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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 - 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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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:
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.
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.
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.
Authors - Sowmini Devi Veeramachaneni 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.
Authors - Marybell Materum, Daniel Dasig Jr, Lucila Magalong, Emelyn Libunao, Shirley Padua, Sonia Pascua, Rizza Gerente and Sharon Sanchez 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.
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.
Authors - John Julius M. Orillana, Loyd S. Echalar 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.
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.
Authors - Carolina Ditan, Daniel Dasig Jr, Sushil Kumar Singh, Isagani Valenzuela II, Catherine Catalan, Bablu Khumar Dhar, Jewelyn Ciocon and Maricris Ediza 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.
Authors - Alyssa C. Vicente, Cedirick Santiago, Elmer M. Alino, Ma. Yvonne Czarina C.Angcaya, Benedict G. Bautista, St. Joseph M. Lumbog 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.
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.
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.
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 - 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.
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.
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.
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.
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.
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.
Authors - 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.
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.
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.
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.
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.
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.
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.
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.
Authors - Bryly Brord Mirah, Anderes Gui 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.
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.
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.
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.
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.
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.
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.
Authors - Christian Vasta, Rolf Dornberger, Thomas Hanne 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.
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.
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.
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.
