BEGIN:VCALENDAR
VERSION:2.0
X-WR-CALNAME:internationalconferencetibs
X-WR-CALDESC:Event Calendar
METHOD:PUBLISH
CALSCALE:GREGORIAN
PRODID:-//Sched.com International Conference on Technological Intelligence and Business Strategies//EN
X-WR-TIMEZONE:UTC
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T010000Z
DTEND:20260622T013000Z
SUMMARY:Registration with Networking Tea / Coffee
DESCRIPTION:\n
CATEGORIES:INAUGURAL SESSION
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:38dc16eed55e2a152025852cae9aec28
URL:http://internationalconferencetibs.sched.com/event/38dc16eed55e2a152025852cae9aec28
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T013000Z
DTEND:20260622T014000Z
SUMMARY:Welcome Remarks by Masters of Ceremonies & Welcoming of Guests
DESCRIPTION:\n
CATEGORIES:INAUGURAL SESSION
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:250ff8e754aad323162304f152dae15a
URL:http://internationalconferencetibs.sched.com/event/250ff8e754aad323162304f152dae15a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T014000Z
DTEND:20260622T015000Z
SUMMARY:Welcome Address By
DESCRIPTION:\n
CATEGORIES:INAUGURAL SESSION
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:bf67ad1ee6756d9c1264e18f900c353f
URL:http://internationalconferencetibs.sched.com/event/bf67ad1ee6756d9c1264e18f900c353f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T015000Z
DTEND:20260622T020000Z
SUMMARY:Address By Local Conference Chair
DESCRIPTION:\n
CATEGORIES:INAUGURAL SESSION
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:06e4d3f829c87f732e56697cc311f14a
URL:http://internationalconferencetibs.sched.com/event/06e4d3f829c87f732e56697cc311f14a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T020000Z
DTEND:20260622T021000Z
SUMMARY:Address By Special Guest & Speaker
DESCRIPTION:\n
CATEGORIES:INAUGURAL SESSION
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:2beca9f67a8c7f42c03d1d45e80d9bf7
URL:http://internationalconferencetibs.sched.com/event/2beca9f67a8c7f42c03d1d45e80d9bf7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T021000Z
DTEND:20260622T022000Z
SUMMARY:Address By Special Guest & Speaker
DESCRIPTION:\n
CATEGORIES:INAUGURAL SESSION
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:a4aa9612a1b8aede084c57e868523106
URL:http://internationalconferencetibs.sched.com/event/a4aa9612a1b8aede084c57e868523106
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T022000Z
DTEND:20260622T023000Z
SUMMARY:Address By Keynote Speaker
DESCRIPTION:\n
CATEGORIES:INAUGURAL SESSION
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:ecb62458ddb163bd46995a8e55166e48
URL:http://internationalconferencetibs.sched.com/event/ecb62458ddb163bd46995a8e55166e48
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T023000Z
DTEND:20260622T024000Z
SUMMARY:Address By Keynote Speaker
DESCRIPTION:\n
CATEGORIES:INAUGURAL SESSION
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:07429c3bc0450548f1b8387aaa08185c
URL:http://internationalconferencetibs.sched.com/event/07429c3bc0450548f1b8387aaa08185c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T024000Z
DTEND:20260622T025000Z
SUMMARY:Address By Keynote Speaker
DESCRIPTION:\n
CATEGORIES:INAUGURAL SESSION
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:1ce3eec2b1412e2027e809e941816182
URL:http://internationalconferencetibs.sched.com/event/1ce3eec2b1412e2027e809e941816182
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T025000Z
DTEND:20260622T030000Z
SUMMARY:Address By Keynote Speaker
DESCRIPTION:\n
CATEGORIES:INAUGURAL SESSION
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:8fee51c1ddbcc824b49d9ef0c6af5916
URL:http://internationalconferencetibs.sched.com/event/8fee51c1ddbcc824b49d9ef0c6af5916
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T030000Z
DTEND:20260622T031500Z
SUMMARY:Address By Invited Guest
DESCRIPTION:\n
CATEGORIES:INAUGURAL SESSION
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:29e6da57ab3f471ee42ae36f9068f015
URL:http://internationalconferencetibs.sched.com/event/29e6da57ab3f471ee42ae36f9068f015
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T031500Z
DTEND:20260622T031500Z
SUMMARY:Invited Guest of TIBS 2026
DESCRIPTION:\n
CATEGORIES:INAUGURAL SESSION
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:a0a8d49b5b65ca457920147995a49aed
URL:http://internationalconferencetibs.sched.com/event/a0a8d49b5b65ca457920147995a49aed
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T031500Z
DTEND:20260622T031500Z
SUMMARY:Special Invitees to Inaugural Ceremony
DESCRIPTION:\n
CATEGORIES:INAUGURAL SESSION
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:27c0e41f3a3fdd7a0d6d6268bc8f073d
URL:http://internationalconferencetibs.sched.com/event/27c0e41f3a3fdd7a0d6d6268bc8f073d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T031500Z
DTEND:20260622T031500Z
SUMMARY:Special Remarks for TIBS 2026
DESCRIPTION:\n
CATEGORIES:INAUGURAL SESSION
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:b646cafca55c8b05830039fef35a8597
URL:http://internationalconferencetibs.sched.com/event/b646cafca55c8b05830039fef35a8597
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T031500Z
DTEND:20260622T032000Z
SUMMARY:Technical Session Remarks By
DESCRIPTION:\n
CATEGORIES:INAUGURAL SESSION
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:532d2021301b4cbeb435ac273c35cfea
URL:http://internationalconferencetibs.sched.com/event/532d2021301b4cbeb435ac273c35cfea
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T032000Z
DTEND:20260622T033000Z
SUMMARY:Vote of Appreciation\, Felicitation & Group Photograph
DESCRIPTION:
CATEGORIES:INAUGURAL SESSION
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:0fee83fafd6b4d301a31393847fcb311
URL:http://internationalconferencetibs.sched.com/event/0fee83fafd6b4d301a31393847fcb311
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T040000Z
DTEND:20260622T040100Z
SUMMARY:Session Moderator and Manager
DESCRIPTION:\n
CATEGORIES:PHYSICAL TECHNICAL SESSION 1A
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:60676621b215381eab1653857e20b097
URL:http://internationalconferencetibs.sched.com/event/60676621b215381eab1653857e20b097
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T040000Z
DTEND:20260622T040100Z
SUMMARY:Technical Session Chairs
DESCRIPTION:\n
CATEGORIES:PHYSICAL TECHNICAL SESSION 1A
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:08bbefad793b579150f6aa2381edf718
URL:http://internationalconferencetibs.sched.com/event/08bbefad793b579150f6aa2381edf718
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T040100Z
DTEND:20260622T041600Z
SUMMARY:Automated Skin Lesion Diagnosis Using CNN and Autoencoder Frameworks
DESCRIPTION:Authors - Rajendra Yadav\, Pavan Kumar Boori\, Gaurav Kumawat\, Chirag Joshi Abstract - Examining skin images from dermatoscopes can be a challenging endeavor especially when unlabelled skin images are being used. Therefore\, we propose a novel method based on deep learning called Contrastive Clustering Autoencoder (CCA)\, specifically for clustering skin lesions without using la-belled data. CCA employs contrastive learning as well as clean autoencoder architecture based off a truncated ResNet-18 architecture. Internally\, these models separate features into two branches: one for reconstruction of the image and one for clustering similar lesions\, allowing it to learn informative patterns while maintaining image quality. Contrastive loss is used to create tight clusters of similar images and clean boundaries between them. To further increase the results\, a pseudo-labelling approach is employed to take the most confident model predictions and use them to improve the model\, modelling both unsupervised and semi-supervised learning methods simultaneously. CCA is evaluated against the HAM10000 dataset measured in cluster purity\, silhouette score\, and expert re-view. The results demonstrate that CCA can cluster skin lesions with high accuracy and consistency without the need for labeled data. The model's stability\, the level of confidence in the results\, and the expectations in the medical field indicate that CCA has tremendous potential for skin diagnosis-related applications where labelled data cannot be obtained.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1A
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:421f38ab1efaa8216406a3ab01f5ed7e
URL:http://internationalconferencetibs.sched.com/event/421f38ab1efaa8216406a3ab01f5ed7e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T041600Z
DTEND:20260622T043100Z
SUMMARY:HM ‘Yan? Visual Rhetoric and Algospeak as Multimodal Strategies of Digital Camouflage and Algorithmic Assimilation in Philippine Social Commerce
DESCRIPTION:Authors - Ernest Joseph M. Garcia Abstract - This study examines how visual rhetoric and algospeak function as coordinated multimodal strategies of digital camouflage in Philippine Ukay-Ukay social commerce\, and how these strategies evolve under algorithmic assimilation. As social media platforms increasingly use Artificial Intelligence (AI) for content moderation\, recommendation\, and commercial indexing\, local online sellers must adjust their communication practices to remain visible\, trusted\, and economically active. In this context\, sellers use Anti-Design visuals such as glitch-style images\, collage layouts\, and cluttered designs\, together with coded language like “Budol\,” “6–7 Condi\,” and “EA Only\,” to navigate platform rules while maintaining community trust. Using a qualitative case study approach grounded in Multimodal Social Semiotics\, the study analyzes six digital artifacts from Facebook and Instagram marketplaces. Each artifact is examined through machine reading (AI/SMI outputs) and human interpretation (community meaning-making)\, revealing a gap between algorithmic recognition and cultural understanding. Findings show that visual rhetoric produces intentional visual noise that signals authenticity and human labor\, while algospeak functions as coded language that evades algorithm detection and communicates trust\, identity\, and group norms. As AI systems improve pattern recognition\, sellers adapt through emoji substitution\, brand masking\, and participatory formats such as “W or L” posts. However\, a persistent intelligence gap remains between machine interpretation and human cultural meaning. The study concludes that digital camouflage is an integrated multimodal system shaped by human creativity and algorithmic governance\, highlighting the continuing importance of cultural knowledge in digital commerce.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1A
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:28760caeb3f05266e24449e0345c60ed
URL:http://internationalconferencetibs.sched.com/event/28760caeb3f05266e24449e0345c60ed
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T043100Z
DTEND:20260622T044600Z
SUMMARY:Interactive Money Saver : A Digital Till to Enhance Money Saving Interests of Children
DESCRIPTION:Authors - ES Sithpahan\, NH Wanigasingha\, MKA Ariyaratne\, PRS De Silva Abstract - This study presents the design and implementation of an interactive coin classification and saving assistant aimed at enhancing financial literacy in children. The system combines computer vision and machine learning techniques to automatically identify Sri Lankan coins using a custom image dataset. A convolutional neural network (CNN) was developed and evaluated using key performance metrics\, including accuracy\, precision\, recall\, F1-score\, and confusion matrices\, to ensure robustness and reliability. The software was integrated with hardware components comprising a Raspberry Pi\, touchscreen interface\, servo motor\, and webcam\, forming a tangible coin-till device for user interaction. The end-to-end system\, from coin insertion and classification to actuation and feedback\, was validated through both quantitative and qualitative testing. Quantitative evaluation focused on model performance\, while qualitative analysis assessed usability\, engagement\, and educational effectiveness based on feedback from children and parents. Results indicate high usability and engagement\, with participants demonstrating increased interest in saving behavior. The study highlights the feasibility of combining AI and embedded systems to deliver educational experiences for children.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1A
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:7bb255ca76b811d9305e8dfe601ef102
URL:http://internationalconferencetibs.sched.com/event/7bb255ca76b811d9305e8dfe601ef102
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T044600Z
DTEND:20260622T050100Z
SUMMARY:Navigating Digital Sovereignty: A Governance Perspective on Platform Choice between Zoom and BigBlueButton in Higher Education
DESCRIPTION:Authors - Emily Slanina\, Marc Fernandes\, Manfred Rossle\, Daniel Gartner\, Christian Koot Abstract - Video conferencing systems are essential to modern university infrastructure. Beyond functionality\, their selection involves critical questions of digital sovereignty\, institutional control over personal data\, and dependencies on external providers. This paper combines a conceptual analysis of digital sovereignty with a qualitative case study at Aalen University. Based on staff interviews\, technical documentation\, and literature\, it provides a structured comparison of Zoom and BigBlueButton (BBB) across functional\, legal\, organizational\, and infrastructural dimensions. Zoom is perceived as highly reliable and user-friendly\, yet its proprietary cloud-based model and non-European legal framework limit transparency and institutional control. While Zoom has introduced sovereignty-related configurations to meet European requirements\, its governance remains external. Conversely\, the open-source platform BBB offers superior transparency and control over data processing but requires significant institutional resources and technical expertise. Both platforms fulfill comparable functional requirements\; however\, they differ fundamentally in governance\, hosting flexibility\, and institutional influence. Selecting a system requires balancing structural autonomy with practical usability. Digital sovereignty is a context-dependent condition shaped by technical and legal frameworks rather than a static platform feature. BBB serves as a sovereignty-promoting alternative\, provided that institutions possess the necessary technical resources and the willingness to manage structural organizational change.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1A
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:063243eb265f56cf4d1d1243a2ac1d26
URL:http://internationalconferencetibs.sched.com/event/063243eb265f56cf4d1d1243a2ac1d26
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T050100Z
DTEND:20260622T051600Z
SUMMARY:Public Acceptance of Multilingual Electronic Menus in Restaurants: Evidence from Jakarta
DESCRIPTION:Authors - Renzo Giovanka Rasyidin\, Nicho Ras\, Tiurida Lily Anita Abstract - Electronic menus are increasingly used in restaurants to improve ordering convenience\, communication clarity\, and service efficiency. In urban and culturally diverse markets\, multilingual language options within electronic menus can support better customer understanding and reduce communication barriers during the ordering process. However\, the factors that drive public acceptance and continued use of multilingual electronic menus remain underexplored. This study examines how perceived interaction quality and perceived ease of use influence trust in multilingual electronic menus\, and how trust subsequently affects perceived service efficiency\, customer satisfaction\, and continued use intention. A quantitative design was employed using survey data from 230 restaurant customers in Jakarta who had prior experience using digital ordering systems. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that both perceived interaction quality and perceived ease of use significantly enhance trust. Trust\, in turn\, positively influences perceived service efficiency and customer satisfaction\, while customer satisfaction is the strongest predictor of continued use intention. The model shows substantial explanatory power across the endogenous constructs. This study contributes to the literature by extending research on self-service technology and multilingual service interfaces in restaurant settings and by identifying trust as a central mechanism linking electronic menu interaction to post-adoption behavior. The findings also provide practical insights for hospitality operators seeking to design reliable\, user-friendly\, and multilingual electronic menu interfaces.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1A
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:17b7339a7a13565d050b574b79066f0e
URL:http://internationalconferencetibs.sched.com/event/17b7339a7a13565d050b574b79066f0e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T051600Z
DTEND:20260622T053100Z
SUMMARY:Evaluating Stakeholder Adoption of a Smart Tourism Digital Transformation Framework for Sustainable Marine Destinations
DESCRIPTION:Authors - Tiurida Lily Anita\, Farah Levyta\, Teguh Prasandy\, Mohd. Nor Shahizan Ali Abstract - This necessitates accelerating the digital transformation of remote marine destinations under pressure to maintain ecological quality and community value. Yet\, over the course of implementing digitalization\, many such destinations continue to face issues like disjointed stakeholder coordination\, heterogeneous technological preparedness and lack of empirical research on sustainability-oriented evaluations of digital tourism frameworks. This research tests the interrelationships of Smart Tourism Readiness\, Perceived Sustainability Value\, Perceived Usefulness\, Adoption Intention\, and Perceived Destination Competitiveness. Using partial least squares structural equation modelling (PLS-SEM)\, quantitative surveys of 180 local tourism stakeholders were analysed. The results indicate that Smart Tourism Readiness gives a positive effect on Perceived Usefulness\, as well as Perceived Sustainability Value\, where the latter has a superior influence. Conversely\, the results show that Adoption Intention is strongly predicted by Perceived Usefulness\, and that both Adoption Intention and Perceived Sustainability Value significantly contribute to making Perceived Destination Competitiveness a more appealing choice. The findings show that the model has significant explanatory and predictive power regarding endogenous constructs. The findings indicate that stakeholders only tend to support digital transformation initiatives if they are perceived as practical\, relevant and aligned with local sustainability priorities. How this paper contributes · The study extends the theory of technology adoption\, sustainability value and destination competitiveness in a holistic empirical framework. The findings provide strategic insights to managers on how to implement digitally driven sustainable tourism development.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1A
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:752db743f628b1c592cf5e36f9686c11
URL:http://internationalconferencetibs.sched.com/event/752db743f628b1c592cf5e36f9686c11
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T063000Z
DTEND:20260622T063100Z
SUMMARY:Session Moderator and Manager
DESCRIPTION:\n
CATEGORIES:PHYSICAL TECHNICAL SESSION 1B
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:4623c90efb67e47f9a64cf424e60f1e4
URL:http://internationalconferencetibs.sched.com/event/4623c90efb67e47f9a64cf424e60f1e4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T063000Z
DTEND:20260622T063100Z
SUMMARY:Technical Session Chairs
DESCRIPTION:\n
CATEGORIES:PHYSICAL TECHNICAL SESSION 1B
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:de6bd4409a68b5bec882a9957a88167e
URL:http://internationalconferencetibs.sched.com/event/de6bd4409a68b5bec882a9957a88167e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T063100Z
DTEND:20260622T064600Z
SUMMARY:Intelligent Resume Screening Application using Machine Learning
DESCRIPTION:Authors - Mangapuram Sadhguna Sri\, Narendra VG\, Shiva Prasad Gundibail Abstract - This project tackles the inefficiency and possibility for bias in traditional resume screening. When a single job post draws hundreds of applications\, manual review becomes a bottleneck. We designed an intelligent system that uses NLP and machine learning to automate this process. Our system features a complete pipeline that parses PDF resumes and converts their text into structured features using TF-IDF or Sentence Embeddings. A trained classifier then evaluates these features to predict the optimal job role for each candidate. By ranking and classifying applicants based on skill relevance\, our tool allows hiring managers to bypass manual sorting and focus directly on the most promising individuals\, ensuring a faster and more equitable screening process.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1B
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:f6b3205ee1d88dd80597f2b90bee66b5
URL:http://internationalconferencetibs.sched.com/event/f6b3205ee1d88dd80597f2b90bee66b5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T064600Z
DTEND:20260622T070100Z
SUMMARY:The Use of Explainable AI (XAI) Methods for Transparent Cleanliness Detection in Supporting Accountability of Hospitality Services
DESCRIPTION:Authors - Karen Chelentine Natalia\, Trias Septyoari Putranto Abstract - Even though this is now a necessity in the sector\, particularly because of COVID-19\,cleanliness has taken on an even greater concern that reflects itself with the implementation of Artificial Intelligence (AI) for automated hygiene monitoring throughout hospitality environments. Yet\, numerous Al models re-main black boxes and their decision-making process is opaque to operators - a dynamic that can undermine trust in the technology. Bhalearo et al.: Evaluation of Explainable Artificial Intelligence (XAI) in Cleanliness Detection Systems: A Study on Hospitality Services This study used a qualitative case study method with in-depth interviews of four participants: a manager\, supervisor\, staff member and hotel guest as data collector. The findings suggest Al can aid efficiency and consistency in cleanliness monitoring\, but that limited interpretability could also hamper their trustworthiness. XAI is an excellent way to increasing the understanding of system outputs by users\, and this leads to increased trust and ac-countability through a better explanation - especially with visual explanations like heatmaps. We highlight the benefits of technological efficiency but also transparency\, especially in hospitality management.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1B
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:410e3a92ff35f904cfa7cfe14f19eeb8
URL:http://internationalconferencetibs.sched.com/event/410e3a92ff35f904cfa7cfe14f19eeb8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T070100Z
DTEND:20260622T071600Z
SUMMARY:Integrating Digital National IDs with Public Key Infrastructure for Securing IoT Systems
DESCRIPTION:Authors - Eisenhower D. Aldemita II\, Christopher Luis M. Senatin\, Wilson M. Tan Abstract - As the Internet of Things (IoT) scales up to billions of connections\, securing resource-constrained devices remains a challenge. The lack of lightweight trust management systems and weak access control are some of the existing vulnerabilities in IoT systems. This paper proposes a Public Key Infrastructure architecture integrating a digital national ID system to tie human and machine identity together. The system requires users to present their ID for verification with the digital national ID system before allowing registration of their IoT devices\, tying their identity to the certificates issued to their devices. Ownership challenges are implemented during the registration of devices to ensure the user has physical access to and possession of the device. Once registered\, mutual Transport Layer Security is used for secure communication between devices and servers. The system was evaluated on ESP8266\, Raspberry Pi Pico W\, and ESP 32 microcontrollers with 10000 tests per key performance metric. The results show that the proposed architecture provides increased security with minimal impact on device performance.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1B
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:d0e5faa6e1e6be2a45b7af477dc9022e
URL:http://internationalconferencetibs.sched.com/event/d0e5faa6e1e6be2a45b7af477dc9022e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T071600Z
DTEND:20260622T073100Z
SUMMARY:OpenLGU ID: A Customizable and Open Source LGU DIS Leveraging PhilSys
DESCRIPTION:Authors - Lanz Anjelo Conanan\, James Ernest Geraldo\, Wilson M. Tan Abstract - With the increasing push for digital transformation and egovernance in order to improve service delivery capacity and crisis handling\, some Philippine Local Government Units (LGU) have begun to develop their own Digital Identity Systems (DIS) as the foundation of their digital infrastructure. Due to the recognition of LGUs’ constraints in developing their own DIS and the interoperability opportunities with the Philippine Identification System (PhilSys)\, the mature National DIS of the Philippines\, this paper developed OpenLGU ID\, a customizable and open source LGU DIS which integrates with PhilSys. Its architecture implements a variety of recognized LGUs needs such as ID issuance\, service claiming\, and sector group management. It is designed with standards like Digital Public Goods (DPG) in mind given it’s minimal storage of Personally Identifiable Information (PII) and it’s signing of QRs. Ultimately\, the researchers present an open source system ready to be implemented by LGUs to assist them in their digital transformation.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1B
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:323f4c539def0ffa215e18892e557e88
URL:http://internationalconferencetibs.sched.com/event/323f4c539def0ffa215e18892e557e88
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260622T073100Z
DTEND:20260622T074600Z
SUMMARY:Explainable Artificial Intelligence Guided Semantic Feature Learning and Histogram Indexing for Efficient Content-Based Image Retrieval
DESCRIPTION:Authors - Gokaramaiah Thota\, Sathya Babu Korra\, Nagaraju K\, Suman Prakash\, Perumalla\, V Ramanjaneyulu Yannam Abstract - Content-based image retrieval (CBIR) has become an important research area in computer vision for retrieving visually similar images from large-scale image collections using intrinsic image content rather than manually assigned annotations. Although deep feature learning has substantially improved retrieval accuracy\, existing retrieval systems continue to encounter challenges\, including limited interpretability\, high computational complexity during similarity search\, and insufficient integration of semantic attention information into retrieval and indexing mechanisms. Most retrieval acceleration techniques operate primarily in feature space and often neglect region-level semantic cues that contribute to retrieval relevance. This work presents a hybrid framework that integrates complete optimization of convolutional features\, explainable artificial intelligence (XAI)-guided feature representation\, and histogramdriven indexing to improve retrieval effectiveness while reducing computational cost. The complete convolutional neural network architecture is optimized to learn domain-adaptive visual representations\, while class activation information is employed to identify semantically important image regions and generate activation-aware feature embeddings that preserve discriminative visual characteristics. The extracted features are normalized and these are subsequently organized into histogram partitions to reduce the retrieval search space. This indexing strategy limits similarity computation to semantically related candidate regions while preserving retrieval quality. Experiments conducted on Corel-10K and Oxford-5K evaluate retrieval performance using Precision@K\, average precision\, mean average precision\, retrieval time\, and average feature comparisons. Experimental results demonstrate that the proposed framework reduces the retrieval search space by approximately 35 to 55% while maintaining competitive retrieval accuracy. Comparative analysis shows competitive retrieval performance with lower computational complexity and improved interpretability.
CATEGORIES:PHYSICAL TECHNICAL SESSION 1B
LOCATION:JV Del Rosario Room\, AIM CONFERENCE CENTER (ACC)\, Manila\, Philippines
SEQUENCE:0
UID:0b461de563b79843c9b06325e443bdea
URL:http://internationalconferencetibs.sched.com/event/0b461de563b79843c9b06325e443bdea
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T025800Z
DTEND:20260623T030000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:4deea56c06705b449fdefbb07fcf9c83
URL:http://internationalconferencetibs.sched.com/event/4deea56c06705b449fdefbb07fcf9c83
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T025800Z
DTEND:20260623T030000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:f6884f023cac2c97e2af961d781ffda3
URL:http://internationalconferencetibs.sched.com/event/f6884f023cac2c97e2af961d781ffda3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T025800Z
DTEND:20260623T030000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:f19dfc6d10fa4327f05e9cc83b480c44
URL:http://internationalconferencetibs.sched.com/event/f19dfc6d10fa4327f05e9cc83b480c44
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T025800Z
DTEND:20260623T030000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:59cb197baedbc72c1812cbfd73d63b28
URL:http://internationalconferencetibs.sched.com/event/59cb197baedbc72c1812cbfd73d63b28
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T030000Z
DTEND:20260623T050000Z
SUMMARY:A Systematic Review of Evolution of Personalization Techniques in Federated Learning for Heterogeneous Clients
DESCRIPTION:Authors - Aneesah Sabar\, KA Dilini T Kulawansa Abstract - Federated Learning has emerged as a robust privacy-preserving framework that enables joint model training across multiple distributed clients without sharing raw data. However\, the effectiveness of traditional federated learning frameworks is hindered by client heterogeneity\, where participants differ in data distribution\, computational resources\, and communication capabilities. This survey investigates evolution of personalization techniques in Federated Learning that address these challenges by tailoring models to individual clients while maintaining the benefits of global collaboration. The paper categorizes existing personalization approaches into five major groups: local fine-tuning\, model interpolation\, meta-learning methods\, clustered federated learning\, and regularization-based techniques. Each method’s core idea\, strengths\, limitations\, and suitability under different heterogeneity conditions are analyzed in detail. The findings indicate that personalization significantly improves fairness\, accuracy\, and adaptability across heterogeneous clients\, though it introduces trade-offs in communication cost\, scalability\, and privacy. This review concludes that personalization is essential for deploying federated learning in realistic\, diverse environments and highlights emerging directions in fairness-aware\, resource-efficient\, and privacy-preserving personalization. Future research should focus on scalable and dynamic personalization strategies capable of handling evolving client behaviors and large-scale federated systems.
CATEGORIES:VIRTUAL ROOM 2A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:3c4c5df5bbe923d351eaebce003dafc8
URL:http://internationalconferencetibs.sched.com/event/3c4c5df5bbe923d351eaebce003dafc8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T030000Z
DTEND:20260623T050000Z
SUMMARY:An Efficient Deep Learning Model for Driver Drowsiness Detection
DESCRIPTION:Authors - Bhavanam Sruthi\, Mettu Sai Preethi\, Krishna Reddy Abstract - Driver drowsiness is a major reason for accidents on the road\, hence it is important to detect it early to increase safety on the road. A driver drowsiness detection system based on deep learning algorithms is proposed and it uses images captured through a camera installed inside a car. Various deep learning algorithms\, namely CNN\, VGG16\, DenseNet121\, MobileNet\, LeNet\, AlexNet\, RNN\, patchTST\,Vision Transformer and Swin Transformer are implemented and compared to assess their performance.The system detects the conditions of the driver\, whether eyes are open\, closed\, yawning\, or not yawning. Among all these algorithms\, the highest accuracy of 97.61% was obtained by using the MobileNet model\, which proves that deep learning can play a vital role in detecting drowsiness. In addition\, an alert can also be sent to warn the driver.
CATEGORIES:VIRTUAL ROOM 2A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:8eab9a75253d02fd55b9084ebaadbb01
URL:http://internationalconferencetibs.sched.com/event/8eab9a75253d02fd55b9084ebaadbb01
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T030000Z
DTEND:20260623T050000Z
SUMMARY:HARNESSING PREDICTIVE ANALYTICS TO IMPROVE PATIENT OUTCOMES: A FOCUS ON EARLY DIAGNOSIS AND TREATMENT
DESCRIPTION:Authors - Prachita Chaudhari\, Shubham Kishor Kadam\, Shiwani wagh\, Pankajkumar Anawade\, Deepak Sharma\, Chhitij Raj Abstract - One of such industries is healthcare\, where data-driven methods attract much attention\, and one among them is the field of predictive analytics that is already making a great difference in the healthcare industry concerning its capacity to enhance early diagnosis and treatment. Through this\, whole care is provided\, and this implies that the problem of fragmented care addresses systemic issues such as inefficiencies and inflation of costs. Predictive analytics is the most effective in the prediction of risks of disease\, i.e. making care a thing related to the individual patient. Besides\, it can be utilized to monitor populations and enhance the management of population health utilizing its combination of machine learning\, natural language processing and deep learning. Solutions that offer the following benefits\, including reduced misdiagnosis\, re-source utilization\, and affordable access to health care\, are also being created with the help of the main enabling technologies including AI\, IoT\, and big data. Nevertheless\, issues like the quality of the data\, technical issues\, ethical concerns and compliance with laws persist. Future work still to be done areas can be seen in the application of new technologies like quantum computing to answer questions about the public health\, real time data that uses IoT\, and the application of other mediating technologies in underserved locations that can instill equity and sustainability. Being fueled by the collaboration of various professionals\, e.g.\, clinicians\, data scientists\, and policy-makers\, predictive analytics is bound to enhance patient outcomes and catalyze the better provision of preventive\, personalized\, and responsibility healthcare solutions.
CATEGORIES:VIRTUAL ROOM 2A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:d3bdcedd2346282935a8762ba1e9ec62
URL:http://internationalconferencetibs.sched.com/event/d3bdcedd2346282935a8762ba1e9ec62
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T030000Z
DTEND:20260623T050000Z
SUMMARY:KAN in ResNet: Effects of Low-Level and High-Level Layer Integration on Hierarchical Skin Lesion Classification
DESCRIPTION:Authors - Binh Pham Nguyen Thanh\, Chau M. Truong\, Nhan Thi Cao Abstract - ResNet is widely used in medical image classification due to its strong hierarchical feature extraction capability. This study investigates the integration of Kolmogorov–Arnold Networks (KAN) and ConvKAN into ResNet to analyze the effect of increasing nonlinearity at different stages within a hierarchical skin lesion classification framework. Convolutional KAN is applied at the initial layer to enhance low-level feature extraction\, while KAN is introduced at the final layer to improve high-level decision boundary modeling. A combined configuration is also evaluated to examine potential complementary effects across different levels of label granularity. Results show that performance depends on both the integration stage and dataset characteristics. Convolutional KAN at early layers provides limited and inconsistent improvements\, whereas KAN at the final layer yields more stable gains. In addition\, models incorporating KAN-based architectures generally achieve better performance across metrics such as accuracy\, precision\, F1-score\, and ROC AUC. As classification becomes more fine-grained\, Recall consistently decreases despite high ROC AUC\, indicating challenges in decision thresholding across hierarchical levels. Overall\, KAN is more effective for high-level decision making\, while dataset complexity has a greater impact than architectural modifications.
CATEGORIES:VIRTUAL ROOM 2A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:744c2fe1dfd93a765190d7d0c4a28d88
URL:http://internationalconferencetibs.sched.com/event/744c2fe1dfd93a765190d7d0c4a28d88
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T030000Z
DTEND:20260623T050000Z
SUMMARY:Multimodal Orchestrator for Real-Time Cognitive Mirroring in Social Anxiety
DESCRIPTION:Authors - Mouna Meghana Nagala\, Anjan Babu G Abstract - Social Anxiety Disorder (SAD) remains one of the most pervasive mental health challenges globally\, characterized by a debilitating “perception gap” where individuals consistently overestimate the visibility of their internal distress while underestimating their social performance. This paper introduces an Explainable AI (XAI) multi-modal sensing system designed for automated social anxiety monitoring and self-perception recalibration. The architecture is founded on an event-driven framework integrating real-time threedimensional facial feature encoding (DeepFace)\, acoustic prosody extraction (Librosa)\, and Natural Language Processing (NLP) for cognitive distortion detection. The system implements a Cognitive Behavioral Therapy (CBT) logic layer that provides interpretable feedback on linguistic patterns. System performance was benchmarked against the FER-2013 and RAVDESS repositories\, yielding an anxiety detection sensitivity of 92.4% and a specificity of 94.7%. The findings affirm that coupling volumetric affective computing with generative AI constitutes a viable pathway toward trustworthy computer-aided detection (CAD) in behavioral health screening programs.
CATEGORIES:VIRTUAL ROOM 2A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:a48e79541f3f1031c51945b8654c9783
URL:http://internationalconferencetibs.sched.com/event/a48e79541f3f1031c51945b8654c9783
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T030000Z
DTEND:20260623T050000Z
SUMMARY:PU-SERV: A TOOL IN ANALYZING STUDENT SERVICES USING MACHINE LEARNING
DESCRIPTION:Authors - Theresa T. Limos\, Sheena Sapuay-Guillen Abstract - This study developed PU-Serv: A Tool in Analyzing Student Services Using Machine Learning\, a web-based system designed to enhance the evaluation of student services through automated sentiment analysis. The study assessed the existing student services evaluation form in terms of adequacy\, efficiency\, and reliability and aimed to develop a machine learning–based model to support the analysis of student feedback.A descriptive and developmental research design guided by Agile methodology and the CRISP-DM framework was employed. Data were gathered from focus group discussions\, questionnaires\, and institutional student feedback records. Natural language processing techniques were used to preprocess narrative feedback\, and the Support Vector Machine (SVM) algorithm was integrated into the system due to its high accuracy in sentiment classification. The developed PU-Serv system automatically analyzes student feedback and presents summarized results through a web-based dashboard. The system provides administrators with actionable insights that support data-driven decision-making\, helping institutions identify service issues\, improve responsiveness\, and enhance the overall quality of student services.
CATEGORIES:VIRTUAL ROOM 2A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:807fef7dc0e4756bacc529f05f7266ae
URL:http://internationalconferencetibs.sched.com/event/807fef7dc0e4756bacc529f05f7266ae
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T030000Z
DTEND:20260623T050000Z
SUMMARY:Zero-Click QR Code Attacks: A Comprehensive Survey of Threats and Defenses
DESCRIPTION:Authors - Neel Lathiya\, Akshita Kadam\, Amit Thakkar Abstract - Industrial tracking tools have led to the development of Quick Response codes\, which are an essential component of digital engagement and provide simple access to payments\, authentication\, and online services with a single scan. However\, they are very vulnerable to exploitation\, particularly zero-click attacks\, which start destructive operations without the user’s consent\, due to their architecture\, which is based on visual legitimacy\, automatic intent execution\, and plaintext encoding. This survey looks at the technical aspects of making and reading QR codes\, charts the evolution of threats based on QR codes\, ranging from physical manipulation to silent deep link hijacking\, and explains how these attacks go beyond the robust security models of iOS and Android by utilizing trusted system paths. Based on five significant studies\, we analyze real-world attack scenarios\, user behavior gaps\, and the efficacy of novel defenses like scanner assessment frameworks\, zero-trust architecture approaches\, and AI-driven payload inspection (AP3X\, QRShield). Certain recommendations are made regarding system hardening\, cryptographic integration\, and user awareness in order to transform QR codes from a latent risk into a safe and verifiable medium.
CATEGORIES:VIRTUAL ROOM 2A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:31f1bc548d8611f983bb030c4e2345f6
URL:http://internationalconferencetibs.sched.com/event/31f1bc548d8611f983bb030c4e2345f6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T030000Z
DTEND:20260623T050000Z
SUMMARY:A Hybrid ViT–GRU Architecture for Myanmar-Script Video Captioning
DESCRIPTION:Authors - Nway Nway Zaw Win\, Aye Nyein Mon\, Win Lelt Lelt Phyu Abstract - Generating natural language descriptions for visual content is a key task bridging Computer Vision and Natural Language Processing. Conventional CNN-based approaches often struggle to capture global contextual information\, limiting semantic consistency. This paper presents a multimodal video captioning framework for Myanmar-script generation based on a Vision Transformer (ViT) encoder and a Gated Recurrent Unit (GRU) decoder. Global visual representations are derived from transformer-based self-attention\, while a class-prefixing mechanism is introduced to improve semantic grounding in a low-resource language setting. Experimental results evaluated using BLEU\, CHRF\, and TER metrics demonstrate that the proposed ViT–GRU model outperforms CNN–RNN baselines. PCA and t-SNE visualizations further confirm the effectiveness of transformer-based visual representations.
CATEGORIES:VIRTUAL ROOM 2B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:f490d12db241ced7d8cf4e094b65ecbb
URL:http://internationalconferencetibs.sched.com/event/f490d12db241ced7d8cf4e094b65ecbb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T030000Z
DTEND:20260623T050000Z
SUMMARY:A Unified Perspective on Bias Detection and Fairness Auditing in Large Language Models
DESCRIPTION:Authors - Kaveti Nani Kartik\, Tanuja Pattanshetti Abstract - The proliferation of Large Language Models (LLMs) has raised concerns about embedded social biases and violations of fairness. Previous work has explored bias detection in word embeddings\, fairnessaware algorithmic interventions\, and system-level auditing frameworks. However\, these approaches are still scattered across datasets\, evaluation strategies and implementation pipelines. In this paper\, we present a comprehensive literature survey to summarize the previous work on bias detection and fairness auditing\, and categorize the contributions based on multiple phases of the research. Moreover\, coverage and consistency limitations on popular benchmark datasets are analyzed. To address these problems\, we present a unified dataset integration pipeline and a modular bias auditing framework. Identified critical research gaps include lack of intersectional bias modeling\, lack of standardized metrics\, and limited scalability in real-time auditing systems.
CATEGORIES:VIRTUAL ROOM 2B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:1cbe7a4f76f6b991077e4a310f6ced97
URL:http://internationalconferencetibs.sched.com/event/1cbe7a4f76f6b991077e4a310f6ced97
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T030000Z
DTEND:20260623T050000Z
SUMMARY:Algorithmic Statistical Arbitrage: Walk-Forward Machine Learning and Dynamic Risk Gating in Intraday Commodity-FX Markets
DESCRIPTION:Authors - Mohit Apte Abstract - We develop systematic pairs trading strategies exploiting price adjustment lags between commodity-exporting currencies and their underlying commodities using CME futures. Two signal generation methods are compared: a rolling Z-score with Optuna-optimized hysteresis\, and walk-forward Ridge regression on fourteen engineered features. Backtests on nine currency-commodity pairs over ten years of hourly data (2016–2026) show the ungated fundamental signal achieves Sharpe 0.56 under realistic costs. Adding rolling cointegration gating improves Sharpe to 0.64 while halving maximum drawdown from 23% to 12%. The ML signal reaches Sharpe 0.92\, with strongest results on INR-Gold\, AUDCopper\, and CAD-Copper pairs. PCA-denoised Equal Risk Contribution sizing pushes ML Sharpe above 1.0 at the cost of higher drawdowns. Results confirm a tradable but risk-sensitive commodity-currency relationship at intraday frequencies.
CATEGORIES:VIRTUAL ROOM 2B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:55f7b9e27c8084aacf4ead1b64316b6e
URL:http://internationalconferencetibs.sched.com/event/55f7b9e27c8084aacf4ead1b64316b6e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T030000Z
DTEND:20260623T050000Z
SUMMARY:Intelligent Synergies: How AI Systems\, Big Data Analytics\, IoT and E-Procurement Drive Sustainable Supply Chain Performance through The Mediating Role of Supply Chain Resilience
DESCRIPTION:Authors - Sayra Islam Saki\, Qaium Hossain\, Nadia Jahan\, Abir Sen Gupta\, Md. Tafshir Jaman Takib\, Rajia Sultana\, S.M. Sayem Abstract - This study examines how AI Systems\, Big Data Analytics\, Internet of Things (IoT) and E-Procurement enhances Sustainable Supply Chain Performance (SCP)\, with a particular focus on Supply Chain Resilience (SCR) as mediator. Primary data were obtained from 307 respondents of manufacturing industries through structured questionnaire. Partial Least Squares Structural Equation Modelling (PLS-SEM) approach was utilized for data analysis. The findings indicate that AI Systems\, Big Data Analytics\, Internet of Things and Supply Chain Resilience positively influence Sustainable Supply Chain Performance. On the contrary\, E-Procurement doesn’t portray any significant direct effect. In terms of indirect pathways\, SCR has positive mediating relationships between AI Systems and SSCP\, as well as between IoT and SSCP. The mediation effect of SCR in the links between Big Data Analytics and E-Procurement with SCP is however not significant. These results provide subtle guidance to the practitioners in the industrial contexts\, highlighting the need to prioritize those technologies that will promote resilience\, specifically to AI Systems and IoT and re-examine the strategic contribution of E-Procurement to Sustainable Supply Chain models.
CATEGORIES:VIRTUAL ROOM 2B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:4f00a71a3ba8ee8f2b8edce74539ab5c
URL:http://internationalconferencetibs.sched.com/event/4f00a71a3ba8ee8f2b8edce74539ab5c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T030000Z
DTEND:20260623T050000Z
SUMMARY:Myanmar News Classification using Mbert-GraphSAGE
DESCRIPTION:Authors - Swe Swe Htun\, Aye Nyein Mon Abstract - Text classification has become a crucial task in natural language processing\, especially for low-resourced languages in which limited annotated data and linguistic resources remain main challenges. This work presents inductive graph-based approach\, GraphSAGE (Graph Sample and Aggregate)\, for text classification applying different word embedding models for Myanmar News classification. Experiments are conducted on eight Myanmar News categories (Business\, Crime\, Culture&Tourism\, Educa-tion&Technology\, Entertainment\, Health\, Politics\, and Sports). The experiments show the effectiveness of BiLSTM (Bidirectional Long Short-Term Memory) and GraphSAGE architectures integrated with traditional and contextual embedding meth-ods\, including TF-IDF (Term Frequency–Inverse Document Frequency)\, MyanBERTa\, and mBERT (Multilingual Bidirectional Encoder Representations from Transformers). In the proposed work\, transformer-based embeddings from pre-trained language models are extracted and combined with graph neural networks to capture both semantic and structural relationships among documents. A similarity graph is built by utilizing cosine similarity and k-nearest neighbor methods\, and GraphSAGE is used to aggregate neighborhood information for inductive learning. The performance of graph-based models is compared to sequential deep learning technique based on BiLSTM. Experi-mental results reveal that graph-based approaches achieve better performance than BiLSTM-based models in all embedding settings. Among the evaluated models\, mBERT with GraphSAGE gets the highest classification accuracy of 63%\, followed by MyanBERTa with GraphSAGE with 60%. In contrast\, MyanBERTa with BiLSTM and mBERT with BiLSTM yield 47% and 52% accuracy\, respectively\, whereas TF-IDF with GraphSAGE obtains 57% accuracy. The findings show that combining the contex-tual transformer embeddings with graph neural networks substantially enhance text classification performance by efficiently modeling semantic and relational information.
CATEGORIES:VIRTUAL ROOM 2B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:e1dd89906f34f9b3bd0c98a3e4207814
URL:http://internationalconferencetibs.sched.com/event/e1dd89906f34f9b3bd0c98a3e4207814
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T030000Z
DTEND:20260623T050000Z
SUMMARY:Navigating Artificial Intelligence Integration in Business Organizations: A Qualitative Exploration of Leadership Strategies and Employee Adaptation
DESCRIPTION:Authors - Edgar G. Cue\, Felix JR Q. Pocong\, Darwin Catalan\, Santa M. Faltado\, Ethel Reyes-Chua\, Randy Joy M. Ventayen Abstract - This qualitative study examined business leaders' use of artificial intelligence (AI) in the workplace and employees' adaptation to AI-related changes. To gain a more thorough understanding of how various leadership practices are used\, the employee experience\, and the organizational response to the integration of AI into their operations\, the researcher employed a qualitative research design. The research included business leaders and employees from organizations that have previously implemented or are currently implementing AI technologies in their operations as the study's subject population. Participants were selected using purposive sampling based on their direct involvement or knowledge of AI integration efforts within their organizations. The researcher collected data through interviews and coded it using thematic analysis to identify recurring themes and patterns related to leadership strategy\, employee adaptation\, organizational challenges\, and workplace transformational changes resulting from the integration of AI. Major findings of this study indicate that\, in implementing AI\, leaders pre-dominantly used phased\, strategic methods while considering employee readiness\, continuous training\, ethical governance\, and partnerships to successfully implement AI within their organizations. On the other hand\, employees exhibited both optimism and anxiety about AI adoption\, with particular concerns about job security\, technological skills\, and organizational support. The study also established that transformational leadership\, participative decision-making\, transparent communication\, a supportive leadership culture\, and continuous capacity development are the most effective practices for facilitating employee adaptation and successful AI integration. The study concludes that successful AI integration requires not only technology but also a high degree of human-centered leader-ship\, ethical accountability\, and an organizational commitment to continuous development and change management to facilitate sustainable transformational change in organizations.
CATEGORIES:VIRTUAL ROOM 2B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:b4b9fd38e771c10c59340d97afacce98
URL:http://internationalconferencetibs.sched.com/event/b4b9fd38e771c10c59340d97afacce98
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T030000Z
DTEND:20260623T050000Z
SUMMARY:Towards Safe AI: A Four-Layer Survey of Risks\, Mitigations\, and Alignment Directives
DESCRIPTION:Authors - Divy Awasthi\, Rushil Jariwala\, Pearl Patel\, Dhiren Patel Abstract - Artificial intelligence is deployed at scale across high-stakes domains—healthcare\, autonomous systems\, finance\, and critical infrastructure— yet the pace of capability development has outrun our ability to ensure these systems behave safely\, transparently\, and in accordance with human values. While individual aspects of AI safety have been studied in isolation\, a unified treatment spanning technical vulnerabilities\, ethical risks\, security threats\, and governance failures remains lacking. This paper addresses that gap with a structured survey of Safe AI organized around a four-layer taxonomy of challenges—data\, model\, system\, and societal—and a corresponding set of mitigation strategies at each layer. We trace AI’s evolution across three generations of increasing capability and opacity\, examine domain-specific safety risks in healthcare\, autonomous vehicles\, manufacturing\, and large language models\, analyze the alignment problem through robustness\, interpretability\, controllability\, and ethical adherence\, and consolidate ten cross-layer directives for safe deployment. We review the global regulatory landscape\, including the EU AI Act\, GDPR\, and national AI safety initiatives across the US\, UK\, and India\, and identify open challenges in scalable oversight\, formal verification\, and the governance of increasingly autonomous AI systems.
CATEGORIES:VIRTUAL ROOM 2B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:5e3637913854c72f185af9db7324e077
URL:http://internationalconferencetibs.sched.com/event/5e3637913854c72f185af9db7324e077
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T030000Z
DTEND:20260623T050000Z
SUMMARY:A Smart Digital Lock System for Zero Trust Architecture Authentication and AES For Secure Data Sharing in Maritime Industry
DESCRIPTION:Authors - Thaw Thaw May Oo\, Khaing Khaing Wai Abstract - Modern maritime industry depends largely on digital communications and access control systems for their operation and security maintenance. On the other hand\, digital communication and access control systems make maritime industry more vulnerable to cybersecurity attacks\, such as unauthorized access\, data leaks\, and insiders' malicious actions. Centralized security measures become inefficient against modern and advanced cyber threats. In that regard\, this paper presents a Smart Digital Lock System using Zero Trust Architecture and AES Encryption. The suggested approach assumes the implementation of zero trust policy in terms of continuous user identity validation requiring tight access control\, including strict user authentication and monitoring. Multifactor authentication and real-time monitoring are the key characteristics of the suggested system\, especially considering such potential high-risk zones as ships and ports. Communication of authorized parties will be performed using the AES encryption to protect the information's privacy and integrity. As a result\, the presented system will be assessed from three perspectives: authentication accuracy\, data protection effectiveness\, and response latency.
CATEGORIES:VIRTUAL ROOM 2C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:ffcd1c59bb5896aa5487e27db18d0871
URL:http://internationalconferencetibs.sched.com/event/ffcd1c59bb5896aa5487e27db18d0871
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T030000Z
DTEND:20260623T050000Z
SUMMARY:ECG Beat Classification Based on Wavelet Attention Mechanisms
DESCRIPTION:Authors - Ei Marlar Win\, Amy Tun\, Khant Kyawt Kyawt Theint Abstract - Electrocardiogram (ECG) signal analysis plays an important role in the early detection and diagnosis of cardiovascular diseases. Manual interpretation of ECG recordings is time-consuming and highly dependent on clinical expertise\, creating a need for automated and accurate classification systems. This study presents an automated ECG classification model using signal preprocessing\, heartbeat segmentation\, wavelet\, feature extraction\, and deep learning. ECG signals are preprocessed to remove noise using filtering and normalization methods. Features are extracted heartbeat segments-based windows around each R peak and classified into five different arrhythmias N (Normal)\, V (Ventricular)\, S (Supraventricular)\, F (Fusion) and Q (Unknown/noisy /unclassified) using wavelet Convolutional Neural Network (CNN) Self Attention model. Experiments on MIT-BIH ECG dataset and analyze the model performance evaluation across a single-lead ECG\, multi lead ECG\, lead fusion and feature fusion techniques by wavelet attention. The results indicate that the proposed approach yields high classification performance and effectively distinguishes heartbeats abnormalities. Class weighting techniques were applied to address the issue of imbalanced class labels in the ECG dataset. The lead fusion approach achieved classification accuracies of 0.98. Single lead\, multi lead and feature fusion experimental approaches were evaluated\, resulting in classification accuracies of 0.97\, 0.98\, and 0.97\, respectively. The class-weighting method combined with lead fusion feature extraction obtained an accuracy of 0.95. Furthermore\, class weight additional techniques achieved accuracies of 0.91\, 0.92 and 0.92\, demonstrating variations in model performance across different methodologies. This automated system can support clinicians to assist in the early diagnosis of heart abnormalities and improve healthcare efficiency.
CATEGORIES:VIRTUAL ROOM 2C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:5cf7be5e131a6b1ce393edd3ebdc366b
URL:http://internationalconferencetibs.sched.com/event/5cf7be5e131a6b1ce393edd3ebdc366b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T030000Z
DTEND:20260623T050000Z
SUMMARY:Enhancing User Experience through Technology Acceptance and Service Efficiency: A Service Design Perspective in O2O F&B Retail
DESCRIPTION:Authors - Caroline Sutiono\, Ronald Gunawan\, Silvina Chandra\, Maria Pia Adiati Abstract - Online to Offline applications (O2O) have transformed a service style to a new level\, since consumers increasingly rely on digital technology to access daily food and beverage products and services based on their needs and preferences. Prior to their arrival\, the customer browses the menu\, place the order and finish the payment and afterwards the product will be collected at the store. The application required to provide details menu information\, options and preference as well as payment details. To use of O2O applications requires customers to have sufficient digital literacy to navigate the ap-plication\, place orders\, and complete upfront payments. Meanwhile\, outlet staff must be able to accurately interpret and process each order specification to ensure service accuracy. Therefore\, this study is examining the relationship between O2O application usage\, service efficiency\, and customer experience in F&B retail businesses. This research uses a quantitative research method\, with a survey approach with 160 eligible respondents and analyzed thru SEM PLS. This result emphasizes the importance of user experience and service design into interaction in O2O application usage experience\, where customers prioritize applications that are intuitive\, convenient\, and aligned with their needs. Therefore\, the effectiveness of O2O applications is influenced not only by operational efficiency but also by how well the technology supports user-friendly and meaningful user experiences.
CATEGORIES:VIRTUAL ROOM 2C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:6e7a90589e288eeac95da69000c407d2
URL:http://internationalconferencetibs.sched.com/event/6e7a90589e288eeac95da69000c407d2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T030000Z
DTEND:20260623T050000Z
SUMMARY:Fine-Tuning CNN-Based Detection of Real Vs AI-Generated Artwork Images
DESCRIPTION:Authors - Su Thet Oo\, Ah Nge Htwe\, Nilar Aye Abstract - The automatic detection of AI-generated art images is essential for distinguishing authentic human creations from artificial ones. This process is critical for authenticity verification\, provenance control\, misinformation management\, and digital forensics. With the rapid evolution of deep learning content generation\, the existing detection approaches within artistic imagery remain an underexplored domain characterized by artworks that differ widely in style and often contain non-standard\, complex\, or distorted visual patterns. The proposed model is an empirical study of a fine-tuned CNN-based generative art detection to classify real and human-created art accurately by learning discriminative visual features such as texture\, structure\, and statistical patterns\, adapting a pre-trained CNN model and also finetuning architecture layers and defining the spatial dimension\, which is used to determine the level of detail captured in feature extraction and classification. In our system\, utilizing a balanced dataset consisting of real and AI-generated art images\, the system was trained and evaluated\, where a base VGG16 net in traditional architecture and this architecture of pre-trained and fine-tuned VGG16 with hyperparameter tuning of task-specific input representation and data augmentation\, layer optimization strategies using the same balanced dataset\, with results benchmarked against a strong baseline.
CATEGORIES:VIRTUAL ROOM 2C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:ed6feb75091ad67d7040285a7839bd8a
URL:http://internationalconferencetibs.sched.com/event/ed6feb75091ad67d7040285a7839bd8a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T030000Z
DTEND:20260623T050000Z
SUMMARY:Intelligent Transformation of On-the-Job Training in Philippine Higher Education: A Systematic Literature Review Through the Lens of Artificial Intelligence\, Data Analytics\, and Digital Strategy
DESCRIPTION:Authors - Ferdinand V. Dalisay\, Gerli Ryza DS. Reyes Abstract - On-the-Job Training (OJT) in Philippine higher education institutions (HEIs) stands at a decisive inflection point. Historically constrained by misaligned curricula\, weak industry-academe partnerships\, and inadequate quality assurance mechanisms\, the OJT system is now confronted simultaneously with the disruptive potential of artificial intelligence (AI)\, the transformative power of data analytics\, and the imperatives of broader digital transformation. This systematic literature review synthesizes 35 peer-reviewed studies and policy documents published between 2020 and 2026 to examine how these three technological forces are reshaping and should further reshape the design\, implementation\, supervision\, and evaluation of OJT programs across Philippine colleges and universities. Guided by the TIBS 2026 conference tracks on AI and Intelligent Systems\, Data Analytics and Business Intelligence\, and Digital Transformation and Technology Strategy\, the review constructs a crosscutting analytical framework that interrogates the current state of Philippine OJT against the backdrop of these technological paradigms. Four thematic clusters are identified: (1) AI-mediated supervision\, mentoring\, and competency scaffolding\; (2) data-driven OJT quality assurance and outcome analytics\; (3) digital platform ecosystems and virtual work-integrated learning\; and (4) strategic alignment between OJT curricula and the emerging digital economy. Findings reveal that while Philippine HEIs have begun to engage with digital tools in OJT administration\, deep integration of AI and analytics into OJT pedagogy and governance remains nascent. The review concludes with a multi-stakeholder digital transformation roadmap for the Philippine OJT system\, offering implications for CHED policymakers\, HEI administrators\, industry partners\, and technology developers.
CATEGORIES:VIRTUAL ROOM 2C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:4ffac420506f3662c5c04c17f0281448
URL:http://internationalconferencetibs.sched.com/event/4ffac420506f3662c5c04c17f0281448
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T030000Z
DTEND:20260623T050000Z
SUMMARY:The Impact of Virtual Try-On Technology on Consumer Buying Impulse and Purchase Behavior
DESCRIPTION:Authors - Janssen Emmanuel Jahja\, Anderes Gui Abstract - The rapid development of e-commerce also raises the need for new innovations such as Virtual Try-On (VTO) to address the physical limitations of online product evaluation. Nevertheless\, the interaction of functional and psychological factors of VTO is poorly understood as influencing its adoption\, while their influence on purchase decisions also remains limited. This study investigates these factors with respect to online purchasing intentions. Incorporating an extended Technology Acceptance Model (TAM) with consumer behavior theories\, the conceptual model assesses Perceived Ease of Use\, Perceived Usefulness\, Perceived Enjoyment\, Attitude\, Personal Innovativeness in IT\, and Self-Efficacy. Using a quantitative approach\, information was gathered from consumers who shop on e-commerce sites and analyzed using Structural Equation Modeling (SEM). The results show that the hypotheses suggested are well supported. This study contributes theoretically by extending digital retail literature and offers managerial implications for designing VTO features that not only improve the shopping experience but also yield higher sales conversions.
CATEGORIES:VIRTUAL ROOM 2C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:a2b70ceed21ecac5a602c693f0e3d35d
URL:http://internationalconferencetibs.sched.com/event/a2b70ceed21ecac5a602c693f0e3d35d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T030000Z
DTEND:20260623T050000Z
SUMMARY:WealthBridge: A Hybrid Deep Learning Framework for Personalized Financial Risk Profiling and Portfolio Allocation for the Sandwich Generation
DESCRIPTION:Authors - Jayanthi J\, Krishna Kanwar\, Divansh Tarun Mittal\, Akash Kumar\, Srikanta Pradhan\, Arun Kumar K Abstract - The problem of financial distress faced by the sandwich generation-who are held responsible for both the elderly parents and dependent children simultaneously\, but not accommodated by available tools-motivates this research. In this work\, we developed a portfolio intelligence system named WealthBridge that leverages an AI framework\, which includes Random Forest model for risk profiling and an LSTM network for market regime detection. While the model accurately classify investors (with 95% accuracy) and market regimes\, it forecasts market trends using time series of various features. A fusion engine then provides recommendation for allocation to different portfolio asset classes and investment in particular stock. It is accessible through the deployment of a Streamlit dashboard\, making it an efficient tool for data-driven financial planning. The accuracy was assessed with robust performance of models that caters to financial services of the Indian middle income sandwich generation.
CATEGORIES:VIRTUAL ROOM 2C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:85e7873d19ed756b1b215d3236d5bb43
URL:http://internationalconferencetibs.sched.com/event/85e7873d19ed756b1b215d3236d5bb43
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T030000Z
DTEND:20260623T050000Z
SUMMARY:Advancing Women’s Financial Inclusion Through Digital Investment Platforms
DESCRIPTION:Authors - Rajkiran R Nair\, Arya k\, Durga K V Abstract - The role of digital investment platforms in facilitating the participation of people in financial markets cannot be overstated. However\, despite the ease of access\, convenience\, and cost savings provided by these platforms\, their adoption among women in developing countries seems to be low. The objective of this research was to examine the factors that affect women’s intention to use digital investment platforms. The factors identified in this study were technology acceptance\, trust\, financial literacy\, perceived security\, social influence and perceived risk. The research findings showed that the perceived security was the most influential factor affecting the adoption intention of digital investment platforms among women\, while the other factors had an indirect influence. The research also found that the traditional technology acceptance model has the limitations of predicting the behaviour of women in investing in the stock market. The research provided helpful insights for FinTech companies to create safer environments for their customers.
CATEGORIES:VIRTUAL ROOM 2D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:08089ba756bed248de43acae8de401e1
URL:http://internationalconferencetibs.sched.com/event/08089ba756bed248de43acae8de401e1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T030000Z
DTEND:20260623T050000Z
SUMMARY:AGRIMITRA: EMPOWERING RURAL INDIAN FARMERS THROUGH BLOCKCHAIN AND AI-DRIVEN MARKET INTELLIGENCE
DESCRIPTION:Authors - Akruti Dabas\, Madhura Jangale\, Rujuta Medhi\, Shravani Patil and Kajal Joseph Abstract - Indian agriculture faces significant challenges due to the opacity of the supply chain system with exploitation by intermediaries that reduce profitability and market reach for the farmers. The Agrimitra project uses blockchain and machine learning technologies to solve these issues by facilitating farmer-to-consumer transactions while recording prices in immutable ledgers. In this platform\, blockchain is used for implementing smart contracts to maintain transparent pricing whereas machine learning is used to analyze past market trends and generate price-recommendation. Through the use of blockchain technology\, farmers can set prices autonomously while keeping transactional records on a blockchain ledger\, which cannot be tampered with. The Agrimitra platform addresses several challenges faced by the rural communities in India such as access to real-time market analysis\, transparent credit history\, buyers’ database\, and a community at the regional level to take care of logistics in the transportation network efficiently and effectively.
CATEGORIES:VIRTUAL ROOM 2D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:2649f02c1c313c5d9bf4f4b46fa80bc3
URL:http://internationalconferencetibs.sched.com/event/2649f02c1c313c5d9bf4f4b46fa80bc3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T030000Z
DTEND:20260623T050000Z
SUMMARY:Consumer Behavior towards ICT-Enabled Credit and Discount Mechanisms in the Petrochemical Retail Sector: Implication for Policy Framework
DESCRIPTION:Authors - Arunangshu Giri\, Dipanwita Chakrabarty\, Manash Routray Abstract - Credit-based transaction in petrochemical retail sector is widely practiced\, though highly challenging\, as the sector operates with a thin margin\, in spite of high transaction volume. The present study identifies crucial determinants for adoption of ICT-enabled credit and discount process in the petrochemical retail sector. Diffusion of Innovation (DOI) Theory was adopted to evaluate the factors that influence trialability of ICT and there by leads to users’ understanding of relative advantage\, which subsequently enhances their demand and purchase intention. Structured questionnaire was used as survey tool and hypotheses were tested using Structural Equation Modelling (SEM). The findings show that trialability get induced by social\, relational and economic factors\, which in consequence enhance relative advantage and improves purchase intention. The result shows that traditional to ICT-enabled payment transition does not solely depend on the financial parameters rather behavioral factors play a pivotal role. The study extends DOI theory in the context of infrastructure-based retail operations and provides deep insights to managers so that they can adopt customeroriented ICT strategies to improve credit-to-cash payment transformations.
CATEGORIES:VIRTUAL ROOM 2D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:3b2485972df67f99b2e758acfced1b0d
URL:http://internationalconferencetibs.sched.com/event/3b2485972df67f99b2e758acfced1b0d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T030000Z
DTEND:20260623T050000Z
SUMMARY:Hybrid U-Net with Attention Gates for Lung Nodule Segmentation in Low Dose CT Scans
DESCRIPTION:Authors - Mamatha Kurra\, Ochin Sharma\, G S Pradeep Ghantasala Abstract - Accurate segmentation of pulmonary nodules in low-dose CT (LDCT) scans plays a crucial role in the early detection of lung cancer. However\, small and irregular nodules remain difficult to detect due to low contrast\, anatomical variability\, and imaging artifacts. In this study\, we perform a comparative evaluation of widely used deep learning-based segmentation architectures-namely\, vanilla U-Net\, Feature Pyramid Network (FPN)\, and Mask R-CNN-on benchmark datasets LIDC-IDRI and LUNA16. Building on the observed limitations of these models\, we introduce a refined Hybrid U-Net architecture augmented with attention gates and Squeeze-and-Excitation (SE) blocks. This enhancement improves the model’s ability to focus on clinically relevant features while maintaining strong spatial consistency across encoder-decoder layers. Preprocessing involves Hounsfield Unit (HU) windowing (−1000 to 400 HU) to isolate lung parenchyma\, followed by patch extraction (128×128) to better represent small nodules and manage class imbalance. The model is trained using a compound loss function that combines Dice loss and Boundary loss in a 0.7:0.3 ratio to balance volumetric overlap and edge accuracy. Experimental results on the LIDC-IDRI dataset show that the proposed attention guided model achieves a Dice coefficient exceeding 0.85\, outperforming the baseline U-Net (average Dice 0.78). Evaluation metrics such as sensitivity (true positive rate) further confirm the effectiveness of our approach in capturing subtle nodule features. This work demonstrates that integrating attention mechanisms and feature recalibration into U-Net significantly boosts segmentation performance on challenging medical imaging tasks. Our results provide a strong foundation for deploying more accurate and interpretable tools in computer-aided diagnosis pipelines for lung cancer screening.
CATEGORIES:VIRTUAL ROOM 2D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:503566e95a9ecc6a24485a746cf26ed2
URL:http://internationalconferencetibs.sched.com/event/503566e95a9ecc6a24485a746cf26ed2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T030000Z
DTEND:20260623T050000Z
SUMMARY:Regional Disparities in BMI and WHR Using FNRI Big Data Analytics: Random Forest Classification with Cloud-Based Processing
DESCRIPTION:Authors - Jonalyn Joy B. Labayne\, Joey Aviles\, Ronald Cordova Abstract - This empirical study examines regional and demographic disparities in BMI and Waist-Hip Ratio (WHR) as indicators of nutritional and cardiometabolic health in the Philippines. Using the 2013 FNRI National Nutrition Survey dataset (n = 69\,505 adults aged 20 years and above)\, data were processed with Apache Spark for distributed handling of large-scale heterogeneous records. A Random Forest classifier was trained with 10-fold stratified cross-validation and inverse class-weighting to mitigate severe class imbalance. The model achieved an accuracy of 0.81\, macro-F1 score of 0.67\, and area under the precision-recall curve (AUCPR) of 0.75. These FNRI-specific results are discussed in the context of existing literature. Genc and Arıcan (2025) compared eight machine learning algorithms on a Latin American obesity dataset (n = 2\,111)\, excluding height and weight variables\; Random Forest achieved the highest ROC AUC of 0.98 and macro-F1 of 0.87 in that study. The inclusion of WHR alongside BMI in the FNRI analysis provides enhanced cardiometabolic risk stratification. The findings underscore the value of ensemble methods in future Philippine research to better detect minority classes and support regionally targeted public health interventions in resource-limited settings.
CATEGORIES:VIRTUAL ROOM 2D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:58a8077cf2eb909f1dfa944335766436
URL:http://internationalconferencetibs.sched.com/event/58a8077cf2eb909f1dfa944335766436
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T030000Z
DTEND:20260623T050000Z
SUMMARY:The Human-Digital Trust Bridge Framework for Mediated Mobile Governance in ICT Adoption in Rural India
DESCRIPTION:Authors - Ruhi Sethi\, Sambhram Pattanayak\, Prachi Trivedi Abstract - Despite substantial investments in India's digital governance infrastructure\, the adoption of mobile governance in ICT services among rural citizens remains critically low. Existing literature treats trust as a monolithic construct and fails to distinguish trust in government institutions\, digital platforms\, intermediaries\, and social trust. This conceptual paper proposes the Human Digital Trust Bridge framework which integrates multidimensional trust theory with three mediation mechanisms. These mechanisms include human intermediaries such as Common Service Centre operators\, low technology interfaces like voice-based services and assisted kiosks\, and social networks including peer influence. Synthesizing evidence from 50 studies published between 2020 and 2026 and contrasting the cases of UMANG success and Sanchar Saathi trust failure\, the framework demonstrates that rural adoption of mobile governance in ICT is not driven by digital trust alone but is mediated through these bridges. The paper deconstructs trust into four distinct dimensions: institutional\, technological\, intermediary\, and social. It shows how each dimension differentially affects rural and urban adoption. The framework yields testable propositions for empirical research and offers actionable policy implications for inclusive mobile governance in ICT design.
CATEGORIES:VIRTUAL ROOM 2D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:b5301c50fd2a659ebceae5149c0cc165
URL:http://internationalconferencetibs.sched.com/event/b5301c50fd2a659ebceae5149c0cc165
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T030000Z
DTEND:20260623T050000Z
SUMMARY:The roles of graphic design principles and AI‑driven design in advertising effectiveness
DESCRIPTION:Authors - Dimo Valev\, Sambhram Pattanayak Abstract - In an era saturated with visual communication and digital interaction\, the effectiveness of advertising increasingly depends on the strategic integration of graphic design principles\, artificial intelligence (AI)\, and Digital Marketing and Social Media Intelligence. Good design can determine whether an advertisement is ignored or remembered\, making visual communication a central component of successful contemporary advertising campaigns. This study investigates the roles of graphic design principles and AI‑driven design in advertising effectiveness in contemporary media environments. It focuses on how core visual elements—such as visual hierarchy\, color theory\, typography\, layout composition\, branding consistency\, imagery\, and interactivity interact with data‑driven and generative technologies to shape consumer perception\, engagement\, and recall. Drawing on theoretical frameworks from visual communication and empirical evidence from real‑world campaigns\, the research analyzes how these principles are applied across print\, digital\, and social media platforms\, often augmented by AI systems for personalization\, layout optimization\, and content generation. The findings show that advertisements that systematically apply key graphic design principles while integrating AI‑driven design tools—such as generative visuals\, dynamic layouts\, and data‑driven personalization—tend to achieve higher levels of attention\, comprehension\, and brand recall than those with weak or inconsistent design. The study also highlights how artificial intelligence has expanded the role of graphic design into functional\, predictive\, and experiential dimensions\, enabling responsive\, real‑time\, and context‑aware advertising. The paper concludes with practical guidelines for designers and marketers on integrating graphic design principles with AI‑driven design processes to optimize attention\, message retention\, and overall campaign effectiveness.
CATEGORIES:VIRTUAL ROOM 2D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:b0a646432a33edb57f0f4e5825c66b8c
URL:http://internationalconferencetibs.sched.com/event/b0a646432a33edb57f0f4e5825c66b8c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T050000Z
DTEND:20260623T050200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:51fb30dbed784350bc01c8a8be7e0d49
URL:http://internationalconferencetibs.sched.com/event/51fb30dbed784350bc01c8a8be7e0d49
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T050000Z
DTEND:20260623T050200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:db7d554eab382ca897025712fc35392a
URL:http://internationalconferencetibs.sched.com/event/db7d554eab382ca897025712fc35392a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T050000Z
DTEND:20260623T050200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:d96cf6c18454c6db60d60e6e951e5631
URL:http://internationalconferencetibs.sched.com/event/d96cf6c18454c6db60d60e6e951e5631
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T050000Z
DTEND:20260623T050200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:e2bf4e47a75aba6b02997dfb9f88c9f1
URL:http://internationalconferencetibs.sched.com/event/e2bf4e47a75aba6b02997dfb9f88c9f1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T050200Z
DTEND:20260623T050500Z
SUMMARY:Session Closing & Information to Author
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:c45f61910a0bff4db2fd9ede3abc3bae
URL:http://internationalconferencetibs.sched.com/event/c45f61910a0bff4db2fd9ede3abc3bae
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T050200Z
DTEND:20260623T050500Z
SUMMARY:Session Closing & Information to Author
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:5ad5eb0434e5c426fe5596cf0f68db95
URL:http://internationalconferencetibs.sched.com/event/5ad5eb0434e5c426fe5596cf0f68db95
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T050200Z
DTEND:20260623T050500Z
SUMMARY:Session Closing & Information to Author
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:36d122f582ff324ceebbe2069285e5bc
URL:http://internationalconferencetibs.sched.com/event/36d122f582ff324ceebbe2069285e5bc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T050200Z
DTEND:20260623T050500Z
SUMMARY:Session Closing & Information to Author
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 2D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:639c30009f41d9159be14681bb6154d3
URL:http://internationalconferencetibs.sched.com/event/639c30009f41d9159be14681bb6154d3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T055800Z
DTEND:20260623T060000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:0cb634085c4adc50d13459cc3289b4bb
URL:http://internationalconferencetibs.sched.com/event/0cb634085c4adc50d13459cc3289b4bb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T055800Z
DTEND:20260623T060000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:cc596283f95539994e89e26ddd0242c4
URL:http://internationalconferencetibs.sched.com/event/cc596283f95539994e89e26ddd0242c4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T055800Z
DTEND:20260623T060000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:207fcc290e48ddab818ee7ab7b34ed55
URL:http://internationalconferencetibs.sched.com/event/207fcc290e48ddab818ee7ab7b34ed55
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T055800Z
DTEND:20260623T060000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:c09f1c955483856e92462a4b43d31ee5
URL:http://internationalconferencetibs.sched.com/event/c09f1c955483856e92462a4b43d31ee5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:Advancing Internationalization in Higher Education through Technology-Driven Innovations
DESCRIPTION:Authors - Maria Cecilia L. Pangan \, Jolou Vincent M. Jala\, Ralph Vendel E. Musni\, Everly A. Nacalaban\, Nenon Roy A. Sandinao\, Randy Joy M. Ventayen Abstract - As digital revolution\, globalization\, and cross-border collaboration re-shape academic landscapes\, internationalization of higher education has emerged as a strategic focus for institutions globally. With this\, technology-driven innovations particularly learning management systems (LMS)\, digital platforms\, virtual mobility tools and artificial intelligence (AI) have augmented international engagement beyond physical boundaries. Artificial intelligence has immense potential to be a universal technology that boosts product innovation and productivity across a range of industries. This study explores how technological innovations improve internationalization in higher education. Most specifically by how technological innovations improve internationalization in higher education through Technology-Enabled Teaching and Learning\, Virtual Mobility and Global Collaboration\, Research and Knowledge Exchange\, Institutional Governance and Global Competitiveness. Notably\, the rapid growth of digital and global academic engagement also causes meaningful implications for students’ and faculty members’ mental health and well-being. This is because when internationalization becomes progressively technology-mediated\, matters such as academic pressure\, digital fatigue\, time-zone differences in global collaboration\, and constant online connectivity may contribute to anxiety\, stress\, and burnout. To attain this goal\, the proponents critically examined 156 papers in the body of literature that were indexed by Scopus to examine the advancement of Internationalization in Higher Education through Technology-Driven Innovations Using a systematic review of recent literature\, this paper synthesizes global and international perspectives. The findings emphasize that Technology-driven revolutions have redefined the practices and scope of internationalization in higher education. Conversely\, obstacles and challenges such as deficient infrastructure\, Digital divides\, and unfair access to technology deter inclusive involvement and participation
CATEGORIES:VIRTUAL ROOM 3A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:60b0b4c7f47cc0263e22c538ada7d9b3
URL:http://internationalconferencetibs.sched.com/event/60b0b4c7f47cc0263e22c538ada7d9b3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:Assessing Statistical and Machine Learning Models for Dengue Incidence Forecasting in Chandigarh\, India
DESCRIPTION:Authors - Gaurav Gupta\, Kumar Shashvat\, Gunjan Abstract - In India\, dengue fever poses a significant threat to public health which continues to worsen. Forecasting methods are crucial to developing effective disease surveillance systems. This study provides an empirical comparison between classical time series forecasting methods\, and various machine learning techniques\, applied to dengue forecasting for the period of 2013 - 2019 in Chandigarh\, India. Seven methods are explored - ARIMA\, SARIMA\, Exponential Smoothing (ETS)\, AutoReg\, Linear Regression with lagged variables\, Decision Tree Regression\, and Random Forest Regression. The models are evaluated on multiple criteria which include Mean Absolute Error (MAE)\, Root Mean Square Error (RMSE)\, Mean Absolute Percentage Error (MAPE) and Mean Absolute Scaled Error (MASE)\, and for the statistical models\, the Akaike Information Criterion and the Bayesian Information Criterion (AIC/BIC) are used. Random Forest Regression produced the lowest predicted error (MAE 26.95\, MASE 0.19)\, while SARIMA\, with seasonal modeling\, demonstrated the best and most useful epidemiological interpretability (MAE 45.36\, MASE 0.39) of the models. The outcome of the study shows the balance between predictive power of a public health forecasting model\, and the interpretability of the model. In this case\, SARIMA had the best balance of both and thus\, is recommended as the best model for dengue surveillance systems.
CATEGORIES:VIRTUAL ROOM 3A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:d8aabe5cec647579c71622f37a4b31ec
URL:http://internationalconferencetibs.sched.com/event/d8aabe5cec647579c71622f37a4b31ec
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:Driving Sustainable Firm Value Through Green Banking Disclosure: The Role of Audit Committee
DESCRIPTION:Authors - Made Ratih Nurmalasari\, Putu Diah Kumalasari\, Mirah Candra Adi Saputri Abstract - Through an emphasis on the function of Audit Committee in trying to enhance the correlation of Green Banking Disclosure and also Sustainable Firm Value\, this study tried to do investigating how banking firms in Indonesia might have benefits from this practice. As sustainability gets increasingly significant for businesses and also stakeholders alike\, banks are under pressure to show transparent of the environmental initiatives. By applying data from Indonesian banks registered in the year of 2021\, 2022\, and also 2023\, this study tended to examine whether banks that actively disclose their efforts of green banking are better allocated to help enhancing their value. The Committee is defined as a moderating variable\, shown the significant role to help ensuring good governance and also the disclosures credibility. Data analysis was done by using SPSS with models of multiple regression\, as like terms of interaction to help assessing the moderation influences. The outcomes stated that Sustainable Firm Value is greatly improved by Green Banking Disclosure. It is also getting amplified at the time an effective Audit Committee is in place\, hoping that good governance is able to increase the influence and also value of sustainability. This research also emphasizes the merging necessity of transparent sustainability measures with strong frameworks of governance to help providing enduring value. The out-comes have actionable information for banking regulators\, executives\, and also legislators to help integrating sustainability with expansion of corporate.
CATEGORIES:VIRTUAL ROOM 3A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:ac57a1fb821d65826682b6cb618a1669
URL:http://internationalconferencetibs.sched.com/event/ac57a1fb821d65826682b6cb618a1669
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:Driving Sustainable MSMEs Through Digital Innovation and Entrepreneurial Mindset
DESCRIPTION:Authors - Made Ermawan Yoga Antara Abstract - This study is to examine how sustainable MSMEs are impacted by digital innovation and entrepreneurial mindset\, mediated by entrepreneurial resilience. This study was carried out in the province of Bali using a quantitative methodology\, focusing on MSMEs in the creative economy craft sub-sector. The study sample consisted of 361 MSME owners and leaders selected using proportional random sampling from a total population of 3\,745 business units. Data were collected using a Likert-scale questionnaire and analyzed using SEM-PLS with the assistance of SmartPLS software. The results showed that digital innovation and entrepreneurial mindset have a positive and significant effect on both entrepreneurial resilience and sustainability in MSMEs. Additionally\, sustainable MSMEs benefit greatly from entrepreneurial resilience. The association between digital innovation and entrepreneurial mindset on sustainable MSMEs is partially mediated by entrepreneurial resilience\, according to the mediation test results. Digital innovation has the largest influence on entrepreneurial resilience\, while entrepreneurial mindset has the largest direct influence on sustainable MSMEs. These findings emphasize the importance of integrating digital technology adoption and internal entrepreneurial capabilities in driving business sustainability. This research supports dynamic capabilities theory\, which emphasizes sensing\, seizing\, and transforming capabilities in enhancing the resilience and sustainability of MSMEs. Practically\, MSMEs need to strengthen digital innovation\, entrepreneurial mindsets\, and business resilience to adapt to environmental dynamics. This research contributes to the development of sustainable entrepreneurship literature\, particularly in the creative economy in developing countries.
CATEGORIES:VIRTUAL ROOM 3A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:d9861f303cbfa3b8ddd2601e9b51c1c6
URL:http://internationalconferencetibs.sched.com/event/d9861f303cbfa3b8ddd2601e9b51c1c6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:Evaluating Online Tourist Feedback Through Sentiment and Topic Analysis Using Natural Language Processing: A Case Study of the Chocolate Hills\, Carmen\, Bohol\, Philippines
DESCRIPTION:Authors - Criscel Jay F. Nayve\, Lord Francis B. Navarro\,Karen Aparicio Doblas\, Elvan Budiongan\, Darrel A. Cardana\, Max Angelo D. Perin Abstract - This study evaluates online tourist feedback on the Chocolate Hills in Carmen\, Bohol\, Philippines\, using Natural Language Processing (NLP) techniques. Although the destination consistently receives high ratings\, negative reviews contain critical insights that can guide tourism management. A total of 4\,059 Google Maps reviews were collected\, of which 2\,011 contained textual content suitable for analysis. The dataset underwent preprocessing using Python and Orange Data Mining before applying sentiment analysis and Latent Dirichlet Allocation (LDA) topic modeling. Results show that\, while overall sentiment toward the Chocolate Hills remains strongly positive\, negative reviews highlight key concerns related to accessibility\, and crowding. Topic modeling identified five dominant themes: scenic appreciation\, environmental ambience\, crowd density and photo-taking behavior\, physical effort required for climbing viewpoints\, and perceived cost–benefit value. Sentiment trends from 2020 to 2025 indicate stable positive perceptions despite pandemic-related fluctuations in review volume. Findings suggest that tourists’ satisfaction is primarily driven by the site’s natural beauty\, but logistical challenges require targeted management interventions. The study contributes to localized tourism analytics in the Philippines and demonstrates the usefulness of NLP for extracting actionable insights from large volumes of user-generated content.
CATEGORIES:VIRTUAL ROOM 3A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:e011a95f9fa162c95a7f979b2653acb0
URL:http://internationalconferencetibs.sched.com/event/e011a95f9fa162c95a7f979b2653acb0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:From Detection to Prediction: A Machine Learning Framework for Cyber Threat Intelligence and Forensics
DESCRIPTION:Authors - Sarvesha Nakharekar\, Seedhi Kundap\, Suman Madan Abstract - When cyberattacks become ever more extensive and complicated\, the demand for intelligent systems capable of executing cyber threat intelligence\, digital forensics\, and risk management efficiently has increased. We have focused on the important point where digital forensics and cyber threat intelligence meet through this article. In order to build and evaluate the classification models\, a publicly accessible intrusion detection dataset was used. The models are Logistic Regression\, Decision Tree\, Random Forest\, Support Vector Machine\, K-Nearest Neighbors\, and Multilayer Perceptron .The models were evaluated from the perspective of their probable employment in cyber threat intelligence and forensics\, based on their performance indicators such as accuracy\, precision\, recall\, F1- score\, and computing efficiency .Through a critical discussion\, the article also contains a number of significant problems that have been touched upon: the explainability of the attacks\, the existence of adversarial attacks\, the data imbalance problem\, and the limitations of real time processing. The investigation\, however\, brings up the possibility of using machine learning based on detection outcomes to improve cyber risk management by threat prioritization and thereby making informed decisions. The document is an essential resource for both researchers and field specialists interested in exploring the use of ML to significantly improve threat forecasting\, speed incident handling\, and strengthen risk management even in a more and more unfriendly domain of cyberattacks.
CATEGORIES:VIRTUAL ROOM 3A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:5258e37327ba7b72d619a8f03716bd35
URL:http://internationalconferencetibs.sched.com/event/5258e37327ba7b72d619a8f03716bd35
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:IoT-Based Smart Contract Framework for Rice Supply Chain Traceability Recall System and Consumer Safety
DESCRIPTION:Authors - Md Tanzid\, Md. Foridul Haque\, Md. Ismail Hossain\, Mohammad Golam Sarowar Abstract - The rice supply chain has been susceptible to quality deterioration\, expiry\, and a low level of transparency\, which are risks to consumer health and food security. To overcome these challenges\, the current research suggests a consortium-based blockchain and IoT-enabled smart contract framework to provide a holistic\, traceable\, and automated governance model. The framework facilitates a consortium of all key stakeholders in the rice supply chain from farmers to retailers as a blockchain network that is co-controlled and resistant to tampering. At key storage infrastructures\, Internet of Things (IoT) sensors are deployed to provide the variable storage conditions (humidity and temperature) in real-time that are important in storing rice. The monitoring variables are sent to smart contracts that generate a two-tiered governance system. Upon data showing that a rice lot reached 90% of its shelf life\, the intervening automated process will promulgate notifications. Upon expiration of the rice\, monitoring will render a smart contract disables the ability to purchase or distribute in the supply chain. The automated process therefore notifies users of public health risks by preventing the introduction and sale of products deemed unsafe for consumption. The framework ensures the sustainable\, validated\, and tamper evident functionality for continuous monitoring and rule-based execution of perishable products on a public ledger to facilitate enhanced food governance\, to lower food safety risks to consumer health\, and to promote consumer trust in the rice supply chain.
CATEGORIES:VIRTUAL ROOM 3A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:d211c0473f0d4a30076f4279d9e070e7
URL:http://internationalconferencetibs.sched.com/event/d211c0473f0d4a30076f4279d9e070e7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:Lightweight YOLOv8s-Based Coral Bleaching Classification Outperforms Vision Transformers for Real-Time Edge Deployment
DESCRIPTION:Authors - Aksh Modi\, Agrim Gairola\, Suryansh Shah\, Sahil Singh\, Malvinder Singh Bali Abstract - The increasing degradation of global coral reef ecosystem heavily needs scalable\, automated monitoring solution that are capable of operating in resource constrained underwater ecosystem. Though the ongoing State of the Art approaches\, such as Vision Transformer and Efficient Net\, achieve high classification accuracy\, they heavily suffer from computational latency and power requirement that makes them unsuitable for Autonomous Underwater Vehicles (AUVs) or diver held devices. This paper presents a lightweight\, real time detection model using the YOLOv8s-cls architecture\, which is optimized for edge deployment. Our model achieves a Top 1 Accuracy of 89.84%\, conquering the official NOAA Vision Transformer baseline (85.0%) and recent YOLOv8 benchmark at 88.0% accuracy when tested on NOAA-PIFSC-ESD dataset. Crucially\, this performance is achieved with a fraction of the computational overhead\, enabling high-frequency inference without reliance on cloud connectivity. These results demonstrate that lightweight Convolutional Neural Networks (CNNs) can outperform complex Transformerbased models in texture-centric underwater tasks\, providing a viable pathway for immediate\, in-situ bleaching assessment by low-power marine robotics.
CATEGORIES:VIRTUAL ROOM 3A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:1a9657fb9f99bfcaac071b7d130b2469
URL:http://internationalconferencetibs.sched.com/event/1a9657fb9f99bfcaac071b7d130b2469
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:AI-Based Writing Tools as Intelligent Decision-Support Systems: Effects on Academic Performance\, Autonomy\, and AI Integration in Higher Education
DESCRIPTION:Authors - Rowena Ocier Sibayan\, Hazel C. Tagalog\, Salvacion M. Domingo Abstract - Artificial intelligence (AI)–based writing tools are increasingly integrated into higher education as part of institutional technological‑intelligence infrastructures\, providing automated feedback that can improve students’ writing quality and efficiency. This study evaluates AI writing tools as intelligent decision‑support systems and examines their impact on academic performance\, student learning behavior\, and institutional decisions about AI integration in higher education. A convergent parallel mixed‑methods design was adopted\, combining quantitative analysis of writing performance with qualitative insights into student experiences. Data were collected from 100 undergraduate students with prior exposure to AI writing tools\; quantitative measures included pre‑ and post‑intervention writing scores\, rubric‑based assessments\, and usage frequency\, while qualitative data were gathered through structured questionnaires and reflective responses. Findings reveal statistically significant\, large improvements in writing confidence\, perceived clarity\, and assignment performance\, with mean grades increasing from 68.5% to 73.2%. Students also reported greater perceived independence in writing\, although qualitative data indicate variability in engagement\, ranging from critical use of AI feedback to more passive reliance. Concerns about data privacy showed minimal change and remained an area of uncertainty\, underscoring the importance of governance and risk management in institutional AI deployments. The study concludes that AI writing tools enhance measurable writing outcomes but do not automatically foster deeper cognitive development. Their effectiveness depends on how students interpret and engage with AI feedback\, underscoring the need for pedagogically guided and ethically responsible integration of AI in higher education.
CATEGORIES:VIRTUAL ROOM 3B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:233be19209c9808b991e78291500699c
URL:http://internationalconferencetibs.sched.com/event/233be19209c9808b991e78291500699c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:Bi-Level PSO–LP Framework for Carbon-Aware Business Optimization
DESCRIPTION:Authors - Sowmini Devi Veeramachaneni Abstract - This paper addresses the challenge of balancing economic performance and environmental sustainability in supply chain optimization. We propose a bi-level hybrid optimization framework that integrates Particle SwarmOptimization (PSO) with Linear Programming (LP) for carbonaware business decision making. At the upper level\, PSO dynamically optimizes the carbon penalty parameter\, while at the lower level\, LP ensures optimal and feasible operational decisions under supply chain constraints. The proposed framework automatically learns the trade-off between profit and emissions\, eliminating the need for manual parameter tuning. Experimental results on both synthetic and real-world datasets demonstrate that the method effectively identifies Pareto-optimal solutions\, achieves stable convergence\, and exhibits strong robustness compared to standalone optimization approaches.
CATEGORIES:VIRTUAL ROOM 3B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:32cc3f7cab1da59ac4927aef4ad272ea
URL:http://internationalconferencetibs.sched.com/event/32cc3f7cab1da59ac4927aef4ad272ea
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:Customer Experience with Robot Waiter Services: The Role of Trust in Technology and Perceived Enjoyment in Driving Revisit Intention
DESCRIPTION:Authors - Vinca Valenia\, Chelsea Calissta Liman Lim\, Ichwan Masnadi Abstract - The swift embrace of artificial intelligence (AI) in the hospitality field has deeply modified the way services are provided and how customers interact\, especially in the context of robot waiter systems in restaurant settings. Previous research mainly focused on operational efficiency\; however\, little has been done to understand how such technologies affect customer experience and their subsequent behaviors. This paper first determines customers' perception factors of AI-based robot waiter systems and their emotional involvement and satisfaction as consequences of the service encounter. Based on the Technology Acceptance Model (TAM)\, this study examines perceived usefulness and perceived ease of use in their contribution to customer attitudes formation toward AI-enabled services. Furthermore\, emotional involvement as the main affective reaction that alters the customer attitudes-satisfaction link has been included in this investigation. Participants were selected based on their familiarity or interest in AI-based service technologies\, and the quantitative method was used for the model testing. These results may shed light on the ways in which customer experience and satisfaction can be improved through AI-driven service innovations that take into account the cognitive and emotional aspects of consumer behavior. This paper is a significant addition to the field.
CATEGORIES:VIRTUAL ROOM 3B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:99b73649305b3b554ba225db4c381645
URL:http://internationalconferencetibs.sched.com/event/99b73649305b3b554ba225db4c381645
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:Enhancing Customer Experience through Human-Centered AI in Self Ordering Restaurant Systems
DESCRIPTION:Authors - Brandon Octavianus\, Charles Jonathan\, Julia Christina\, Ichwan Masnadi Abstract - The introduction of AI-driven self-service in restaurants has been swift\, fundamentally altering the nature of customer service interactions. Customers’ experiences dining at these AI-enabled restaurants have also revealed that intelligent systems need to be more human-centered. The intention of this research is to discover the influence of Technology Readiness to Attitudes Toward Using restaurant self-order technology device with Perceived Ease of Use\, Perceived Usefulness\, and Perceived Speed as the mediators. Through a quantitative analysis of 200 respondents located in the JABOTABEK region that have experience using restaurant self-ordering technology. The data was evaluated through PLS-SEM system. This research reveals a positive effect of Technology Readiness on each variable\, but it does not have considerable direct impact on Attitude Toward Using. The analysis of mediations revealed that customer attitude was positively impacted by Perceived Ease of Use and Perceived Speed\, whereas Perceived Usefulness displayed insignificant effect. Overall\, Perceived Speed was revealed as the strongest predictor implying that customers prioritize fast and easy service over useful functionality when interacting with intelligent restaurant systems. This study builds upon existing knowledge with an additional layer of understanding about human-centric AI implementation. Intelligent service technologies are meant to benefit both humans and organizations\, but restaurants should also focus on providing quick\, seamless\, and easy customer experience through this technology. Keywords:
CATEGORIES:VIRTUAL ROOM 3B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:27762582204a6e4a78805cbed53fa48f
URL:http://internationalconferencetibs.sched.com/event/27762582204a6e4a78805cbed53fa48f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:Extending the Technology Acceptance Model in Quick-Commerce Mobile Applications: The Roles of Interface Usability and Trust
DESCRIPTION:Authors - Agnes Gracia Hosiana\, Catherine Puspita Sari\, Tiurida Lily Anita Abstract - The rapid expansion of quick-commerce mobile applications has re-shaped how consumers purchase everyday essentials through digital platforms. Unlike traditional e-commerce\, quick-commerce operates in a time-sensitive and mobile-first environment\, making interface usability and trust particularly important in shaping user adoption. In this research\, Technology Acceptance Model (TAM) is extended by adding interface usability and trust into the model with the aim of understand the factors that affect the users' behavioral intention toward the usage of ASTRO mobile application. This research used quantitative methodology through surveys conducted among 258 active users of ASTRO. The pro-posed model in this research was evaluated utilizing Partial Least Square Structural Equation Modeling (PLS-SEM). The findings show that interface usability significantly influences perceived ease of use and perceived usefulness. Further-more\, trust positively impacts both attitude toward use and behavioral intention to use. Both perceived usefulness and perceived ease of use also positively impact user attitude. These results confirm that TAM remains relevant in the quick-commerce context\, while also demonstrating that interface usability and trust enhance its explanatory power in mobile retail environments. This research offers contributions to the technology adoption literature by providing a context-sensitive ex-tension of TAM for quick-commerce applications and delivers practical recommendations for platform developers to optimize user experience\, strengthen trust\, and encourage sustained adoption.
CATEGORIES:VIRTUAL ROOM 3B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:d56a57de30c653a91c269d90922cf8fc
URL:http://internationalconferencetibs.sched.com/event/d56a57de30c653a91c269d90922cf8fc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:Improving Sarcasm Detection Stability using Biphasic Differential Learning Rates
DESCRIPTION:Authors - Tanvi Pawar\, Sachin S. Pande\, Emmanuel M Abstract - Sarcasm detection in social media text is a NLP challenge\, as sarcastic statements inverse meaning of the statement as sarcastic statements hide the real meaning. This problem intensified on platforms like Reddit by informal phrasing\, community-specific references\, and implicit cultural knowledge. This paper introduces a RoBERTa-based classification framework which addresses three core issues: contextual impoverishment of isolated comments\, unstable training caused by random initialization\, and catastrophic forgetting during fine-tuning. These are handled via inline textual metadata fusion (encoding subreddit identity and upvote score into the input sequence)\, a structured multi-layer classification head\, and a biphasic two-stage training method with differential learning rates. Trained on a balanced 500\,000-sample subset of the SARC dataset\, the model achieves 68.36% accuracy with stable\, monotonic convergence across all training epochs. Near-symmetric false positive and false negative rates shows that the model does not favor a single class. Future directions include knowledge graph integration\, model distillation\, multi-class sarcasm taxonomy\, and multilingual extension.
CATEGORIES:VIRTUAL ROOM 3B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:d7defc7694e66a9e6070eaeb60c0553a
URL:http://internationalconferencetibs.sched.com/event/d7defc7694e66a9e6070eaeb60c0553a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:Learning Management Systems and Academic Achievement: The Role of System Features and Demographic Moderators in Higher Education
DESCRIPTION:Authors - Augustus Abbey\, Benjamin Ghansah\, Stephen Opoku Oppong\, Joseph Kwabena Essibu\, Charles Buabeng-Andoh\, Christopher Yarkwah\, Mathias Abgeko Abstract - The adoption of Learning Management Systems (LMSs) in higher education has transformed teaching and learning by enhancing digital content delivery\, assessment processes\, and collaborative engagement. Despite their widespread use\, variations in students’ learning experiences and academic outcomes suggest that the effectiveness of LMS platforms is influenced by both system features and learner characteristics. This study investigates the extent to which specific LMS functionalities contribute to students’ academic performance and examines how demographic and learner-related factors moderate LMS usage and learning outcomes. A cross-sectional survey design was employed\, involving 381 students from the University of Education\, Winneba. Data were collected using structured questionnaires and analyzed through descriptive statistics\, correlation analysis\, and multiple regression techniques. The findings reveal that key LMS dimensions\, including content delivery mechanisms\, communication and interaction tools\, navigation usability\, and system accessibility\, significantly influence students’ academic performance and learning experiences. Further-more\, demographic and learner-specific variables such as age\, socioeconomic back-ground\, language proficiency\, and learning preferences were found to shape the effectiveness and utilization of LMS platforms. The study underscores the importance of inclusive and user-centered LMS design approaches that accommodate diverse learner needs and promote equitable access to digital learning environments. The findings con-tribute to the growing discourse on technology-enhanced learning by providing empirical insights for educational institutions\, LMS developers\, and policymakers seeking to optimize the accessibility\, usability\, and pedagogical effectiveness of LMS platforms in higher education.
CATEGORIES:VIRTUAL ROOM 3B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:9fd39e881050bbe8e234a58e965ed73f
URL:http://internationalconferencetibs.sched.com/event/9fd39e881050bbe8e234a58e965ed73f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:Scalable Supply Chain Optimization via Feature-Aware Clustering
DESCRIPTION:Authors - Sowmini Devi Veeramachaneni\n Abstract - Modern supply chain systems must balance economic efficiency with environmental sustainability. Traditional optimization approaches\, such as linear programming (LP)\, provide optimal solutions but often struggle with scalability in large-scale networks. This paper proposes a clustering-based framework to reduce the computational complexity of supply chain optimization while preserving solution quality. The method groups suppliers and demand points using feature-aware clustering based on cost and emission profiles\, and solves a reduced transportation problem using LP. Experimental results on a real-world dataset demonstrate that the proposed approach achieves near-optimal performance\, with less than 7% deviation in profit and less than 2% deviation in emissions\, while reducing computation time by nearly an order of magnitude. An ablation study further highlights the trade-off between computational efficiency and solution fidelity controlled by the number of clusters. The proposed framework provides a practical and scalable solution for large-scale\, sustainability-aware supply chain optimization.
CATEGORIES:VIRTUAL ROOM 3B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:4a5608eb32590ba4e25e582b515f9e6e
URL:http://internationalconferencetibs.sched.com/event/4a5608eb32590ba4e25e582b515f9e6e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:A Hybrid Technological Intelligence Framework for Broadband Analytics: Machine Learning-Driven Business Strategy Insights from Multi-Country Digital Infrastructure Data
DESCRIPTION:Authors - Marybell Materum\, Daniel Dasig Jr\, Lucila Magalong\, Emelyn Libunao\, Shirley Padua\, Sonia Pascua\, Rizza Gerente and Sharon Sanchez\n Abstract - Broadband infrastructure has become a critical enabler of digital trans formation\, technological competitiveness\, and economic sustainability across OECD economies. This study proposes a hybrid technological intelligence framework integrating descriptive analytics\, temporal trend modeling\, compara tive broadband evaluation\, and predictive business interpretation using OECD broadband subscription datasets. The dataset comprised 11\,324 broadband obser vations covering fixed\, mobile\, and fiber-optic technologies across multiple countries and annual periods. A quantitative explanatory research design was em ployed using statistical preprocessing\, longitudinal analysis\, and machine learn ing-oriented analytical procedures to identify broadband growth dynamics and digital infrastructure disparities. Results revealed substantial asymmetry in broadband adoption patterns\, with the United States\, Japan\, Korea\, France\, and the United Kingdom demonstrating dominant subscription trajectories and accel erated digital infrastructure expansion. Fiber-optic and mobile broadband tech nologies exhibited the highest growth rates\, particularly after 2018\, reflecting in tensified digital transformation and remote connectivity demands. The findings demonstrate that broadband intelligence analytics can support strategic business forecasting\, digital competitiveness evaluation\, telecommunications planning\, and evidence-based policy formulation within Industry 4.0 and smart governance ecosystems.
CATEGORIES:VIRTUAL ROOM 3C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:8d7e377ef9339e2585a79c2843c5ae99
URL:http://internationalconferencetibs.sched.com/event/8d7e377ef9339e2585a79c2843c5ae99
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:An AI-Driven Neighborhood Recommendation System Based on User Lifestyle Preferences
DESCRIPTION:Authors - Govind Kumar\, Amresh Kumar\, Ajeet Singh Abstract - The process of selecting the right Indian city to live in is an extremely crucial one\, which can have a huge impact on One’s life\, safety\, work and happiness every day. However\, the tools available today\, The kind of websites that tell about a property\, or simple map applications\, aren’t smart enough. They Do not know what each member of a neighbourhood really wants. This paper introduces Neighbor- Fit\, an innovative AI-driven solution that suggests neighborhoods. Based on the actual need of the user. The system has three new ideas\, the first of which is: A composite neighborhood suitability score (CNSS) as a six-part score that perates safety\, facilities in the area\, travel time\, cost of living\, green areas\, and community life\; (2) a smart algorithm called Preference-Adaptive Cascade Hybrid (PACH) which alters its style of recommendation according to the amount of recommendation it already has knows about the user\; and (3) an explanation system based on LIME which explains to the user in simple words why a neighborhood was suggested. Tests done on 250 PIN codes In three major cities of India\, namely\, Delhi\, Mumbai and Bengaluru\, Preci- shows across. sion@10 of 87.3%\, Recall@10 of 84.1%\, and F1-Score of 85.7% — better than all There were five methods of comparison (p ¡ 0.05). The system reacts in an average of 340ms time even for 50 users using simultaneously.
CATEGORIES:VIRTUAL ROOM 3C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:6aeb7f2f2567c97d4d79cccd55f61cd0
URL:http://internationalconferencetibs.sched.com/event/6aeb7f2f2567c97d4d79cccd55f61cd0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:CRABSMART: A Smart Container-Based System for Mud Crabs (Scylla serrata) With Integrated Water Quality Monitoring and Growth Prediction
DESCRIPTION:Authors - John Julius M. Orillana\, Loyd S. Echalar\n Abstract - Mud crab fattening supports aquaculture\, local food supply\, and income for small-scale farming communities. In container-based culture systems\, farmers face two common problems. They need to keep water quality stable. They need to track crab growth on time. Manual monitoring takes time\, changes from one checking period to another\, and slows response when water conditions shift. These problems affect crab health\, survival\, and growth. This study developed CRABSMART\, a smart container-based fattening system for mud crabs\, Scylla serrata\, with integrated water quality monitoring and growth prediction. The system tracks temperature\, pH\, dissolved oxygen\, and salinity through sensors linked to a microcontroller platform. The platform sends the data to a web-based dashboard for real-time display\, historical monitoring\, and system status tracking. The study also includes a growth prediction component. This component estimates growth trends from recorded water quality conditions and culture duration. The study used a developmental research approach for design\, integration\, and implementation of the prototype. Functional assessment examined sensor operation\, data transmission\, dashboard performance\, and integration of the prediction component. CRABSMART supports faster decisions\, reduces manual monitoring\, and improves daily management in mud crab fattening. The system provides a practical approach for smart aquaculture\, especially in container-based mud crab production.
CATEGORIES:VIRTUAL ROOM 3C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:fcab97f2305439552fc83573c5abd901
URL:http://internationalconferencetibs.sched.com/event/fcab97f2305439552fc83573c5abd901
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:Explainability-Driven Leukemia Diagnosis: An Experimental Study
DESCRIPTION:Authors - Sarita Thummar\, Amit Thakkar\, Gayatri Patel\, Vaishali Koria\, Yug Mordiya Abstract - Leukemia is a malignancy that afflicts blood and bone marrow and requires a precise diagnosis and care to be effective. False diagnosis and diagnosis at a late stage result into death. Diagnostic capabilities have been greatly improved by recent developments in Artificial Intelligence (AI)\, especially machine learning and deep learning. However\, many AI models\, also known as black boxes\, are opaque and thus restricted to use in a clinical scenario where interpretability and transparency is important. This paper will look at the application of Explainable AI (XAI) to diagnose leukemia\, with a particular focus on how it can be used to provide clear and intelligible explanations of AI-driven decisions. The experimental results prove that the given ensemble model can be useful in classifying the subtypes of leukemia. Explainable AI methods like SHAP and LIME also enable more trust since the insights obtained are transparent and clinically relevant. This demonstrates the possibility of interpretable models being applicable to practice to aid clinical diagnosis. By using XAI techniques on trained model\, the potential of XAI to bridge the gap between high-performance AI and clinical applicability is demonstrated. Despite its potential\, XAI is faced with several challenges to address\, including the need to integrate it into existing clinical workflows\, technical complexity\, and issues of data protection. At the end of the paper\, the importance of developing domain-specific XAI methods and collaborative structures to succeed is outlined.
CATEGORIES:VIRTUAL ROOM 3C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:5a30a8c5537efa0e7df32b442a358b32
URL:http://internationalconferencetibs.sched.com/event/5a30a8c5537efa0e7df32b442a358b32
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:Federated Technological Intelligence for Sustainable Governance Analytics Framework Using Machine Learning
DESCRIPTION:Authors - Carolina Ditan\, Daniel Dasig Jr\, Sushil Kumar Singh\, Isagani Valenzuela II\, Catherine Catalan\, Bablu Khumar Dhar\, Jewelyn Ciocon and Maricris Ediza\n Abstract - The increasing complexity of sustainable governance ecosystems re quires advanced analytical models capable of integrating multidimensional soci oeconomic\, environmental\, governance\, and technological indicators into inter pretable strategic intelligence systems. This study proposes a Federated Techno logical Intelligence Framework (FTIF) utilizing the World Bank Sustainable and Social Governance Database (WB_SSGD) to analyze governance resilience\, en vironmental sustainability\, institutional effectiveness\, and digital transformation patterns across multiple countries. The study integrates explainable artificial in telligence (XAI)\, federated analytics\, ensemble machine learning\, and nonlinear predictive modeling to identify strategic relationships among governance indica tors\, energy transition variables\, democratic participation metrics\, and environ mental sustainability indicators. The methodology combines Random Forest Re gression\, Gradient Boosting Machines\, Long Short-Term Memory (LSTM) tem poral learning\, SHAP explainability mechanisms\, and panel-based econometric validation. Findings reveal that governance effectiveness\, access to civil justice\, corruption control\, democratic participation\, and carbon intensity significantly influence sustainable development trajectories. The hybrid architecture achieved high predictive reliability with strong convergence stability and reduced predic tion variance across heterogeneous country clusters. The SHAP-based explaina bility analysis further demonstrates that institutional quality variables contribute more significantly to sustainability outcomes than isolated economic indicators. The proposed framework contributes to technological intelligence literature by introducing a scalable and interpretable governance analytics architecture for strategic policymaking and digital sustainability planning. The study offers prac tical implications for governments\, higher education institutions\, business strate gists\, and international development organizations pursuing evidence-based gov ernance transformation.
CATEGORIES:VIRTUAL ROOM 3C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:35258e4ead364f7296395a089c02b2ea
URL:http://internationalconferencetibs.sched.com/event/35258e4ead364f7296395a089c02b2ea
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:Machine Learning Applications in Computer-Aided Screening and Early Detection of Autism Spectrum Disorder: A Systematic Review
DESCRIPTION:Authors - Alyssa C. Vicente\, Cedirick Santiago\, Elmer M. Alino\, Ma. Yvonne Czarina C.Angcaya\, Benedict G. Bautista\, St. Joseph M. Lumbog\n Abstract - This systematic review investigates the application of machine learning (ML) and deep learning (DL) in the early detection of Autism Spectrum Disorder (ASD)\, a neurodevelopmental condition characterized by social and communication deficits. Adhering to the 24-step framework by Muka et al. and PRISMA 2020 guidelines\, the methodology involved a rigorous search of four academic databases—IEEE Xplore\, Scopus\, PubMed\, and ACM Digital Library— identifying 67 records. Ultimately\, 10 peer-reviewed studies published between 2020 and 2024 were analyzed based on their use of real-world datasets and quantitative metrics. Results indicate that ML models\, particularly Convolutional Neural Networks (CNNs) and ensemble classifiers\, achieve high predictive performance with accuracies between 80% and 94%. The findings highlight that behavioral data from home videos and eye-tracking scan paths serve as effective indicators for remote\, scalable screening. However\, the review identifies significant gaps\, including small\, homogeneous datasets and a lack of model interpretability. To advance the field\, future research must focus on Explainable AI (XAI)\, multimodal fusion\, and the development of large-scale\, multicultural\, open-access datasets to ensure clinical trust and global generalizability.
CATEGORIES:VIRTUAL ROOM 3C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:d1f72d872b419d1c4f2dfb31acb85651
URL:http://internationalconferencetibs.sched.com/event/d1f72d872b419d1c4f2dfb31acb85651
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:Symmetrical Houses Are Environmentally Friendly: Its Effects from the Perspective of Apartment Residents
DESCRIPTION:Authors - Takumi Kato Abstract - According to Processing Fluency Theory\, the more fluently people can process an object\, the more positive their aesthetic response becomes\, making symmetrical designs more desirable. Furthermore\, symmetry is also expected in the context of ethical products\, as simplicity is effective in fostering an impression of environmental and health considerations. However\, symmetry is a highly symbolic and essential design. Based on Construal Level Theory\, people prefer essential objects when they feel a greater psychological distance from them\, and prefer objects when they feel a greater psychological distance. Through this theoretical lens\, the evaluation of essential symmetrical designs may differ depending on the psychological distance from the product. This study posed the research question: "Do people who feel a greater psychological distance from the product rate products with symmetrical designs more highly than those who feel a greater psychological distance?" Focusing on detached houses\, a randomized controlled trial was conducted with 1\,000 Japanese people aged 20-60. The results showed that in detached house designs\, symmetrical designs were significantly more favorably received than asymmetrical designs in terms of living intention\, healthy impression\, and environmental impression. However\, these effects were more pronounced in people living in apartments than in those currently living in detached houses. Therefore\, it can be inferred that symmetry is more effective for luxury goods than for inexpensive goods\, for gifts to others than for personal use\, and for goods that will be useful in the future than for goods that will be useful immediately.
CATEGORIES:VIRTUAL ROOM 3C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:b8b4bdc3bacea530fdebe93b4e0c61f3
URL:http://internationalconferencetibs.sched.com/event/b8b4bdc3bacea530fdebe93b4e0c61f3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:Teacher Strategies for Developing Learners’ Digital Literacy Competencies in Ghanaian Basic Schools
DESCRIPTION:Authors - Jemima Achiah\, Benjamin Ghansah\, Stephen Opoku Oppong\, Charles Buabeng Andoh\, Joseph Kwabena Essibu\, Christopher Yarkwah Abstract - The integration of digital literacy within basic education has become increasingly important in preparing learners with the competencies required for participation in twenty-first-century society. This study investigates how basic school teachers in Ghana foster learners’ dig-ital literacy competencies within the context of the Standards-Based Curriculum. Specifically\, the study examines the instructional strategies employed by teachers\, the contextual challenges influencing implementation\, and the extent to which these practices shape learner engagement and digital skill acquisition. An embedded mixed-methods research design was adopted\, com-bining qualitative and quantitative approaches to provide a comprehensive understanding of classroom practices and learner experiences. Qualitative data were collected through semi-struc-tured interviews with six teachers and observations of school digital infrastructure\, while quan-titative data were obtained from 122 learners across three public basic schools in Komenda\, Ghana. The findings revealed that teachers predominantly employed learner-centered pedagogi-cal approaches\, including hands-on instruction\, collaborative learning activities\, and the integra-tion of learner-owned digital devices to facilitate practical engagement. Despite persistent chal-lenges relating to inadequate infrastructure\, limited access to digital resources\, and insufficient professional development opportunities\, these instructional practices contributed positively to learners’ motivation\, confidence\, and practical ICT competencies. The study contributes to the limited empirical literature on teacher-driven digital literacy development within Ghanaian basic education and highlights the critical need for sustained teacher capacity building\, improved dig-ital infrastructure\, and supportive policy interventions to strengthen effective digital literacy in-tegration in resource-constrained educational contexts.
CATEGORIES:VIRTUAL ROOM 3C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:bfeb10fca8eda3f654fdbe876e443d2b
URL:http://internationalconferencetibs.sched.com/event/bfeb10fca8eda3f654fdbe876e443d2b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:Benchmarking Lightweight Transformer Models for Aspect-Conditioned Sentiment Analysis of Tourist Reviews Under Philippine LGU Deployment Constraints
DESCRIPTION:Authors - Lord Francis B. Navarro\, Chris Jordan G. Aliac\, Larmie S. Feliscuzo Abstract - This study benchmarks three Transformer-based encoder models for the sentiment classification stage of an aspect-based sentiment analysis pipeline applied to tourist reviews of the Chocolate Hills Complex in Bohol\, Philippines. The work is motivated by the need for tourism analytics that remain usable under the computing constraints of Philippine local government units. A corpus of 5\,885 Google Maps and TripAdvisor reviews was cleaned to 3\,288 English textual reviews and transformed\, through LLM-assisted silver-standard annotation\, into 7\,555 aspect-sentiment pairs across six tourism aspects and three sentiment classes. Three models — RoBERTa\, DistilBERT\, and TinyBERT — were finetuned for aspect-conditioned sentiment analysis and compared with TF-IDF baselines. Classification was evaluated on a held-out test set\; deployment efficiency was tested on CPU-only hardware using latency\, memory footprint\, and parameter count. RoBERTa achieved the highest accuracy and macro-F1 but required substantially more memory and higher latency. TinyBERT achieved the lowest latency and memory use while maintaining usable macro-F1\, making it the most deployment-practical option under the tested conditions. The results suggest that model selection for local tourism analytics should consider both predictive performance and operational feasibility.
CATEGORIES:VIRTUAL ROOM 3D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:303d29fa6fc7beba0939627d41464e0c
URL:http://internationalconferencetibs.sched.com/event/303d29fa6fc7beba0939627d41464e0c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:Business Strategy Formulation for Neulla\, a Local Indonesian Fashion Brand: An Integrated BMC\, SWOT\, and TOWS Matrix Approach
DESCRIPTION:Authors - Adeline Aulia Darsonoputri\, Farah Alfanur Abstract - Indonesia’s local fashion industry has grown alongside digital marketplaces\, social media\, and live commerce\, expanding market opportunities while increasing competition\, customer switching\, and digital platform dependence. Neulla\, a Bandung-based Indonesian fashion brand with the concept of “Basic with a Twist\,” faces the need to strengthen differentiation\, customer relationships\, and competitive positioning. This study formulates renewed business development strategies for Neulla using an integrated Business Model Canvas (BMC)\, PESTLE\, Porter’s Five Forces\, SWOT\, and TOWS Matrix approach. A descriptive qualitative case study was conducted through in-depth interviews with internal and external informants supported by company documentation. The findings show that Neulla’s current business model has implemented the nine BMC blocks\, with strengths in brand identity\, digital sales channels\, and product design capability. However\, Neulla faces challenges related to competition\, changing fashion trends\, marketplace dependency\, production capacity\, and creative team turnover. The TOWS Matrix generated 30 alternative strategies\, which were consolidated into 18 renewed strategies and classified into short-term\, mid-term\, and long-term priorities. These strategies were integrated into a new BMC to strengthen design differentiation\, sales channels\, customer engagement\, internal systems\, and partnerships. The proposed business model offers practical strategic direction for enhancing Neulla’s competitive positioning in Indonesia’s local fashion industry.
CATEGORIES:VIRTUAL ROOM 3D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:e006ed22937fb862fbc739771cc908c3
URL:http://internationalconferencetibs.sched.com/event/e006ed22937fb862fbc739771cc908c3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:Comparative Analysis of Machine Learning Models for Customer Churn Prediction
DESCRIPTION:Authors - Josephine Florencia Chan\, Anderes Gui\, Riki\, Huynh Trong Thua\, Nguyen Minh Tuan\, Chau Van Van Abstract - Losing customer in the telecommunication may lead to significant financial losses. Machine learning approaches have shown promising potential for predicting churn\, but many studies still focus primarily on Accuracy\, which can be misleading when using an imbalanced dataset. This study compares three ma-chine learning algorithms: Logistic Regression\, Linear Support Vector Machine (SVM)\, and Decision Tree. The goal is to determine which algorithms prioritizes Recall. The Iranian Churn dataset was used for the experiment\; this dataset con-sists of 3151 customer records with 14 behavioral and demographic attributes. This study used an 80:20 train-test split with standardized features\, and model performance was evaluated based on Recall\, F1-score\, Precision\, Specificity\, and Accuracy. The Decision Tree model achieved the highest Recall\, while Logistic Regression and Linear SVM showed slightly lower Recall but similar Accuracy. These results suggest that for small and structured customer datasets\, simpler or appropriately constrained models may perform effectively while prioritizing the identification of churners. Model selection should consider dataset characteris-tics. Prioritizing Recall over Accuracy can also help guide effective customer retention strategies.
CATEGORIES:VIRTUAL ROOM 3D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:e100eb66c9d8d04cc420599b12845253
URL:http://internationalconferencetibs.sched.com/event/e100eb66c9d8d04cc420599b12845253
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:Partner-Country Trade Digitalization and National Export Competitiveness: Evidence from an Extended Gravity Model
DESCRIPTION:Authors - Thanh Hien Hoang\, Thi Dieu Linh Huynh\, Le Hoang Linh Chi Abstract - This study examines whether the institutional digitalization of trade procedures in importing partner countries is associated with Vietnam’s bilateral export performance. While existing studies have widely examined trade facilitation\, e-commerce\, and general ICT adoption\, less attention has been paid to the role of partner-country digital trade readiness in shaping export market access for an export-oriented economy such as Vietnam. Using panel data on Vietnam’s exports to 27 major trading partners over the period 2013–2022\, this study applies an extended gravity model incorporating the Paperless Trade Index\, the Cross-border Paperless Trade Index\, and the aggregate Trade Digitalization Index. The model also controls for importer GDP\, Vietnam’s GDP\, geographical distance\, partner-country innovation capacity\, and the COVID-19 period. The random-effects estimates show that paperless trade\, cross-border paperless trade\, and over-all trade digitalization in importing markets are positively and significantly associated with Vietnam’s export values. The findings also confirm the relevance of conventional gravity variables\, with GDP showing positive associations and distance showing a negative association with exports. These results suggest that digital trade readiness in destination markets can function as an external institutional condition supporting export competitiveness. The study contributes to the literature by distinguishing between domestic paperless trade and cross-border paper-less trade in importing markets and by providing Vietnam-specific evidence on the strategic importance of interoperable digital trade procedures.
CATEGORIES:VIRTUAL ROOM 3D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:27ff4678b30c2452d0d1b64e5dbeb427
URL:http://internationalconferencetibs.sched.com/event/27ff4678b30c2452d0d1b64e5dbeb427
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:Strategic Prioritization for Student Accommodation Survey Services Using IFE–EFE\, SWOT\, IE Matrix\, and QSPM
DESCRIPTION:Authors - A. Muhammad Maheswara Iporennu\, Siska Noviaristanti Abstract - This study formulates and prioritizes business strategies for Survei Kos Incaran by Kospace\, a student accommodation property management service operating around Telkom University. The study is motivated by the increasing shift of accommodation search activities from conventional channels to digital and platform-based services\, which raises the importance of information accuracy\, service reliability\, and field verification. The research applies a descriptive qualitative case study approach using semi-structured interviews\, limited observation\, internal documents\, and operational data. The analytical process integrates Internal Factor Evaluation (IFE)\, External Factor Evaluation (EFE)\, Internal-External (IE) Matrix\, SWOT/TOWS Matrix\, and Quantitative Strategic Planning Matrix (QSPM). The results show that Kospace is positioned in the Hold and Maintain cell of the IE Matrix with an IFE score of 2.300 and an EFE score of 2.510. QSPM prioritizes the digitalization of real-time service monitoring and scheduling as the highest-ranked strategy with a total attractiveness score of 5.450\, followed by survey deck quality standardization and strengthened positioning as a trusted sur-vey service. The findings indicate that the central strategic challenge for Kospace is not only market visibility\, but also the operational reliability of field verification as the foundation of trust-based student accommodation management.
CATEGORIES:VIRTUAL ROOM 3D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:333cab7523acc6a28573d7f67b114287
URL:http://internationalconferencetibs.sched.com/event/333cab7523acc6a28573d7f67b114287
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:Sustainability Meets Hype Culture: An SBERT-Based Analysis of Emotional and Rational Consumer Processing in Nike Space Hippie Discourse
DESCRIPTION:Authors - Sri Bramantoro Abdinagoro\, Enda Panggati Abstract - This study examines how audiences process sustainability-oriented hype sneakers through emotional\, rational\, and hybrid responses in YouTube discourse on the Nike Space Hippie Sneaker. Using the Elaboration Likelihood Model and a semantic NLP approach\, this study applies Sentence-BERT (SBERT)-based semantic similarity to identify dual-process consumer responses beyond conventional positive-negative sentiment classification. A corpus of YouTube comments was analyzed using a prototype-based se-mantic embedding approach. Audience comments were classified into emotion-al\, rational\, hybrid\, and ambiguous processing orientations. Robustness checks were conducted using all-MiniLM-L6-v2 and all-mpnet-base-v2 models. The findings show that emotional processing became the most dominant category\, followed by hybrid processing\, while rational processing appeared in smaller proportions. The results indicate that sustainability in Nike Space Hip-pie discourse is mediated not only by environmental evaluation but also by aesthetic appeal\, hype culture\, and symbolic sneaker identity. In addition\, the emergence of hybrid processing suggests that emotional and rational evaluations may coexist simultaneously within sustainability-oriented sneaker dis-course.
CATEGORIES:VIRTUAL ROOM 3D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:71b5375f9b30dc792535c9384be17108
URL:http://internationalconferencetibs.sched.com/event/71b5375f9b30dc792535c9384be17108
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:Technology Accepted Models in Augmented Reality (AR) : Measuring Effectiveness and Acceptance
DESCRIPTION:Authors - Darma Rika Swaramarinda\, Eka Dewi Utari\, Alifah Kusumaningrum\, Sri Kartikowati\, Muh. Darwis\, Triesninda Pahlevi\, Zsany Zahra Ailliya Abstract - This study aims to determine the effectiveness and acceptance of Augmented Reality (AR) among office administration lecturers in Indonesia by adopting the Technology Acceptance Model (TAM). This study examines the relationship between Perceived Ease of Use (PEOU)\, Perceived Effectiveness (PU)\, Attitude Toward Use (ATU)\, Behavioral Intention to Use (BITU)\, and Actual Behavior (AB). Data were collected through a survey of lecturers from three state universities in Indonesia and analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method. The results showed that Perceived Effectiveness had a positive and significant effect on Attitude Toward Used (β = 0.613\; p < 0.001)\, which indicates that lecturers tend to have a positive attitude towards the use of AR when they feel the benefits of the technology in the learning process. In addition\, Attitude Toward Use had a positive and significant effect on Behavioral Intention to Use (β = 0.419\; p < 0.001)\, while Behavioral Intention to Use had a positive and significant effect on Actual Behavior (β = 0.405\; p = 0.001). The results of the mediation analysis also showed that Attitude Toward Use partially mediated the relationship between Perceived Effectiveness and Behavioral Intention to Use\, while Behavioral Intention to Use mediated the relationship between Attitude Toward Use and Actual Behavior. The results of this study provide strategic implementation for universities in strengthening AR based learning innovation to improve the quality and competitiveness in the era of digital transformation.
CATEGORIES:VIRTUAL ROOM 3D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:03e3c7606befa3c14912b237f2305b96
URL:http://internationalconferencetibs.sched.com/event/03e3c7606befa3c14912b237f2305b96
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T060000Z
DTEND:20260623T080000Z
SUMMARY:The Effect of Corporate Governance Mechanisms on Financial Distress: Evidence from Energy Sector Companies Listed on the Indonesia Stock Exchange
DESCRIPTION:Authors - Ain Nasthashia Nasrul\, Roy Budiharjo Abstract - This study looks at how corporate governance practices affect financial distress in firms in the energy industry registered as companies under the Indonesia Stock Exchange (IDX) listing throughout the years spanning 2020 until 2024. The study applies institutional shareholding\, audit committee capacity\, diversity of gender among board directors\, and company age as independent variables\, while the Altman Z″ Score framework is utilized to evaluate financial dis-tress. This research utilised a quantitative methodology with a causal descriptive framework\, employing secondary data sourced from annual reports and financial statements. The sample comprised 28 firms chosen by purposive selection\, yielding 140 company-year observations. Results obtained from the model comparison stage revealed the suitability of the Common Effects Model as the selected specification in the panel regression estimation. Research outcomes reveal that corporate governance implementation together with company age significantly affects financial hardship. Meanwhile\, institutional ownership\, audit committee proportion\, and gender composition within the board of directors do not show a statistically meaningful influence on financial distress. On the other hand\, financial hardship is positively and significantly impacted by firm age\, suggesting that\, under some circumstances\, older businesses are more likely to face financial trouble. This analytical model is capable of accounting for approximately 24.76% of the changes occurring in financial distress conditions. These results imply that the efficacy and calibre of governance processes are more crucial in reducing financial hardship than the mere existence of a governance organisation.
CATEGORIES:VIRTUAL ROOM 3D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:4fcec4077ddca505c52e785996c5919d
URL:http://internationalconferencetibs.sched.com/event/4fcec4077ddca505c52e785996c5919d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T080000Z
DTEND:20260623T080200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:aa0fb2d8123061552f20e23cc18e5384
URL:http://internationalconferencetibs.sched.com/event/aa0fb2d8123061552f20e23cc18e5384
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T080000Z
DTEND:20260623T080200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:80eb7131248e1996c54638f094d9828f
URL:http://internationalconferencetibs.sched.com/event/80eb7131248e1996c54638f094d9828f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T080000Z
DTEND:20260623T080200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:12b936f2fc272bc8d7a35047ada2d764
URL:http://internationalconferencetibs.sched.com/event/12b936f2fc272bc8d7a35047ada2d764
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T080000Z
DTEND:20260623T080200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:222566029f32a21e99dfcb12aa1c859d
URL:http://internationalconferencetibs.sched.com/event/222566029f32a21e99dfcb12aa1c859d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T080200Z
DTEND:20260623T080500Z
SUMMARY:Session Closing & Information to Author
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:e2775a874c2cfef29712997046e6c93b
URL:http://internationalconferencetibs.sched.com/event/e2775a874c2cfef29712997046e6c93b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T080200Z
DTEND:20260623T080500Z
SUMMARY:Session Closing & Information to Author
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:3bdb80aab16fcb4d1425b5cca5d552bc
URL:http://internationalconferencetibs.sched.com/event/3bdb80aab16fcb4d1425b5cca5d552bc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T080200Z
DTEND:20260623T080500Z
SUMMARY:Session Closing & Information to Author
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:538ab9b370eeaf328a4febe1e0033aee
URL:http://internationalconferencetibs.sched.com/event/538ab9b370eeaf328a4febe1e0033aee
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T080200Z
DTEND:20260623T080500Z
SUMMARY:Session Closing & Information to Author
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 3D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:d8f4ceabce2780ca3afbe742b699d6f8
URL:http://internationalconferencetibs.sched.com/event/d8f4ceabce2780ca3afbe742b699d6f8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T085800Z
DTEND:20260623T090000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:1e9de8dfb4bfd61b8e49ba439e9807d3
URL:http://internationalconferencetibs.sched.com/event/1e9de8dfb4bfd61b8e49ba439e9807d3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T085800Z
DTEND:20260623T090000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:6cbbd56e99a1b906b9f53b690f9fafd6
URL:http://internationalconferencetibs.sched.com/event/6cbbd56e99a1b906b9f53b690f9fafd6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T085800Z
DTEND:20260623T090000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:5e3e260c3c587c9dc1d2c1449d781e3c
URL:http://internationalconferencetibs.sched.com/event/5e3e260c3c587c9dc1d2c1449d781e3c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T085800Z
DTEND:20260623T090000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:abc3636127fe6e771744caa94eb58bea
URL:http://internationalconferencetibs.sched.com/event/abc3636127fe6e771744caa94eb58bea
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:A study in analyzing the impact of implementing wearable devices in enhancing patient health
DESCRIPTION:Authors - Reepu Abstract - Wearable devices are gaining more importance in the present day as they offer various advantages to the users and thereby enabling in generating better outcomes. Wearable devices gained higher focus mainly in the healthcare sector as they support in tracing the critical signs of the patients on a real time basis\, provide better support to them as and when needed. The application of wearable devices enables in addressing different health care concerns in an effective manner\, the current context witnessed many advancements in the wearable device’s domain. With the integration with other techniques like AI\, Internet of Things and other sophisticated tools it can undergo major shift in the health care domain in protecting the lives of the individuals\, move from being reactive mode of providing treatment to the patients to more predictive method\, enhance clinical skill and outcomes. The overall value of implementing these tools and technologies support in enhancing the life of the patients and supports the practitioners in making better progress in clinical advancements and other areas.
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:579b15e6de9e47dc02a2243cb064c0b9
URL:http://internationalconferencetibs.sched.com/event/579b15e6de9e47dc02a2243cb064c0b9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:ADHD Classification Using Vision Transformers and Deep Learning: A Survey of fMRI/sMRI-Based Diagnostic Approaches
DESCRIPTION:Authors - Shreya Shukla\, Mishti Kukreja\, Ruchika Katariya Abstract - Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder with a prevalence rate ranging between 5 to 7.2% among children and between 2.5 to 6.7% among adults worldwide. In spite of its high prevalence rate\, its diagnosis still relies mostly on clinical ratings that have the tendency to show inter-rater differences and confusions with symptoms of other conditions associated with ADHD. Neuroimaging methods\, particularly rs-fMRI and sMRI\, offer an innovative approach towards providing objective measures for understanding the neurobiological underpinnings of ADHD. This paper offers a systematic narrative review of deep learning methods for ADHD classification using fMRI/sMRI data from 2012 to 2025\, with a specific focus on the recent period from 2021 to 2025 characterized by architectural diversity. We classify the literature into three main streams according to the neural networks adopted: (1) Convolutional Neural Networks (CNNs)\, which involve 2D CNNs\, 3D CNNs\, residual CNNs\, dense CNNs\, attention-based CNNs\, and graph-based CNNs\; (2) Vision Transformers (ViTs)\, which encompass conventional ViTs\, Swin transformers\, self-supervised ViTs\, multi-modal ViTs\, and brain foundation model ViTs\; and (3) hybrid CNN-ViT models\, which combine both local and global context representations. This work highlights the problems of heterogeneity among multiple sites\, inconsistent evaluations\, fairness\, efficient inference\, and clinical deployment. Note: This review does not follow the guidelines for systematic reviews (PRISMA 2020). It is an organized narrative review. Numerical comparisons between different works should be considered approximate due to variations in training/testing sets and data preprocessing.
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:dca04fed587e79fe98ef50ca53388801
URL:http://internationalconferencetibs.sched.com/event/dca04fed587e79fe98ef50ca53388801
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:AI-Powered Multi-Agent Self-Evolving Cybersecurity Intelligence System
DESCRIPTION:Authors - Madhumati Pol\, Rutuja Chaudhari\, Sai Jadhav\, Sri Sai Preethi Munnaluri\, Rudrani Sarangdhar Abstract - The necessity of developing adaptive\, autonomous\, and intelligent security systems has developed significantly over time because of the increased volume and complexity of cyber attacks. Therefore\, this research project will present an AI-powered multi agent self-evolution cybersecurity intelligence system. The purpose of the system will be for the real-time identification\, classification\, and prediction of cyber threats. The system will consist of three working agents: Network Monitoring Agent\, System-Metrics Surveillance Agent and Threat Intelligence Agent. These agents will be supported by interpretable machine learning classifiers and light-weight Python-based data collection tools. A universal dataset converter will enable it to operate on all types of cybersecurity datasets\, and a self-evolving element will allow it to continually update itself with additional information regarding current threats. Dashboards will be provided through the use of Streamlit in order to provide real-time timelines of attacks\, CVE intelligence\, anomaly detection\, and real-time visualization of threats. Results from experimental testing show that the system can improve the accuracy of its threat detection as time progresses and perform well across various datasets. Overall\, this work provides a self-learning\, scalable\, modular\, and dataset-agnostic architecture for use within modern enterprise-level cybersecurity environments.
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:d12603cc290e03a4fa2a17a2ab309b9c
URL:http://internationalconferencetibs.sched.com/event/d12603cc290e03a4fa2a17a2ab309b9c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:Big Data Survival Analysis of Breast Cancer Patients Using the METABRIC Dataset and Hadoop Infrastructure
DESCRIPTION:Authors - Elmar B. Noche\, Randy Joy M. Ventayen Abstract - Breast cancer remains one of the leading causes of mortality among women\, highlighting the need for reliable survival prediction tools. This study applied big data analytics and Cox Proportional Hazards regression to the METABRIC dataset\, which contains clinical\, pathological\, and genomic records from over 2\,000 breast cancer patients. Hadoop HDFS was used for distributed storage\, while PySpark supported preprocessing and data transformation. After feature selection\, six significant predictors were identified: inferred menopausal state\, Nottingham Prognostic Index\, oncotree code\, type of breast surgery\, cohort classification\, and tumor size. The findings show that combining Hadoop-based infrastructure with interpretable survival modeling can support patient risk stratification\, treatment planning\, and precision oncology.
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:088e706940623cfe8b6bea53a6b9f68c
URL:http://internationalconferencetibs.sched.com/event/088e706940623cfe8b6bea53a6b9f68c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:ECG-Based Cardiac Arrhythmia Detection and Classification
DESCRIPTION:Authors - Phat Ly Tan\, My Nguyen Kieu\, Phung Nguyen Thi Kim Abstract - This paper presents an automated approach for cardiac arrhythmia detection using ECG signals from the CPSC2018 database. The proposed pipeline includes band-pass filtering\, normalization\, and segmentation of raw ECG recordings\, conversion of ECG segments into 2D grayscale images\, and multi-label arrhythmia classification using CNN based on a DenseNet architecture. According to the official CPSC2018 labeling scheme\, ECG segments are categorized into multiple clinically relevant rhythm types\, including normal sinus rhythm and major arrhythmias such as first-degree atrioventricular block\, atrial fibrillation\, right bundle branch block\, left bundle branch block\, ventricular ectopic beat\, premature atrial contraction\, ST-segment elevation\, and ST-segment depression. The DenseNet-based architecture combines an oversampling training strategy to alleviate class imbalance. Experimental results on the CPSC2018 database demonstrate the effectiveness of the proposed image-based ECG classification approach\, highlighting its potential to assist clinicians in ECG interpretation and early diagnosis of cardiac disorders.
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:423a778ee8dee7f5f7660789ed28d6f0
URL:http://internationalconferencetibs.sched.com/event/423a778ee8dee7f5f7660789ed28d6f0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:Institutionalizing Artificial Intelligence in Education for Sustainable Development: A Systematic Review of Higher Education Policies and Practices in the Asia-Pacific
DESCRIPTION:Authors - Renato E. Salcedo\, Elmar B. Noche Abstract - The rapid proliferation of Artificial Intelligence (AI) in education has prompted growing scholarly and policy interest in how higher education institutions across the Asia-Pacific region are systematically incorporating AI into teaching\, learning\, research\, and governance. This paper presents a systematic review of 68 peer-reviewed studies\, institutional policy documents\, and government reports published between 2018 and 2024\, examining the extent to which AI is being institutionalized within Asia-Pacific higher education systems in alignment with Education for Sustainable Development (ESD) principles. Using the PRISMA framework and a content analysis methodology\, this review identifies four dominant institutionalization pathways: curriculum integration\, research infrastructure development\, policy formalization\, and faculty capacity-building. Findings indicate significant heterogeneity across countries\, with East Asian economies particularly China\, Japan\, and South Korea exhibiting more advanced levels of AI policy coherence\, while Southeast Asian and Pacific Island nations remain in nascent stages of formal AI institutionalization. Critical barriers include data governance deficits\, algorithmic inequity risks\, underfunded professional development pipelines\, and insufficient alignment between national AI strategies and ESD frameworks. The review concludes with a set of evidence-based recommendations for regional policymakers\, university administrators\, and international development organizations to accelerate equitable\, sustainable AI institutionalization across the Asia-Pacific higher education landscape.
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:21a8c750e43ba719825d25eeeacb0237
URL:http://internationalconferencetibs.sched.com/event/21a8c750e43ba719825d25eeeacb0237
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:Optimal Recloser Placement for Reliability Enhancement Using Steady-State Genetic Algorithm
DESCRIPTION:Authors - \n Abstract - This research investigated the potential for improving the reliability of the Central Pangasinan Electric Cooperative (CENPELCO) San Carlos 20MVA Substation distribution system through optimized placement of Automatic Circuit Reclosers (ACRs). The study employed a Steady-State Genetic Algorithm (SSGA) implemented in MATLAB to identify optimal ACR locations that minimized SAIDI\, SAIFI\, and maximized EENS. The algorithm was validated using the IEEE 34-node test feeder\, demonstrating its effectiveness in balancing competing objectives. The validated SSGA was then applied to the CENPELCO system\, resulting in significant improvements in SAIDI values across all feeders. The research introduced a novel approach to using EENS to represent the number of connected customers at each node\, refining the SAIDI calculation and providing a more accurate measure of the impact of outages on consumers. The optimized ACR placement strategy consistently brought SAIDI values below the NEA standard for on-grid electric cooperatives\, indicating a substantial enhancement in the reliability of the CENPELCO distribution system. The study provides valuable insights for CENPELCO and other power system operators seeking to improve system reliability and minimize the impact of power outages.
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:fd45c3c9d70f61ef32f2b41264e77071
URL:http://internationalconferencetibs.sched.com/event/fd45c3c9d70f61ef32f2b41264e77071
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:YOLOv9 Based Multi-Object Tracking System Using Improved DeepSORT with GIoU Association and ReID Memory Based Class Filtering
DESCRIPTION:Authors - Phway Phway Aung\, Tin Zar Thaw Abstract - Multi-object tracking (MOT) is widely applied in surveillance\, traffic monitoring\, and autonomous systems. Most MOT systems are created by combining DeepSORT and YOLO series. The original DeepSORT relies on IoU as-sociation-based matching and a fixed age threshold deletion algorithm which of-ten leads to incorrect associations\, premature track removal\, and frequent ID switches under occlusion or fast motion. To address these limitations\, YOLOv9-Based Multi-Object Tracking System is proposed by using GIoU for more reliable geometric association and the enhanced filtering algorithm that are integrated class validation\, motion uncertainty estimation with consign similarity\, and a Re-Identification (ReID) memory buffer for reducing ID switching. To analyze the performance of the proposed MOT system we compare two cases: IoU and GIoU on Original DeepSORT and the improved DeepSORT and original DeepSORT based on MOT16 videos’ sequences. Experimental evaluation demonstrates that the proposed YOLOv9-Based Multi-Object Tracking System achieves more sta-ble and accurate tracking performance compared to the original DeepSORT system in two cases.
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:f9e2c81fada7f571d7d797d453d7a104
URL:http://internationalconferencetibs.sched.com/event/f9e2c81fada7f571d7d797d453d7a104
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:A Novel Deep Learning Framework for Predicting Obesity Risk with reference to Tumkur City
DESCRIPTION:Authors - SMT. DIVYASHREE D V\, D RAMESH Abstract - In India\, obesity has become a serious public health concern\, especially in urban and semi-urban areas that are seeing fast changes in diet and lifestyle. Predictive modelling has advanced globally\, but there are still very few techniques tailored to a given region that take into consideration Indi's distinct socioeconomic\, environmental\, and cultural context. The study is conducted from the local population in Tumkur city by creating an ANN model that predicts the obesity risk from diverse age groups. The model is built with the physiological\, behavioural and environmental parameters that make deeper study to analyse the risk through multi-faceted dataset. A mobile application is developed to close the gap and monitor the obesity risk through recommendation given by interactive monitoring tool. This tool will provide the real time risk evaluations to the individuals by giving warnings and progress updates that supports health tracking for timely behavioural and physiological changes. The research mainly focusses on predicting the obesity risk\, designing a mobile health monitoring tool and assessing the obesity risk by validating the hypothesis risk framework by one-way ANOVA statistical analysis on primary data on region specific.
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:ae244be62d96ff9806c20b9c40a681f3
URL:http://internationalconferencetibs.sched.com/event/ae244be62d96ff9806c20b9c40a681f3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:Augmenting AI Literacy in Bachelor of Science in Information Technology Using the TPACK and SAMR Frameworks
DESCRIPTION:Authors - Jann Alfred A. Quinto\, Mark Teddy D. Quiban Abstract - The increasing integration of generative artificial intelligence (AI) in educational settings calls for stronger development of AI literacy\, particularly among students at the start of their academic programs. This study explored the extent to which exposure to generative AI technologies can enhance the AI literacy of first-year Bachelor of Science in Information Technology (BSIT) students. Instructional design was informed by the Technological Pedagogical Content Knowledge (TPACK) and Substitution–Augmentation–Modification–Redefinition (SAMR) frameworks to support meaningful and pedagogically aligned use of technology. The intervention emphasized early conceptual grounding\, critical engagement\, and responsible interaction with AI systems. To examine its impact\, a quasi-experimental one-group pretest–posttest design was employed with 45 participants. A validated AI literacy instrument was administered before and after the intervention. Learning activities incorporated guided interaction with generative AI technologies\, structured tasks\, and reflective exercises addressing both functional use and ethical considerations. Statistical analysis using a paired-sample t-test was conducted to evaluate changes in performance. Results indicated a statistically significant improvement in posttest scores (p < 0.05). These outcomes suggest that a structured and framework-guided approach to integrating generative AI can strengthen students’ conceptual understanding\, applied capabilities\, and awareness of ethical issues. Introducing AI literacy early in the BSIT curriculum may help prepare students for the demands of AI-influenced academic and professional environments.
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:c96386978ee90023e89ab19a66911e5e
URL:http://internationalconferencetibs.sched.com/event/c96386978ee90023e89ab19a66911e5e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:Designing for Inclusion Strategies and Practices in Online Distance Education at A Philippine Open University
DESCRIPTION:Authors - Phillip Queroda Abstract - This study examined the implementation of inclusive education strategies within the Open and Distance e-Learning (ODeL) system of Pangasinan State University–Open University Systems (PSU-OUS). Utilizing a quantitative descriptive research design with stratified random sampling\, data were collected via an online questionnaire from faculty and students. Findings revealed a high level of implementation across four domains: Universal Design for Learning (UDL)-based instructional design\, collaborative learning\, accommodations and modifications\, and personalized learning. Instructional resources and activities consistently provided multiple means of representation\, engagement\, and expression\, successfully fostering learner interaction and addressing diverse needs. However\, implementation gaps were identified in the integration of assistive technologies and the development of systematic monitoring and evaluation mechanisms. The study concludes that while PSU-OUS demonstrates a strong institutional commitment to inclusive online education\, enhancing technological integration and establishing data-driven monitoring systems are essential for long-term sustainability and effectiveness.
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:20fd51e5d3451c5313ea846a0d0d7373
URL:http://internationalconferencetibs.sched.com/event/20fd51e5d3451c5313ea846a0d0d7373
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:Entrepreneurial Efforts and Opportunity Cost as Determinants of Monetisation Performance Among Micro Valorant Streamers in Indonesia
DESCRIPTION:Authors - Zahrah Meidila Hafizhah\, Jurry Hatammimi Abstract - The digital creative economy is increasingly driving live streaming to become one of the most promising business models\, particularly within the gaming community. Here\, creators are competing to produce the most engaging content possible in order to generate revenue from their social media channels. This study examines how entrepreneurial efforts and opportunity costs influence the monetisation performance of Valorant micro-streamers in Indonesia. A quantitative method was employed\, utilising data from 100 respondents via an online questionnaire\, which was subsequently analysed using SEM-PLS. The results support all three hypotheses: entrepreneurial effort has a positive effect on monetisation performance\, whilst opportunity cost has a stronger positive effect. Together\, these two variables account for 30.3 percent of the variation in monetisation. The significant difference in effect sizes suggests that monetisation outcomes are not primarily determined by the extent of effort expended\, but rather by economic conditions that influence how deeply an individual can commit to streaming. These findings extend the study of digital entrepreneurship to the context of streaming outside western countries\, which tends to be mobile-based\, whilst also suggesting that platforms wishing to support micro-streamers need to consider not only content quality\, but also the incentive systems that influence creators’ sustainability.
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:e78df8b6bd1c3652428f37480886789b
URL:http://internationalconferencetibs.sched.com/event/e78df8b6bd1c3652428f37480886789b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:Predictive Analytics of Faculty Promotions in State Universities: Using Machine Learning and Document Image Processing on Personal Data and Individual Performance Commitment Reviews
DESCRIPTION:Authors - Ronnel A. dela Cruz Abstract - This study presents a machine learning–based predictive analytics framework[1][2][3] for forecasting faculty promotion outcomes in state universities using institutional performance data and OCR-based document processing[ 4][5]. Faculty demographic information\, Individual Performance Commitment Review (IPCR) indicators\, and digitized faculty documents were utilized to develop predictive classification models. A dataset consisting of 1\,000 faculty records was preprocessed through data cleaning\, normalization\, feature engineering\, and SMOTE balancing applied only to the training dataset. Ada- Boost\, Gradient Boosting\, and XGBoost classifiers were evaluated using Accuracy\, Precision\, Recall\, F1-score\, and ROC-AUC metrics. Among the evaluated models\, AdaBoost achieved the strongest performance with 97.33% accuracy and 98.36% ROC-AUC. Feature importance analysis identified teaching effectiveness\, curriculum development\, and mentorship services as dominant predictors of promotion outcomes. The findings demonstrate the potential of integrating machine learning and OCR-driven document processing to support transparent\, scalable\, and evidence-based faculty promotion systems in higher education institutions.
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:ec1b04edd6c36bc8d1edd50edcab0055
URL:http://internationalconferencetibs.sched.com/event/ec1b04edd6c36bc8d1edd50edcab0055
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:SATISFACTION IN EVERY BITE: CONSUMER PREFERENCES FOR TRADITIONAL VS MODERN ADOBO PREPARATIONS
DESCRIPTION:Authors - Apolinar P. Datu\, Jesielitlyn B. Gloria\, Barnard J. Maraon\, Jhoan P. Sarimos\, Jenny B. Unico\, Garry G. Garcia Abstract - Adobo\, often regarded as the Philippines’ unofficial national dish\, holds significance both as a culinary staple and as a symbol of cultural heritage. This study explores consumer satisfaction and preferences between traditional and modern adobo preparations. Specifically\, it aims to: (1) identify sensory and cultural factors influencing consumer choices\, (2) compare satisfaction levels between traditional and modern versions\, and (3) examine how demographics such as age\, lifestyle\, and exposure to food trends shape these preferences. Using a quantitative survey design\, data were collected through a structured questionnaire administered to a diverse group of respondents. Perceptions were measured across five dimensions—taste\, aroma\, presentation\, health considerations\, and cultural relevance—while descriptive statistics and comparative analyses were employed to assess variations in consumer satisfaction. The findings reveal that traditional adobo remains preferred for its authenticity\, flavor consistency\, and nostalgic value\, reflecting its cultural importance. In contrast\, modern adaptations—marked by fusion styles\, innovative presentation\, and health-conscious alternatives—resonate with younger and lifestyle-driven consumers. Satisfaction\, therefore\, extends beyond taste\, encompassing identity\, innovation\, and cultural pride. This study highlights how culinary heritage evolves within modern gastronomy\, offering insights for restaurateurs\, culinary educators\, and food entrepreneurs to balance tradition with innovation in sustaining adobo’s cultural significance.
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:bc25eb81d865b7a70aba113b6f6d7389
URL:http://internationalconferencetibs.sched.com/event/bc25eb81d865b7a70aba113b6f6d7389
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:The Influence of Technology Acceptance and Perceived Value on the Intention to Use Artificial Intelligence in Digital Finance
DESCRIPTION:Authors - Bryly Brord Mirah\, Anderes Gui \n Abstract - The rapid integration of Artificial Intelligence (AI) in the financial sector has fundamentally transformed service delivery through the emergence of Digital Human Advisors. This research examines the factors influencing the intention to adopt these AI-driven services in Indonesia by synthesizing the Technology Acceptance Model (TAM) with Perceived Trust and Multidimensional Perceived Value\, including functional\, emotional\, and conditional dimensions. Employing a quantitative methodology with purposive sampling\, data were gathered from a predominantly Generation Z population. The analysis\, conducted through Partial Least Squares Structural Equation Modeling (PLS-SEM)\, reveals that Perceived Ease of Use serves as the primary cornerstone in shaping Perceived Usefulness\, indicating that the simplicity of the interface is a critical pre-requisite for users to recognize the technology's benefits. Furthermore\, Intention to Use is significantly driven by Perceived Trust\, Functional Value\, and Perceived Usefulness. Conversely\, the insignificance of emotional and conditional values suggests a highly pragmatic mindset among users in high-stakes financial environments. These findings imply that financial institutions should prioritize a "utility-first" strategy\, focusing on systemic integrity and seamless navigation to foster long-term adoption.
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:886a4cbf0e20716d1c4b7c60243f2d27
URL:http://internationalconferencetibs.sched.com/event/886a4cbf0e20716d1c4b7c60243f2d27
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:Treading Artificial Intelligence in Education Through Competency Framework for Teachers (CFT)
DESCRIPTION:Authors - Jann Alfred A. Quinto Abstract - Artificial Intelligence (AI) continues to modify education hence\, need for AI Literate teachers becomes increasingly critical. There remains limited data on teachers’ AI competency in terms of knowledge\, attitudes\, ethical understanding\, and use of technologies. This study sought to assess the level of AI literacy progression among Teachers using a UNESCO Competency Framework for Teachers (CFT)\, profile\, relationships\, differences in the competency levels. Findings showed that majority of teacher are novice (1-5 Years in service) in the teaching profession\, and with no trainings attended related to AI\, and occupying a position equivalent to Teacher 1 to 3. Teachers strongly agree that they “Acquired” basic AI knowledge\, skills and ethics along Human-centered mindset\, Ethics of AI\, Foundations and applications\, AI pedagogy\, and AI for professional growth. In addition\, there is no significant difference in AI literacy competency progression level across profile. This shows that teachers\, with or without training and new in service “Acquired” the basic principles and applications of AI competencies through self-exploration. Literacy competency among the respondents is on the “acquired level”. Furthermore\, there was a significant gap between Human-centered mindset and AI professional growth domain. This implies\, awareness on AI importance\, belief that AI is human led and appreciating AI capacities is high while exploration of AI tools to enhance professional development\, utilization of AI tools confidently for sharing resources is low. This suggest that there should be training on the use of AI tools in teaching before useful programs become obsolete due to rapid change in technology.
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:7726f9fa41d3964326c5c3c3a2fda04b
URL:http://internationalconferencetibs.sched.com/event/7726f9fa41d3964326c5c3c3a2fda04b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:Domain-Agnostic KG-RAG: A Lightweight Framework with LLM-Driven Ingestion and Temporal-Semantic Capabilities
DESCRIPTION:Authors - Shivam Kumar\, Dinesh Kumar Saini Abstract - KG-RAG (Knowledge Graph-Retrieval Augmented Generation) is an advanced AI framework that combines structural knowledge graphs with LLMs to make them smarter\, more accurate\, robust\, and less prone to hallucination. However\, existing KG-RAG pipelines are often tightly coupled with specific domains. In addition\, most of the systems lack proper schema validation and have limited support for temporal knowledge. GenericKG is a modular framework designed to decouple knowledge ingestion\, validation\, storage and retrieval across domains. The framework includes an agentic ingestion pipeline with schema-driven knowledge graph construction\, supported by multi-level validation (L1-L3) to ensure structural\, semantic and temporal consistency. Temporal attributes and semantic embeddings are integrated at framework level\, enabling time-aware querying and hybrid retrieval without domain-specific reengineering. This paper is evaluated on three benchmarks: the BC5CDR biomedical corpus (87.92% entity F1 with 100% precision)\, the WebNLG crossdomain dataset (85.6% entity F1 across 15+ relation types on 100 records)\, and HotpotQA multi-hop question answering (58.0% accuracy on bridge and comparison questions). A raw-LLM baseline without schema guidance scores 0% on all metrics\, confirming the importance of the schemadriven pipeline. This framework is implemented in TypeScript and it will be released as open source.
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:7a9cf0f318f4b7f1b51f8312b6a1e55b
URL:http://internationalconferencetibs.sched.com/event/7a9cf0f318f4b7f1b51f8312b6a1e55b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:Efficient Gated Recurrent Unit Architectures for Univariate Time-Series Forecasting: A Benchmark Analysis Using the Libra Framework
DESCRIPTION:Authors - Abraham Gezehei\, Thomas Hanne\, Rolf Dornberger Abstract - This study benchmarks twelve recurrent neural network (RNN) architectures for univariate macroeconomic time-series forecasting\, covering LSTM and GRU baselines\, width/depth scaling\, bidirectional encoders\, an attention-like pooling variant\, convolutional–recurrent hybrids\, and strong regularization. Following the Libra benchmarking philosophy and the multi-metric evaluation advocated by Prater et al.\, we compare all configurations under identical protocols on 100 series from the Libra Economics collection. A bidirectional GRU yields the best RNN accuracy (sMAPE 41.0\, MASE 0.0447)\, improving over a comparable 2-layer GRU baseline (sMAPE 41.9) at higher wall-clock runtime. Most architectural additions and capacity increases do not improve performance over the simple GRU baseline (e.g.\, deeper/wider models\, pooling-based attention\, CNN–RNN hybrids\, and heavy dropout). The results suggest that short input windows (dynamically sized at 10% of series length\, minimum 10 steps) limit the benefits of architectural complexity in this setting. Classical statistical methods (sNaive\, ETS\, Theta) outperform all neural models by a wide margin while requiring substantially less computation. For these low-frequency macroeconomic series\, shallow GRU variants—especially bidirectional encoders—are the strongest RNN option\, but classical baselines remain the practical choice.
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:cd2a181d2d7986a36778c4763926a44a
URL:http://internationalconferencetibs.sched.com/event/cd2a181d2d7986a36778c4763926a44a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:Enhancing Transparency and Accountability in E-Procurement Using Big Data Analytics and Information processing capabilities
DESCRIPTION:Authors - Alfito Athar Rayyansyah\, Abdurrahman Faris Indriya Himawan\, Galuh Sudarawerti Abstract - Governance challenges remain a major concern in large-scale procurement activities\, particularly regarding transparency\, accountability\, and operational effectiveness. This study investigates the role of Big Data Analytics (BDA) and Information Processing Capability (IPC) in enhancing governance outcomes within the e-procurement environment of PT PLN Indonesia Power. Specifically\, the study examines how these capabilities contribute to transparency and accountability and how they affect both financial and non-financial procurement performance. A quantitative research design was employed\, and data were gathered from employees engaged in procurement-related activities. The proposed model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0. The findings reveal that all proposed hypotheses are statistically supported. BDA emerged as the primary factor driving transparency and accountability\, which subsequently improves procurement performance\, particularly non-financial outcomes. The findings reveal that IPC serves as a key enabler in maximizing the value of BDA while increasing the ability of e-procurement systems to support data-driven analysis. These findings offer practical implications for state-owned enterprises by emphasizing the importance of integrating analytical capabilities and information-processing resources to strengthen governance quality and improve procurement effectiveness in digital environments.
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:ae0f9ec2e8091617ac1c6cc240a98719
URL:http://internationalconferencetibs.sched.com/event/ae0f9ec2e8091617ac1c6cc240a98719
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:How Smart Lighting Shapes Green Hotel Image and Revisit Intention
DESCRIPTION:Authors - Ichwan Masnadi\, Renza Fahlevi\, Elda Nurmalinda Abstract - The purpose of this study is to analyze how Perceived usefulness of smart lighting can affect Revisit Intention through the mediation of Green Hotel Image. This study was conducted on hotel guests who stayed at hotels that implemented smart lighting technology in Jakarta. This study uses quantitative methods by sending questionnaires online via Google Forms to 150 hotel guests who have previously stayed at hotels with smart lighting technology implemented. The data was then processed using SEM-PLS (Structural Equation Modeling–Partial Least Square) through SmartPLS 3 software. The results showed that Perceived usefulness of smart lighting had a positive and significant impact on Green Hotel Image. Green Hotel Image also had a positive and significant effect on Revisit Intention. Perceived usefulness of smart lighting had no effect on Revisit Intention. Furthermore\, results from the analysis showed that Green Hotel Image fully mediated the effect of Perceived usefulness of smart lighting on Revisit Intention. In conclusion\, guests are not inclined to revisit hotels that implement smart technology such as smart lighting. Smart technology indirectly fulfills its role by increasing the hotel’s green (environment-friendly and sustainability focused) image which leads to customer revisit intention. This study contributes to the SOR Theory by showing how Perceived usefulness of smart lighting is the Stimulus factor\, Green Hotel Image is the Organism factor\, and Revisit Intention is the Response factor. Hotel managers can benefit from this study by properly branding their hotels’ sustainability to leverage their use of smart technology in order to compete with other hotels.
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:f18fa6f19a11aeb73af53430a9dde409
URL:http://internationalconferencetibs.sched.com/event/f18fa6f19a11aeb73af53430a9dde409
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:Nondestructive Avocado Ripeness Assessment Using Microwave Sensing and Neural Networks
DESCRIPTION:Authors - Luong Vinh Quoc Danh\, Truong Minh Nhan\, Nguyen Tan Dat\, Nguyen Vinh Thanh\, Do Chi Tam\, Le Tan My\, Nguyen Chanh Nghiem Abstract - Accurate avocado ripeness assessment is essential for ensuring product quality and effective postharvest management\, yet conventional evaluation methods remain largely destructive\, time-consuming\, and limited to representative samples. This paper presents a non-destructive ripeness assessment method combining microwave sensing with feedforward neural network (FNN) classification. A custom-designed open-ended coaxial probe connected to a vector network analyzer was employed to measure the complex reflection coefficient S11 of avocado samples over a frequency range of 1.1–3.1 GHz. Variations in the dielectric properties of avocado flesh during ripening produce corresponding and measurable changes in the S11 characteristics\, from which magnitude\, phase\, and frequency features were extracted and used as inputs to the FNN classifier. The proposed system achieved an overall classification accuracy of 87% in discriminating among three ripeness stages – unripe\, ripe\, and overripe – thereby demonstrating its viability as a rapid\, costeffective\, and non-destructive alternative to conventional destructive ripeness assessment methods.
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:69b92b125c30ad87e2b1e1c795444b2e
URL:http://internationalconferencetibs.sched.com/event/69b92b125c30ad87e2b1e1c795444b2e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:Optimizing an Inventory Routing Problem Using Simulated Annealing
DESCRIPTION:Authors - Christian Vasta\, Rolf Dornberger\, Thomas Hanne\n Abstract - The Inventory Routing Problem (IRP) is a critical challenge in logistics\, combining vehicle routing with inventory management under a unified objective. Recent research in computational intelligence has advanced the use of metaheuristics for tackling such combinatorial problems. Among these\, Simulated Annealing (SA) remains underexplored for IRP compared to more commonly applied methods. In this study\, we address this gap by implementing a custom SA algorithm to solve a deterministic five-day IRP. The goal is to minimize total transportation costs while satisfying daily customer demand using a single-vehicle fleet with fixed capacity. The algorithm's performance is evaluated with 20 independent runs and compared to a modified Tabu Search benchmark using the same deterministic instance. Our results show that Simulated Annealing performs competitively\, producing high-quality solutions\, with moderate variation observed across different cooling schedules and repeated runs. Although it shows greater sensitivity to initial parameters and stochastic behavior\, its exploratory nature allows it to overcome local optima more effectively than Tabu Search in some cases. The outcomes suggest that SA is a viable alternative for IRP under deterministic conditions\, particularly when flexibility in parameter tuning is prioritized.
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:1b0c43f6a037dd5628c6fd753f608792
URL:http://internationalconferencetibs.sched.com/event/1b0c43f6a037dd5628c6fd753f608792
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:Python and MATLAB-based automated waveform pattern analysis method for ECU validation using the Hardware-in-the-Loop test framework
DESCRIPTION:Authors - Febin Koshy Jacob\, Indranil Bose\, Sarika D Tavhare\, Sandhya Anilkumar Abstract - Modern automotive Electronic Control Unit (ECU) systems demand robust and accurate validation frameworks to address increasing system complexity while minimizing manual test effort and development cost. This paper novels an automated Hardware-in-the-Loop (HIL) testing framework for validation of automotive systems\, with a primary focus on automated waveform pattern analysis method. The framework integrates a dSPACE real-time interface with a hardware test bench and algorithm developed using a MATLAB-based simulation model of the Body Control Module (BCM) to generate and analyze input signals. Python-based automation scripts are utilized for test execution control\, synchronized data acquisition\, and automated result analysis\, ensuring repeatable and scalable testing across multiple application domains. The core contribution is a reference-driven waveform comparison methodology\, where signals captured from the Device Under Test (DUT) are evaluated against predefined golden reference waveforms. The approach quantifies Root Mean Square Error (RMSE) percentage and timing deviations across individual channels\, enabling precise detection of mismatches in waveform sequences. The framework is demonstrated through automotive tail lamp animation pattern validation\, where output sequences are compared against reference waveforms for accuracy and robust assessment. Additionally\, the solution is extendable to electric vehicle subsystems such as Battery Management Systems (BMS)\, Traction Motor Control Units (TMCU)\, and Off-Board Chargers (OFBC)\, supporting both dynamic and steady-state validation such as torque-speed curve\, Battery profile testing\, Sensor accuracy etc. The implementation achieves approximately 45.8% automation of test cases and reduces overall validation time by about 41.2%\, resulting in improved repeatability\, reduced manual intervention\, and faster development cycles\, ultimately enabling faster time-to-customer and providing a scalable and efficient solution for modern automotive and electric vehicle system validation.
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:bb03474d6a2cb4bd90fc1d15764948f6
URL:http://internationalconferencetibs.sched.com/event/bb03474d6a2cb4bd90fc1d15764948f6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:THE CONTRIBUTION OF ON-THE-JOB TRAINING TO THE DEVELOPMENT OF COLLABORATIVE SKILLS IN STUDENT INTERNS: IMPLICATIONS FOR WORKFORCE TRANSFORMATION
DESCRIPTION:Authors - Apolinar P. Datu\, Jeferson C Mojica\, Pamela Daphne R. Busog\, Kelvin M. Custodio\, Desiree Anne D. Mendoza\, Kristel Shane C. Paminter\, Rose Ann T. Genova\, Keno A. Villavicencio Abstract - This study explores how on-the-job training (OJT) helps student interns improve their ability to work with others. It focuses on how real workplace exposure strengthens teamwork\, communication\, and adaptability. Data were collected from 150 interns from different academic programs using a survey that examined their experiences during training. The findings show that most interns felt a noticeable improvement in their collaborative skills. Many were actively involved in meetings\, team activities\, and workplace discussions\, which gave them valuable opportunities to interact and contribute. These experiences not only helped them communicate more confidently but also made them more comfortable working as part of a team. The results also indicate that supportive work environments—those that encourage communication and teamwork—play an important role in helping interns grow. In addition\, OJT helped boost their confidence\, sense of responsibility\, and readiness for future employment. Overall\, the study highlights the importance of OJT as a bridge between academic learning and real-world practice. It reinforces the idea that hands-on experience is essential in preparing students for a workplace that values collaboration and adaptability.
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:723dc94866189bb092c8d99280d05320
URL:http://internationalconferencetibs.sched.com/event/723dc94866189bb092c8d99280d05320
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:Artificial Intelligence in High-End Cameras: Enhancing Autofocus\, Exposure\, and Image Processing
DESCRIPTION:Authors - Sambhram Pattanayak\, Pallavi Mishra\, Ruhi Sethi\, Prachi Trivedi Abstract - Rapid advances in Artificial Intelligence (AI) have significantly transformed high-end camera systems\, particularly in autofocus\, exposure control\, and image processing. This study examines the growing integration of AI in high-end camera systems\, focusing on its impact on these areas. By leveraging deep learning models and edge-based computational frameworks\, modern cameras perform real-time scene analysis\, subject recognition\, and predictive parameter optimization. The research adopts a hybrid methodology that combines controlled experimental evaluation\, particularly for AI-assisted autofocus\, with a systematic review of contemporary industry and academic developments. Key performance indicators such as focus accuracy\, response latency\, exposure consistency\, and subject-tracking reliability are analyzed under challenging conditions\, including low light\, dynamic motion\, and complex scene compositions. The results demonstrate that AI-driven imaging systems significantly outperform traditional manual and semi-automated approaches by improving precision\, reducing operational complexity\, and enabling intelligent decision-making at the point of capture. The study also highlights AI’s dual role as a technical enabler and a creative support tool\, allowing photographers and cinematographers to focus more on artistic expression while maintaining high technical standards. Overall\, the research contributes to the evolving field of computational cinematography by offering a balanced evaluation of AI’s technical benefits and its implications for creative workflows.
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:508ca909eb88c8696cb06d765a7194b4
URL:http://internationalconferencetibs.sched.com/event/508ca909eb88c8696cb06d765a7194b4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:Educational Information System\, Artificial Intelligence\, and Allocative Efficiency: Toward Performance-Based Educational Governance in Morocco
DESCRIPTION:Authors - Mohamed Boujarfaoui\, Amal Azeroual\, Mourad Azhari\, Abdessamad Dibi\, Mustapha Esghir Abstract - Amid the progressive adoption of results-based management and the steady expansion of public expenditure on education\, the question of allocative efficiency has become increasingly central to educational governance in Morocco. Despite substantial financial investment\, the Moroccan education system continues to face persistent challenges related to learning outcomes\, territorial inequalities\, and school dropout rates. This paper examines the role of the Educational Management Information System (EMIS) as a strategic instrument for improving governance mechanisms and enhancing the allocation of educational resources. The analysis identifies several structural limitations affecting the current system\, including fragmented data structures\, weak interoperability between digital platforms\, limited analytical exploitation of information\, and insufficient integration of pedagogical\, administrative\, and budgetary dimensions. The study further explores the potential contribution of artificial intelligence (AI) to the transformation of educational governance. Through predictive analytics\, intelligent dashboards\, and budget simulation models\, AI technologies can support more accurate decision-making\, strengthen territorial targeting\, and improve the anticipation of school dropout risks. Such tools also offer new opportunities for optimizing the distribution of financial and human resources while reinforcing performance-oriented public management. The paper argues that the modernization of Morocco’s educational information system\, combined with the integration of AI\, could significantly enhance the efficiency\, equity\, and responsiveness of educational policies. However\, this transition requires substantial improvements in data governance\, analytical capabilities\, interoperability standards\, and ethical safeguards to ensure transparency\, accountability\, and data security.
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:3ebeff9c3800295b2aaaef9c9788d624
URL:http://internationalconferencetibs.sched.com/event/3ebeff9c3800295b2aaaef9c9788d624
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:Factors leading to Reduced Brand sacralization in Zero waste lifestyle product brands among Gen Z consumers
DESCRIPTION:Authors - Z. Aadhil\, T.A. Alka\, M. Suresh Abstract - This research explores the factors leading to less brand sacralization in zero-waste lifestyle product brands among Gen Z consumers by examining the influence relationships among these factors. For this\, a quantitative analysis using Grey Influence Analysis (GINA) is conducted\, and the grey influence and response coefficients are calculated to understand interdependencies among the critical\, ideal\, and typical models. The study identified that Lack of Emotional Connection and Low Trust in the brand’s Green Claims are the most influential factors in the system. These are influenced by other factors\, including utility perception\, expected value\, sustainability\, weak visuals and design\, and the feeling that the price feels high. The findings reveal that for brand sacralization\, the emotional connection or meaning and authenticity are important compared to the functional attributes. Therefore\, this study provides decision-making to practitioners like brand managers to develop zero-waste lifestyle brands with more credibility\, emotional resonance\, storytelling\, brand narratives in the case of Gen Z consumer perception and values\, transparency\, building trust\, and calls for pol-icymakers’ actionable interventions on standardized policy measures to enhance trust and reduce greenwashing. The study identified the future research scope in conducting longitudinal research and statistical analysis to understand the perception of the brand\, cross-cultural differences\, barriers to the zero-waste life-style products\, and the role of digital platforms in increasing emotional connection and trust for higher brand sacralization among the millennials and Gen Z consumers. Hence\, this research\, by providing a system-based understanding of brand sacralization\, is novel and highly contributes.
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:664ee7e615d8ea8650987815832724a3
URL:http://internationalconferencetibs.sched.com/event/664ee7e615d8ea8650987815832724a3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:Financial Impact of Cosmetic Purchase Behaviour Among Working Women: An Empirical Study of Beauty Consumption and Personal Budget Allocation
DESCRIPTION:Authors - Mariya Joseph\, Vinodkumar K.\, Dayana Das Abstract - The beauty and cosmetics industry has emerged as one of the fastest-growing consumer sectors\, significantly influencing the consumption behaviour of working women. Cosmetic purchases are no longer limited to grooming needs but have become associated with workplace confidence\, professional identity\, social media visibility\, and lifestyle aspirations. This study investigates the financial impact of cosmetic purchases on working women\, with special emphasis on monthly budget allocation\, savings behaviour\, financial stress\, and impulse purchase tendencies. A quantitative sur-vey-based methodology is proposed using a structured questionnaire among 250 working women across urban sectors. Statistical tools including descriptive analysis\, reliability testing\, correlation\, regression\, and ANOVA are used to examine the relationship between cosmetic spending and personal financial well-being. The findings indicate that frequent cosmetic expenditure significantly reduces discretionary savings and con-tributes to moderate financial stress\, especially among younger professionals and middle-income groups. The study contributes to consumer behaviour literature by integrating beauty expenditure with financial decision-making among women professionals.
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:dc77b07bc9dc4648b566af2bb95c6cf6
URL:http://internationalconferencetibs.sched.com/event/dc77b07bc9dc4648b566af2bb95c6cf6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:GST and Financial Inclusion: An Empirical Study of Small Business Formalization in Kerala
DESCRIPTION:Authors - Sreesankar R S\, Pranav Vinod\, S Kailas\, Vivek V\, Durgalashmi C V Abstract - The insertion of the Goods and Services Tax (GST) in 2017 was assumed not only to simplify India’s indirect tax system but also to encourage small firms to shift from the informal to the formal economy. For micro and small enterprises\, enactment through GST registration can open doors to bank credit\, digital payment systems and government schemes\, all of which are central to financial inclusion. Kerala offers a particularly interesting setting because it combines a strong micro-enterprise culture with active financial inclusion initiatives such as self-help groups\, microfinance and financial literacy programmes supported by institutions like Kerala bank and NABARD. This paper explores the influence of goods and services tax (GST) on small business formalization in Kerala\, and its impact on their access to formal financial services. The study which is based on primary data collected from small enterprises that are both in and out of GST cover selected districts in Kerala also supplemented with secondary data mainly derived from official and policy sources\, aims to examine what trends emerge in relation to patterns of GST adoption\, perceived costs and benefits associated formalization as well as changes in bank accounts credit digital transaction access post-GST. Objective of the findings is to provide evidence on whether GST acts as a facilitator for small businesses in accessing formal finance and then identifying policy actions that could make tax reforms more enabling and supportive for grassroots entrepreneurship.
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:113f3fa3fc661ef72c5f1f283cd3f894
URL:http://internationalconferencetibs.sched.com/event/113f3fa3fc661ef72c5f1f283cd3f894
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:Integrating Halal and Green Supply Chains as Drivers of Port Operational Sustainability at Tanjung Priok Port
DESCRIPTION:Authors - Melly Azwari\, Abdurrahman Faris Indriya Himawan Abstract - Operational sustainability is a strategic imperative for ports\, requiring efficiency\, cargo integrity\, environmental responsibility\, and stakeholder trust. This study investigates how integrating halal and sustainable supply chain practices influences operational sustainability at Tanjung Priok Port\, Indonesia\, using the Triple Bottom Line perspective. Halal supply chain employs physical segregation\, traceability via barcode/RFID\, and segregated waste handling\, while sustainable supply chain implements energy efficiency\, emission reduction\, and green logistics practices. A quantitative associative design was employed with 169 purposively sampled port logistics operations respondents. Data were analyzed using multiple linear regression. Results indicate that both halal supply chain (B = 0.248\; t = 4.016\; p < 0.001) and sustainable supply chain (B = 0.702\; t = 10.281\; p < 0.001) significantly and positively affect operational sustainability\, with sustainable supply chain emerging as the dominant predictor. Jointly\, the integration explains 60.5% of the variance (F = 129.567\; p < 0.001). These findings offer practical insights for port managers seeking responsible\, resilient\, and integrated port operations.
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:4b9220f6db9fa92bff47d1ad65dd4704
URL:http://internationalconferencetibs.sched.com/event/4b9220f6db9fa92bff47d1ad65dd4704
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:Magnetic Extraction of Microplastics from Simulated Human Blood Using PEG–Chitosan–Coated Superparamagnetic Iron Oxide Nanoparticles (SPIONs)
DESCRIPTION:Authors - Suramrit Kohli\, Nikunj Parikh Abstract - Microplastics have been found in human plasma under various research studies\, with 77% of the samples tested containing concentrations greater than 1\,000 particles/litre. Microplastics are so small that they are able to pass through cell walls\, and without a standardized clinical test for them\, there is a heightened risk of inflammation\, oxidative stress\, neurotoxicity\, haemolysis\, and damage to the DNA. The aim of this proof-of-concept study was to test the feasibility of using PolyEthylene Glycol-coated chitosan Super Paramagnetic Iron Oxide Nanoparticles (PEG-chitosan SPIONs) to selectively capture and extract Polyvinyl Chloride(PVC) microplastics from blood-like solutions such as synthetic plasma Fetal Bovine Serum(FBS). The PEG-chitosan SPION approach was evaluated with respect to external magnetic dialysis and by measuring the efficiency with which they bound to and separated from PVC microplastics. The data demonstrated that PEG-chitosan SPIONs are highly effective in removing PVC microplastics from blood-like fluids with a decrease of 85.1% in turbidity and a reduction of 45.6% in the total contaminants present\, while also capturing approximately 0.17 g of PVC microplastics within the SPION-PVC-microplastic mixture. These data suggest that PEG-chitosan SPIONs are able to bind and remove PVC microplastics efficiently and have considerable biocompatibility under in vitro conditions. These preliminary data will serve as a foundation for conducting future studies. The results suggest that magnetically assisted nano-separation systems hold promise for large-scale development of commercial microplastic removal options in the future.
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:7218054957b9cc82efbe28f25f24d762
URL:http://internationalconferencetibs.sched.com/event/7218054957b9cc82efbe28f25f24d762
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T090000Z
DTEND:20260623T110000Z
SUMMARY:UCF101 Dataset-Based Video Steganography using 2LSB Embedding and ML-based Steganalysis
DESCRIPTION:Authors - Anamika Saini\, Kavita Rathi Abstract - Video steganography has emerged as an effective approach for secure multimedia communication by concealing secret information inside video frames while maintaining visual imperceptibility. This work presents a UCF101 datasetbased video steganography framework using a 2LSB embedding technique for hiding secret image data inside video frames. 101 video samples from the UCF101 benchmark dataset were utilized to evaluate the robustness and scalability of the proposed framework under diverse motion and background conditions. The visual quality of stego frames was analyzed using Peak Signal-to- Noise Ratio (PSNR)\, Mean Squared Error (MSE)\, and Root Mean Squared Error (RMSE). In addition\, machine learning-based steganalysis models including Support Vector Machine (SVM)\, Convolutional Neural Network (CNN)\, and Extreme Gradient Boosting (XGBoost) were implemented to detect hidden information from cover and stego frames. Experimental results demonstrate that the proposed embedding method maintains high visual quality with low distortion values. However\, the steganalysis results indicate that advanced machine learning approaches\, particularly XGBoost\, can effectively identify hidden embedding patterns present in stego frames. The study highlights the trade-off between visual imperceptibility and resistance against machine learning-based steganalysis in modern video steganography systems.
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:2045779a1a3b34ac4141a9dde1e0fd9f
URL:http://internationalconferencetibs.sched.com/event/2045779a1a3b34ac4141a9dde1e0fd9f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T110000Z
DTEND:20260623T110200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:9448c3aa5283ddbdefeda57c05c4714f
URL:http://internationalconferencetibs.sched.com/event/9448c3aa5283ddbdefeda57c05c4714f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T110000Z
DTEND:20260623T110200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:96f413207756dbcab095472218732615
URL:http://internationalconferencetibs.sched.com/event/96f413207756dbcab095472218732615
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T110000Z
DTEND:20260623T110200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:88ec268fdf2f49e8f1634811bb1a376c
URL:http://internationalconferencetibs.sched.com/event/88ec268fdf2f49e8f1634811bb1a376c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T110000Z
DTEND:20260623T110200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:7ee6ae3727736e3815b3b8ee50351610
URL:http://internationalconferencetibs.sched.com/event/7ee6ae3727736e3815b3b8ee50351610
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T110200Z
DTEND:20260623T110500Z
SUMMARY:Session Closing & Information to Author
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 4A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:99533949e3600bf5662cf15db4b6ea15
URL:http://internationalconferencetibs.sched.com/event/99533949e3600bf5662cf15db4b6ea15
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T110200Z
DTEND:20260623T110500Z
SUMMARY:Session Closing & Information to Author
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 4B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:6670633df2946df66e2774951bf77664
URL:http://internationalconferencetibs.sched.com/event/6670633df2946df66e2774951bf77664
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T110200Z
DTEND:20260623T110500Z
SUMMARY:Session Closing & Information to Author
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 4C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:eb22922d4be338436eca17ba7250ce45
URL:http://internationalconferencetibs.sched.com/event/eb22922d4be338436eca17ba7250ce45
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260623T110200Z
DTEND:20260623T110500Z
SUMMARY:Session Closing & Information to Author
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 4D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:eaf5c21e763dd233cf3c8ad6ed15ff1b
URL:http://internationalconferencetibs.sched.com/event/eaf5c21e763dd233cf3c8ad6ed15ff1b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T025800Z
DTEND:20260624T030000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:6a47bac2decc74bcd02b61d2d64f9c02
URL:http://internationalconferencetibs.sched.com/event/6a47bac2decc74bcd02b61d2d64f9c02
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T025800Z
DTEND:20260624T030000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:31aecc1ce3553bca9963dfd49bc6d341
URL:http://internationalconferencetibs.sched.com/event/31aecc1ce3553bca9963dfd49bc6d341
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T025800Z
DTEND:20260624T030000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:59a85886720119d1b3d713c982a38790
URL:http://internationalconferencetibs.sched.com/event/59a85886720119d1b3d713c982a38790
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T025800Z
DTEND:20260624T030000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:56bc6fe7f2eef2d91a8511b992564915
URL:http://internationalconferencetibs.sched.com/event/56bc6fe7f2eef2d91a8511b992564915
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T030000Z
DTEND:20260624T050000Z
SUMMARY:A Low-Cost Drone-Mounted Multispectral Imaging Framework for Early Detection of Maize Leaf Diseases in Smallholder Farming Systems
DESCRIPTION:Authors - Mainford Mutandavari\, D. Hemavathi Abstract - Maize (Zea mays L.) is an essential staple produce for smallholder farmers in developing nations\, yet Northern Corn Leaf Blight (NLB)\, Grey Leaf Spot (GLS)\, and Common Rust foliar diseases cause yield losses of 30–70%. Infection detection is done at advanced stages due to labor intensity resulting from the conventional disease monitoring methods. A Low-Cost Drone-Mounted Multispectral Imaging (LCDMI) framework for resource-constrained smallholder systems is presented in this paper\, pairing a consumer-grade UAV with a five-band multispectral sensor. The vegetation-index features are fused with multispectral band data using a Spectral-Spatial Attention Vision Transformer (SSAViT) classifier and a Spectral-Constrained Synthetic Data Generation (SC-SDG) module addresses training-data scarcity. A hardware cost of USD1\,940 is projected for field evaluations across twelve plots in Zimbabwe over two growing seasons yielding 95.8% detection accuracy\, identifying diseases 7–12 days before visible symptom onset. A multi-label extension enables simultaneous classification of co-occurring infections. Georeferenced disease maps are delivered within 6.3 min/ha. With perhectare costs as low as USD2.10 on a scale\, the economic analysis projects ROI within two seasons for cooperatives managing 50+ hectares.
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:adb86d58df1ddcdeab9944794c496a9f
URL:http://internationalconferencetibs.sched.com/event/adb86d58df1ddcdeab9944794c496a9f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T030000Z
DTEND:20260624T050000Z
SUMMARY:AI in Higher Education: Cultivating Critical Thinking in Social Learning Environments
DESCRIPTION:Authors - IGN Oka Ariwangsa\, Komang Widhya Sedana Putra P\, Wayan Sri Maitri Abstract - The rapid adoption of artificial intelligence (AI) in higher education has transformed how students access information and engage in academic activities. While AI-powered technologies enhance efficiency and provide personalised support\, their uncritical use may weaken independent reasoning and reduce meaningful social-academic participation. This raises concerns in digitally mediated environments where individuals must interpret complex information\, evaluate uncertainty\, and make informed judgments. Despite growing attention\, most studies emphasise functional outcomes such as academic performance\, overlooking the mechanisms through which AI-integrated teaching can foster deeper\, more sustainable learning. This study examines how AI-aware pedagogy—defined as the intentional and reflective integration of AI in instructional design—enhances critical thinking through social-academic engagement. A quantitative approach was employed\, involving 200 undergraduate students in Indonesia. Data were collected via structured questionnaires and analysed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that AI-aware pedagogy has no significant direct effect on critical thinking. However\, it significantly influences critical thinking indirectly through social-academic engagement. This indicates that higher-order thinking develops not merely through technological integration\, but through socially embedded learning processes that encourage interaction\, reflection\, and evaluation. Theoretically\, this study links digital pedagogy with cognitive and social learning processes. Practically\, it highlights the need for AIsupported environments that foster critical evaluation and responsible decisionmaking under conditions of uncertainty. Future research should explore its applicability across contexts and its long-term cognitive implications.
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:594f1aa25a5fa98a9b8438ffcb48b1ab
URL:http://internationalconferencetibs.sched.com/event/594f1aa25a5fa98a9b8438ffcb48b1ab
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T030000Z
DTEND:20260624T050000Z
SUMMARY:DATA-DRIVEN ANALYSIS OF ACADEMIC PERFORMANCE OF BSOAD STUDENTS AT TAGBILARAN CITY COLLEGE USING DATA MINING TECHNIQUES
DESCRIPTION:Authors - Mary Diana C. Yamzon\, Janelli M. Mendez Abstract - This study provides a data-driven analysis of the academic performance of Bachelor of Science in Office Administration (BSOAD) students at Tagbilaran City College from Academic Year 2021 to 2024\, employing data mining clustering techniques to ascertain the five most challenging subjects. The study specifically aimed to: (1) construct and preprocess a dataset of pertinent academic attributes\; (2) employ K-Means\, K-Medoids\, and Agglomerative Hierarchical Clustering algorithms to discern groupings of subject difficulty\; (3) validate clustering results utilizing the Davies-Bouldin Index (DBI)\; and (4) develop evidence-based recommendations for curriculum enhancement and academic assistance. The analysis involved a dataset of 26\,965 valid student grade records across 68 subjects\, all of which were processed using RapidMiner Studio. The research utilized the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework within the context of Educational Data Mining (EDM). The DBI for K-Means (DBI = 0.461\; Excellent) and K-Medoids (DBI = 0.9145) were used to check the clusters\, and the visual dendrogram was used to check the Agglomerative Hierarchical Clustering. All three algorithms consistently recognized OA113 Advanced Shorthand and OA111 Foundations of Shorthand as the two most challenging subjects in the program. The results offer statistically substantiated\, evidence-based insights to facilitate curriculum evaluation\, instructional enhancement\, and the formulation of specialized academic intervention programs for BSOAD students.
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:4b95c6dc92a285693bdbf9560201e31c
URL:http://internationalconferencetibs.sched.com/event/4b95c6dc92a285693bdbf9560201e31c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T030000Z
DTEND:20260624T050000Z
SUMMARY:EmpowerSK: A Data-Driven Framework for Boosting Youth Engagement Using Data Mining Tools
DESCRIPTION:Authors - Hussein P. El Sayed Ahmed\, Ardee Joy T. Ocampo Abstract - Youth participation in local governance remains a persistent challenge despite institutional mechanisms designed to promote engagement. In the Philippines\, the Sangguniang Kabataan (SK) serves as a formal platform for youth involvement in local decisionmaking\; however\, many SK programs continue to experience low participation\, limited feedback integration\, and repetitive activity design. This study presents EmpowerSK\, a data-driven framework that leverages data mining techniques to enhance youth engagement in SK programs. Using structured survey data from 1\,055 youth respondents aged 18–25 across the nine barangays of Alilem\, Ilocos Sur\, the study applies the Knowledge Discovery in Databases (KDD) framework\, K-Means clustering\, and sentiment analysis to transform raw feedback into governance intelligence. K-Means clustering (k=3) identified three statistically validated engagement profiles: Highly Active (61.6%)\, Moderately Involved (17.0%)\, and Disengaged (21.3%). Sentiment analysis of open-ended responses revealed appreciation (77.8% positive)\, diagnosis (73.2% negative)\, and aspiration (85.5% neutral-aspirational) as a coherent three-phase youth governance narrative. An overall weighted mean of 3.75 ("Very Good") across eleven Likert-scale items confirmed a critical institutional gap: Digital Engagement (4.14) significantly outpaced SK Support Initiatives (3.52)\, with SK Training recording the lowest item score (3.44). A five-pillar data-driven action plan—Awareness and Inclusion\, Program Diversification\, Digital Transformation\, Capacity Building\, and Monitoring and Evaluation—was developed\, validated by SK officials\, and aligned with SDG 4\, 11\, and 16. The findings demonstrate that freely available data mining tools can transform rural youth governance into an annually replicable\, evidence-based participatory system.
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:0b066da10ff5d63b36b5d46f96133682
URL:http://internationalconferencetibs.sched.com/event/0b066da10ff5d63b36b5d46f96133682
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T030000Z
DTEND:20260624T050000Z
SUMMARY:Forecasting Enrolment\, Retention\, and Graduation Trends Using Predictive Analytics: A Cohort-Based Analysis
DESCRIPTION:Authors - Reynaldo F. Agunod\, Janelli M. Mendez Abstract - Higher education institutions collect large volumes of student data but these are underutilized for institutional planning. This study applies the CRISP-DM framework to enrolment records of a freshman cohort of 1\,916 students across four academic years (2021-2025) across 28 academic programs from a private higher education institution in Central Visayas\, Philippines\, to forecast institutional progression metrics using predictive analytics. Descriptive analytics and three predictive models were applied based on their suitability for the dataset with 3-4 data points\, namely: Linear Regression\, Holt-Winters Exponential Smoothing\, and ARIMA. Six institutional performance metrics were analyzed: enrolment\, retention\, persistence\, attrition\, program shifts\, and graduation. Key findings reveal a continuous 29.6% enrolment decline within the cohort\, an im-proving retention and persistence profile\, a program-shift surge largely due to migrations from Accountancy to Finance\, and a rapidly increasing graduation rate. Linear Regression (OLS) was identified as the most effective forecasting model for the study’s single-cohort dataset.
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:fb01ad0350a1b82f1e9b0e7cc894caad
URL:http://internationalconferencetibs.sched.com/event/fb01ad0350a1b82f1e9b0e7cc894caad
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T030000Z
DTEND:20260624T050000Z
SUMMARY:PREDICTIVE MODELING OF STUDENT ATTRITION AND RETENTION USING MACHINE LEARNING ALGORITHMS AT TAGBILARAN CITY COLLEGE
DESCRIPTION:Authors - Edimar J. Rato\, Janelli M. Mendez Abstract - Student dropout has remained a major problem in all higher education institutions globally\, including in the Philippines\, where the total college dropout rate in the country was recorded at about 35.15% in the Academic Year 2023–2024. This study aimed to develop a predictive analytics model that identifies dropout and retention patterns among students of Tagbilaran City College to support evidence-based intervention strategies. offered by the school from Academic Year 2021-2024. The algorithms implemented for the supervised learning process include Random Forest and Gradient Boosting\, while the algorithm for the unsupervised learning process is K-Means Clustering implemented using the RapidMiner Studio tool. Results revealed that both supervised models had a poor performance due to class imbalance issues as well as a small feature set\; the Random Forest model had an accuracy of 59.59%\, while it had an AUC of 0.575. The Gradient Boosting model had an accuracy of 60.51%\, while it had an AUC of 0.508. The K-Means Clustering model had a good performance since it resulted in three interpretable student risk clusters: a moderate-risk group with a dropout rate of 27.3%\, a highest-risk group with a dropout rate of 44.7%\, and a lower-risk but larger group with a dropout rate of 41.9%. The Davies-Bouldin Index of 0.967 confirmed adequate cluster separation. The K-Means model demonstrated the most practical utility as an early-warning risk stratification tool applicable at the start of each academic year\, forming the foundation of an evidence-based intervention plan to improve student retention at Tagbilaran City College.
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:8a75fa6ec0cea1691faa4d50d38269c4
URL:http://internationalconferencetibs.sched.com/event/8a75fa6ec0cea1691faa4d50d38269c4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T030000Z
DTEND:20260624T050000Z
SUMMARY:Smart Technology Adoption in Tourism Operations for Innovation and Sustainability Outcomes: A Systematic Literature Review
DESCRIPTION:Authors - Ni Made Prasiwi Bestari\, Jonathan Jacob Paul Latupeirissa\, Suryanto Nugroho\, Iwan Adinugroho\, Melati Budi Srikandi\, Ayu Made Bianca Juarez Abstract - The Fourth Industrial Revolution has been transforming the global tourism industry\, shifting toward a dynamic Tourism 4.0 ecosystem. Given that the adoption of AI is expected to increase the revenue of the tourism industry\, it is necessary to conduct a Systematic Literature Review to fill the gap in empirical research on the relationship between technological innovation and long-term sustainability. Most studies on smart tourism from different perspectives\, including tourist behavior\, tourist service quality\, innovation\, and sustainability\, focus on the "hardware construction" at the macro level and its implementation based on related policies\, ignoring the psychological mechanisms affecting tourists' experiences at the micro level. This study aims to identify the key technological drivers\, including AI\, IoT\, and computer vision\, and their influence on operational innovation and Sustainable Development Goals. A total of 23 core manuscripts from 2020 to 2025 gathered from Scopus database were synthesized and analyzed based on PRISMA guidelines. The results showed that smart tourism technologies can greatly improve efficiency and enhance hyper-personalization. However\, most current applications of smart tourism technologies do not take adequate account of social and environmental metrics. Also\, many digital tourism strategies prioritize revenue over social inclusion. For the future of smart tourism destinations\, frameworks such as Society 5.0 that integrate high-tech with the human touch of hospitality and tourism are needed. Destinations should also seek governance models that ensure long-term resilience by moving the focus away from infrastructure and toward "Smart People" initiatives and the development of standardized real-time sustainability metrics.
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:fd3774e87a0b4d4db4491977db034ee1
URL:http://internationalconferencetibs.sched.com/event/fd3774e87a0b4d4db4491977db034ee1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T030000Z
DTEND:20260624T050000Z
SUMMARY:Assessing the Implementation of the Intellectual Property and Technology Business Management(IPTBM) in a State University in Bohol\, Philippines
DESCRIPTION:Authors - Angeline B. Elegio\, Darrel A. Cardana\, Kathlyn L. Quion Abstract - This study assesses the implementation of the Intellectual Property and Technology Business Management (IPTBM) framework at Bohol Island State University (BISU)\, a geographically peripheral state university in the Philippines. Using a longitudinal descriptive case study design\, the study reviewed institutional documents\, policies\, IP records\, training reports\, curricular materials\, and technology utilization records from 2019 to 2025\, with emphasis on the IPTBM implementation period from 2023 to 2025. Findings show that IP applications increased from 89 in 2023 to 156 in 2025\, representing a 75.3% overall increase\, while IP registrations rose from 45 to 112\, equivalent to a 148.9% increase. Sectoral filings were concentrated in Food with 74 filings or 24.7%\, ICT with 64 filings or 21.3%\, and Creative Works with 54 filings or 18.0%\, reflecting the university’s regional innovation priorities. Capacity-building also expanded\, with trained participants increasing from 723 in 2023 to 1\,839 in 2025\, or a 154.4% increase. However\, IP utilization remained largely extension-mediated\, accounting for 26 of 29 recorded utilization activities or 89.7%\, while commercialization through licensing and revenue generation remained emerging. Viewed through the Triple Helix framework\, the findings suggest that IPTBM functioned as an enabling mechanism for strengthening IP governance\, human capital development\, and university-level innovation management. The study contributes longitudinal\, institution-level evidence on how resource-constrained HEIs can move toward integrated IP protection\, utilization\, and innovation governance.
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:74403de99835243a5c9a8f5d2b11b626
URL:http://internationalconferencetibs.sched.com/event/74403de99835243a5c9a8f5d2b11b626
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T030000Z
DTEND:20260624T050000Z
SUMMARY:From Cash to Digital: Exploring the Pathways to a Cashless Economy in Bangladesh with mediating roles of Intrinsic Motivation and Initial Trust
DESCRIPTION:Authors - Ovia Rizvi\, Sadman Kabir\, Md. Tafshir Jaman Takib\, Abir Sen Gupta\, Sayra Islam Saki\, S.M. Sayem Abstract - The global shift toward cashless payment systems has transformed financial transactions\, yet adoption in developing countries such as Bangladesh remains limited. This study investigates the determinants of cashless payment adoption in Bangladesh by examining user perceptions and behavioral drivers. Drawing on survey-based evidence from 369 respondents\, the PLS-SEM analysis identifies facilitating conditions\, perceived security\, initial trust and intrinsic motivation as the most influential factors shaping adoption. In contrast\, digital financial literacy\, social influence and IT innovation acceptance were found to have little impact\, suggesting that peer effects and novelty alone do not encourage sustained use. Moreover\, initial trust and intrinsic motivation showed significant mediating impact between the drivers and the adoption of cashless payment systems. The findings highlight the importance of robust infrastructure\, strong security protocols and user-centric design in promoting digital financial inclusion. Policy implications emphasize collaborative efforts by regulators and service providers to expand infrastructure\, enforce cybersecurity standards and foster user trust. These measures are critical for accelerating Bangladesh’s transition toward a secure and inclusive cashless society.
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:81a9c95872f5000621b7df5960a1c067
URL:http://internationalconferencetibs.sched.com/event/81a9c95872f5000621b7df5960a1c067
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T030000Z
DTEND:20260624T050000Z
SUMMARY:Human-Centric AI-Driven Social Media Intelligence: Linking Consumer Trust\, e-WOM\, Purchase Intention\, and Perceived Business Sustainability
DESCRIPTION:Authors - Aditya Nova Putra\, Tri Wiyana\, Setiani Putri Hendratno\, Nora Fitriawati\, Ida Bagus Putu Aditya Abstract - The world of social media marketing is shifting from traditional con-tent delivery to personalized solutions\, algorithmic recommendation systems\, AI generated content and Automated Customer Service Chat. While these technologies can increase relevance and responsiveness\, the consumer impact of AI-powered brand communications is conditional upon perceptions of brand messaging as being trustworthy and human-centered\, socially meaningful. The study constructs a consumer-behavior framework of the impact of human-centric AI-driven social media intelligence on trust in AI-based brand content\, e-WOM\, pur-chase intention and perceived sustainability. Situated in the fields of digital marketing\, social media intelligence and behavioral consumer analytics\, this study aims to investigate a quantitative survey conducted among Indonesian social media users who have been exposed to AI-assisted or AI-generated brand communication. Data was analyzed with PLS-SEM with trust being treated as the inner psychological mechanism and e-WOM as the outer social amplification mechanism transferring AI-enabled marketing to purchase intention and perception of Sustainability. Moving beyond technological adoption\, this research on AI marketing highlights customer intelligence\, consumer trust construction\, online recommendations and responsible digital value creation. In practice\, the framework guides firms in designing AI-enabled social media strategies that are persuasive\, credible\, customer- and sustainability-oriented.
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:480d031aa5bfe7f6b123f735c03b6133
URL:http://internationalconferencetibs.sched.com/event/480d031aa5bfe7f6b123f735c03b6133
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T030000Z
DTEND:20260624T050000Z
SUMMARY:Implementation of an Artificial Intelligence Based EcoVision Framework for Economic Forecasting
DESCRIPTION:Authors - Chaithra G\, Ambika P R\, Manjunath R\, Shivashankar\, Niranjan R\, Sowmya Naik P Abstract - Gross Domestic Product (GDP) forecasting using traditional Econometric models is indispensable for evidence-based decision-making. However\, these models are often limited in their ability to handle linear relationships and adapt to high-dimensional data. This paper introduces EcoVision\, an open-source web-based forecasting platform that incorporates AI and machine learning to accurately predict GDP and other associated socio-economic variables using the Gap minder dataset (175 countries\, 1998-2018). Four machine learning models were used: Support Vector Machine Regression\, Polynomial Regression\, Decision Tree Regression\, and Random Forest Regression. These were built using Python and the Flask/Scikit-learn stacks. Models were evaluated using Average Absolute Error\, Squared Error\, and R² values. Results show that the Decision Tree Regression model has a perfect fit (R² = 1.0\, AAE = 0\, SE = 0)\, making it the best model compared to the other models. The web interface is built using pure HTML5/CSS3/Chart.js. The integrated "Gemini API Module" enables the automatic generation of easily understandable policy summaries\, thus allowing for faster extraction of insights. Results from testing the system on 3532 clean data records proved that the system is accurate in forecasting ≥ 85%\, Artificial Intelligence summary relevance ≥ 80%\, and export success 100%\, making it a potential decision-support system for economists\, researchers\, and governments.
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:62d632e49e6aa5069c33b8f5b3a5af79
URL:http://internationalconferencetibs.sched.com/event/62d632e49e6aa5069c33b8f5b3a5af79
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T030000Z
DTEND:20260624T050000Z
SUMMARY:Smart Surveillance System for Weapon and Violence Detection
DESCRIPTION:Authors - R Suganya\, S Priya\, Sheeja Pon Chakravarthy\,Pragadheesh Thirumal M Abstract - This paper presents a real-time intelligent surveillance system designed to detect weapons and violent activities using deep learning techniques [2]. The system integrates the YOLOv7 object detection model [7] for weapon recognition and a CNN-based violence detection module for behavioral analysis. Real-time video streams from CCTV cameras are processed to identify potential threats\, and alerts are transmitted via MQTT for immediate notification. Experimental evaluation demonstrates that the YOLOv7 model achieves a mean Average Precision (mAP@0.5) of 55.3% for weapon detection\, while the CNN model [11] attains 96% accuracy in classifying violent actions. The system operates at an average speed of 25–30 frames per second with low latency\, confirming its feasibility for live surveillance applications. The proposed architecture enhances public safety by providing automated\, accurate\, and real-time monitoring capabilities.
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:f8ca66ddcdaefef6398489c7bf8a898f
URL:http://internationalconferencetibs.sched.com/event/f8ca66ddcdaefef6398489c7bf8a898f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T030000Z
DTEND:20260624T050000Z
SUMMARY:The Role of AI in Information Curation on Social Media and Its Impact on Public Agenda Setting
DESCRIPTION:Authors - Melati Budi Srikandi\, Jonathan Jacob Paul Latupeirissa\, Yolanda Masnita\, Rizki Dewantara\, Ni Made Prasiwi Bestari\, Ayu Made Bianca Juarez Abstract - The shift from human gatekeepers to AI-driven algorithmic curation has fundamentally changed the concept of "Agenda-Setting" theory in the digital age. This change is significant because AI now influences public issue salience\; however\, there is a notable gap in public awareness. This study examines AI's role in social media information curation and its effects on public discourse and agenda setting. To do this\, the research employs a systematic literature review guided by PRISMA principles\, analyzing data collected from the Scopus database to identify current research trends. Moving from “handheld” to “automatic” curation results in more personalized interfaces that foster “filter bubbles” and “echo chambers\,” according to the analysis. It demonstrates that understandings of “algorithmic news bias” are more influenced by user partisanship and ideological cues than purely technical causes. In conclusion\, this suggests that media theories need to be refined to include automated gatekeeping as a core component. Algorithmic literacy serves as a filter against distortions\, aiming to reduce disinformation and digital conflict within society. To address the bottlenecks in communication processes\, future research and policy should focus on improving algorithmic literacy\, given its undeniable influence over human decision-making.
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:984f45297f54bce5a577f6e5848522c2
URL:http://internationalconferencetibs.sched.com/event/984f45297f54bce5a577f6e5848522c2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T030000Z
DTEND:20260624T050000Z
SUMMARY:Understanding Customer Behavioral Intentions Toward Hotel Online Check-In: Insights from the Technology Acceptance Model
DESCRIPTION:Authors - Helmy Wijaya\, Vallencia Ricca Widjaja\, Fernand Jetshen Clevanno\, Ichwan Masnadi Abstract - With rapid digitalization happening in the hospitality industry today\, hotels are now able to interact more digitally with their guests using innovative customer service solutions. Aspects of technology adoption can be studied from the perspective of customer intelligence to gain behavioral insights about customers\, which in turn can help hoteliers improve their user experience with such technologies and create a higher rate of adoption amongst customers. This research explores what factors affect hotel customers' intention to adopt online check-in technology by implementing the Technology Acceptance Model (TAM) with an exploratory factor analysis aimed at customer behavioral insights. Using quantitative explanatory research methods\, data from 150 respondents in Jakarta was gathered through online questionnaires. Structural equation modeling was analyzed through Partial Least Squares SEM (PLS-SEM). Empirical results showed that PU and PEOU affected users' attitude toward using online check-in technology. The users' disposition exerted a considerable influence on their aspiration to utilize the digital check-in technology. The effect of PU and PEOU on intention was fully mediated by attitude\, implying how affective evaluation by customers has an impact on customers' behavioral intention.
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:65e358c5a4cc1ae9c8c8cb3df4758cf0
URL:http://internationalconferencetibs.sched.com/event/65e358c5a4cc1ae9c8c8cb3df4758cf0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T030000Z
DTEND:20260624T050000Z
SUMMARY:A comprehensive Survey on GAN-Driven Intrusion Detection and Security Enhancement in IoT Systems
DESCRIPTION:Authors - Bhavya Balakrishnan\, Srinivasa HP Abstract - The massive deployment of heterogeneous\, resource- con-strained and always-on devices underlying the Internet of Things (IoT) has introduced complex cybersecurity challenges. The rapid growth of the Internet of Things (IoT) due to the large-scale deployment of heterogeneous\, resource-constrained and always-on devices has resulted in complex cybersecurity challenges. The physical and digital components in the IoT systems are tightly bound which increases the attack sur-face and makes them highly prone to threats of malware infections\, data theft\, unauthorized access and distributed denial of service. Traditional security mechanisms and rule-based intrusion detection systems cannot manage the dynamic\, large-volume and evolving IoT traffic. The solutions provided by machine learning have been widely concerned due to its capability of learning data patterns and finding abnormal and malicious activities. However\, existing machine learning models have serious constraints such as lack of labelled information\, extreme class imbalance\, and inability to generalize to new and never-seen attacks. In recent years\, Generative Adversarial Networks (GANs) have emerged as a promising paradigm to improve the cybersecurity of IoT through artificial generation of realistic synthetic data\, adversarial sample enhancement\, alleviating data imbalance and modelling adversarial attack-defense dynamics. GAN based models have showed great gains in intrusion detection\, anomaly detection and malware analysis in the IoT networks . However\, modern studies are still divided on this issue due to variations in GAN architectures\, datasets\, evaluation procedures\, and experimental procedures. In addition\, most of the researches have been more concentrated on offline benchmark databases\, with less focus on checking through realistic IoT testbeds\, which could be more precise in capturing the actual deployment conditions.
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:8383bf2d4c9fb321561d63cbc1f60811
URL:http://internationalconferencetibs.sched.com/event/8383bf2d4c9fb321561d63cbc1f60811
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T030000Z
DTEND:20260624T050000Z
SUMMARY:Democratizing Digital Archive Learning for SDG 4 Inclusive Education Using a Cost-Effective VBA Excel Framework
DESCRIPTION:Authors - Ferry Setyadi Atmadja\, Sabo Hermawan\, Eka Dewi Utari\, Suciati Putri Nurjanah\, Siti Dwi Hastuti Abstract - The exorbitant costs associated with professional Content Management Systems (CMS) have precipitated a severe theory to practice gap in digital archive education. This infrastructural barrier disproportionately disadvantages institutions with constrained budgets\, fundamentally threatening the inclusive education mandates of Sustainable Development Goal (SDG) 4. To bridge this ped-agogical divide\, this study developed and validated a zero-license educational framework utilizing Microsoft Excel's Visual Basic for Applications (VBA) to simulate a professional electronic records environment. Employing an R&D methodology (ADDIE model) with a cohort of 40 undergraduate students\, the proposed framework circumvented hardware and financial constraints by operating offline on low-specification devices. Results indicated high expert validation (4.35/5.0) and a statistically significant enhancement in students' practical archival skills\, evidenced by a moderate to high Normalized Gain (N-Gain) of 0.61. Furthermore\, the system demonstrated exceptional usability with a System Usability Scale (SUS) score of 76.5. These findings provide empirical evidence that strategic\, low-cost technological interventions can effectively democratize digital archive learning\, offering a highly scalable solution for marginalized educational ecosystems in developing regions.
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:8e3e2effe5a9cbf0ebbe0b3c8273df3d
URL:http://internationalconferencetibs.sched.com/event/8e3e2effe5a9cbf0ebbe0b3c8273df3d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T030000Z
DTEND:20260624T050000Z
SUMMARY:From Acceptance to Continuance: Investigating Trust and Privacy Risk in Mandatory AI-Based Biometric Boarding Systems at Indonesian Railways
DESCRIPTION:Authors - Rayyan Naufal Anandito\, Muhammad Fedylopa Ginting\, Trias Septyoari Putranto Abstract - The rise of Automated Biometric Boarding Systems (ABBS) for public transportation\, driven by the potential to enrich convenience while integrating artificial intelligence into their activities has not been without the desire among policymakers and business leaders to get a better grasp on how biometry could be integrated in mandatory adoption contexts. Abstract This study aims to investigate passenger acceptance and continuance intention of AI-based face recognition boarding system in PT Kereta Api Indonesia (KAI) Gambir Railway Station 2023. Based on an integrated framework of Technology Acceptance Model (TAM) and Expectation-Confirmation Model (ECM)\, complemented with Trust and Perceived Privacy Risk\, this study explores the pathways through which affective factors and institutional factors influence long-term behavioral intentions in a compulsory acceptance context. Data from cross-sectional\, quantitative. 150 purposively sampled passengers were analyzed by PLS-SEM using SmartPLS 4.0. This is the first time that these findings challenge many of the assumptions about technology adoption and provide relevant policy recommendations for transport authorities based on a framework for AI governance aligned with Indonesia's Personal Data Protection Law (UU No. 27/2022).
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:33b75b5f5a0d9c9ae0f67488278762d4
URL:http://internationalconferencetibs.sched.com/event/33b75b5f5a0d9c9ae0f67488278762d4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T030000Z
DTEND:20260624T050000Z
SUMMARY:Leveraging Information-Theoretic Measures for Feature Selection in High-Dimensional Data Mining
DESCRIPTION:Authors - Ridhi Sharma\, Ashok Kumar Abstract - This manuscript discovers the role of information theoretic measures for feature selection while dealing with high dimensional data sets. The study uses entropy\, mutual information and divergence measures to address the issues of classification and high computational complexity of real data set which is affect by redundant and irrelevant features\, by analyzing the dependency patterns and feature relevance in complex data set. Under different data conditions\, the proposed approach for feature selection\, in comparison to traditional methods\, handles the non-linear relationships and noisy attributes effectively in terms of relevance\, classification and interpretation. In-formation theoretic methods provide more precise feature selection and pattern identification results in the data sets. Despite the challenges of computational cost and scalability\, the study shows that information theoretic measures can perform better in feature selection and decision making of the data mining.
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:da41a8293c89cdaf69960c337352095c
URL:http://internationalconferencetibs.sched.com/event/da41a8293c89cdaf69960c337352095c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T030000Z
DTEND:20260624T050000Z
SUMMARY:Night-Window Batching versus Carbon-Aware Scheduling for Clinical AI GPU Workloads
DESCRIPTION:Authors - Nishi Doshi\, Shrey Shah Abstract - Hospitals run more machine learning on GPUs while the carbon footprint of grid electricity rises and falls through the day. Using a computer simulation\, we compare 13 scheduling rules on mixed GPU hardware\, with synthetic patient-style jobs\, urgency tiers\, and time-ofday carbon traces. We do not study patient outcomes\; every percentage we report is a simulator queue number\, not a clinical finding. We ask whether running non-urgent jobs overnight is almost as good as a richer rule that mixes urgency and carbon (CUCA at weight 0.45\, written CUCA 0.45). The comparison keeps carbon reduction secondary to clinical priority and deadline compliance\, so each policy is judged on both average kg CO2e and missed-deadline behavior. CarbonGreedy and CarbonShift are carbon-first stress tests that demonstrate how poorly wrong vendor presets can disrupt clinical priorities\, and are not meant for production. Numbers are averages over many test settings\, with wide run-to-run spread and no statistical adjustment\, so headline ratios are exploratory. On an eight-GPU baseline\, the overnight rule closes about 78% of the carbon gap between urgency-only and CUCA 0.45 while missing fewer urgent deadlines than either. CarbonShift lets about 46% of the most urgent jobs miss their deadline\; this is simulated queueing\, not bedside harm. At 48 jobs per hour\, the carbon footprints almost tie\, yet the overnight rule still misses fewer urgent deadlines. A geography test\, where regions share one daily carbon shape with only timezone shifts\, trims under one percentage point of average carbon\; a twelve-hour routine window saves a little carbon for CUCA 0.45 but raises overall missed deadlines. Overnight batching stays competitive on average modelled carbon\; carbon-only rules belong only in stress tests.
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:d4ed4efa89f69ad9083b87c3ea3bf6d2
URL:http://internationalconferencetibs.sched.com/event/d4ed4efa89f69ad9083b87c3ea3bf6d2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T030000Z
DTEND:20260624T050000Z
SUMMARY:Towards Explainable and Multimodal Deep Learning for IVF: A Comprehensive Survey and a Hybrid AI Framework for Embryo Selection
DESCRIPTION:Authors - Abhijit Dnyaneshwar Jadhav\, Prashant G. Ahire\, Madhuri Hiwale Abstract - In vitro fertilization (IVF) is currently one of the most powerful assisted reproductive technologies for infertility treatment. However\, the embryo selection process still represents a bottleneck that greatly influences the rates of implantation and live birth. Traditional methods of embryo evaluation involve embryo morphology grading. But this approach suffers from subjectivity\, variability\, and heavily depends on the skill and experience of the embryologist. To go beyond the limitations of human assessment\, the latest improvements in artificial intelligence (AI)\, machine learning (ML)\, and deep learning (DL) have made possible the automated embryo evaluation using pictures\, time-lapse morphokinetics\, and clinical data. This paper reviews comprehensively the currently available AI-enabled IVF systems while also first introducing the conventional embryo assessment and later presenting the most sophisticated multimodal deep learning frameworks. The paper also discusses some of the major outstanding issues such as the poor performance of models on new datasets\, the lack of the shared and agreed upon benchmarks\, and the limited explainability of the models. We have also developed a Multimodal Explainable Artificial Intelligence Frame-work for IVF (MEAIF-IVF) to fill in these gaps in which image of the embryo\, time-lapse video of the embryo\, and clinical patient information are all combined into one deep learning model. This system uses convolutional neural networks and vision transformers for spatial feature extraction\, recurrent neural networks for temporal modeling\, and attention-based fusion for multimodal integration.
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:1fc8a76efea3f6d5aaafc05676fdaa2f
URL:http://internationalconferencetibs.sched.com/event/1fc8a76efea3f6d5aaafc05676fdaa2f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T030000Z
DTEND:20260624T050000Z
SUMMARY:TTP Detection and Prediction of Cyber Threat Techniques using LogBERT and Graph Neural Network
DESCRIPTION:Authors - Peruru Gayathri\, Rohini M\, Anand R Nair Abstract - Cyber threats are getting more sophisticated and conventional security solutions are not keeping up with detecting cyber-attack. In this research\, a hybrid detection and prediction system for TTP (Tactics\, Techniques and Procedures) based on deep learning and graph-based is presented. The planned study is based on an analysis of data originating from cyber security systems at large scale\, which can be used to detect attack patterns and correlations of attacks. Host logs and threat intelligence data are trained using deep learning models to detect discriminative features\, while graph-based models are used to model the structural relationships between users\, systems\, and attack patterns. Combined these techniques will result in more complex attacks and lateral movement being easier to detect. It also assumes probable attack methods to move to the next level\, so that it can predict the attacks and take proactive actions to mitigate attacks in the future. The entire predictive and graph based solution enhances threat visibility and threat response speed\, while boosting threat detection accuracy. The system enables the detection of the APTs and real time monitoring them by the Cyber Security analysts. The experimental results show that the highest accurate transformer is able to achieve 95% classification accuracy\, and the graph neural network is demonstrated to achieve 78.26% accuracy for predicting next technique. The framework has been shown end-to-end\, with the intent of showing it can be utilized as an extra layer of Intelligence on the enterprise security side\, with Splunk.
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:595bbf5caffe104ea6af2e7f786b3df2
URL:http://internationalconferencetibs.sched.com/event/595bbf5caffe104ea6af2e7f786b3df2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T030000Z
DTEND:20260624T050000Z
SUMMARY:A STUDY ON STUDENTS’ AWARENESS OF STARTUPS AND SUSTAINABLE DEVELOPMENT GOALS
DESCRIPTION:Authors - Anis A\, Kasi B Anand\, Adithyan B\, Navaneeth A Nayan\, Durgalashmi CV Abstract - The advent of the prospects of using startups especially tech-based having unprecedented significance and continuous severity with regard to Sustainable Development Goals (SDGs) has increased the understanding of how important it is to understand students' knowledge and perceptions in this field. This study is designed for survey to understand and measure students' depths of knowledge regarding startup companies\, the SDGs\, as well as potentially precautionary attitudes towards startups in achieving sustainable development by 2030. The robust quantitative research design\, were a well-designed and systematically developed questionnaire was employed to identify a systematic collection of questionnaires from students who are studying in the different higher educational institutions by incorporating cross-sectional survey methodology data collection technique. In general\, the results show that students agree that startups are good for economy\, society as a whole and even environment. However\, research also mentions that there is low perception about startup doing Sustainable development work along-with moderate awareness and moderate belief in strong government support. These parts together suggest deep necessary work by the policymakers\, educators and other stakeholders to raise the level of awareness and support. Furthermore\, the study demonstrates the need for systematic integration of sustainability and entrepreneurship education to enhance students' knowledge on sustainability issues as well as their involvement in sustainable development-oriented tasks. The findings of this study offer new and practical lessons to policymakers\, educators and researchers facing the continuing challenge of building and reshaping startup ecosystems that reflect or foster their successful fulfilment of sustainable development goals.
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:c20e38d4e4ced95f79011cf670194ce2
URL:http://internationalconferencetibs.sched.com/event/c20e38d4e4ced95f79011cf670194ce2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T030000Z
DTEND:20260624T050000Z
SUMMARY:Analyzing the Current and Evolving Cyber Threat Landscape: A Comprehensive Study of Organizational Security Impact Using Machine Learning Approaches
DESCRIPTION:Authors - Chethana R.M. and Dr S.P. Manikandan\n Abstract - The rapid evolution of cyber threats has intensified risks to organisational security\, necessitating intelligent\, data-driven approaches to threat assessment and mitigation. This study presents a comprehensive analysis of the evolving cyber threat landscape and its impact on organizational security using a dataset of 1\,200 cybersecurity incidents reported across major sectors in India from 2019 to 2024. The dataset includes diverse incident categories such as phishing\, ransomware\, data breaches\, online fraud\, identity theft\, and hacking\, along with associated financial losses\, geographic distribution\, and affected organizational domains. To investigate threat patterns and predict incident behavior\, three machine learning models\, Support Vector Machine (SVM)\, Random Forest (RF)\, and Logistic Regression (LR) were employed for classification and regression tasks. Experimental results reveal significant challenges posed by class imbalance and feature complexity\, leading to relatively low classification accuracies\, with Random Forest marginally outperforming other models. Regression analysis for predicting financial losses also demonstrated limited explanatory power\, indicating the influence of latent factors beyond the available attributes. Despite these constraints\, the study identifies important sector-specific vulnerability patterns\, highlighting significant financial impacts across healthcare\, financial services\, and government. The findings emphasize that conventional machine learning models alone may be insufficient for capturing the highly dynamic and nonlinear nature of cyber threats\, underscoring the need for advanced threat intelligence frameworks\, richer datasets\, and adaptive security analytics. This research contributes empirical insights into cyber risk modeling and offers practical implications for policymakers and organizations seeking evidence-based cybersecurity strategies.
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:5b9399285feeb40009dd7031075c6885
URL:http://internationalconferencetibs.sched.com/event/5b9399285feeb40009dd7031075c6885
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T030000Z
DTEND:20260624T050000Z
SUMMARY:Architecture and Components of an Information System for Sentiment Analysis of Uzbek
DESCRIPTION:Authors - Xamdamov Utkir Raxmatillaevich\, Elov Botir Boltayevich\, Alavutdinova Nadira Ganiyevna\, Malika Suyunova Odil qizi\, Sharipov Soxib Salimovich \, Narimova Gulnora Abdumanonovna Abstract - In this article\, the architecture of an information system for sentiment analysis of Uzbek-language texts and its key components are examined from both scientific and practical perspectives. The system is based on a multi-layered and microservice architecture\, consisting of a user interface (front-end) and a server (back-end) that provides services through a REST API. The back-end components\, implemented via a Flask-based RESTful API server\, carry out the business logic and sentiment classification. Deep learning models\, especially transformer-based architectures (BERT\, XLM-RoBERTa)\, were utilized for analyzing Uzbek texts and demonstrated effective results. The system ensures security\, provides integration capabilities\, and offers a user-friendly interface to enhance user experience. The modular architecture of the system allows broad scalability and integration with various platforms. As a result of scientific and practical experiments\, the system achieved high accuracy (90%) and proved effective for real-time sentiment analysis tasks.
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:042681734a2d9d0bde760131a9eda84b
URL:http://internationalconferencetibs.sched.com/event/042681734a2d9d0bde760131a9eda84b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T030000Z
DTEND:20260624T050000Z
SUMMARY:Examining Mobile App Attributes as Driving Force of Shopping Engagement
DESCRIPTION:Authors - Sunandita Adhikary\, Dipanwita Chakrabarty\, Arunangshu Giri\, Shamba Chatterjee\, Dibyendu Rath\, Soumya Kanti Dhara\, Solanki Pattanayak\, Samik Bagchi Abstract - The study evaluates how shopping engagement gets influenced by mobile apps in digital retail platform. In e-commerce platform it is important to understand user preference in the context of customization\, quality of information\, usability and interactivity. The present study investigates how these contextual parameters play a pivotal role in shaping users’ emotional and cognitive reactions. These reactions subsequently influence user engagement and purchase-related decisions. The study has proposed a structured framework to identify the antecedents’ influence on shopping engagement and how it shapes user satisfaction. The findings of the study shows that mobile app plays a crucial role in engaging user in digital retail platform and consequently users’ shopping engagement influence their choice satisfaction. The study has notable contribution for marketers and mobile app developers so that they can enhance users’ satisfaction and can achieve competitive advantage. The study has enriched existing literature as well by extending expectation confirmation theory in the context of shopping engagement through mobile application in digital retail platform.
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:66a5c8b63ba1724f37d26dff6f35abc3
URL:http://internationalconferencetibs.sched.com/event/66a5c8b63ba1724f37d26dff6f35abc3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T030000Z
DTEND:20260624T050000Z
SUMMARY:Strengthening Document Management at the Water Secretariat of Portoviejo\, Ecuador\, through Archive Centralization and Business Intelligence Platforms
DESCRIPTION:Authors - Maria Genoveva Moreira Santos\, Eric Geovanny Cedeno Zambrano Abstract - Document management in public institutions constitutes a strategic element for improving administrative efficiency and strengthening decision making. In this context\, the present study analyzes the implementation of a centralized repository in Ecuador’s Water Secretariat\, aimed at the use of business intelligence tools and platforms to optimize access to\, control of\, and utilization of institutional information. The study was conducted using a quantitative methodology\, supported by interviews applied across different departments of the institution\, in order to identify needs\, limitations\, and practices related to records and process management. The results revealed a low adoption of specialized technological solutions and a limited appreciation of their strategic potential within the public sector. A total of 83.7% of the results supported the need to establish clear regulations for document management. It is concluded that the integration of document management systems with business intelligence platforms promotes the generation of timely information\, institutional monitoring\, and evidence based decision making.
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:8014c3067bcaee3e61ab4f5031f90eab
URL:http://internationalconferencetibs.sched.com/event/8014c3067bcaee3e61ab4f5031f90eab
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T030000Z
DTEND:20260624T050000Z
SUMMARY:Towards Intelligent Academic Web Services: A Data Driven Quality Evaluation Using Integrated WebQual 4.0 and EUCS Models
DESCRIPTION:Authors - Eka Dewi Utari\, Darma Rika Swaramarinda\, Maulana Amirul Adha\, Triesninda Pahlevi\, Yuliansyah\, Dewi Nurmalasari\, Agung Kresnamurti Rivai P\, Ferry Setyadi Atmadja\, Fauzan Fadlullah\, Alifah Kusumaningrum\, Sabo Hermawan\, Renata Rachel Abstract - Academic websites have emerged as critical intelligent digital infrastructures for delivering institutional information and services in higher education. However\, existing evaluation frameworks often capture either technical quality dimensions or subjective user experience in isolation. This study proposes and empirically validates an integrated evaluation model combining WebQual 4.0 with the Ease of Use construct from the End User Computing Satisfaction (EUCS) model\, applied to the official website of the Faculty of Economics and Business\, Universitas Negeri Jakarta (www.feb.unj.ac.id). The integration is motivated by the growing imperative to align academic web services with intelligent service design principles encompassing data-driven content governance\, responsive interaction channels\, and user centred personalization as foundations for future AI augmented academic portals. A quantitative descriptive design collected data from 124 respondents (93.5% students\; 6.5% lecturers) via a 17-item validated questionnaire across four dimensions: Usability\, Information Quality\, Interaction Quality\, and Ease of Use. Multiple linear regression (IBM SPSS 23) revealed that Information Quality (β = 0.419\, p < 0.001) and Interaction Quality (β = 0.260\, p = 0.002) exerted statistically significant partial effects on user satisfaction\, whereas Usability and Content did not reach partial significance. Collectively\, the four dimensions explained 70.8% of satisfaction variance (R² = 0.708\; F = 72.074\; p < 0.001). Bibliometric keyword-network analysis contextualises the study within the broader digital-services literature. The integrated WebQual–EUCS model offers a replicable diagnostic tool for higher education institutions seeking to align web services with intelligent user expectations.
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:26694b5f3811d039157d04f3a3273d58
URL:http://internationalconferencetibs.sched.com/event/26694b5f3811d039157d04f3a3273d58
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T030000Z
DTEND:20260624T050000Z
SUMMARY:Understanding Visit Intention in Urban Tourism: The Rules of Cognitive Perception\, Destination Trust\, and Social Media Influencers
DESCRIPTION:Authors - Anggita Sharon Simanjuntak\, Eva Nurhazizah Abstract - This study investigates the impact of cognitive perception on destina tion trust and intention to visit\, while examining the moderating role of social media influencers at Taman Impian Jaya Ancol\, an urban tourism destination in Indonesia. Utilizing a quantitative approach\, data were collected from 385 re spondents via purposive sampling and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS. The results reveal that cognitive perception significantly enhances both destination trust and intention to visit. Similarly\, destination trust and social media influencers exhibit a signif icant positive effect on visit intention and destination trust\, respectively. How ever\, social media influencers do not significantly moderate the cognitive per ception-destination trust relationship. Ultimately\, these findings highlight the ne cessity of cultivating positive perceptions and trust\, offering strategic insights for destination managers to optimize social media marketing.
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:8113f161d41d27f7b611a06454aba0b4
URL:http://internationalconferencetibs.sched.com/event/8113f161d41d27f7b611a06454aba0b4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T050000Z
DTEND:20260624T050200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:b848bd5ed34a17ca9068e7716659c435
URL:http://internationalconferencetibs.sched.com/event/b848bd5ed34a17ca9068e7716659c435
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T050000Z
DTEND:20260624T050200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:4829409dccf518299597cfa0e181c809
URL:http://internationalconferencetibs.sched.com/event/4829409dccf518299597cfa0e181c809
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T050000Z
DTEND:20260624T050200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:91072c48826df16811d80cbfdfc4e2d9
URL:http://internationalconferencetibs.sched.com/event/91072c48826df16811d80cbfdfc4e2d9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T050000Z
DTEND:20260624T050200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:13ee939e063d8716313838957dbe1128
URL:http://internationalconferencetibs.sched.com/event/13ee939e063d8716313838957dbe1128
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T050200Z
DTEND:20260624T050500Z
SUMMARY:Session Closing & Information to Author
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 5A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:59df5515c8ab53805d87ac4a8bde3fd1
URL:http://internationalconferencetibs.sched.com/event/59df5515c8ab53805d87ac4a8bde3fd1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T050200Z
DTEND:20260624T050500Z
SUMMARY:Session Closing & Information to Author
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 5B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:46440d788798dfa59ebddd8a06bbb210
URL:http://internationalconferencetibs.sched.com/event/46440d788798dfa59ebddd8a06bbb210
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T050200Z
DTEND:20260624T050500Z
SUMMARY:Session Closing & Information to Author
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 5C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:cf4a7f4f1c73aab9d1b43d2290943982
URL:http://internationalconferencetibs.sched.com/event/cf4a7f4f1c73aab9d1b43d2290943982
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T050200Z
DTEND:20260624T050500Z
SUMMARY:Session Closing & Information to Author
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 5D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:0dfa677089216d04b5393356d66ffb0f
URL:http://internationalconferencetibs.sched.com/event/0dfa677089216d04b5393356d66ffb0f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T055800Z
DTEND:20260624T060000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:4538a0de688e68af11e033c5ec0b9790
URL:http://internationalconferencetibs.sched.com/event/4538a0de688e68af11e033c5ec0b9790
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T055800Z
DTEND:20260624T060000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:86ff40ce371a67c24942377237a44394
URL:http://internationalconferencetibs.sched.com/event/86ff40ce371a67c24942377237a44394
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T055800Z
DTEND:20260624T060000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:08b4044e7d92bc98606cc88325889d84
URL:http://internationalconferencetibs.sched.com/event/08b4044e7d92bc98606cc88325889d84
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T055800Z
DTEND:20260624T060000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:a4f8bda085bd3fe2bd79f2dfd64f61f6
URL:http://internationalconferencetibs.sched.com/event/a4f8bda085bd3fe2bd79f2dfd64f61f6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:AI-Assisted 7S Compliance Analytics for Campus Operations: A Data-Driven Decision Support Case Study at BISU Bilar Campus
DESCRIPTION:Authors - Max Angelo D. Perin\, Lenie B. Maligmat\, Darrel A. Cardana\, Renante S. Digamon\, Joan Mae G. Lagumbay\, Cecilia T. Gumanoy Abstract - The Quality Assurance Office of a Philippine state university campus conducts 7S evaluations across all offices each semester\, producing numeric scores and written evaluator comments. Consolidating the narrative comments has depended on manual review\, which is time-consuming across more than a hundred offices per cycle. This paper describes a two-phase AI-assisted analytics pipeline. Phase 1 retrieves audit records from a MySQL database via a stored procedure\, formats them with a Python ETL script\, and submits them to Grok (xAI) to draft scorecards and action items\; evaluators then review the drafts be-fore consolidation into the official PDF report. Phase 2 parses the validated PDF with Python to extract structured fields and compute descriptive statistics\, office rankings\, a priority index\, and TF-IDF text clustering. Applied to the November 2025–January 2026 cycle (112 offices\; 107 scored\, 5 with no submission)\, most units cluster in the moderate-to-great compliance range while a meaningful minority fall below threshold. Among the top 25 priority offices\, Standardize (20/25) and Safety (19/25) are the most frequently flagged dimensions. The pipe-line shows that AI assistance structured around human review can accelerate QA consolidation while preserving evaluator accountability.
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:8bd7c3c684df497a8afec4348c120a4b
URL:http://internationalconferencetibs.sched.com/event/8bd7c3c684df497a8afec4348c120a4b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:Digital Transformation Capability and Sustainable Supply Performance: The Role of Stakeholder Integration and Absorptive Capacity
DESCRIPTION:Authors - Nur Fajrina\, Felina C. Young\, Rosita Widya Putri Abstract - This study investigates the relationships among Stakeholder Integration (STI)\, Digital Transformation Capability (DTC)\, Absorptive Capacity (AEC)\, and Sustainable Supply Performance (SSP) within a knowledge-intensive supply chain context. Employing a quantitative methodology alongside Partial Least Squares Structural Equation Modeling (PLS-SEM)\, data were collected from 262 respondents involved in strategic and operational functions. The results reveal that stakeholder integration significantly enhances digital transformation capability\, thereby strengthening absorptive capacity. Both digital transformation capability and absorptive capacity have direct positive effects on sustainable sup-ply performance. However\, stakeholder integration does not directly influence sustainable supply performance. Instead\, its effect becomes significant only when mediated by absorptive capacity\, indicating that internal knowledge assimilation and utilization mechanisms are essential for translating collaborative efforts into sustainability outcomes. The results highlight the critical role of dynamic capabilities in accomplishing sustainable supply performance\, particularly in environments characterized by digital transformation and stakeholder complexity. The study contributes theoretically by integrating stakeholder theory and dynamic capability perspectives\, emphasizing absorptive capacity as a key mediating mechanism. The results suggest that firms should complement external stakeholder collaboration with investments in digital infrastructure and organizational learning systems to enhance long-term sustainability performance.
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:eac02e473127d8680e3162f88260f2c0
URL:http://internationalconferencetibs.sched.com/event/eac02e473127d8680e3162f88260f2c0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:Mobile App Development: Trends and Challenges
DESCRIPTION:Authors - Ayush Ghumare\, Reena S. Satpute Abstract - Mobile application development has evolved rapidly with the emergence of advanced technologies such as 5G connectivity\, Artificial Intelligence (AI)\, Machine Learning (ML)\, and Mobile Edge Computing (MEC). These technologies are transforming the mobile ecosystem by enabling the development of intelligent\, data-driven applications and accelerating development cycles. Mod-ern mobile applications are expected to provide real-time services\, personalized user experiences\, and seamless connectivity\, which has significantly increased the complexity of mobile application design and implementation. It is resulting into many challenges. One of the major challenges in mobile application development is the inherent limitation of mobile devices\, including restricted pro-cessing power\, limited memory capacity\, and battery constraints. Developers must optimize application performance while ensuring energy efficiency to pre-vent excessive battery consumption and degraded user experience. Additionally\, the increasing reliance on third-party libraries and analytics tools may introduce security vulnerabilities\, creating potential security gaps within applications. These risks are often intensified by the lack of specialized security expertise within development teams\, raising concerns related to data privacy\, application security\, and software supply chain vulnerabilities. Another challenge is platform fragmentation\, particularly within the Android ecosystem\, where diverse devices\, operating system versions\, and hardware configurations complicate compatibility and performance optimization. This diversity increases testing complexity and development costs. Furthermore\, integrating AI and ML models into mobile ap-plications requires careful decisions regarding cloud-based versus on-device pro-cessing. Therefore\, developers must balance scalability\, performance\, security\, and energy efficiency when designing modern mobile applications. This study presents systematic literature evaluation methodology\, comparative analysis of native and cross-platform paradigms\, software supply chain security frameworks\, measurable energy optimization strategies\, and practical industry case studies from healthcare\, fintech\, and mobile commerce sectors.
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:7ae15f500e0fdd5211b0e8b8a0868bc4
URL:http://internationalconferencetibs.sched.com/event/7ae15f500e0fdd5211b0e8b8a0868bc4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:Neuro-Symbolic AI Agents for Zero-Touch Salesforce DevOps Pipelines
DESCRIPTION:Authors - Murali Mohan Reddy Seelam\, VyshnaviThanneeru\, Ajay Kumar Reddy Vemireddy\, Srilatha Kudumula Abstract - This paper shows a new approach to implement the Agentforce-NS framework to provide zero touch salesforce deployment pipelines by integrating it with the Neuro Symbolic AI Agents. Even though the complete salesforce deployment pipelines have been automated end to end\, it has been very difficult to achieve zero touch deployments due to its nature of the handling of metadata due to the interdependency of the components within the salesforce. The regular pipeline processes still heavily depend on the manual intervention to resolve the merge conflicts\, resolve the dependency errors\, working on the roll back deployments and following the compliances. The architecture we are proposing will solve all these problems by integrating the adaptive and predictive capabilities of the neural networks with rule based\, transparent precision of the symbolic reasoning. The proposed Agentforce architecture will have five agents that will collaborate and will execute the deployments without any human intervention. These five agents are used to learn the deployment strategies\, roll back planning\, analyzing the metadata\, autonomous execution and verification of the governance. After many tests in the enterprise level environments\, we see that it is resolving so many blockers\, issues and increasing the deployment success rate\, improving the governance\, and reducing the meantime to recover. By covering the technical gap between logical interface and the deep learning\, the Agentforce-NS represents a break through advancement to have the fully automated\, autonomous and auditable salesforce devops pipelines.
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:e22c1d3333799434c49ada71a55c7e66
URL:http://internationalconferencetibs.sched.com/event/e22c1d3333799434c49ada71a55c7e66
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:Pragmatics and Contextual Understanding in Large Language Models: A Unified Analysis
DESCRIPTION:Authors - Shreya S. Partake\, Reena S. Satpute Abstract - Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing\, achieving human-level performance on many semantic and syntactic benchmarks. However\, their competence in pragmatics—the study of how context shapes meaning—remains a critical and underexamined frontier. This paper presents a unified analysis of the “pragmatic gap” in LLMs\, arguing that it stems from a fundamental distinction between the co-textual statistical patterns LLMs are trained on and the contextual world knowledge humans use for inference. We first establish a theoretical baseline by reviewing foundational linguistic concepts\, including Grice’s maxims\, implicature\, presupposition\, speech acts\, and deixis. We then systematically evaluate LLM performance\, contrasting successes in pattern-rich tasks like coreference resolution with systemic failures in tasks requiring novel inference\, such as non-conventionalized indirect speech acts and irony. We analyze the development of new evaluation tools\, particularly the Pragmatics Understanding Benchmark (PUB)\, which quantifies the persistent gap between model and human performance. Subsequently\, we synthesize emerging technical solutions\, including “thought-based” fine-tuning and the injection of Gricean principles into Retrieval-Augmented Generation (RAG) frameworks. Finally\, we dissect the profound cognitive and philosophical implications of this gap\, critically examining the debates on the Symbol Grounding Problem and Theory of Mind (ToM). We conclude that while LLMs can pass “literal” ToM tests\, they fail “functional” ToM\, revealing them to be sophisticated co-text manipulators rather than context-aware agents. We propose that future progress lies in developing a “machine pragmatics” based on probabilistic models rather than flawed anthropomorphic imitation.
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:53d70021b1cf7004bf99a37ae1bc08e3
URL:http://internationalconferencetibs.sched.com/event/53d70021b1cf7004bf99a37ae1bc08e3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:Social Media and Society: Understanding Digital Communication through Natural Language Processing
DESCRIPTION:Authors - Ayushi Chapate\, Reena S. Satpute Abstract - Natural language helps us to interact with the computer through human language. This article investigates how Natural Language Processing (NLP) can enhance our understanding of social media changes. To its audience\, social media provides a large - arguably unlimited - and otherwise untapped linguistic re-source\, revealing information about government behavior\, civic participation\, in-dividual mental well-being\, and consumption behavior\, among many other things. Using machine learning analytical methods such as sentiment analysis\, topic modeling\, stance detection\, and misinformation tracking\, researchers can begin to study the social\, psychological\, and economic implications of web-based inter-action. In terms of civic and political implications\, to analyze user-generated con-tent\, discourse networks\, and hashtags using NLP applications can produced new insights into online mobilization and collective action. For example\, researchers studying the political movement’s #MeToo and #BlackLivesMatter\, based on analysis of Twitter data\, have employed topic modeling techniques to reveal their influence and significance in innovative ways. From a psychological perspective\, NLP methods make it possible to examine prevalent mental health indicators across separated populations\, through the analysis of emotional tone\, pronoun use\, and distress markers. In studies conducted between 2020–2025\, the application of BERT based embedding models were found to detect online indicators of depression\, anxiety\, and social comparison leverage's based on word meaning. Further\, understanding the depth of these psychological consequences remains nebulous and limited to a range of social categories in the digital landscape\, similar to previous notions of 'self-checking' across the digital commons exploring citizen engagement.
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:871c6f99a597a3a06a6aca816b3f0964
URL:http://internationalconferencetibs.sched.com/event/871c6f99a597a3a06a6aca816b3f0964
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:Student Experience Intelligence for Educational Tours Using Survey Analytics and Text Mining
DESCRIPTION:Authors - Jes Maries M. Mendez\, Max Angelo D. Perin\, Joan Mae G. Lagumbay\, Mae S. Dagupan\, Elizabeth A. Orapa\, Marcelina S. Butlig Abstract - Educational tours are widely used in higher education to connect class-room learning with real settings\, yet evaluations often stop at overall ratings that do not explain why students endorse a tour or which delivery issues weaken the experience. This study applies a student experience intelligence workflow that integrates survey analytics with offline text mining to produce planning-relevant evidence. A survey of 156 students captured demographics\, three 10-item Likert constructs—motivation\, perceived effectiveness\, and problems encountered (4-point scale)—a recommendation rating\, and open-ended comments. Responses were cleaned through category standardization and rule-based numeric conversion. Internal consistency was good for motivation (α = 0.877) and excellent for effectiveness (α = 0.960) and problems (α = 0.958). Learning beyond classroom instruction (M = 3.71) and interest in tour inclusions (M = 3.68) led motivation\; creative learning (M = 3.67)\, resourcefulness (M = 3.66)\, and social skills (M = 3.65) led effectiveness\; tour expense (M = 3.21) and short time per attraction (M = 2.60) led problems. 73.1% gave the top recommendation. Recommendation correlated positively with motivation (ρ = 0.317\, p < 0.001) and effectiveness (ρ = 0.328\, p < 0.001)\; a binary logistic model showed perceived effectiveness as the strongest predictor of the top recommendation category. Open-ended comments (171 entries) were summarized through TF–IDF with K-Means clustering (k = 6) and complemented with a VADER polarity pass on 155 meaningful entries (68.4% positive\, 21.9% neutral\, 9.7% negative\; mean compound = +0.365). The combined evidence points to improvements that preserve educational value while addressing cost and pacing\, and shows that the workflow is portable to other programs and experiential learning activities.
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:9f6d98207f12d2b35ab5f7a9cb9081f9
URL:http://internationalconferencetibs.sched.com/event/9f6d98207f12d2b35ab5f7a9cb9081f9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:Uncovering Insights Beyond Metrics: A Machine Learning Approach to Service Evaluation in the Provincial Government of La Union
DESCRIPTION:Authors - Kent Cyryl A. Campit\, Christian Kelvin Gonzales Abstract - This study explored a data-driven approach to evaluating citizen feedback within the Provincial Government of La Union (PGLU) by integrating quantitative and qualitative analytical techniques. Traditional feedback systems in government offices often rely on averages and summary reports\, limiting the ability to capture deeper citizen experiences and concerns. To address this gap\, the research transformed paper-based feedback forms into a structured digital dataset covering responses from 34 frontline offices and service units from July 2025 to January 2026. The study applied Customer Satisfaction Score (CSAT)\, Weighted Mean\, and Range of Interval to measure and classify service performance levels. For qualitative analysis\, Latent Dirichlet Allocation (LDA) was used to identify recurring themes in open-ended responses\, while a dual-model sentiment analysis approach combining VADER and RoBERTa classified citizen feedback into positive\, neutral\, and negative sentiments. The analytical pro-cesses were implemented using Microsoft Excel\, Google Sheets\, and Python through Google Colaboratory. Findings revealed consistently high satisfaction ratings across offices\, while qualitative analysis uncovered recurring themes related to service efficiency\, staff assistance\, facility conditions\, and operational concerns. RoBERTa demonstrated better contextual understanding and achieved higher performance metrics compared to VADER. The study further developed an Observed Satisfaction Classification Framework to support evidence-based decision-making and service improvement. Ultimately\, the re-search demonstrated how citizen feedback can be transformed into actionable governance insights that promote transparency\, accountability\, and continuous improvement in public service delivery\, aligned with Sustainable Development Goal 16.
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:96e3049bf078ce0a02e1de2165968064
URL:http://internationalconferencetibs.sched.com/event/96e3049bf078ce0a02e1de2165968064
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:A Hybrid LSTM-Autoencoder Model for Anomaly Detection in Chip Fabrication Processes
DESCRIPTION:Authors - Megha Potdar\, Andhe Dharani\, Ch.Ram Mohan Reddy Abstract - Semiconductor fabrication processes suffer significant yield losses\, often exceeding 20%\, due to equipment anomalies in critical stages like plasma etching and lithography\, where traditional Statistical Process Control fails to detect subtle\, non-linear drifts in multivariate sensor data such as temperature\, pressure\, and gas flow. This paper proposes a novel hybrid AI framework combining Long Short-Term Memory Autoencoder for unsupervised reconstruction-based anomaly detection with Isolation Forest for robust outlier scoring and severity ranking\, enabling real-time predictive maintenance and Remaining Useful Life estimation. The LSTM-AE compresses temporal sequences into a latent space and flags anomalies via elevated Mean Squared Error thresholds (>95th percentile)\, while Isolation Forest filters multivariate errors to minimize false positives. RUL prediction employs linear regression on error trends for proactive scheduling. Implemented in a Keras/TensorFlow MLOps pipeline with
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:2f5094ddc0c4e754d91ee8187cbc019c
URL:http://internationalconferencetibs.sched.com/event/2f5094ddc0c4e754d91ee8187cbc019c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:A Hybrid Retrieval Architecture for Intelligent Campus Assistants: Combining Semantic Search with Factual Consistency
DESCRIPTION:Authors - Kamasani Vishnuvardhan Reddy\, Anjan Babu G Abstract - Universities maintain extensive repositories of institutional knowledge\, yet students struggle to extract accurate information from disparate sources such as PDF circulars\, web portals\, and notice boards. Rule-based chatbots handle only narrow query sets\, while general-purpose large language models (LLMs) produce fluent but sometimes fabricated responses—a phenomenon termed hallucination. This paper presents the Intelligent Campus Assistant Chatbot for Sri Venkateswara University (SVU)\, employing a Retrieval-Augmented Generation (RAG) pipeline that grounds every response in verified institutional documents via dense semantic vector search and deterministic keyword retrieval fused through Reciprocal Rank Fusion (RRF). Evaluation on a 200-query benchmark yields 94.2% factual correctness\, hallucination rates below 1%\, mean latency of 0.8 s\, and inter-rater agreement κ = 0.87 across English\, Telugu\, and Hindi.
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:ddceaf97da79f24202b3e0bde7200d65
URL:http://internationalconferencetibs.sched.com/event/ddceaf97da79f24202b3e0bde7200d65
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:Choosing Algorithms for Customer Segmentation and Promotion Response: A Comparative Study with Explainable Benchmarks for Digital Marketing
DESCRIPTION:Authors - Ronald S. Cordova\, Rowena O. Sibayan\, Hazel C. Tagalog Abstract - Digital marketing teams often struggle less with access to algorithms than with choosing the right one for a specific decision. This paper presents a comparative study on the selection of the three most suitable algorithms for two related digital marketing tasks: customer segmentation and promotion-response prediction. Based on the example of Oman's retail industry\, a benchmark is established using first-party customer data\, including recency\, purchase frequency\, monetary value\, product-category behavior\, campaign participation\, website visits\, and engagement ratio. For customer segmentation\, the study focuses on Kmeans\, DBSCAN\, and Gaussian mixture model because they provide a practical balance of scalability\, noise handling\, and probabilistic customer-state representation. For promotion-response prediction\, the selected models are logistic regression\, random forest\, and XGBoost because they offer a staged balance between transparency\, nonlinear learning\, and campaign-ranking performance. For benchmarking and explainability\, the same preprocessing approach\, leakage prevention\, temporal splitting\, tuning strategies\, and metrics such as silhouette quality\, stability\, ROC-AUC\, PR-AUC\, Brier score\, calibration\, and top-decile lift are employed. Explainability is treated as a condition for adoption rather than an optional reporting activity.
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:9097d242278f065c5301d39feb9ac628
URL:http://internationalconferencetibs.sched.com/event/9097d242278f065c5301d39feb9ac628
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:Deep Learning Approach for Freshwater Plankton Classification using Convolutional Neural Network and Transfer Learning
DESCRIPTION:Authors - Mike Philip T. Ramos\, Andres R. Vicedo\, Jocelyn O. Padallan\, Jonardo R. Asor\, Genemarck B. Catedrilla Abstract - This research aims to develop a model for plankton species classification by analyzing images utilizing a convolutional neural network or CNN to simplify the task of classifying plankton species. The use of CNN and other transfer learning models will be used to recognize different freshwater plankton species in order to identify the genus of plankton easily. There were several layers in the CNN architecture used in this study\; (1) Layer 1 has convolutional data with 32 filters and 3x3 kernel with max pool of 2x2 kernel\; (2) Layer 2 has convolutional data with 64 filters and 3x3 kernel with max pool of 2x2 kernel\; and (3) Layer 3 has conventional data with 128 filters and 3x3 kernel with mas pool of 2x2 kernel. After the validation and training in terms of accuracy and loss for CNN and pre-trained models\, it is observed that MobileNetv2 showed the highest positive scores with 0.99 in train accuracy\, 0.93 in validation accuracy\, 0.07 in train loss\, and 0.12 in validation loss\, which makes it more viable to be used in this study. CNN's capacity to extract characteristics from photos has shown to be highly effective at classifying images. Additionally\, it has been determined that transfer learning strategies can aid CNN in enhancing its picture categorization capabilities. The use of pre-trained learning like MobileNetv2 with a small data set and image classification studies can be a greater help for identification than CNN\, Convolutional Network\, Rest- Net50 and EfficientNetB0
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:6d59cc175a5cb13cb43def4ae956d908
URL:http://internationalconferencetibs.sched.com/event/6d59cc175a5cb13cb43def4ae956d908
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:Educational Data Analytics for Understanding Students Digital Behavior and Academic Achievement Using Descriptive and Cognitive Analytics
DESCRIPTION:Authors - Wannakorn Phornprasert\, Nisarat Onthong\, Thapanapong Sararat\, Wongpanya S. Nuankaew\, Pratya Nuankaew Abstract - This study proposes an Educational Data Analytics approach to understanding students' digital behavior and academic achievement using Descriptive and Cognitive Analytics. Data were collected from 40 purposively selected students using questionnaires that covered general information\, social media usage\, sleep behavior\, Kolb-based learning style\, and GPA. Descriptive Analytics was applied to summarize frequencies\, percentages\, means\, and key behavioral patterns\, while Cognitive Analytics was used to interpret these patterns in relation to learning readiness\, self-regulation\, and academic achievement. The findings showed that students had an average GPA of 3.38\, spent an average of 7.53 hours on social media per day\, and most frequently used social media between 20:01 and 00:00. The most common bedtime was 01:00\, and Concrete Experience was the dominant learning style. The results suggest that small-scale learner data can support understanding of digital behavior\, sleep patterns\, and academic achievement in Thai higher education.
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:bd3dac52a6e7da1f8f37a03bfa5bdde5
URL:http://internationalconferencetibs.sched.com/event/bd3dac52a6e7da1f8f37a03bfa5bdde5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:Explainable Intelligent Document Recognition and Automated Decision Support for Applied Thai Tax Deduction Eligibility Assessment
DESCRIPTION:Authors - Kuljira S. Nuankaew\, Kaewpanya S. Nuankaew\, Wongpanya S. Nuankaew\, Keingkrai Buakeaw\, Thapanapong Sararat\, Pratya Nuankaew Abstract - This research presents the development of an Explainable Intelligent Document Recognition system and a decision support system for assessing tax deductions in Thailand. The system uses image processing and data extraction technologies to analyze photographic documents and PDF files. It incorporates image quality enhancement\, text recognition\, key information extraction\, and tax condition assessment\, along with a rationale to enhance transparency in decision-making. Experimental results demonstrate efficient and accurate data recognition and extraction\, and the system can handle diverse document types. Furthermore\, a web-based prototype evaluation by 30 users showed high satisfaction\, particularly regarding understanding the results and explanations. However\, the system exhibits limitations with low-quality and complex documents. This research highlights the potential for applying such technology to taxation and for future expansion to improve flexibility and efficiency.
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:00dda32d9ef5b720a8c581cf441e292c
URL:http://internationalconferencetibs.sched.com/event/00dda32d9ef5b720a8c581cf441e292c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:Integrating AI Tools in Academic Writing: Faculty Experiences and Responsible Adoption in Higher Education
DESCRIPTION:Authors - Najera R. Umpar\, Apolinar P. Datu\, Minsoware S. Bacolod\, Soraya R. Umpar\, Darwin B. Reyes\, Albert Lee A. Catibayan\, Klifford L. Carlos\, Francia F. Murao Abstract - The widespread use of artificial intelligence (AI) tools into the world of academia has brought about substantial changes to the way scholars write. This paper examines how faculty view AI tools for use in academic writing through their own experiences of using these tools. Also\, it explored their capacity to produce research publications and the integrity of the research being produced. Using a qualitative research design\, the study gathered data through semi-structured interviews with faculty selected purposively using AI enabled tools such as ChatGPT\, Sci.ai and Grammarly) during their writing process to collect data. Thematic Analysis was utilized to identify common themes within faculty member's accounts of their experiences. The findings of the study indicate that faculty perceive AI tools as valuable to enhance the speed in which they complete writing tasks\, and also to improve grammar usage while writing\, and to assist in idea generation\; however\, there were concerns voiced about overusing AI tools\, ethical concerns with using AI tools\, and how AI tools affect a faculty member's ability to think critically and produce work that is original. Additionally\, the digital literacy level of faculty members who participated in this study reflects their ability to be able to adopt and incorporate these technologies into their daily teaching and research activities\; thus\, varying levels of digital literacy influence how a faculty member adopts and incorporates these technologies into their academic productivity. The study underscores the need for clear institutional guidelines and capacity-building initiatives to ensure the responsible and effective use of AI in academic writing. By providing insights into faculty experiences\, this research contributes to the growing discourse on AI integration in higher education and offers implications for policy development\, pedagogical practices\, and future research.
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:7db9f1d5abbacc7309cbfd6678eb2048
URL:http://internationalconferencetibs.sched.com/event/7db9f1d5abbacc7309cbfd6678eb2048
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:Narrating Responsibility: Archetypal Branding and Cultural Meaning in India’s Jaago Re Cause Marking Campaign
DESCRIPTION:Authors - Pradeep Kumar\, Balasubramanian\, Dhivyalakshumi Abstract - This paper analyzes the role of archetypal storytelling as an ethical brand meaning construction strategic tool in cause-related advertising as applied longitudinally to the Jaago Re campaign created by Tata Tea. Jaago Re is a cause marketing effort spanning more than 10 years and dealing with civic engagement\, gender equity\, community health and climate accountability. Based on the theory of archetypal branding\, the paper examines thirteen aired and online advertisements published between 2008 and 2023 to learn how archetypes are utilized and redefined based on the changing socio-cultural and ethical issues. Based on the principles of a qualitative content analysis\, guided by the Archetypal Criticism framework and Cultural Branding theory\, the research paper recognizes the primary and secondary archetypes and investigates their narrative and ideological roles. The results have shown that archetypes that include the Hero\, Sage\, Caregiver\, Everyman\, and Outlaw\, together with the Magician are well-planned layers that deploy civic actions\, promote ethical contemplation\, and maintain symbolic continuity in the campaign stages. The work proves that Jaago Re goes beyond episodic cause promotion\, including the responsibility and social awareness as a part of the cultural identity of the brand. This study can be of use in the literature on ethical branding and responsible advertising because it links the progression of archetypal arrangements through time\, providing a conceptual framework to build a sustained social and cultural value in the marketing communications.
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:0ffa0928d512e31adb6e19b0c7251099
URL:http://internationalconferencetibs.sched.com/event/0ffa0928d512e31adb6e19b0c7251099
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:An Interpretable Warning-to-Action Layer for Multi-Echelon Supply-Chain Digital Twins
DESCRIPTION:Authors - Vishwa Kumaresh Abstract - A local supplier delay or demand shock in multi-echelon supply chains can make upstream orders volatile long before the full costs appear in planning dashboards. In this study\, we propose an interpretable warning-to-action layer for supply-chain digital twins. This layer sits above the replenishment controller: it estimates disruption-regime risk from rolling demand\, inventory\, backlog\, order\, and lead-time telemetry\, then maps that risk to bounded changes in responsiveness\, safety stock\, and order caps. We calibrate a gradient-boosted stump classifier that combines standard warning indicators\, cross-echelon imbalance measures\, and nonlinear stress descriptors. A small mode table converts the resulting probability into five auditable replenishment modes. This method is tested on twelve disruption scenarios grouped into six mechanism classes\, using ten baselines and an untouched lockbox of 576 observations. The proposed policy reduces aggregate system expenditure by 15.2% and cross-echelon volatility (bullwhip) by 44.5%\, relative to a linear guard that uses the same broad action family. The largest gains occur in lead-time disruptions and backlog cascades. Compound shocks demonstrate marginal performance gains\, as existing linear guards effectively capture these dynamics within standard monitoring frameworks.
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:b4f739bb4769734e87e0ae3e235fbc25
URL:http://internationalconferencetibs.sched.com/event/b4f739bb4769734e87e0ae3e235fbc25
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:Applying Learning Analytics to University Students’ Eye Health Risk: A Descriptive and Diagnostic Exploration Using Social Media Usage Data
DESCRIPTION:Authors - Wannakorn Phornprasert\, Waraporn Phothirin\, Thapanapong Sararat\, Wongpanya S. Nuankaew\, Pratya Nuankaew Abstract - This study uses Learning Analytics to assess university students’ eye health risks based on social media usage data\, focusing on descriptive and diagnostic analyses. Data collected from 44 undergraduates via a self-reported questionnaire with 82 key questions covered general details\, social media habits\, device and screen environments\, symptoms of Computer Vision Syndrome\, and Felder–Silverman learning styles. The descriptive analysis revealed Instagram as the most popular platform\, frequent nighttime use after 20:00\, and many students spend over six hours daily on social media. While most respondents were categorized as low risk\, symptoms such as watery eyes\, eye pain\, light sensitivity\, and neck pain were commonly reported. The diagnostic analysis linked risky sitting postures\, looking below eye level\, prolonged daily usage\, and nighttime social media activity to increased eye health risks. These findings support initiatives for digital well-being and learning support in higher education.
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:0d6a59bece0a4a96f0f9cfc5ae570ca8
URL:http://internationalconferencetibs.sched.com/event/0d6a59bece0a4a96f0f9cfc5ae570ca8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:Blockchain-Based Academic Credential Issuance and Verification Using Hyperledger Fabric in Higher Education Institutions
DESCRIPTION:Authors - Mariel Leo T. Violeta Abstract - The increasing incidence of academic credential fraud\, inefficient verification procedures\, and reliance on centralized record management systems present significant challenges for higher education institutions. This study proposes and evaluates a blockchain-based academic credential issuance and verification platform using Hyperledger Fabric to improve the security\, authenticity\, and efficiency of academic credential management. The platform enables university registrars to issue digital academic credentials\, allows students to securely access and share academic records\, and provides employers and external entities with a reliable credential verification mechanism. To ensure data integrity while maintaining scalability and privacy\, the framework integrates blockchain-based cryptographic hashing with off-chain cloud storage. A quantitative descriptive research design was employed using the Technology Acceptance Model (TAM) as the theoretical framework. Data were collected from 40 registrar personnel at the Polytechnic University of the Philippines through a structured survey instrument measuring Perceived Usefulness and Perceived Ease of Use. Findings revealed that respondents strongly agreed that the platform improves security\, credential verification\, operational efficiency\, accessibility\, and flexibility. The results demonstrate that Hyperledger Fabric can provide a secure\, tamper-resistant\, and efficient infrastructure for managing academic credentials in higher education institutions. The study contributes to the growing adoption of blockchain technology in education by presenting a practical and institution-oriented framework for secure and verifiable digital credential management.
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:256b6aca5172f26674d1d3c12aa4c00f
URL:http://internationalconferencetibs.sched.com/event/256b6aca5172f26674d1d3c12aa4c00f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:Customer Awareness and Adoption of Green Banking Initiatives in India: An Empirical Study
DESCRIPTION:Authors - Bhagyalakshmi S Pai\, Jeevanand E S\, Radhika P.C\, Krupa B Nair\, Sreeja Radhakrishnan\, Dhanalakshmi Menon Abstract - The present study attempts to empirically investigate how the customers’ awareness relates to the adoption of green banking initiatives of commercial banks in Kerala\, India. The study employs data gathered from 540 customers of five banks (SBI\, Canara\, PNB\, ICICI Bank\, HDFC Bank\, and Axis Bank) by using a structured questionnaire\, and builds and validates the structural model for green banking adoption. Customer awareness is considered as a higher order construct which consists of Environmental Awareness and General Awareness. The analysis used descriptive statistics\, reliability analysis\, Confirmatory Factor Analysis (CFA)\, two-stage analysis of Structural Equation Modeling (SEM)\, and Z test and One-Way ANOVA test to determine awareness levels and differences in demographic data. The results show that there exists a high Awareness–Adoption Gap\, that is\, a superficial awareness of green banking\, which is not yet accompanied by a conceptual understanding of it. The study also reveals that adoption of e-banking is mainly for convenience and that practice in key life-stages and occupations have a strong bearing on adoption behaviour.
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:c92d4b0d0051eb3e76c7895d483e92d4
URL:http://internationalconferencetibs.sched.com/event/c92d4b0d0051eb3e76c7895d483e92d4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:Learning Well-Being and Academic Burnout Signal Analytics for Assessing Pseudo-Depression Risk Among University Students
DESCRIPTION:Authors - Wannakorn Phornprasert\, Ratchanin Intham\, Thapanapong Sararat\, Wongpanya S. Nuankaew\, Pratya Nuankaew Abstract - This study explored learning well-being and indicators of academic burnout associated with pseudo depression risk among university students at the University of Phayao. Data collection involved a general information questionnaire\, an academic burnout assessment scale\, and the DASS-21. Descriptive and diagnostic statistics were applied. Results indicated a moderate level of overall academic burnout\, with academic fatigue scoring higher than academic withdrawal. Emotional risk assessment found that 50.0% of students showed mild to severe pseudo depression symptoms. Additionally\, scores for academic fatigue\, academic withdrawal\, and overall burnout were positively linked to depression\, anxiety\, and stress. These results suggest that descriptive and diagnostic approaches can serve as initial tools for screening and promoting students' learning well-being in Thai higher education.
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:327d4a2c80b73fd638c0783c09eeb61d
URL:http://internationalconferencetibs.sched.com/event/327d4a2c80b73fd638c0783c09eeb61d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:Student Behavioral Data Analytics: Descriptive and Diagnostic Analysis of Factors Associated with Second Hand Fashion Consumption in the Digital Era
DESCRIPTION:Authors - Pratya Nuankaew\, Panisara Paksasuk\, Thanapon Thiradathanapattaradecha\, Thapanapong Sararat\, Wongpanya S. Nuankaew Abstract - This study analyzes student behavioral data to understand factors influencing secondhand fashion purchases in the digital age. A survey was conducted with 40 University of Phayao students who are experienced in buying secondhand fashion items. Data analysis included descriptive statistics and diagnostic approaches to profile students\, their purchasing habits\, perceptions\, and key factors. Results indicated that all participants had prior secondhand shopping experience\, using both physical stores and online platforms as key channels. Product quality received the highest average score of 4.20\, followed by a positive attitude toward second-hand fashion at 4.05\, frugality at 4.00\, and brand reputation and environmental responsibility at 3.85\, with sustainable fashion close behind at 3.83. These findings suggest that students’ choices are influenced more by quality\, value\, personal attitudes\, and sustainability awareness than by social media influencers alone. The research provides valuable insights for promoting sustainable fashion\, designing platforms\, and developing future predictive analytics.
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:9caf482e77772367b7643a81bcb2eaf1
URL:http://internationalconferencetibs.sched.com/event/9caf482e77772367b7643a81bcb2eaf1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:Using Student-Pet Interaction Data to Support Mental Well-Being Prediction in Universities
DESCRIPTION:Authors - Pratya Nuankaew\, Duangjai Pongsawan\, Supan Tongphet\, Thapanapong Sararat\, Wongpanya S. Nuankaew Abstract - This research aimed to examine the use of student-pet interaction data to enhance understanding of university students' mental well-being. Descriptive and diagnostic data analyses were conducted. The sample comprised 40 students. Data collection was conducted using questionnaires to collect baseline information\, characteristics of interaction with pets\, and evaluations with the CCAS\, PSS-10\, and ST-5 instruments. The analysis revealed that the majority of students experienced a high level of attachment and comfort with their pets\, with an average CCAS score of 3.57. The average PSS-10 score was 20.48\, indicating moderate stress levels\, and the mean ST-5 score was 7.43. Diagnostic analysis suggested that the duration of contact with pets\, pet type\, living conditions\, and pet ownership status were potentially associated with students' stress levels. These findings may serve as an initial guideline for developing monitoring and support programs to promote the mental well-being of university students.
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:024c82d8c69e1656539b96eb63403e9a
URL:http://internationalconferencetibs.sched.com/event/024c82d8c69e1656539b96eb63403e9a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:VARK Learning Style Data and Ergonomic Analytics for Screening Office Syndrome Risk Among University Students
DESCRIPTION:Authors - Wannakorn Phornprasert\, Papimon Novichai\, Thapanapong Sararat\, Wongpanya S. Nuankaew\, Pratya Nuankaew Abstract - This investigation aimed to analyze the VARK learning style and ergonomic data to identify the risk of office syndrome among university students. A quantitative\, cross-sectional approach was employed\, utilizing questionnaire data from 40 students. The analysis used descriptive statistics to summarize general characteristics\, learning styles\, and risk levels\, and diagnostic analyses to identify factors associated with office syndrome risk. The most prevalent learning styles identified were Read/Write (30.0%) and Kinesthetic (25.0%). Ergonomic assessments revealed that 42.5% of students were at high risk\, while 35.0% were at moderate risk. Factors correlated with risk included excessive phone usage (exceeding 4 hours per day)\, inappropriate chair height\, unsuitable armrests\, incorrect screen positioning\, and improper keyboard posture. These findings indicate that combining learning preferences with ergonomic data can serve as an initial screening tool for risk assessment and facilitate the development of learning environments tailored to students in the digital era.
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:d301e80ebfa5b90cd78786ab7efda887
URL:http://internationalconferencetibs.sched.com/event/d301e80ebfa5b90cd78786ab7efda887
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:Developing a Dynamic Landslide Susceptibility Model for Benguet Province Using Machine Learning
DESCRIPTION:Authors - Virgel William Afaga\, Patrick Andrew Balang\, Dana Wynnette Binwag\, Emmanuel Paolo Bromeo\, Mark John Bumacod\, Carl Allan Calsiman\, Juliana April Cendana\, Roderick Makil\,Dulthe Carlo Munar Jr. Abstract - Benguet Province\, Cordillera Administrative Region\, Philippines\, is highly susceptible to landslides due to its rugged topography\, complex geology\, frequent typhoon tracks\, and extensive mining and road construction. Existing hazard maps rely on static statistical methods and coarse rainfall averages that cannot capture the dynamic triggering conditions of individual storm events. This paper presents a dynamic landslide susceptibility model built on Random Forest (RF) and Extreme Gradient Boosting (XGBoost) trained on thirteen environmental conditioning factors across five domains (topographic: elevation\, slope\, aspect\, distance to streams\; geological: rock type\, soil type\; land cover: LULC\, NDVI\, NDWI\; climatic/hydrological: mean annual rainfall\, event rainfall\, antecedent rainfall\; anthropogenic: distance to roads) derived from high-resolution satellite imagery and event-specific rainfall records. Training used a balanced 16\,158-sample dataset (50:50 landslide/non-landslide) from the MGB-CAR geohazard inventory\, split 60:20:20 for training\, validation\, and testing. XGBoost outperformed RF on all metrics: AUC-ROC 0.8903\, accuracy 81.78%\, precision 81.87%\, recall 81.62%\, and F1 81.75%\; the performance difference was statistically significant (McNemar's test: χ² = 6.22\, p < .013). Spatial validation via the Seed Cell Area Index (SCAI) confirmed that High and Very High susceptibility classes captured 69.87% of inventoried landslides within only 36.3% of the provincial area. Expert review by four MGB-CAR geoscientists yielded Likert mean scores above 4.0 for conditioning factor appropriateness\, inventory quality\, and feature importance plausibility. A fully automated monthly update pipeline was deployed—completing the full cycle from remote-sensing data retrieval to web-map publication in approximately 31 minutes—demonstrating operational feasibility for dynamic hazard monitoring using open-source tools and free satellite data.
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:06599a6f4daed9a30a3e8bffdd6c6c57
URL:http://internationalconferencetibs.sched.com/event/06599a6f4daed9a30a3e8bffdd6c6c57
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:Development of a Real-Time EMF Monitoring System and its Application in Assessing Electromagnetic Exposure Effects on Animals
DESCRIPTION:Authors - Adibhav Agrawal\, Nikunj Parikh Abstract - This article is about how a new configuration of devices has been created for a compact\, low-cost\, real-time monitoring system for measuring electromagnetic fields on dairy farms. The Electromagnetic Field Monitoring System (EMFMS) is composed of an ESP32 micro-controller\, MLX90393 three-axis magnetometer\, TP4056 based boosting supply module\, and a 0.96-inch OLED screen\, which are all encased in a unique 3D printed PETG enclosure. The EMFMS can store and transmit wirelessly time-stamped activity levels of the earth’s magnetic field on three axes through MQTT protocol. The EMFMS was placed into three different areas of an operational dairy barn over 28 days where EMF levels of up to 17 times higher were observed between different areas\, and a statistical finding was noted between EMF levels and lower levels of milk production (r = −0.61\, p = 0.003) and higher levels of cortisol in serum (r = +0.44\, p = 0.03) in Holstein-Friesian dairy cows. The findings of this pilot study demonstrate that this method of continuous measurement of electromagnetic fields for animals using IoT technology could serve as a more feasible and low-cost alternative to existing spot measurements.
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:6c548d07be7c3bf7fd559b65f98bb5f3
URL:http://internationalconferencetibs.sched.com/event/6c548d07be7c3bf7fd559b65f98bb5f3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:Edge-Optimized YOLOv8 for Real-Time Military Camouflage Detection on NVIDIA Jetson Nano
DESCRIPTION:Authors - Viraj Bhatt\, Rajvi Bhimani\, Bhupendra Fataniya\, Dhaval Shah Abstract - Cross-border security remains a critical concern for global stability\, particularly in jungle or forested terrains where soldiers face significant risks. Military camouflage is engineered to blend in with natural surroundings using advanced concealment techniques that match local textures and color patterns. Consequently\, the identification of concealed threats is a challenging task where human observation is prone to error due to poor visibility and fatigue. Traditional surveillance methods often rely on optical sensors which may fail to efficiently detect modern military camouflage. To address this\, an automated detection model was developed using the YOLOv8-Nano architecture and deployed on NVIDIA Jetson Nano hardware. The framework was validated using a 5- fold cross-validation strategy to ensure robust and reliable performance. Experimental results yielded a peak mean Average Precision (mAP) at an Intersection over Union (IoU) threshold of 0.5 of 0.955 and an average mAP of 94.8%. The model was further optimized into a TensorRT engine using FP16 quantization\, achieving a final footprint of 5.9 MB. These results demonstrate that low-power\, portable hardware can effectively perform real-time surveillance as an edge-AI system. This also results in minimizing risks to human lives and directly supporting the core mission of Sustainable Development Goal-16 (SDG-16).
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:3b72b359024a8332917d1e366aa31f9c
URL:http://internationalconferencetibs.sched.com/event/3b72b359024a8332917d1e366aa31f9c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:ESG ratings prediction: A study using Machine Learning approaches
DESCRIPTION:Authors - Kapil Mohan\, Ritu Chauhan\, Harleen Kaur Abstract - ESG (Environment\, Social and Governance) rating in today’s financial world is becoming a good indicator for investors in decision making and risk analysis. There has been stress on E and S in the recent past as Governments and Regulators stress these parameters and benefits to those who are working towards this improvement rating. The rating is a clear indicator of sustainability and promising business and thus is gaining popularity. The analytics firms have combined this indicator and have come up with this calculation using certain scientific and mathematical models from the published data and/or requested data that are provided exclusively to do this calculation for the indicator. These ratings are published annually by analytics firms like Sustain analytics and Bloomberg ESG data service for global but limited firms. This study’s focus is to fit financial data of firms on machine learning models and predict ESG rating with changing market fundamentals and firm’s business value indicators. The result can be com-pared to passed ratings\, category averages\, deviation and outliers which can benefit venture capitalists and investors to refine their investment strategies. The re-search also captures and compares this output and suggests the approach that best suits this problem by building an architecture that can update the model and can predict live data from the market.
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:42637ef7bd587b49b81a8854564c8f2c
URL:http://internationalconferencetibs.sched.com/event/42637ef7bd587b49b81a8854564c8f2c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:Internal Assessment Module for Educational Institute
DESCRIPTION:Authors - Anuj Kothawade\, Ishan Patra\, Pravin Chavare\, N. S. Shirude Abstract - The rapid digital transformation of higher education emphasizes the need for robust\, data-driven platforms to monitor and enhance student learning. However\, many institutions rely on closed\, third-party learning management systems that restrict direct access to raw educational data and limit customized analytical capabilities. To address this gap\, this paper proposes a scalable educational assessment and learning analytics platform that grants educators complete data sovereignty. Built on a modern stack of TypeScript\, React and Tailwind CSS over an owned\, directly accessible Firebase backend\, the system enables secure\, unhindered access for granular data mining. The platform monitors a range of college assessment activities\, targeting quizzes and practical coding tests\, and uses role-based authentication and custom data-fetching hooks to process student interactions into comprehensive performance metrics. A distinguishing feature is its integrated client-side PDF generation\, which instantly produces detailed analytical score reports that serve a dual pedagogical purpose: empowering teachers with actionable insights to adapt instruction\, while giving students personalized\, self-reflective feedback for continuous improvement. Validated on a controlled pilot\, the system achieved 95% overall accuracy\, an 85% quiz-evaluation accuracy\, a 28% improvement in student engagement\, a 40% reduction in report-generation time\, and a 92% system-usability score.
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:20c7ab02c44bf1089507dc4b5959598e
URL:http://internationalconferencetibs.sched.com/event/20c7ab02c44bf1089507dc4b5959598e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:RIVERCAST: Forecasting Marikina River Level Using Auto-Regressive Transformer with Kernel PCA and Euclidean Kernel
DESCRIPTION:Authors - Aleta Fabregas\, Nathanael Almazan\, Jordan Garcia\, Shaina Laman\, Paolo Morato\, Armin Coronado\, Montaigne Molejon\, Mariel Leo Violeta Abstract - The Philippines is frequently affected by tropical storms\, typhoons\, and flooding events that threaten communities located near major river systems. Accurate river level forecasting is essential for improving disaster preparedness and reducing flood-related risks. This study proposes RIVERCAST\, a forecasting system that utilizes an Auto-Regressive Transformer with Kernel Principal Component Analysis (Kernel PCA) and Euclidean Kernel to predict Marikina River water levels across the Nangka\, Sto. Niño\, and Montalban monitoring stations. Meteorological\, hydrological\, and topographical datasets were collected from PAGASA\, MMDA\, DPWH\, and OpenWeather API records from January 2012 to January 2023. Eighty percent of the collected records were allocated for training while the remaining twenty percent were utilized for testing. The pro-posed model was compared with the Transformer model developed by Xu et al. (2023) using rolling window testing and mean absolute error analysis. Results revealed that the proposed Auto-Regressive Transformer with Kernel PCA and Euclidean Kernel achieved an overall forecasting accuracy of 93.19%\, outperforming the Bidirectional Transformer model\, which obtained 92.57% accuracy. Findings further indicated that precipitation\, rainfall intensity\, and temperature significantly influenced forecasting performance\, while humidity exhibited the least contribution. The developed model demonstrated reliable twelve-hour river level forecasting capability and may support flood preparedness and early warning initiatives within flood-prone communities along the Marikina River.
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:3024618742d6e6ec0a5cb27ed8de5505
URL:http://internationalconferencetibs.sched.com/event/3024618742d6e6ec0a5cb27ed8de5505
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:Smart Technology and Integrated Systems in Subscription Hospitality: The Role of Service Personalization in Guest Satisfaction
DESCRIPTION:Authors - Syafira Aulia Azzahra\, Christina Angelica Himawan\, Brigitta Vellia\, Indra Kusumawardhana Abstract - The hospitality industry is increasingly shaped by smart technology\, integrated systems\, and subscription-based service models that require consistent and personalized guest experiences. In Indonesia\, particularly in the Greater Jakarta area\, hotels are adopting Internet of Things-based devices\, cloud-based property management systems\, and data-driven service platforms to improve guest convenience and strengthen customer retention. This study examines the effects of smart technology devices and integrated systems on customer satisfaction in subscription-based hospitality\, with service personalization positioned as a central mechanism in the guest experience. A quantitative cross-sectional survey was conducted with 400 hotel users in the Greater Jakarta area who had experience using smart technology in hotel services. The data were analyzed using Partial Least Squares Structural Equation Modeling. The findings show that smart technology devices and integrated systems positively influence customer satisfaction and service personalization. Service personalization also emerges as the strongest predictor of customer satisfaction\, indicating that technology creates greater value when it enables relevant\, adaptive\, and individualized services. The study contributes to hospitality technology and customer intelligence literature by explaining how digital infrastructure and system integration support personalized subscription-based hotel experiences. Practically\, the findings suggest that hotel managers should prioritize investment in interoperable systems\, guest data integration\, and personalization capabilities to improve satisfaction and sustain long-term customer relationships in technology-enabled hospitality services.
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:41eb6f9de75e578511a70b750adc4f52
URL:http://internationalconferencetibs.sched.com/event/41eb6f9de75e578511a70b750adc4f52
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T060000Z
DTEND:20260624T080000Z
SUMMARY:Understanding the Effect of Temporal and Attention Learning in GMFlow-Based Fall Detection Systems
DESCRIPTION:Authors - Aye Mya Mya Win\, Ah Nge Htwe Abstract - In recent years\, optical flow-based deep learning methods have pro vided evidence of impressive performance in recognizing human behavioral movements from video sequences\, revealing high applicability for fall detection functions. This paper analyzes GMFlow-based architectures by experimenting with three different approaches that merge TCN\, Attention\, and CNN compo nents. These methods are GMFlow-TCN\, GMFlow-TCN-Attention\, and GMFlow-CNN-TCN-Attention. The experiments were executed on URFD Da taset\, Le2i Dataset\, and a combined\, URFD-Le2i dataset to analyze and evalu ate their performance. According to the experimental results\, the method that combines GMFlow-CNN-TCN-Attention achieved better performance than the other proposed models. This model obtained test accuracies of 100% on the URFD dataset\, 92% on the Le2i dataset\, and 94% on URFD-Le2i dataset. These results point out that the presented method is capable of effectively cap turing both spatial features and temporal features required for fall detection. This approach provides useful insights for developing effective real-time vi sion-based fall detection applications.
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:848de22710f37b3b2ff05985b47f6911
URL:http://internationalconferencetibs.sched.com/event/848de22710f37b3b2ff05985b47f6911
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T080000Z
DTEND:20260624T080200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:94ff7c7785139e66a7454b3e7a45cd2b
URL:http://internationalconferencetibs.sched.com/event/94ff7c7785139e66a7454b3e7a45cd2b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T080000Z
DTEND:20260624T080200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:99ecd296a4a29a385d889bfc7dc119e1
URL:http://internationalconferencetibs.sched.com/event/99ecd296a4a29a385d889bfc7dc119e1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T080000Z
DTEND:20260624T080200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:707aa35ab3ae1a7740164e28be95df62
URL:http://internationalconferencetibs.sched.com/event/707aa35ab3ae1a7740164e28be95df62
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T080000Z
DTEND:20260624T080200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:82960dc9a385113c123cd9ed2443627c
URL:http://internationalconferencetibs.sched.com/event/82960dc9a385113c123cd9ed2443627c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T080200Z
DTEND:20260624T080500Z
SUMMARY:Session Closing & Information to Author
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 6A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:e72b04e6e7b5be8075e3932b39905bc2
URL:http://internationalconferencetibs.sched.com/event/e72b04e6e7b5be8075e3932b39905bc2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T080200Z
DTEND:20260624T080500Z
SUMMARY:Session Closing & Information to Author
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 6B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:b5e21305bec2ffb9477ea846407744fa
URL:http://internationalconferencetibs.sched.com/event/b5e21305bec2ffb9477ea846407744fa
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T080200Z
DTEND:20260624T080500Z
SUMMARY:Session Closing & Information to Author
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 6C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:ab4b7668f4f64d69fed7b768fa36367e
URL:http://internationalconferencetibs.sched.com/event/ab4b7668f4f64d69fed7b768fa36367e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T080200Z
DTEND:20260624T080500Z
SUMMARY:Session Closing & Information to Author
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 6D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:d9258959cb107c989a9a3d6038323871
URL:http://internationalconferencetibs.sched.com/event/d9258959cb107c989a9a3d6038323871
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T085800Z
DTEND:20260624T090000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:8203b1e3448eac0086400f603803c0e7
URL:http://internationalconferencetibs.sched.com/event/8203b1e3448eac0086400f603803c0e7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T085800Z
DTEND:20260624T090000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:709c80ab772ae97ed0c230b97dfee199
URL:http://internationalconferencetibs.sched.com/event/709c80ab772ae97ed0c230b97dfee199
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T085800Z
DTEND:20260624T090000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:86b9adf32f68a469b56d4398b25ed1aa
URL:http://internationalconferencetibs.sched.com/event/86b9adf32f68a469b56d4398b25ed1aa
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T085800Z
DTEND:20260624T090000Z
SUMMARY:Opening Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:a677b8ed084ff808c5a5dfe26380a733
URL:http://internationalconferencetibs.sched.com/event/a677b8ed084ff808c5a5dfe26380a733
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T090000Z
DTEND:20260624T110000Z
SUMMARY:3P-VAD: A Layered Three-Phase Framework for Intelligent Phishing URL Detection
DESCRIPTION:Authors - Kalva Yamini\, Kapilesh C\, Hari Kishore R\, Giri Karthick S Abstract - Phishing attacks remain among the most prevalent cybersecurity threats\, exploiting deceptive URLs that imitate legitimate domains. Traditional blacklist and heuristic-based methods fail to detect zero-day phishing URLs\, leaving users exposed to novel attack vectors. This paper presents 3P-VAD (Three-Phase Verification and Detection)\, an AI-powered system for real-time URL classification integrating three complementary layers: (i) threat intelligence dataset lookup against live feeds\, (ii) multi-engine verification via the VirusTotal API aggregating results from 70+ security vendors\, and (iii) a Convolutional Neural Network (CNN)-based zero-day detection model operating exclusively on URL character sequences. A selection-based scanning mechanism enables on-demand URL verification\, enhancing user privacy by preventing inadvertent submission of sensitive internal URLs to third-party services. Evaluated on 2 million URLs\, the framework achieved 95.0% accuracy\, 94.5% precision\, 86.0% recall\, and 90.0% F1-score on the CNN zero-day component\, with 100% combined detection rate across all three phases. Ablation experiments confirm non-redundant\, complementary coverage.
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:e61d0bf968ef20ed78d7822847dcc7d6
URL:http://internationalconferencetibs.sched.com/event/e61d0bf968ef20ed78d7822847dcc7d6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T090000Z
DTEND:20260624T110000Z
SUMMARY:A Systematic Review of Deep Autoencoder and HDBSCAN Clustering for Explainable Customer Segmentation in the Banking Sector
DESCRIPTION:Authors - Farai C. Jonha\, Arthur Ndlovu\, Mainford Mutandavari Abstract - This study presents a systematic review on the use of deep learning and density-based techniques for explainable segmentation of banking customers. We analyze 71 peer-reviewed papers published between 2015 and 2025 to investigate their methodological trends\, validation approaches\, and the degree of incorporation of interpretability into proposed models. Our findings suggest that autoencoders and variational autoencoders provide better separation of clusters than models using raw data. In terms of clustering methods\, density-based clustering algorithms perform better than clustering algorithms based on centroids since banking data exhibit highly skewed and non-Gaussian patterns. We also observe a common deficiency in explainability\, with less than 26% of the re-viewed papers considering approaches such as SHAP or LIME. Furthermore\, considerations of external validity\, operational governance\, regulation\, and scalability of implementation are rare. We therefore propose an explainable customer segmentation (XCS) framework based on deep representation learning\, density-based clustering\, post-hoc explainability\, and an operationally ready pipeline that is suitable for use in regulated banking environments
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:a5f29b8e70614d1ae63872071656d208
URL:http://internationalconferencetibs.sched.com/event/a5f29b8e70614d1ae63872071656d208
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T090000Z
DTEND:20260624T110000Z
SUMMARY:AV-SHIELD: A Hybrid Machine Learning Framework for Real-Time\, Low-False-Positive Credential Leakage Detection in Enterprise DevSecOps
DESCRIPTION:Authors - Mohammed Sulaiman I\, Shreevatsa DS\, Kavitha Sooda\, Revanth L\, Dhanush M Abstract - The unintentional release of API keys\, tokens\, and any other credentials in the source code is an obvious security threat to contemporary software development. Old rule-based scanners produce too many false positives and cannot scan through obfuscated secrets or secrets that are unknown. This paper introduces AV-SHIELD (Automated Vulnerability Scanning Hybrid\, Implementing integrated Leakage Detection) which is a hybrid framework that brings together pattern matching and machine learning to identify credential leaks in real time. The system serves to monitor development spaces in event-driven fashion and scan repositories in GitHub up to size limitations. One uses a Random Forest type of classifier\, which is trained on entropy based features to combatSecret vs Benign strings and a risk scoring engine which gives priority to create alerts. Records of the identified exposures are archived in a fingerprint-tracked vault\, batch-processed into mail notifications\, and include professionally-formatted PDF records. A trade analysis using an interactive Streamlit dashboard allows viewing trends of exposure\, provider profiles\, and risk allocations. The synthetic data generated has demonstrated a high precision and recall rate that is much lower than the explanation of the uses of regex alone\, tested through experimental evaluation. The framework was implemented as a systemd service\, which shows its applicability to the enterprise DevSecOps pipelines.
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:4e0eb4517ad5437e8cebe48f5d1ea9c5
URL:http://internationalconferencetibs.sched.com/event/4e0eb4517ad5437e8cebe48f5d1ea9c5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T090000Z
DTEND:20260624T110000Z
SUMMARY:Detection and Treatment of Rice Diseases in Benin Using AI: A Systematic Review
DESCRIPTION:Authors - Alfred ADINSI\, Pelagie HOUNGUE Abstract - This systematic review evaluates AI-based techniques for rice dis-ease detection with a focus that existing surveys have not adopted: their deploy-ability in West African smallholder conditions\, using Benin as the reference case. Based on 220 studies selected from 390 Scopus publications (2019–2025) via PRISMA\, it goes beyond performance benchmarking to assess what actually works under resource constraints. Rice blast (70.9% of studies)\, brown spot (60.9%)\, and bacterial blight (44.5%) dominate the literature. Deep Learning accounts for 64.5% of approaches\, hybrid methods for 21.8%\, and classical Machine Learning for 13.6%. Mean accuracy reaches 94.2% for pure Deep Learning and 95.8% for hybrid architectures. Res-Net+ViT (96.4% ± 2.1%) and CNN+SVM (94.1% ± 4.1%) are the strongest per-formers\, but performance alone is not the right metric for Benin. While 85% of studies apply to tropical climates\, only 30.5% propose solutions running on limited hardware. Three approaches clear both bars: MobileNet+SVM (89.4%)\, optimized YOLOv8 (89.2%)\, and ResNet-based Transfer Learning (91–94% after fine-tuning). That AI can detect rice diseases accurately is no longer in question. The harder problem is which systems beninese farmers and extension agents can actually use. This review provides an answer.
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:b17f7127edb2aef29063b3b4c98b3006
URL:http://internationalconferencetibs.sched.com/event/b17f7127edb2aef29063b3b4c98b3006
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T090000Z
DTEND:20260624T110000Z
SUMMARY:From Tradition to Transformation: Digital Public Service Innovation and Sustainable Governance in Bali
DESCRIPTION:Authors - I Gusti Ayu Agung Dewi Sucitawathi Pinatih\, Jonathan Jacob Paul Latupeirissa Abstract - The objective of this study is to examine and analyze the integration of technology\, governance\, and sustainability in the context of e-government and public services\, with a particular focus on the implementation of these three dimensions at the global and local levels\, specifically in the Province of Bali. This study employs a Systematic Literature Review (SLR)\, beginning with the identification of relevant keywords such as “e-government\,” “public service\,” and “sustainabil-ity\,” which were validated using WordCloud. Next\, strict inclusion and exclusion criteria were used to select articles. These criteria included relevance to the topic\, year of publication (2016-2026)\, and the journal’s peer-review status. Initial identification\, screening of titles and abstracts\, and in-depth reading of articles were part of the article selection process. The research findings indicate that in the digital transformation of the public sector\, technology\, governance\, and sustainability are interrelated\, and Bali serves as an example of how the integration of these three dimensions is reinforced by local values such as Tri Hita Karana and the subak system. These findings underscore that the digitization of public services in Bali will succeed if the principle of sustainability is applied in tandem with technology\, governance\, and local culture.
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:33413c4aa8923bc2dd61a9d031fab19d
URL:http://internationalconferencetibs.sched.com/event/33413c4aa8923bc2dd61a9d031fab19d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T090000Z
DTEND:20260624T110000Z
SUMMARY:Mapping Global Research on Blockchain in Supply Chain Management Performance: A Scientometric Review
DESCRIPTION:Authors - Aymane Chekira\, Aziz Hmioui Abstract - The rapid expansion of digital technology in recent years has significantly changed the way international supply chains (SCs) are structured\, operated\, and how well they perform. Among these transformations\, blockchain has grown to be a major enabler for addressing continuous concerns with transparency\, traceability\, collaboration\, and trust throughout supply chain networks. As companies seek more and more to raise supply chain performance and sustainability\, scholarly investigations of blockchain-based supply chain management have grown dramatically. Descriptive and content analysis of co-occurrence key-words using Biblioshiny and VOSviewer software revealed the main research subjects and their linkages across 145 peer-reviewed Scopus-indexed publications spanning 2019–2026. Scientometrically speaking\, this study examines this expanding body of research. The results point to two primary research directions: (i) how blockchain uptake influences organizational performance and supply chains\, and (ii) how transparency\, traceability\, decision-making\, and sustainable development enabled by blockchain are present in supply chains. The data analysis reveals that blockchain technology is a key and unifying feature that connects performance improvement with the goals of governance and sustainability. It emphasizes new ways for more investigation in blockchain-enabled supply chain performance and offers a systematic overview of the intellectual environment of blockchain research in supply chain management\, as well as comprehensible in-sights on its thematic evolution.
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:7a0f9a90696c0f32d2ec38b43194fbcd
URL:http://internationalconferencetibs.sched.com/event/7a0f9a90696c0f32d2ec38b43194fbcd
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T090000Z
DTEND:20260624T110000Z
SUMMARY:When Algorithms Meet Auditing: Unmasking Fraud Hexagon Schemes in the Digital Era
DESCRIPTION:Authors - Putu Putri Prawitasari\, Shefali Saluja\, Jonathan Jacob Paul Latupeirissa Abstract - Financial statements are vital for conveying a company's performance and financial health\, yet fraudulent financial reporting remains a significant concern\, especially involving fraud hexagon schemes. This study investigates the integration of advanced technologies to combat fraud hexagon schemes and improve auditing effectiveness in the digital era. Through a comprehensive literature review of academic sources from the Scopus database\, this research identifies the limitations of traditional auditing in detecting complex fraud patterns. Findings reveal that the adoption of technology-based tools such as data analytics\, artificial intelligence\, machine learning\, and blockchain enhances auditors’ ability to detect anomalies and suspicious activities more efficiently and accurately. Furthermore\, combining these technologies with robust corporate governance and auditor expertise strengthens fraud prevention mechanisms. The study concludes that leveraging digital innovations within a holistic fraud detection framework significantly advances audit quality and fraud mitigation strategies in contemporary financial environments.
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:64b98b53f9293276be830ee6283824e4
URL:http://internationalconferencetibs.sched.com/event/64b98b53f9293276be830ee6283824e4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T090000Z
DTEND:20260624T110000Z
SUMMARY:A Reproducible Indonesian NLP Pipeline for Multiclass Sentiment Classification of Hospitality Reviews
DESCRIPTION:Authors - Valencia Vannessa Taslim\, Melissa Anastasia\, Shalva Andena Rizaldi\, Tiurida Lily Anita Abstract - Online hospitality reviews provide valuable insights into guest experiences\, service quality\, and operational performance. However\, the unstructured and noisy nature of review text makes large-scale analysis difficult\, especially for Indonesian-language reviews that often contain informal expressions\, abbreviations\, spelling variations\, and inconsistent sentence structures. Although sentiment analysis has been widely applied in hospitality research\, studies focusing on Indonesian-language hospitality reviews remain limited\, and few have presented a reproducible Natural Language Processing (NLP) workflow for multiclass sentiment classification. This study proposes a reproducible Indonesian NLP pipeline for classifying hospitality reviews into positive\, neutral\, and negative sentiment categories. The workflow integrates review collection\, sentiment annotation\, Indonesian text preprocessing\, TF-IDF feature extraction\, and super-vised classification using Naïve Bayes\, Support Vector Machine (SVM)\, and Logistic Regression. The dataset consists of 450 Indonesian-language hotel reviews collected from Google Reviews across three hotel segments: budget\, mid-scale\, and upscale. The experimental results show that SVM achieved the best overall performance\, with 91.78% accuracy\, 91.35% precision\, 91.78% recall\, and 91.50% F1-score\, outperforming Naïve Bayes and Logistic Regression under the same experimental setting. These findings indicate that classical machine learning\, when supported by systematic preprocessing and consistent feature representation\, remains highly effective for Indonesian hospitality review analytics. This study contributes a practical and reproducible baseline for Indonesian-language sentiment classification and provides a foundation for future intelligent review monitoring systems in the hospitality sector.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:dc5a68f20e8872fa274aa3472e0bc277
URL:http://internationalconferencetibs.sched.com/event/dc5a68f20e8872fa274aa3472e0bc277
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T090000Z
DTEND:20260624T110000Z
SUMMARY:Adoption of Artificial Intelligence in Financial Management Systems of Higher Education Institutions
DESCRIPTION:Authors - Jolou Vincent M. Jala\, Everly A. Nacalaban\, Nenon Roy A. Sandinao\, Erlinda D. Rivera\, Hilfiger L. Cubarrubia Abstract - This article explored the adoption of Artificial Intelligence in Financial Management Systems of Higher Education Institutions (HEIs) by utilizing a systematic review of related literature. The study focuses on reviewing pre-sent literature on Artificial Intelligence adoption in financial management systems\, recognizing the benefits of AI integration\, scrutinizing the challenges and barriers to implementation\, and offer recommendations for effective and successful AI integration in HEIs. The findings disclosed that artificial intelligence has the capability to meaningfully enhance financial management systems in Higher Education Institutions through automated financial reporting systems\, budgeting forecasting and predictive analytics\, fraud detection and risk management\, and expense tracking and optimization. Adoption of Artificial Intelligence improves efficiency\, enhances accuracy\, provides better decision-making and cost optimization. More-over\, it enhances operational efficiency by systematizing monotonous financial tasks\, enhances accuracy by plummeting human faults\, supports better decision-making through actual financial data and predictive analytics\, and helps to long-term cost optimization and financial sustainability. These improvements permit institutions to alter from manual and volatile financial management routines toward more data-driven\, calculated and strategic\, financial planning and re-source provision. Conversely\, the study also found several challenges that deter AI adoption in Higher Education Institutions\, specifically in developing countries such as the Philippines. These challenges include high initial investment and maintenance costs\, limited technical skills among staff\, data privacy and cybersecurity risks and resistance to organizational change. Numerous HEIs are still in the developing stage of digital transformation and depend chiefly on enterprise systems and basic accounting rather than advanced Artificial Intelligence technologies. The article concludes that successful AI in- corporation requires institutional readiness\, strategic planning\, capability building\, infrastructure progress\, and robust data governance policies to completely maximize the advantages of Artificial Intelligence in financial management systems.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:c2da5b09365897676ce23652212f7e9f
URL:http://internationalconferencetibs.sched.com/event/c2da5b09365897676ce23652212f7e9f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T090000Z
DTEND:20260624T110000Z
SUMMARY:Architecture and Development of a Cloud-based Information System with Integrated Decision Support
DESCRIPTION:Authors - Denver Novencido Abstract - An organization’s operational efficiency\, productivity\, and reliability can be adversely affected by using manual-based systems. Some of the issues associated with using a manual-based approach include inefficient processes\, inconsistent documentation\, difficulty in monitoring and validating records\, and limited accessibility. The development of information systems provides a solution to address the limitations and challenges of a manual-based approach in organizations. This study presents the design and implementation of a cloud-based information system integrated with decision support capabilities to streamline organizational operations\, enhance data storage and retrieval\, and facilitate strategic planning. The system was created using the Agile Unified Process (AUP) software development methodology. Evaluation results indicate strong compliance with ISO software quality standards\, making it a suitable tool for managing organizational operations.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:958ba023b77c95b9cb1497fa6a606a05
URL:http://internationalconferencetibs.sched.com/event/958ba023b77c95b9cb1497fa6a606a05
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T090000Z
DTEND:20260624T110000Z
SUMMARY:Comparative assessment of blockchain-powered identity management in digital financial services
DESCRIPTION:Authors - Felix Kabwe\, Jackson Phiri Abstract - This study explores how blockchain-based Identity and Access Management (IAM) systems can enhance the security and efficiency of Digital Financial Services (DFS). As DFS environments grow more complex and involve multiple stakeholders\, traditional IAM systems face challenges such as centralization\, limited interoperability\, and scalability constraints. Blockchain offers a compelling alternative by enabling decentralized\, transparent\, and tamper-resistant identity management. The study compares three main IAM models: centralized systems supported by blockchain\, federated identity management\, and Self-Sovereign Identity (SSI). Using the Technology-Organization-Environment (TOE) framework alongside a semi-quantitative scoring approach\, the research evaluates these models across key factors including security\, privacy\, usability\, scalability\, governance\, cost\, and regulatory alignment. The findings highlight clear trade-offs. Centralized systems excel in performance\, cost efficiency\, and regulatory compliance but are vulnerable to single points of failure. Federated models strike a balance by improving interoperability and user experience\, though they introduce governance complexity. SSI provides strong privacy and user control but faces challenges in usability\, scalability\, and regulatory acceptance. Overall\, no single model fully meets DFS needs. Federated systems are currently the most practical\, while hybrid federated–SSI approaches offer the most flexible\, scalable\, and user-focused solution.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:a69f598a8bb2ae9c4dd744b3aa42628a
URL:http://internationalconferencetibs.sched.com/event/a69f598a8bb2ae9c4dd744b3aa42628a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T090000Z
DTEND:20260624T110000Z
SUMMARY:Enhanced Image Captioning using Dual-Encoder Networks and Transformer Decoding
DESCRIPTION:Authors - Md. Monowar Hossain\, Fahima Hossain\, Md. Shahidul Islam\, Md. Tanvir Ahmed\, Reduan Ahmed Abstract - This automated image captioning is on one hand a Computer Vision (CV) and Natural Language Processing (NLP) application\, but on the other hand\, conventional CNN-RNN models suffer from feature loss and long-range dependency. The proposed model in this study is a parameter balanced multi-modal model that consists of a dual-encoder network which combines Effi-cientNet-B4 for hierarchical features and MobileNetV2 for geometric efficiency\, as well as a multi-head Transformer decoder. The model was evaluated on Flickr8k\, and tested with a dynamic scalar weight mechanism and teacher-forced optimization\, the BLEU-1 was 0.5774 and METEOR was 0.4129. Interestingly\, the ablation results also showed that although the dual-encoder method is competitive\, the pathway of the standalone MobileNetV2 is slightly better than the fused pathway in terms of BLEU-4 (0.2284 vs. 0.20). This indicates that the pathway may be redundant during the concatenation process. This study validates the possibility of using Transformer decoders instead of RNN bottle-necks and offers important considerations for the optimization of real-time feature fusion for vision tasks.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:8b6b4f7e5f752c3079ffba4202475ee4
URL:http://internationalconferencetibs.sched.com/event/8b6b4f7e5f752c3079ffba4202475ee4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T090000Z
DTEND:20260624T110000Z
SUMMARY:Explainable Feature Importance Analysis for Skin Disease Classification
DESCRIPTION:Authors - Aarthi R\, Aniketha Prasad\, Dhamini Manoj\, Manasvi G\, Meghaa Sunil Abstract - Early and accurate diagnosis of dermatological disorders remains one of the main issues in clinical dermatology\, especially with regard to diseases with similar appearances. Despite the achievements of deep learning methodologies in the classification of cutaneous lesions with the help of images\, structured clinical metadata is not used to the fullest\, despite its significant diagnostic potential. In a practical clinical setting\, dermatologists do not solely use visual evaluation of the case but also use patient-specific metadata\, which includes age\, lesion progression\, pruritus\, hemorrhage\, anatomic location\, prior biopsy\, and family history. The current study presents a fully explainable\, metadata based\, multi-class classification of skin diseases\, using the PAD-UFES-20 database\, and concentrated on 6 distinct diagnostic categories. Although the dataset is dermoscopic\, the predictive quality of formal metadata variables are mainly under consideration in the present work. The explainability analyses revealed that biopsy status\, elevation\, itch\, region and age are attributes that have significant effects on the classification results. However\, empirical evidence shows that the reduced model consisting of the premier five features lowers accuracy\, which highlights the importance of a thorough combination of metadata features to determine skin disease rather than limited combination. Comparative studies indicate that the Multi-Layer Perceptron shows an improvement in a model performance with a corresponding increase of the number of selected features. The suggested framework thus highlights interpretability in line with predictive efficacy thus enhancing the importance of transparent artificial intelligence systems in medical decision-making.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:3f0316a25c76d2e9f83b598180c0c744
URL:http://internationalconferencetibs.sched.com/event/3f0316a25c76d2e9f83b598180c0c744
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T090000Z
DTEND:20260624T110000Z
SUMMARY:Gesture Controlled Interface for Smart Devices using MediaPipe and Android Debug Bridge
DESCRIPTION:Authors - Veeravalli Sri Satya\, Anjan Babu G Abstract - Human-computer interaction with smart consumer electronics predominantly requires physical peripherals\, which introduce limitations regarding hardware degradation\, shared-surface hygiene\, and usability in hands-free environments. Voice-activated systems provide an alternative but exhibit high latency and degraded performance under ambient noise. This paper presents a multi-layered touchless gesture control framework that translates human hand kinematics into direct system actuation. The architecture utilizes a standard web camera and the Google MediaPipe framework to extract 21 three-dimensional hand landmarks in real time. To bypass the computational bottlenecks of traditional Convolutional Neural Networks (CNNs)\, the system employs a custom heuristic algorithm to classify eight distinct static and dynamic gestures by analyzing the geometric relationships between finger joints. The framework processes these classifications locally and actuates Android-based Smart TVs over Wi-Fi utilizing Android Debug Bridge (ADB) protocols [11]. Evaluated in a controlled environment\, the pipeline achieved an average processing time of 35 milliseconds per frame (approximately 30 frames per second) with a network transmission delay of 50 to 80 milliseconds. The results suggest that computationally lightweight computer vision models\, when paired with structured state-machine logic\, can effectively replace physical remote controls without requiring dedicated GPU hardware.
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:3ac040e3584a07a55d3e682dd26102ea
URL:http://internationalconferencetibs.sched.com/event/3ac040e3584a07a55d3e682dd26102ea
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T090000Z
DTEND:20260624T110000Z
SUMMARY:Crowdsourced Civic Issue Reporting and Resolution System
DESCRIPTION:Authors - Sachin Ramling Jadhav\, Rajveer Nandkar\, Srushti Rajput\, Rajvardhan Desai\, Gunjan Ramteke\,Samruddhi Rajput Abstract - Dealing with city problems like cracked roads\, trash piles\, leaks in pipes\, or dark lamp posts keeps urban teams busy. When fixes depend on old paper methods\, pieces of info get lost\, trust dips\, responses drag. A new setup steps in - CCIRS - running through a basic website made with PHP tools. Instead of guessing what comes first\, supervisors follow a clear score called PI\, shaped by how bad things look\, where many reports cluster\, plus how long issues wait. Behind the scenes\, staff watch live updates\, study trends\, trace progress using their control view online. Half a year of testing in three city areas of Pune cut response times by 59.0%. Because of this change\, meeting service targets got better by nearly half. Old ways of handling issues were clearly outperformed. Math behind sorting locations was built and tested. Ranking urgency used formulas that matched real outcomes well.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:338b1f6c3edfdb0ddf9921d997170ef4
URL:http://internationalconferencetibs.sched.com/event/338b1f6c3edfdb0ddf9921d997170ef4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T090000Z
DTEND:20260624T110000Z
SUMMARY:Finfluencer Impact on Young Retail Investors’ Behavioural Biases
DESCRIPTION:Authors - Mrityunjaya Chavannavar\, Melita Simoes \, Nikhil Shetty \, Chirivella Vishal Abstract - Over the years\, there is a rapid growth of social media-based financial content. Finfluencers have been emerging as influential sources that provide investment information to young retail investors. This research is inclined towards understanding the influence of finfluencers on numerous behavioural biases that include herd mentality\, overconfidence\, and FOMO. This study also examines their influence on decision-making when it comes to investments and the overall risk perception in the current digitally enabled investment landscape. There is interplay between social media platforms\, financial influencers\, and behavioural biases and can be observed among young retail investors in India. Most traditional theories in finance assume that a majority of investors behave rationally while behavioural finance acknowledges the impact of cognitive and emotional biases influence investment decisions. This quantitative study makes use of a descriptive-analytical approach. The primary data used here was gathered with the help of structured online questionnaires distributed to 120 young retail investors. Data analysis was carried out with the help of IBM SPSS Statistics. Tests such as correlation analysis\, multiple regression models\, and ANOVA with post-hoc Tukey HSD were undertaken. Findings showed that general social media usage frequency had no significant relationship with the four behavioural biases examined. Perceived credibility of finfluencer content demonstrated significant negative relationships with all four biases (overconfidence: β = -0.387\, p = 0.001\; herding: β = -0.252\, p = 0.044\; confirmation: β = -0.321\, p = 0.006\; availability: β = -0.354\, p = 0.003). This indicates that high-quality financial influencers may serve a corrective rather than amplifying function. Indiscriminate following of numerous finfluencers positively predicted confirmation bias (β = 0.191\, p = 0.025). Investors with over five years of experience revealed significantly lower biases. This study can be used for better investor protection\, and financial literacy initiatives and can be embedded in various regulatory frameworks.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:e2131fa089b7eb5b8c940e5e7942e801
URL:http://internationalconferencetibs.sched.com/event/e2131fa089b7eb5b8c940e5e7942e801
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T090000Z
DTEND:20260624T110000Z
SUMMARY:Fostering Sustainable Innovation Through Transformational Leadership in Entrepreneurial University: Evidence from a Philippine Higher Education Institution
DESCRIPTION:Authors - April L. Macasieb-Gumnad\, Roberto M. Arguelles Abstract - The study focuses on transformational leadership\, entrepreneurship\, and sustainability in higher education. Using Saint Louis University (Philippines) as a case study\, the purpose was to (1) identify the role transformational leadership has in developing (or affecting) the characteristics of an entrepreneurial university\, (2) identify how transformational leadership fosters sustainable innovation\, and (3) assess the effect entrepreneurial university characteristics have on achieving sustainable outcomes. This quantitative research used three different instruments that were previously validated (HEInnovate Questionnaire\; Sustainability Assessment Questionnaire\; and Survey of Transformational Leadership) to gather data from a sample of 795 respondents at SLU and analyzed the resulting data using Spearman-rank correlation analysis and simple linear regression. This study provided practical applications to the literature on higher education management through empirical evidence of relationships between types of leadership styles\, achievement of SDGs\, organizational structures/models/characteristics\, and sustainability of innovation in higher educations.The SLU CARES Innovation Framework was proposed to provide actionable insights for academic and administrative leaders seeking to align Catholic educational missions with contemporary demands for innovation and sustainability.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:5cd940bdf41bc893518777aaab66b176
URL:http://internationalconferencetibs.sched.com/event/5cd940bdf41bc893518777aaab66b176
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T090000Z
DTEND:20260624T110000Z
SUMMARY:Implementation of Intellectual Property and Technology Business Management in Five Higher Education Institutions under the Regional Agri-Aqua Innovation System Enhancement Program in Central Visayas\, Philippines: A Documentary Analysis
DESCRIPTION:Authors - Angeline B. Elegio\, Darrel A. Cardana\, Kathlyn L. Quion\, Max Angelo D. Perin Abstract - Universities are increasingly expected to translate research into usable technologies through effective intellectual property and technology business management (IPTBM). However\, the pathway from intellectual property (IP) outputs to commercialization remains challenging\, particularly in regional innovation programs where institutions operate under varying resources and capabilities. This study examined the IPTBM implementation experience of five higher education institutions (HEIs) in Region 7\, Philippines\, under the Regional Agri- Aqua Innovation System Enhancement (RAISE) Program\, tracing the pathway from IP outputs to commercialization outcomes. A documentary analysis design was employed\, analyzing official IPTBM records including IP filings\, commercialization cases\, capacity-building participation\, and governance milestones. A total of 584 IP filings were documented\, dominated by utility models (63.4%)\, with output concentrated in two institutions (CTU: 46.1%\; Regional IPTBM/BISU: 34.1%). Five commercialization cases were recorded (0.86% conversion rate) generating Php 59\,762.25 in documented licensing returns\, predominantly through agreements with local cooperatives and MSMEs. Core training participation was standardized\, but supplemental training and outreach varied substantially. Policy development stages ranged from drafted to Board of Regents- approved. Findings suggest that output volume alone does not ensure commercialization success\; rather\, market-facing capabilities\, portfolio prioritization\, and partner readiness critically shape technology transfer outcomes.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:3188eff1746baaffe49ee93ed9223898
URL:http://internationalconferencetibs.sched.com/event/3188eff1746baaffe49ee93ed9223898
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T090000Z
DTEND:20260624T110000Z
SUMMARY:Strengthening University-Based Innovation Ecosystems: An Assessment of the Agri-Aqua Technology Business Incubator (ATBI) Implementation in Bohol Island State University under the RAISE Program
DESCRIPTION:Authors - Darrel A. Cardana\, Ethel Zean M. Anosa\, Angeline B. Elegio\, Jes Maries Mendez\, Ivy Corazon Mangaya-ay Abstract - Agri-Aqua Technology Business Incubators (ATBIs) play an important role in promoting innovation\, entrepreneurship\, technology commercialization\, and institutional collaboration within higher education institutions. This study assessed the operational performance and institutional development of the BISU Agri-Aqua Technology Business Incubator (ATBI). Specifically\, the study evaluated the accomplishments of the incubator in terms of personnel capacitation\, partnership and linkage development\, awareness and promotional activities\, incubation services\, technology incubation initiatives\, intellectual property generation\, and policy institutionalization. The study also examined the capacitybuilding activities\, partnership initiatives\, intellectual property outputs\, and the problems and strategic solutions encountered during implementation. The study employed a descriptive-evaluative research design utilizing documentary analysis of the official accomplishment report and supporting institutional documents of the BISU ATBI. Frequency counts\, percentage analysis\, and thematic analysis were utilized in analyzing the collected data. The findings revealed that the BISU ATBI successfully implemented several operational and institutional initiatives. The incubator conducted seventeen (17) trainings and workshops\, forged twelve (12) MOUs with incubatees and six (6) institutional partnerships\, conducted eight (8) awareness seminars\, developed ten (10) business plans\, filed ten (10) trademarks and five (5) copyrights\, and enrolled seventeen (17) incubatees in the incubation program. However\, only two (2) technologies were successfully co-incubated despite the target of ten technologies\, indicating challenges in technology commercialization and adoption. The study also identified regulatory hurdles\, technology readiness concerns\, partnership issues\, and low technology adoption as major implementation challenges. Overall\, the findings indicate that the BISU ATBI established a strong operational and institutional foundation for technology business incubation\, although continuous enhancement of commercialization and technology adoption initiatives remains necessary.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:81f86895f38b3ed7de3506ea90110b14
URL:http://internationalconferencetibs.sched.com/event/81f86895f38b3ed7de3506ea90110b14
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T090000Z
DTEND:20260624T110000Z
SUMMARY:The Impacts of eWOM on Fashion Shopping Intention: The Case Study of TikTok in Vietnam
DESCRIPTION:Authors - Nguyen Quoc Cuong\, Nguyen Ha\, Mai Thi Bich Ngọc Abstract - The proliferation of short-form video platforms has reshaped consumer decision-making\, yet how electronic word of mouth (eWOM) attributes influence Generation Z fashion shopping intention in emerging markets remains underexplored. Grounded in the Information Adoption Model (IAM) and Attitude–Intention framework\, this study examines the impact of TikTok-based eWOM on fashion shopping intention among Vietnamese Generation Z consumers. Using PLS-SEM analysis of 263 valid survey responses\, results reveal that eWOM Information Quality and Credibility significantly predict Attitude toward eWOM\, while Information Usefulness is the strongest predictor of eWOM Adoption\; Information Quantity exerts a positive but weaker effect. Both Attitude and Adoption significantly influence Fashion Shopping Intention\, with Attitude as the dominant predictor\, and mediation analysis confirms their roles as key intervening mechanisms. These findings extend the IAM to short-form video and social commerce contexts\, demonstrating that Generation Z engages in evaluative\, quality-oriented content processing rather than responding passively to volume. Practically\, results offer actionable guidance for fashion marketers to prioritize authentic\, credible\, and informative eWOM strategies on TikTok.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:9a004851452a3d54fcd14e2adb2cd0cc
URL:http://internationalconferencetibs.sched.com/event/9a004851452a3d54fcd14e2adb2cd0cc
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T090000Z
DTEND:20260624T110000Z
SUMMARY:Ways to further improve the efficiency of road border customs posts in facilitating foreign trade using digitalization
DESCRIPTION:Authors - Rakhmonova Nargiza Rashidovna\, Rajapov Shukhrat Zaripbaevich Abstract - The growing volume of international trade is increasing pressure on road border customs posts\, making their operational efficiency a key factor in facilitating foreign trade. Chronic congestion\, long vehicle queues\, and procedural delays at land border crossings hinder logistics efficiency and increase trade costs. Digitalization is increasingly viewed as a strategic solution for modernizing customs administration while ensuring effective control and economic security. This study examines ways to further improve the efficiency of road border customs posts through digitalization\, using the case of Uzbekistan. The analysis is based on data from 322 road border customs posts and employs economic and statistical methods\, including regression analysis and structural equation modeling (SEM). The model assesses the impact of human resources\, infrastructure capacity\, and digital inspection technologies—specifically\, the number of employees\, traffic lanes\, inspection and verification complexes (ISC and Z-portal)\, passenger flows\, and reported violations—on daily vehicle traffic volumes. The results consistently show that human resources are the most significant factor in customs post efficiency. An increase in the number of employees has a strong and statistically significant positive effect on daily vehicle flow across all parameters of the model. In contrast\, the expansion of physical infrastructure\, measured by the number of traffic lanes\, shows a negative or weakly significant relationship\, indicating that infrastructure alone does not guarantee increased throughput. Digital control systems show a positive but statistically insignificant effect\, suggesting incomplete integration into operational processes. The results indicate that to achieve significant efficiency gains\, digitalization must be combined with effective human resource management and organizational optimization. Policy measures should prioritize capacity building\, intelligent traffic management\, and deeper integration of digital systems to reduce congestion\, speed up logistics\, and improve conditions for foreign trade.
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:bacfd2a6630ae144ece125c37d6a0058
URL:http://internationalconferencetibs.sched.com/event/bacfd2a6630ae144ece125c37d6a0058
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T090000Z
DTEND:20260624T110000Z
SUMMARY:An Android Application for Pothole Detection and Severity Analysis Using Sensor Fusion and Deep Learning
DESCRIPTION:Authors - Al John A. Villareal\, Jaime M. Samaniego Abstract - Potholes significantly impact road safety\, vehicle performance\, and infrastructure maintenance\, particularly in developing countries where monitoring systems remain largely manual. This study presents the design and implementation of an Android-based mobile application that utilizes sensor fusion and deep learning for real-time pothole detection and severity analysis. The system integrates a YOLO-based object detection model with smartphone sensors\, including accelerometer\, gyroscope\, and Global Positioning System (GPS)\, enabling simultaneous visual and motion-based detection. A dataset consisting of 9\,253 road surface images containing 16\,123 pothole annotations was used for training and evaluation using a 70:20:10 dataset split for training\, validation\, and testing. Among the evaluated models\, YOLO11s achieved the highest mAP@50– 95 value of 54.2%. However\, YOLO26n was selected and implemented in the developed Android application due to its competitive detection performance\, compact 5.2 MB model size\, and suitability for real-time mobile deployment. Field testing across four road segments covering 18.97 kilometers resulted in 130 detections\, of which 84 were verified potholes and 46 were false detections\, yielding a verification rate of 64.62% and a false detection rate of 35.38%. The system recorded an average detection density of 6.85 potholes per kilometer. Results demonstrate that integrating deep learning and sensor fusion in a mobile platform provides a scalable and cost-effective solution for automated road condition monitoring and intelligent transportation systems.
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:ad31e7307eb03f772c980d1ede8d8909
URL:http://internationalconferencetibs.sched.com/event/ad31e7307eb03f772c980d1ede8d8909
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T090000Z
DTEND:20260624T110000Z
SUMMARY:An Interpretable Hybrid Deep Learning Framework for Cost-Effective Water Quality Classification in Aquaculture
DESCRIPTION:Authors - Nu Yin Khaing\, Win Lelt Lelt Phyu Abstract - Water quality monitoring is essential for sustainable aquaculture management and fish health assessment. However\, monitoring a large number of physicochemical parameters in creases sensor deployment costs and system complexity. While traditional machine learning ap proaches struggle with complex\, nonlinear relationships among water quality variables\, feature reduction can optimize system efficiency. This study proposes a hybrid machine learning and deep learning framework to achieve accurate\, cost-effective water quality classification using the Water Quality Dataset (WQD). The framework integrates Random Forest (RF) and XGBoost for feature selection with Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) models as classifiers\, evaluating four hybrid combinations (XGBoost+SVM\, XGBoost+LSTM\, RF+SVM\, and RF+LSTM) across subsets of 10\, 7\, and 5 features. Experimental results demon strate that hybrid deep learning architectures consistently outperform traditional machine learn ing methods. Specifically\, XGBoost+LSTM and RF+LSTM achieved the highest classification accuracy of 96.05% using 10 selected features\, while maintaining reliable performance at lower dimensions. Furthermore\, Shapley Additive explanations (SHAP) analysis was applied to en hance model interpretability\, identifying Dissolved Oxygen (DO)\, Turbidity\, BOD\, H2S\, and Nitrite as the most important attributes. Ultimately\, the proposed framework minimizes sensor requirements and provides an accurate\, interpretable\, and economically viable solution for aqua culture water quality monitoring systems.
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:7eee98b1a41753c12274a83afa8b28cb
URL:http://internationalconferencetibs.sched.com/event/7eee98b1a41753c12274a83afa8b28cb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T090000Z
DTEND:20260624T110000Z
SUMMARY:EMPOWERING THE NEXT GENERATION: INTEGRATING MULTILINGUAL CUSTOMER SERVICE SKILLS INTO WORKFORCE DEVELOPMENT PROGRAMS FOR HOSPITALITY MANAGEMENT STUDENTS
DESCRIPTION:Authors - Apolinar P. Datu\, Barnard J. Maraon\, Rommel H. Orquiza\, Cristopher T. Takano\, Olivia L. Yosa\, Mark Joseph G. Cruz\, Jeferson D. Talisayon Abstract - This study examines the integration of multilingual customer service skills into workforce development programs for hospitality management students. In today’s globalized environment\, the hospitality industry serves guests from diverse cultural and linguistic backgrounds\, making effective communication an essential skill. Using a quantitative approach\, the study gathered data from 100 hospitality students to assess their communication skills\, performance\, and confidence in multilingual settings. The findings reveal that most students already possess basic multilingual abilities\, particularly in English and Filipino\, which serve as a strong foundation for further development. Results also show that the current curriculum includes elements of multilingual training\, contributing to students’ overall competence. However\, while students demonstrate satisfactory performance and confidence\, there is still a need for increased practical exposure and strengthened training programs. Furthermore\, the study highlights that students generally meet industry expectations but may benefit from more structured and continuous training to enhance their real-world readiness. Overall\, integrating multilingual customer service skills significantly supports the development of hospitality students\, preparing them for the demands of a culturally diverse workforce and improving their competitiveness in the global hospitality industry.
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:e558df17b5ab6b13f58c8f12fdc4b42a
URL:http://internationalconferencetibs.sched.com/event/e558df17b5ab6b13f58c8f12fdc4b42a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T090000Z
DTEND:20260624T110000Z
SUMMARY:Enhancing Minority-Class Detection in ECG Arrhythmia Classification: A Reliability-Oriented Machine Learning Approach
DESCRIPTION:Authors - Sanjana Priyadarsini\, Choudhary Aman Kumar Roy\, Ashlesha Shree Bajpai\, Rajdeep Banerjee\, Shivali Sharma\, Ranjita Kumari Dash Abstract - Today\, machine learning methods are quickly being adopted in healthcare. In numerous instances\, it has been observed that datadriven approaches have increased reliance of medical data analysis and disease detection by about 60-70%. It is important to diagnose cardiac arrhythmias early using electrocardiogram (ECG) analysis\, as timely diagnosis can prevent severe complications and loss of life. Most ECG datasets are however not balanced\, with normal beats by far outnumbering abnormal ones and causing the models to underperform on rare but significant cases. In this work\, Logistic Regression is used as a baseline model. To correct this imbalance\, Class weighting and Synthetic Minority over-sampling Technique (SMOTE) are applied. These techniques help the model detect rare heartbeat patterns more reliably and miss fewer abnormalities. This paper shows that addressing class imbalance can make ECG-based classification systems more accurate and clinically valid.
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:601c47af5c818db57841a5b0a5b7e9b5
URL:http://internationalconferencetibs.sched.com/event/601c47af5c818db57841a5b0a5b7e9b5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T090000Z
DTEND:20260624T110000Z
SUMMARY:Toward Smart SME Governance: Developing an AI Based Digital Audit Maturity Framework Integrated with Internal Control Systems for SDG 9
DESCRIPTION:Authors - Windy Permata Suyono\, Dwi Handarini\, Eka Septariana Puspa\, Surya Anugrah\, Nuramalia Hasanah\, Ratna Anggraini\, Sabo Hermawan\, Rio Firnanda Abstract - This study aims to develop an AI-Based Digital Audit Maturity Frame work integrated with SPIP to support Smart SME Governance and Sustainable Development Goal 9 (SDG 9). The study employs a systematic literature review approach by analyzing 50 relevant articles published between 2020 and 2026 re lated to artificial intelligence in auditing\, digital audit maturity\, internal control systems\, smart governance\, SMEs\, and sustainable innovation. The findings in dicate that artificial intelligence technologies significantly improve audit effec tiveness\, governance transparency\, operational efficiency\, and organizational re silience. The study also reveals that digital audit maturity and SPIP-based internal control systems play important roles in supporting sustainable digital transfor mation and adaptive governance within SMEs. Based on the literature synthesis\, this study proposes a conceptual framework consisting of input factors\, digital transformation processes\, digital audit maturity levels\, SPIP-based internal con trol systems\, smart SME governance\, and SDG 9 achievement. The proposed framework contributes theoretically by integrating technological capability\, gov ernance systems\, internal control mechanisms\, and sustainability perspectives into a unified governance model. Practically\, the framework provides guidance for SMEs\, policymakers\, auditors\, and digital transformation practitioners in strengthening sustainable AI-driven governance implementation.
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:734b61de66e8fdeedfa9eecc7d1f7d78
URL:http://internationalconferencetibs.sched.com/event/734b61de66e8fdeedfa9eecc7d1f7d78
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T090000Z
DTEND:20260624T110000Z
SUMMARY:Zephyr: An AI-Driven Tool for Post-Meeting Productivity and Actionable Intelligence
DESCRIPTION:Authors - Milind Nemade\, Khush Chheda\, Rahul Dhanak\, Durgeshkumar Dubey Abstract - The problem of meeting productivity continues to prevail in the current era in multilingual environments due to frequent language switching between speakers. Most of the existing frameworks for meeting intelligence primarily focus on automatic transcription and lack significant support for Indic languages\, speaker identification\, and task extraction. Additionally\, many of these frameworks depend on metadata associated with specific platforms and\, therefore\, cannot be used in any offline environment or even on other platforms. In this work\, we present a scalable and platform-agnostic framework for meeting intelligence which can automatically an alyze meetings post factors by leveraging speech recognition\, speaker identification\, and contex tual analysis. The system leverages multilingual and code-switched transcription capabilities of Sarvam AI\, generates speaker embeddings using ECAPA-TDNN\, and then uses Large Language Models for context-based analysis. Two different strategies for speaker identification are dis cussed in this paper such that they do not need any platform-based metadata while improving the attribution accuracy. We have further developed an asynchronous framework for extracting tasks\, assigning tasks\, and notifying about them. Experimental results indicate enhanced transcription accuracy as well as speaker identification accuracy in Hindi-English code-switching cases. Fu ture work will focus on implementing advanced privacy protection and end-to-end encryption mechanisms for secure storage and processing of meeting recordings and metadata.
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:62b32f72dfded27cf4a53b6102c7b286
URL:http://internationalconferencetibs.sched.com/event/62b32f72dfded27cf4a53b6102c7b286
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T110000Z
DTEND:20260624T110200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:3f6531d00a0d7fcd700130bd1af6cdeb
URL:http://internationalconferencetibs.sched.com/event/3f6531d00a0d7fcd700130bd1af6cdeb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T110000Z
DTEND:20260624T110200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:7c31a9d9b562963dcecb821dce4fe519
URL:http://internationalconferencetibs.sched.com/event/7c31a9d9b562963dcecb821dce4fe519
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T110000Z
DTEND:20260624T110200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:424fe068230d3fbbcfcfe76369182434
URL:http://internationalconferencetibs.sched.com/event/424fe068230d3fbbcfcfe76369182434
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T110000Z
DTEND:20260624T110200Z
SUMMARY:Session Chair Concluding Remarks
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:0d5c4699ad4bf096abb5804e2a5d081b
URL:http://internationalconferencetibs.sched.com/event/0d5c4699ad4bf096abb5804e2a5d081b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T110200Z
DTEND:20260624T110500Z
SUMMARY:Session Closing & Information to Author
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7A
LOCATION:Virtual Room A\, Manila\, Philippines
SEQUENCE:0
UID:486fe5498eca928f9950c9257e64c3b9
URL:http://internationalconferencetibs.sched.com/event/486fe5498eca928f9950c9257e64c3b9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T110200Z
DTEND:20260624T110500Z
SUMMARY:Session Closing & Information to Author
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7B
LOCATION:Virtual Room B\, Manila\, Philippines
SEQUENCE:0
UID:7d7a9ca1392c123b300c7539bb4ecd4c
URL:http://internationalconferencetibs.sched.com/event/7d7a9ca1392c123b300c7539bb4ecd4c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T110200Z
DTEND:20260624T110500Z
SUMMARY:Session Closing & Information to Author
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7C
LOCATION:Virtual Room C\, Manila\, Philippines
SEQUENCE:0
UID:03f721ce2694793752a76e6a886d9a24
URL:http://internationalconferencetibs.sched.com/event/03f721ce2694793752a76e6a886d9a24
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260616T191730Z
DTSTART:20260624T110200Z
DTEND:20260624T110500Z
SUMMARY:Session Closing & Information to Author
DESCRIPTION:\n
CATEGORIES:VIRTUAL ROOM 7D
LOCATION:Virtual Room D\, Manila\, Philippines
SEQUENCE:0
UID:e6c877f6ca0f92413f6900ec8f46193b
URL:http://internationalconferencetibs.sched.com/event/e6c877f6ca0f92413f6900ec8f46193b
END:VEVENT
END:VCALENDAR