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. Limited research on Cebuano has hindered linguistic documentation and understanding of its grammar and vocabulary. This study introduces a Cebuano POS tagger using the Hidden Markov Model (HMM) to improve Cebuano text processing. The researchers also propose a method for handling unfamiliar words. Results show the algorithm performs well on a news text corpus of 25,000 datasets, with an accuracy of 84 %, precision of 80%, recall of 81.52%, and F1-score of 82%. These outcomes demonstrate the algorithm's effectiveness in addressing language challenges in specific genres. Additionally, the research contributes to the Sustainable Development Goals (SDGs) by promoting linguistic diversity and fostering inclusive language technologies. The study provides insights into Cebuano's linguistic traits and grammatical structures, offering a foundation for further research in natural language processing.

Energy consumption significantly increased in recent decades, notably at the household level, due to economic development, rising population, and technological advancements. To address this sustainability concern, peer-to-peer energy sharing service (P2PESS) is introduced as a solution to household level energy needs. However, P2PESS has yet to be fully explored in terms of development and adoption. As such, this study attempts to provide an understanding of the adoption intention on artificial intelligence (AI) in P2PESS a developing country. This study is realized by developing an extended adoption intention model analyzed through partial-least squares - structural equation modeling (PLS-SEM). Results show that attitude is the most significant predictor of AI in P2PESS adoption intention. This study also reveals that the trust dimension has the strongest effect on attitude, while attitude toward use has the strongest effect on behavioral intention. Also, this study confirms ease of use and usefulness as critical factors in adoption intention. Meanwhile, AI-anxiety is the least significant predictor in the model. Finally, this study is the first evidence of AI in P2PESS adoption intention from the perspective of household level users.

Sustainability is a present concern in many developing countries, where the role of the household is pivotal in realizing its benefits. This study aims to explore artificial intelligence in green energy technologies (AIGET) adoption intention among household-level respondents selected in the National Capital Region (NCR), Philippines. The study has 446 respondents and analyzed using partial least squares and structural equation modeling approaches (PLS-SEM). Among the factors tested, results revealed that perceived usefulness is the strongest predictor of AIGET adoption intention. Factors such as usefulness, ease of use, subjective norms, and perceived risk have a positive effect on attitude. This confirms that attitude has a positive impact on behavioral intention on AIGET. Finally, this study shows that household-level participants have a positive interest in adopting AIGET, considering its usefulness and ease of use. This study presents useful theoretical and practical contributions to further its uptake in the Philippines and other developing countries.

Sustainability in transportation technologies is growing in all parts of the world through electric and micro-mobility sharing services. As such, there is a need to explore the factors that influence its adoption and use behavior. However, this is relatively underexamined in many developing countries. This study attempts to understand the intention and use behavior of micro-mobility as a service (MaaS) in the Philippines, a developing country. This study used survey data, and analysis was performed using partial least squares and structural equation modeling (PLS-SEM). Results show that performance expectancy is the strongest predictor of intention, while satisfaction is the least significant predictor. Factors such as social influence, price value, and habit have a positive effect on intention. Overall, the predictive model is explained by the coefficient of determination, revealing that behavior intention, satisfaction, and use behavior have large predictive relevance. This study provides theoretical and practical implications for further micro-mobility research in the future.

Creativity has long been viewed as the bastion of human expression. With the advent of generative artificial intelligence (AI), there is an emerging notion of artificial creativity that contests traditional perspectives of artistic exploration. This paper explores the complex dynamics of this evolution by examining how generative AI intertwines with and transforms the art world. It presents a comprehensive analysis of the challenges posed by generative AI in art, from questions of authenticity and intellectual property to ethical dilemmas and impacts on conventional art practices. Simultaneously, it investigates the revolutionary opportunities generative AI offers, including the democratization of art creation, the expansion of creative boundaries, and the development of new collaborative and economic models. The paper posits that the integration of generative AI in art is not just a technological advancement but a significant cultural shift, which necessitates a reevaluation of our understanding of art and the artist. It concludes with a forward-looking perspective, advocating for a collaborative approach to harness the potential of this technology in enriching human creativity and ensuring the vibrant evolution of the art world in the era of AI-driven generation.

The application of natural fibers in reinforced composites to create sustainable materials is gaining more attention due to their appealing attributes, such as low cost, low density, and good mechanical properties. The development of Self-Compacting Concrete (SCC) in the construction industry continuously improved over the last decade due to its advantage of having self-consolidation capability. However, a lack of studies on how SCC behaves when introducing natural fiber still arises. Hence, the present work investigates the effect of Coir fiber (CF) as reinforcement to SCC. In addition, this study provides information on the extraction process, surface characterization using Scanning Electron Microscopy (SEM), and tensile strength of the Coir fiber. Finally, the coir fiber was applied to SCC by varying the dosage ranging from 0%, 1%, 1.5%, and 2% by weight of cement to determine the fresh properties and compressive strength. The fresh properties were assessed using slump flow, T500, L-box, and GTM Screen stability tests. Furthermore, the compressive strength of the new composite was determined after 28 days. The results reveal that adding coir fiber significantly affects the fresh properties of SCC because of its hydrophilic nature. Despite the reduction in the flowability, passing ability, and segregation resistance as the fiber increases, the SCC with CF is within the acceptable ranges specified on the EFNARC standard except for the mixture with 2% coir fiber. Additionally, incorporating 1% coir fiber achieves 12.84% higher compressive strength with less cracking pattern and failure mode. Thus, it emphasizes that it can be utilized fully in the construction industry to reinforce SCC.

This project was pushed through to engage people towards proper waste collection, through the utilization of mobile devices of the communities in different municipalities. The study aims to develop and implement a sustainable and efficient waste management collection system by informing the residents of the garbage truck collection schedule available on their mobile devices. Additionally, the platform utilizes optimization and decision support algorithms, including queuing algorithms, to receive and review complaints efficiently. The researcher employed an incremental software development methodology, allowing the software to be developed and tested even when requirements are still evolving. The study is descriptive-correlational, as it involves evaluating the developed system based on feedback from expert respondents. The evaluation yielded an overall mean performance score of 4.75, interpreted as “Strongly Agree,” indicating that the system is well-prepared for deployment.

Children in street situations are prone to physical and emotional abuse, exploitation, medical and financial problems. This research aims to examine the root causes of children in street situations' deprivations, to contribute to the welfare of children in street situations, and to propose strategies for addressing the matter through a multimedia project, specifically by a hybrid 2D & 3D educational animated series and website. It will also explore the impact of educational animated series and websites that will provide a comprehensive understanding of the challenges faced by children in street situations. It serves to raise awareness, promote understanding, and potentially generate solutions to improve the lives of children in street situations. This multimedia project can be an educational tool to various NGOs and organizations that cater to children in street situations. This was found to be effective by providing an educational series and website with information on how to access basic needs and to provide a safe and supportive environment for children in street situations. The study utilizes a mixed qualitative and quantitative research design, it provides valuable insights into the challenges through interviews, pre-assessment, post assessment, and survey questions. The study has important implications for policymakers, educators, government, and practitioners working with children in street situations and highlights the importance of providing a safe and supportive environment for children in street situations to thrive.

This study enabled the researcher to create a graduate tracer monitoring platform. It aimed to provide a centralized channel to monitor institutions' graduates in terms of their job employment, to assess academic programs using modified instruments so necessary interventions may be provided, and to provide a matching algorithm that can be used both by industry partners and respective alumni. This study employed a Decision Support System and mapping recommendation analysis using a rule-based algorithm to evaluate the results of alumni program evaluation on five areas or dimensions, namely curriculum, faculty, facility, laboratory, and student services. It sets the threshold to determine if the results of the areas mentioned above are beyond the passing rate and implements the interventions for each area. The content management system was also used in this study to change the contents of the Alumni Program Evaluation, the interventions, the threshold, and many more. Based on the results, no intervention must be implemented in all areas/dimensions since the mean and the composite mean were more than the 4.0 threshold that was set in the proposed system. The overall rating of the respondents using the technology acceptance model numerical rating is 4.42 with an interpretation of “Agree.” As observed all criteria are rated either agree or strongly agree which indicates a high standard has been set in the development of the system. This means that the system is ready for deployment.

This study explores the beneficial uses of Microsoft Copilot as a support tool for Baliwag Polytechnic College instructors' lesson planning and activity design. Researchers evaluate the influence of Copilot on the creation of instructional content by examining the experiences and opinions of educators. The study demonstrates the advantages, difficulties, and opportunities for customization that come with incorporating Copilot into the curriculum. The results indicate that Copilot can significantly improve the effectiveness and caliber of lesson design, but also highlight certain implementation issues. This research offers insights into the future of technology-enhanced education and contributes to the expanding body of research on AI-assisted teaching strategies.