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.

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.

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.

Multiple sequence alignment (MSA) is a fundamental technique in computational biology that compares protein, DNA, or RNA sequences to identify regions of similarity reflecting functional, structural, or evolutionary relationships. This systematic literature review examines the diverse land-scape of multiple sequence alignment algorithms, categorizing them based on their underlying approaches and analyzing their strengths, limitations, and applications. We explore seven major categories of alignment methods: dynamic programming, progressive alignment, iterative refinement, Hidden Markov Model-based, consistency-based, structure-based, and machine learning-based approaches. Through comprehensive analysis of recent benchmarks and literature, we identify key innovations, performance characteristics, and emerging trends in the field. This review provides a detailed overview of the evolution of multiple sequence alignment algorithms and their applications in modern bioinformatics.

The accuracy of classification algorithms is significantly influenced by the quality and structure of input data. In this light, effective pre-processing is crucial for boosting the generalization capabilities of supervised machine learning models. This study addresses key challenges in data preparation, including the treatment of continuous attributes, imputation of missing values, and management of high-dimensional features. To overcome these obstacles, we propose an innovative hybrid preprocessing strategy that synthesizes multiple techniques into a unified framework. By tailoring specific methods to the characteristics of diverse datasets, this hybrid approach enhances both the accuracy and robustness of the classification results. Through the promotion of intelligent, data-driven solutions that can be applied in multiple sectors, the findings support the Sustainable Development Goal 9 (SDG 9), which focuses on Industry, Innovation and Infrastructure.

Part of speech tagging (POS) is crucial in natural language processing, identifying the grammatical categories of words in sentences. This research highlights the lack of focus on POS tagging for Asian languages, particularly Cebuano. Inadequate research on Cebuano religious text has hindered linguistic documentation and understanding its grammar and vocabulary. This study introduces a Parts-of-Speech Tagging for Cebuano utilizing a Modified Viterbi Algorithm. The researchers also apply a method for handling unfamiliar words. Results indicate that the algorithm performs exceptionally well on a religious text corpus comprising 50,000 datasets, achieving an accuracy of93%,precision of90%, recall of 90. 52%, and an F1-score of92%. These results highlight the algorithm's effectiveness in tackling language challenges within specific genres. Furthermore, the research supports the Sustainable Development Goals (SDGs) by promoting linguistic diversity and advancing inclusive language technologies. The study also provides valuable insights into Cebuano's linguistic characteristics and grammatical structures, laying a solid foundation for future research in natural language processing.

In this study, we use various RNN architectures namely, RNN, Bi-LSTM, and GRU — alongside BERT to analyze sentiment across university departments. Our aim is a comparative analysis of these models in sentiment classification within education. We collected and pre-processed textual data from multiple departments for balanced training and validation. Results showed that traditional RNNs achieved 90% accuracy, Bi-LSTM 93%, and GRU 89%. BERT, leveraging its Transformer architecture, outperformed with 94% accuracy. These findings highlight the superiority of BERT in capturing complex language patterns for sentiment analysis. This study underscores the potential of advanced neural network architectures to gain insights into departmental sentiments, informing policy decisions and educational strategies. Aligning with sustainable development goals in education, we aim to use AI models to develop effective, inclusive, and responsive educational strategies, enhancing quality and accessibility.

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.

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.