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.
