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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Authors - Chethana R.M. and Dr S.P. Manikandan 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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
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.
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.
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.
<b>Authors - </b>Mahi Shah, Sachin Pande, Sumitra Jakhete, Emmanuel Mark<br /> <b>Abstract - </b>Brain-Computer Interfaces (BCIs) operate as systems that translate brain signals into digital commands. They provide a non-muscular channel of communication for individuals with profound motor disabili ties. Cerebral Palsy (CP) is a neurological condition that impairs move ment and muscle tone, frequently making physical or verbal expression difficult. This paper reviews the current state of BCI technology and, building upon these insights, introduces a framework for a non-invasive, low-cost BCI communication system tailored specifically for children with CP, addressing the limited accessibility of assistive communication technologies in low-resource environments. The proposed seven-stage framework targets these ongoing challenges by incorporating OpenBCI hardware, adaptive signal processing, and gamified interfaces. This processing pipeline converts neural signals into structured communication outputs, enhancing accessibility and engagement for CP children. To assess the feasibility of the proposed framework, an offline analysis was conducted using a publicly available EEG dataset. A Linear Discriminant Analysis (LDA) classifier y a classification accu racy of 62.5% and an Information Transfer Rate (ITR) of 11.4 bits/min, demonstrating the computational viability of the approach. The modular design offers scalability, though its efficacy requires further validation in real-world pediatric settings. In summary, this work bridges theoretical insights with practical innovation, offering a promising step toward empowering CP children. While limitations in real-world testing remain, the framework lays a foundation for future refinements. Successful implementation could significantly improve independence and quality of life, marking a milestone in inclusive assistive technology.
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.
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.
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.
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.
