Air quality monitoring has become increasingly critical in urban environments, particularly in densely populated megacities like Manila, Philippines. This research presents the design and conceptual framework for a comprehensive web-based air quality monitoring and mapping system that leverages fuzzy logic algorithms to provide intelligent, real-time assessment of atmospheric conditions across Metro Manila. The proposed system addresses the inherent uncertainties and complexities associated with environmental data by implementing a sophisticated fuzzy inference system specifically calibrated for Manila's unique atmospheric conditions, pollution sources, and regulatory requirements. The research encompasses a thorough analysis of Manila's current air quality challenges, including the identification of primary pollutants such as particulate matter (PM2.5 and PM10), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ground level ozone (O3). The proposed system architecture integrates multiple technological components including a distributed sensor network, centralized data processing infrastructure, fuzzy logic engine, web-based visualization platform, and real-time mapping capabilities. The fuzzy inference system is specifically designed to accommodate Manila's tropical climate conditions, high population density, and diverse pollution sources ranging from vehicular emissions to industrial activities. The methodology incorporates adaptive membership functions that adjust to seasonal variations and local environmental patterns, ensuring accurate and contextually relevant air quality assessments. The system design emphasizes scalability, real-time processing capabilities, and user accessibility through responsive web interfaces optimized for both desktop and mobile platforms. The technical implementation framework encompasses comprehensive hardware specifications for sensor deployment, software architecture for data processing and visualization, database design for efficient time-series data management, and API development for system integration and third-party access. Expected outcomes of this research include improved public awareness of air quality conditions, enhanced decision-making capabilities for environmental authorities, and the establishment of a robust foundation for future environmental monitoring initiatives in Manila and similar urban environments. The fuzzy logic approach provides a more nuanced and human-interpretable assessment of air quality compared to traditional crisp methodologies, enabling better communication of environmental risks to diverse stakeholder groups. This comprehensive study contributes to the growing knowledge in environmental informatics and smart city technologies, demonstrating the practical application of artificial intelligence techniques in addressing real-world environmental challenges. The research provides a detailed roadmap for implementing intelligent air quality monitoring systems in developing urban environments, with particular emphasis on cost-effectiveness, technological accessibility, and community engagement.

In an era of accelerating digital transformation, optimizing resource management through effective data governance has become vital for local governments in developing nations such as the Philippines. This study introduces a data-driven governance platform designed to enhance resource allocation and decision-making in healthcare services through integrated forecasting algorithms and data governance principles. Anchored on a comprehensive framework for local data governance, the system centralizes, analyzes, and forecasts health-related data to support evidence-based planning and resource distribution. Employing both descriptive and developmental research designs, the study developed and tested the DALAY system using forecasting techniques such as exponential smoothing to predict medical supply needs and service demand. The findings demonstrate that integrating forecasting algorithms within a structured data governance framework can significantly improve resource efficiency, transparency, and responsiveness in local government operations. The system thus provides a replicable model for strengthening data-driven governance and optimizing community resource management in the Philippines. The findings indicate that robust data governance can lead to improved operational effectiveness, enhanced accountability, and ultimately better outcomes for citizens of the Philippines. This study aligns with SDG 9 that highlights the role of ICT in modernizing governance, fostering innovation, and improving data-driven decision making.

This study extended a previous paper that focuses on the acceptability of selected Bachelor of Science in Computer Science (BSCS) and Information Technology (BSIT) students on the use of Intelligent Tutoring System (ITS) as an educational technology tool for C/C++ Programming. A one-shot case study research design was carried out in 5 programming classes taught by the author. A Slovin's formula computation from the population was 35.54. A stratified sampling method was employed with the 4 intervals between students to mitigate bias. The study involved 39 participants, out of which 74.36% were male and 25.64% were female computer science and IT students. Utilizing the Technology Acceptance Model (TAM) as an evaluation tool online enabled importing the dataset into IBM SPSS for finding the correlations and factor loading calculations. Cronbach alpha was conducted by the author with a value of 0.947, which signifies the measure of internal consistency. The seven (7) factors of TAM were analyzed to reveal coefficient values for comparisons and derive their relative implications. Research indicates that every factor significantly influences the acceptance of ITS among BSCS and BSIT students. Interestingly, PerUse→Att has the highest coefficient value (0.883) next in the rank was SocNor→Att by a factor of 0.822 signifying their impact on ITS (Att), leaving SocNor→PerEas ranking last amongst relations with a 0.630 coefficient value. Finally, the results implied CS and IT students are open to the notion of incorporating intelligent teaching tools into their laboratory sessions to supplement their programming activity and increase their efficiency when building console applications.

The Digital Academic Information System is an integrated platform designed to support and manage core academic functions, including research, extension, and instruction. The system streamlines research management by tracking proposals, publications, and collaborations; facilitates extension services by organizing community engagement programs and reporting outcomes; and enhances instruction through tools for course management, faculty workload tracking, and student performance monitoring. By centralizing these features, the system promotes efficiency, transparency, and improved decision-making within academic institutions. This study focuses on the evaluation of the developed digital academic system that focuses on research, instruction and extension integration which processes essentials to state universities and colleges. The researcher used the descriptive- developmental type of research. The system provides a real-time overview of the status of the performance and accomplishment of the academic institution in the mentioned areas. The evaluation of a Digital Academic Information System (DAIS) using Agile methodology and software quality model assessment provides a dynamic and structured approach to system development and analysis. Agile enables iterative development with continuous stakeholder feedback, ensuring that evolving academic requirements are met efficiently. By integrating a software quality model—such as ISO/IEC 25010—the evaluation further assesses critical attributes like functionality, usability, reliability, and maintainability The system obtained the overall weighted mean of 3.73 which interpret as Excellent in terms of Product Quality evaluated by the IT Experts. This means that the system is approved by the IT Experts and highly recommended to use.

Effective teaching forms the bedrock of education, directly influencing program accreditation and institutional performance. A competitive and supportive learning environment, fostered by strong faculty performance, is crucial for an academic institution to achieve its vision and mission. This study incorporates Sustainable Development Goals (SDGs) principles, ensuring that faculty performance evaluation contributes to long-term educational sustainability. Addressing the pressing need for robust faculty performance assessment, data mining algorithms are employed to extract insightful information regarding effective instruction, utilizing both structured and unstructured data. The developed system aims to empower institutions to identify their strengths, address areas for improvement, and cultivate continuous growth in teaching and learning processes by discerning trends within faculty data. Furthermore, sentiment analysis methods are utilized to evaluate qualitative input, with Laravel 8.0 serving as the framework for algorithm implementation. Expert evaluations of the system yielded a grand mean score of 4.38, deemed 'Very Acceptable,' thereby affirming its reliability and efficacy in supporting faculty performance reviews and advancing SDG objectives.

Green economy is an approach to economic development while protecting the environment, that focuses on clean energy, resource saving activities, waste reduction and pollution. Several developed countries have aligned their educational focus, integrating outcomes-based education, exposing students to sustainable development goals (SDG), particularly, Green Economy. To date, however, the Philippines, a developing country, is still on its emerging stage of exposing higher education students on green economy, through, outcomes-based education (OBE) implementation. This study aims to predict generation Z's participation in the green economy based on OBE exposure, through survey data and machine learning techniques. Results show that random forest-based model predicts at rate of 95% accuracy, support vector machine (94%), gamma ray boosting (94%), extreme gradient boosting (92%), k-nearest neighbors (89%), k-nearest neighbors (89%), and decision tree (87%). Thus, machine learning models demonstrate the ability to determine participation and non-participation in green economy based on OBE exposure. Research and educational implications are offered.

This study developed the Digital Banks PH Notebook, a mobile application designed to support financial literacy among Filipinos by optimizing savings through high-yield digital banking platforms. The application featured a savings portfolio tracker, interest forecasting calculator, and savings goal management to address gaps in financial planning and savings behavior. Development followed a blended Agile methodology integrating Scrum, Extreme Programming, and Feature-Driven Development, ensuring iterative improvements aligned with user needs. Software quality was assessed using the ISO/IEC 25010 model, while qualitative feedback was analyzed through word cloud visualization to capture user sentiment and key focus areas. Findings indicated that the application effectively enhanced users' understanding of savings strategies and promoted responsible saving practices. The tool successfully connected the opportunities presented by digital banking with the practical requirements of financial education, providing users with actionable insights to manage their savings more strategically. By leveraging agile development practices and rigorous evaluation frameworks, the project demonstrated that technology-driven solutions can play a significant role in advancing financial literacy and supporting sustainable financial behaviors in an evolving digital economy.

An AI-driven academic path forecasting system is proposed to support data-informed advising and early academic intervention in higher education. In the Philippine context, where delayed graduation, student dropouts and lack of personalized academic guidance persist, machine learning in education offers a scalable and intelligent solution. The system combines three educational data mining techniques: a Long Short-Term Memory (LSTM) network for course sequence prediction, a decision tree classifier for student progress classification as regular or irregular and a K-Means clustering algorithm for grouping students based on academic trajectories. These models are developed in TensorFlow and deployed on a web platform built with CodeIgniter, enabling functionalities such as academic path forecasting, curriculum tracking and real-time risk alerts. Evaluation shows that the LSTM model achieves strong precision and recall in predicting next-term courses, while the decision tree classifier accurately detects off-track students with interpretable decision rules. K-Means clustering reveals meaningful groupings aligned with academic outcomes, further supporting early identification of at-risk learners. Confusion matrix analysis confirms high model accuracy across tasks. By integrating AI into higher education through course prediction, student classification and cluster-based insights, the system offers a practical framework for enhancing student success through targeted academic support.

Chloride-induced corrosion (CIC) is a primary reason of deterioration in reinforced concrete (RC), particularly in marine structures which causes cracking, degradation, and decreased service life. Advances in the 4th Industrial Revolution have enabled utilization of machine learning techniques in different fields of civil engineering. This study develops an Artificial Neural Network (ANN) enhanced by Particle Swarm Optimization (PSO) to predict rebar corrosion in polypropylene fiber reinforced concrete (PFRC). Accelerated corrosion tests were performed using the impressed current method on samples with varying polypropylene fiber content, concrete cover (CC), and bar diameter (BD). Experimental results showed that the 3-7-1 network structure (NS) (3 input neurons (IN), 7 hidden neurons (HN), 1 output neuron (ON)) achieved the highest accuracy with correlation coefficient (R) of 0.98969, mean squared error (MSE) of 0.18846, and mean absolute percentage error (MAPE) of 7.832 %. Employing the generated connection weights (CW) from the governing model (GM), through Olden's connection weights approach, observed that the concrete cover had the most significant influence on corrosion (-43.231%), followed by bar diameter (33.717%) and fiber content (-23.052%). It highlights that increasing concrete cover and fiber content significantly reduces corrosion in PFRC, which may be used by civil engineering professionals as it offers insights for enhancing the durability of reinforced concrete structures. This approach supports SDG 9 (Sustainable Development Goal 9: Industry, Innovation, and Infrastructure) by promoting resilient, innovative construction methods and contributes to SDG 11 (Sustainable Development Goal 11: Sustainable Cities and Communities) by enhancing the longevity and sustainability of urban infrastructure.

Part-of-speech tagging (POS) is a vital process in natural language processing, enabling the identification of grammatical categories within sentences. This research emphasizes the lack of attention given to POS tagging for Asian languages, particularly Waray-waray. Limited studies on Waraywaray religious texts have hindered linguistic documentation and the deeper understanding of its grammar and vocabulary. To address this gap, the study introduces a POS tagging system for Waray-waray utilizing a Modified Viterbi Algorithm, which also incorporates a strategy for handling unfamiliar words. Evaluated on a corpus of 50,000 religious text datasets, the algorithm demonstrates outstanding performance-achieving an accuracy of 93%, precision of 90%, recall of 90.52%, and an F 1 score of 92%. These results underscore the algorithm's effectiveness in navigating linguistic challenges across specialized genres. Beyond technical contributions, the study promotes linguistic diversity and fosters inclusive language technologies, advancing the goals of the Sustainable Development Goals (SDGs). Specifically, it enhances language learning and literacy among Waray-waray speakers, supports inclusive education through computational tools for minority languages, and aligns with SDG 4 by providing foundational resources for mother-tongue instruction and educational content development. Additionally, it offers new insights into Waray-waray's grammatical structures, laying a robust groundwork for future linguistic and computational research. Beyond technical contributions, the study promotes linguistic diversity and fosters inclusive language technologies, advancing the goals of the Sustainable Development Goals (SDGs). Specifically, it enhances language learning and literacy among Waray-waray speakers, supports inclusive education through computational tools for minority languages, and aligns with SDG 4 by providing foundational resources for mother-tongue instruction and educational content development. Additionally, it offers new insights into Waray-waray's grammatical structures, laying a robust groundwork for future linguistic and computational research.