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.
