The world is rapidly changing and experiencing a rapid increase in population, especially in cities and urban areas. The growth in population in these urban areas results in a need for a more competitive and sustainable system. In the onset of the fourth industrial revolution, the trend in equipping these cities with advanced mechanisms in improving the quality of life and service in these cities is needed. In this study, a neural network - based approach for factor prioritization was implemented to determine the most influential factor in the smart city (SC) development implementation in the Philippines. Using the neural network internal characteristics including the Levenberg-Marquardt (LM) as the training algorithm (TA) and the hyperbolic tangent sigmoid (HTS) as the transfer function (TF). The study utilized the 18-37-1 network structure for the neural network model with an R value of 0.95003 and MSE of 0.032609. The connection weights (CW) from this network were utilized to calculate the relative importance (RI) of the factors affecting the smart city implementation through Garson's Algorithm (GA). The results of the study revealed that the most influential parameter (MIP) to the smart city implementation is the SCPD2 - analyzing solutions fit with strategic objectives. Moreover, the results and findings of the study could assist the city planners and SC strategy development authorities in the integration of different systems in the SC implementation.

Loan management in transportation cooperatives involves the assessment, approval, disbursal, and monitoring of loans for various member needs, ensuring compliance with regulations, and providing support throughout the loan lifecycle. The traditional manual processes and paperwork used in loan management are prone to errors and data redundancy, are time-consuming and inefficient, lack transparency, and cause difficulty in data analysis and reporting, highlighting the need for a proficient Loan Management Website and Mobile Application. This study presents the development of a comprehensive system that efficiently handles data, integrates the credit scoring system, and leverages the FICO score for accurate loan amount recommendations, enhancing decision-making and enabling improved money monitoring, risk management, and record storage. The system's quality was evaluated using ISO 9126's software characteristics such as functionality, usability, reliability, and portability, yielding an overall mean score of 4.71 for the web application and 4.68 for the mobile application, indicating its seamless facilitation of loan transactions. The integration of additional technologies like Decision Support and SMS notifications enhances risk management and financial transparency, fostering increased capital generation for cooperatives, thereby providing opportunities for business growth and sustainability. The developed Loan Management System offers a streamlined approach compared to traditional methods, facilitating faster loan application, approval, and payment processes. Overall, this research contributes to the advancement of loan management practices in transportation cooperatives by leveraging technology, enhancing decision-making processes, and promoting financial efficiency and transparency.

The adoption of information technology in healthcare has resulted in novel solutions for improving patient care and operational efficiency. This study details the design and implementation of a web-based clinic management system augmented with sentiment analysis to evaluate and enhance patient satisfaction. The system utilizes natural language processing and machine learning to autonomously assess patient feedback, understanding feelings (negative, positive, and neutral) regarding critical aspects such as waiting times, doctor-patient interactions, care efficacy, and overall clinic experience. The system underwent alpha and beta testing, commencing with controlled trials and then involving real-world evaluations with clinic attendants, physicians, and patients. An evaluation conducted in a dermatology clinic revealed the system's effectiveness in detecting service deficiencies and informing enhancements. Thus, this study suggests that the integration of sentiment analysis in clinical management systems enhances data-driven decision-making, hence improving patient experiences and optimizing operations.

Generative AI is transforming multimedia arts education by streamlining creative workflows and expanding artistic possibilities. Its integration into digital platforms allows students to boost productivity and explore new creative techniques, fostering innovation in artistic expression. However, concerns remain about originality, artistic authenticity, and the diminishing role of traditional skills. Using the Task Technology Fit Model, this study finds that while students recognize the efficiency and accessibility of AI tools, they remain cautious about their long-term effects on creative growth. Sentiment analysis reveals a generally positive attitude toward AI adoption, although ethical concerns such as copyright issues and fair attribution continue to be debated. These findings highlight the need for balanced educational strategies that maximize AI’s benefits while preserving essential artistic foundations. Future research should focus on developing pedagogical frameworks that integrate AI as a tool for enhancing rather than replacing human creativity. This approach will ensure that technological advancements support artistic expression without compromising its authenticity.

Biochar also known as ‘biocarbon’ or ‘biocoal’ is a material that has a charcoal similar property. It is obtained from thermolysis (pyrolysis) of biomass feedstocks and plant matters. It can help the process of eliminating carbon dioxide from the atmosphere. The biochar was considered as waste by industrial plants and considered no additional cost except for the transportation. Biochar was tested for its chemical properties in Department of Science and Technology as a parameter for Simapro. Environmental and health impact were analyzed in this study for concrete with biochar as partial replacement for cement. Different mixtures with zero percent to twenty percent biochar replacement was simulated using life cycle assessment with the help of Simapro. Different sources in Luzon island, Philippines were gathered and found out that sources in southern part of Luzon is the best sources for biochar because of its near location that decreases the effect of transportation. Also, concrete with biochar replacement with or without considereing the effect of transportation yields greater health and environmental impact compared to mixture without biochar replacement.

The integration of Internet of Things (IoT) technology in the agricultural sector using advanced sensor systems has garnered significant interest in the recent year, especially in promoting food security. This study shows an application of this to Spondias purpurea (Philippine Pias Prunes), an important fruit in the Philippine Archipelago. The developed system places significant importance on the connection of Internet of Things (IoT) devices and the Google Cloud Platform. This integration enables real-time monitoring, data storage, and analysis, therefore providing valuable insights into enhancing the drying process and mitigating spoilage by maintaining moisture levels within the recommended range of 12-14%. The technology provides farmers with the opportunity to extend the shelf life of the prunes, reduce food wastage and increased profitability. While the system focused on Spondias purpurea, the system is highly adaptable and scalable to other fruits and crops. The research employed a DHT11 sensor that is linked to a Raspberry Pi Microcontroller, together with a Google Cloud-Based Platform for the purpose of data storage and processing. Results of the experiments indicate that the temperature measurements remain consistent at varying conditions. Moreover, the humidity levels remain to be high while the prune’s moisture content continue to be steady. To enhance the system's functionality, future endeavours should focus on integrating the system with other agricultural processes. Additionally, it is recommended to broaden the scope of the cost-benefit analysis by considering aspects such as the initial investment, maintenance costs, energy consumption, and potential rewards in terms of product quality, loss reduction, and increased output.

This study introduces a centralized Business Management System (BMS) tailored for small and mediumsized enterprises (SMEs), with an innovative approach due to the integration of a foot traffic detection system through video processing. The system allows businesses to access common business management features such as point of sales, staff scheduling, inventory management, and reports. With the integration of YOLOv7, foot traffic detection for customer count is made possible through LACAD. By automating data collection and providing foot traffic counts, alongside graphical reports, the system empowers SMEs to make better decisions for their businesses. This research highlights the strategic advantage of leveraging foot traffic insights to drive performance and competitiveness in the modern business landscape. As a guide for the study the researchers used Scrum methodology. The study was then evaluated through a quantitative survey using FURPS with 12 IT professionals and 7 beneficiaries as the respondents where the calculated total weighted mean for both the respondent types resulted in 4.60 which means that the users “Strongly Agree” with the system's overall components.

This study addresses the challenge of identifying students at risk of academic underperformance in a BS Information Technology program. Using a predictive analytics framework aligned with the Philippine Computer Society’s Information and Computing Accreditation Board (PICAB) Criterion 3 on Student Outcomes, a decision tree model was developed in Python using Google Colab. The dataset included grades from key academic indicators such as OJT, Capstone, GPA, Programming, Math, Ethics, and Communication. The trained model achieved an accuracy of 83.33%, effectively distinguishing patterns of academic risk. Specifically, students with Capstone grades of 4.00 or higher, or multiple failing grades in core subjects, were frequently classified as "At-Risk." These findings provide actionable insights for academic intervention, curriculum refinement, and program enhancement. The research supports evidence-based decision-making and contributes to Sustainable Development Goal 4 which is Quality Education by promoting inclusive and data-driven approaches to student success.

This research explores how to optimize online learning environments in support of the United Nations' Sustainable Development Goal 4 (SDG 4), which advocates for inclusive and quality education. It specifically focuses on Network Academy platforms and aims to develop a predictive framework for course completion rates, contributing to SDG Target 4. enhancing technical and vocational skills among youth and adults. By adapting the Technology Acceptance Model (TAM) for educational sustainability, the study integrates traditional constructs like Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) with variables such as inclusive course design, quality instructor feedback, and student self-efficacy. This reframing positions technology acceptance not just as a matter of adoption, but as a strategic pathway to meaningful and equitable learning engagement. Using a mixed-methods approach, the research seeks to produce a robust model that informs educators, instructional designers, and platform developers on how to improve online training programs. Ultimately, the study offers practical, evidence-based recommendations for designing online systems that promote inclusive, high-quality education and directly support the 2030 Agenda for Sustainable Development.

This study focuses on the evaluation of a faculty Performance Modeling System, recognizing the critical role of faculty performance in educational quality and institutional success. Although previous research has often concentrated on the technical development and predictive capabilities of such systems, this paper shifts the focus to user acceptance and system efficacy from the perspective end-users, the faculty. To achieve this, the researchers propose and apply a Modified Technology Acceptance Model (TAM) as the theoretical framework for evaluation. This modified TAM incorporates specific constructs relevant to the academic environment and faculty roles, such as perceived impact on teaching effectiveness and perceived relevance to professional development, alongside traditional TAM constructs like perceived usefulness and perceived ease of use. The evaluation methodology involves assessing faculty perceptions and attitudes towards the system, utilizing both quantitative and qualitative data to understand factors influencing its adoption and continued use. The findings are expected to provide valuable insights into the practical applicability and user acceptance of faculty modeling systems, guiding future design and implementation efforts to ensure these tools effectively support faculty growth and institutional objectives.