Post-disaster reconstruction projects (PDRP) are integral to ensure that a community will recover and return to normal after a major disaster. The project team success (PTS) in PDRPs is essential to ensure that post-construction efforts will be effective and attain its objective of recovery in the community. An Artificial Neural Network (ANN) model was established considering several factors including post-disaster reconstruction project including project manager's leadership style (PMLS), multi-disciplinary project competence (MDPC), project manager's experience and competence (PMEC), high degree of trust within the project management team (HDTPMT), implementing an effective decision (IAED), effective project control (EPC), competent project manager (CPM), project risk and liability management (PRLM), motivated and well-integrated team (MWIT), and team composition (TC). The governing ANN model has a topology of 10-3-1 network structure and showed good performance with correlation plot (R) of 0.99850, MSE and MAPE of 0.00135 and 0.40559, respectively. The relative importance (RI) of the input parameters (IP) was also determined utilizing the connection weights (CWs) via Garson's algorithm (GA). The findings showed that the MWIT factor is the most influential factor (MIF) to project team success in PDRPs. The results in this study could be utilized to focus on improving areas to guarantee the success of PDRPs.

Steel diagrid high-rise structures require repeated finite-element analyses to accurately predict the multi-directional displacements, which is a time-consuming approach for parametric exploration and early-stage design. This paper presents an artificial neural network (ANN) – particle swarm optimization (PSO) informed model for predicting multi-directional displacements of high-rise steel diagrid frames considering different parameters including the number of storeys (NS), diagrid angle (DA), cross-sectional area (CSA), total weight (TW), and mass of the diagrid exterior (MDE). The model was developed from a dataset of 360 simulations from SAP 2000 ranging from 20-80 storeys and 33.69°-90° angles was used to create a Levenberg-Marquardt (LM) ANN with hyperbolic tangent sigmoid (HTS) activation function and 11 hidden neurons. The PSO was integrated into the model to enhance the training by optimizing the weights and biases (WB) of the network. The ANN-PSO achieved excellent model performance results with R values ranging from 0.9931 to 0.9989 and mean squared error (MSE) ranging from 0.000380 to 0.017200. The sensitivity analysis (SA) utilizing Garson's algorithm (GA) revealed that the number of storeys and diagrid angles are primary influencing the X and Y-displacements while the total weight and cross-sectional area were the leading influential factors to the Z-displacement. The proposed ANN-PSO offers an accurate, interpretable and computationally efficient approach for performance-based preliminary design of steel diagrid high-rise structures.

Brain tumor detection using magnetic resonance imaging (MRI) plays a critical role in early diagnosis and treatment planning. However, manual analysis of MRI images can be time-consuming and prone to human error. This study proposes a deep learning framework for brain MRI classification that integrates Contrast Limited Adaptive Histogram Equalization (CLAHE) preprocessing with a Multi-Stage Transfer Learning (MSTL) strategy. The proposed framework evaluates three convolutional neural network architectures, MobileNetV2, ResNet50, and EfficientNet-B0, to analyze their performance in classifying 3D-rendered brain MRI images into tumor categories. CLAHE was applied to enhance image contrast and improve the visibility of structural patterns before training. The MSTL framework progressively fine-tunes pretrained models through multiple stages, enabling better adaptation of learned features to the MRI dataset. Experimental results demonstrate that all three models achieved high classification performance. Among the evaluated architectures, ResNet50 achieved the highest accuracy of 99.06%, followed by EfficientNet-B0 at 98.90% and MobileNetV2 at 98.12%. Training curves and confusion matrix analysis further confirmed stable convergence and strong classification capability across the models. The novelty of this study lies in combining CLAHE-based MRI enhancement with a progressive transfer learning framework to improve deep learning performance in medical image classification. The proposed approach may support AI-assisted diagnostic systems for automated brain tumor detection and improve the efficiency of clinical decision-making processes.

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.

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.

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.

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.

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.

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.

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.