Scopus ID: 105031358679
2025 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), (2025), pp. 227-232
Conference Paper | Published: December 5, 2025
Abstract
The integration of artificial intelligence (AI) into computational materials science (CMS) has introduced powerful approaches for accelerating the discovery and optimization of advanced energy materials. As energy demands shift toward renewable systems, the development of efficient materials for batteries, fuel cells, and electrocatalysts becomes increasingly critical. This paper systematically reviews recent AI methodologies applied within CMS, particularly those leveraging density functional theory (DFT), molecular dynamics (MD), and kinetic Monte Carlo (KMC) simulations. Emphasis is placed on the use of machine learning (ML) models, including supervised learning, deep learning, and hybrid strategies for property prediction, structure optimization, and inverse design. The review categorizes current applications across key energy technologies and discusses how AI is reshaping material screening and development pipelines. It concludes with an outlook on future directions, highlighting the need for standardized datasets, interpretable models, and physics-informed frameworks to improve predictive accuracy and facilitate AI adoption in practical materials research.
Scopus ID: 105031391365
2025 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), (2025), pp. 221-226
Conference Paper | Published: December 5, 2025
Abstract
Artificial Intelligence (AI) is transforming the prediction and optimization of advanced energy materials by enabling accurate, scalable modeling beyond traditional methods. This review evaluates recent AI applications—including Graph Neural Networks (GNNs), Convolutional and Recurrent Neural Networks (CNNs, RNNs), tree-based ensembles, and Gaussian Process Regression (GPR)—for forecasting performance metrics such as overpotential, conductivity, capacity, and degradation. GNNs achieved R2 > 0.90 in structure-sensitive tasks; LSTM models predicted battery degradation with <10% error; and tree-based models balanced accuracy (MAE < 0.15 V) with interpretability. GPR excelled in low-data regimes via uncertainty quantification. Hybrid and physics-informed models improved generalizability and data efficiency. While challenges remain in data quality and integration with experiments, emerging strategies like autonomous labs and generative design offer promising advances. This review provides comparative benchmarks and highlights pathways for robust AI-driven materials discovery.
Scopus ID: 105030208463
2024 IEEE 16th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2025), pp. 1-6
Conference Paper | Published: December 3, 2025
Abstract
URBANIHAN is a capstone project made by Digital Dino wherein the project is a 2D animated informative video and website that is made to inform the youth into the practice of urban gardening benefits and projects and programs made from the said practice. The project consists of five episodes with each story highlighting the benefits of urban gardening as well as showcasing the projects and programs implemented by the client, Agricultural Training Institute (ATI). The study aims to inform and increase the youth enthusiasm on urban gardening. The objective of the study is to inform the youth regarding the practice of urban gardening and how it benefits the people mentally, financially, economically, and socially, and education. Additionally, a website is designed using a web builder that serves as additional information about urban gardening using the supplementary materials and information provided by ATI and their urban gardening programs and projects for the community. The target audience of the study are the youth ages 15 to 30. The methodology used for the study is a combination of qualitative and quantitative approach to gather the necessary data through interviews for subject experts and surveys for the target audience of the study. As per the findings, a weighted mean of 4.51 has been acquired post-assessment, which is a significant improvement on the mean of 3.82 from the pre-assessment; indicating the effectiveness of the project corresponding to its objective. In conclusion, the videos produced provide sufficient information about urban gardening and its benefits, as well as the client. The website is also able to provide additional information about urban gardening through the supplementary materials and the previous projects mentioned in the animated videos.
Scopus ID: 105030206664
2024 IEEE 16th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2025), pp. 1-5
Conference Paper | Published: December 3, 2025
Abstract
This study aims to evaluate the developed predictive dropout risk model in the Alternative Learning System (ALS) by analyzing various demographic, socio-economic, academic, and behavioral factors. The early identification of students who are at risks in dropping out is crucial in order to provide necessary academic intervention programs. The researcher used Knowledge Discovery in Databases (KDD) as methodology in the evaluation of the predictive models. Using Gradient Boosting Decision Trees (GBDT) for predictive modeling. Key findings highlighted that with both classes achieving an F1-score of 93% which demonstrate a balanced performance between precision and recall for both positive and negative classes. In summary, the overall evaluation of the system is 3.59 which indicates that they system can be used for deployment and maybe further be improved.
Scopus ID: 105030206133
2024 IEEE 16th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2025), pp. 1-6
Conference Paper | Published: December 3, 2025
Abstract
This study centers on the essential role played by Speech-Language Pathologists (SLPs) in the diagnosis and treatment of speech and language disorders within the Philippines. It underscores the significant difficulties resulting from the limited availability and effectiveness of Augmentative and Alternative Communication (AAC) tools, particularly in the context of the Filipino language. These limitations impede the progress of Filipino children struggling with speech delay disorders. The study aims to develop AAC software integrated with an intelligent tutoring system in Filipino. This innovative approach incorporates Filipino AAC tools such as AAC boards, assessments, client management, and identification of rapidguessing behavior on AAC assessments on different difficulty levels using a decision tree algorithm, providing a structured and personalized therapy approach. The software was evaluated using FURPS with a total of 50 participants, whom are the 30 or 60 % speech-language pathologists, 8 or 16 % Information Technology and Computer Science (IT/CS) professionals, 2 or 4 % CS Professors, and 10 or 20 % Parents/Guardians. The computed Cronbach's alpha (α
) was 0.95 which indicates the FURPS instrument has excellent internal consistency. The grand mean of the software evaluation was rated at 4.63 which highlights the generally positive evaluation of the system. Precision, recall and F1-score assess the model's performance in binary classification. For the class labeled “0,” the model achieved a precision of 0.99, a recall of 1, and an F1 score of 0.99. This indicates that the model has high accuracy in predicting instances belonging to class “0.” For the class labeled “1,” the model achieved a precision of 0.95, a recall of 0.92, and an F1-score of 0.93, indicating slightly lower performance than class “0.” The findings of this study and the developed software have significant implications in the field of AAC. Additionally, this study's contribution serves as a foundation for future advancements in AAC-related technologies, driving innovation and improvement in the field.
) was 0.95 which indicates the FURPS instrument has excellent internal consistency. The grand mean of the software evaluation was rated at 4.63 which highlights the generally positive evaluation of the system. Precision, recall and F1-score assess the model's performance in binary classification. For the class labeled “0,” the model achieved a precision of 0.99, a recall of 1, and an F1 score of 0.99. This indicates that the model has high accuracy in predicting instances belonging to class “0.” For the class labeled “1,” the model achieved a precision of 0.95, a recall of 0.92, and an F1-score of 0.93, indicating slightly lower performance than class “0.” The findings of this study and the developed software have significant implications in the field of AAC. Additionally, this study's contribution serves as a foundation for future advancements in AAC-related technologies, driving innovation and improvement in the field.
Scopus ID: 105030202679
2024 IEEE 16th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2025), pp. 1-4
Conference Paper | Published: December 3, 2025
Abstract
The project aims to create an AR mobile application about Fort Santiago. This mobile application features the 3D models of the past and present versions of different historical landmarks within Fort Santiago using augmented reality. This also includes 2D map of the location of the historical landmarks as well as audio narration and 3D animation. A promotional website with content management system was also developed to promote the application to the public. To prove that the application is working, the researcher conducted a survey with a total of 61 respondents, consisting of 41 Visitors/Tourists, 5 Intramuros Administrations Staff and Employees, and 15 IT Experts. Based on the results of the survey, the research was deemed to improve its overall capabilities, features, and performance according to the feedbacks provided by the respondents. There were also recommendations for adding notification features for future events related to Fort Santiago and adding gridlines for augmented reality to aid users in precisely scanning landmarks. In addition, adding more featured landmarks and interactable figures could enhance the experience and interactivity of the application as well as the engagement of visitors within Fort Santiago.
Scopus ID: 105030127070
2024 IEEE 16th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2025), pp. 1-6
Conference Paper | Published: December 3, 2025
Abstract
Rice is one of the essential food crops consumed by every Filipino due to it being a good source of energy and protein. However, well-milled rice mostly encounters rice weevil (Sitophilus oryzae) invasion during their prolonged storage, causing severe damage to rice grains. Several solutions have been proposed to address the problem, but these solutions may harm the consumers and oftentimes, lengthy, and tedious. This study developed an automated rice sieving device (ARSD) with a heat-controlled system for the reduction of Sitophilus oryzae, programmed in Arduino IDE (C++) for automation and genetic algorithm using Python for optimum angle of inclination for sieving. Results showed that the device can effectively and efficiently separate rice weevils and rice grains at sieving inclination of 15 degrees and can eliminate rice weevils within 1 minute at constant temperature of 54°C, exhibiting a 100% mortality rate. The optimum angle of inclination at 15.7° is obtained from the genetic algorithm using Python. Furthermore, at optimum angle of inclination and 1 minute heating time, the device is reliable in sieving 1 to 3 kilograms in four rice varieties namely Malagkit, Denorado, Angelica and Sinandomeng with an average percentage value of 97.9167, 96.1458 and 92.6042 with respect to their respective rice weights (1 kg, 2 kg, and 3kg), proving that ARSD is a potential solution in addressing rice weevil problems.
Scopus ID: 105030132921
2024 IEEE 16th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2025), pp. 1-6
Conference Paper | Published: December 3, 2025
Abstract
A sustainable electronics engineering program effectively prepares graduates to tackle the changing technological, environmental, and societal challenges. In this context, this study examines the relationship between Outcome-Based Education (OBE), quality assurance mechanisms, and student performance in the Electronics Engineering Licensure Examination with the goal of enhancing the development of the program and making it more sustainable. To do this, the paper analyzed a five-year dataset to examine key factors such as accreditation by the Philippine Technological Council (PTC), international rankings (QS and THE), and recognition as Centers of Excellence (COE) or Centers of Development (COD) by the Commission on Higher Education (CHED). Regression modeling of the data gathered revealed that the linear interaction model most effectively predicts student performance, with an R-squared value of 0.85, highlighting the emphasis on OBE and quality assurance to improve academic results. The study concluded that emphasizing interactions among program attributes can guide curriculum revisions to enhance student success and ultimately, its sustainability. It suggested that future studies integrate machine learning (ML) techniques to improve the predictive capabilities of model to enhance quality assurance measures. This may be done by utilizing ML methodologies from related fields such as human detection systems and ECG analysis and apply it to educational research. Such an implementation can enhance data-driven decision-making processes, thereby improving the quality of education and student performance in the Electronics Engineering Licensure Examination and ultimately, making the program more sustainable.
Scopus ID: 105030135995
2024 IEEE 16th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2025), pp. 1-4
Florante D. Poso &
Jaychris Georgette Y. Onia
Jaychris Georgette Y. Onia
Conference Paper | Published: December 3, 2025
Abstract
In the Philippines, a country characterized by mountainous landscapes and frequent intense weather events such as typhoons and monsoons, the issue of slope failures has become increasingly pressing. This study investigated the impact of soil cohesion on slope stability using numerical modeling. The SLOPE/W software and Morgenstern-Price method were employed to simulate various slope scenarios with varying cohesion values. Results indicated a strong positive correlation between cohesion and factor of safety (FOS), highlighting the critical role of cohesion in slope stability. Analysis of Variance confirmed the statistical significance of cohesion variations on Factor of Safety. The findings underscore the importance of incorporating cohesion in geotechnical design and slope management, especially in regions like the Philippines with diverse topography. Future research should explore the combined effects of other soil properties and validate numerical results through field testing. By considering cohesion, engineers can optimize slope safety and minimize the risks of slope failures.
Scopus ID: 105030147183
2024 IEEE 16th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2025), pp. 1-5
Conference Paper | Published: December 3, 2025
Abstract
The widespread use of Liquefied Petroleum Gas (LPG) in households and industries presents a significant risk due to its highly flammable nature, making early detection of leaks for preventing accidents. Traditional LPG detection methods often rely on manual monitoring, which may not provide timely alerts or automatic responses to prevent accidents. The Internet of Things (IoT) refers to a network of physical objects, or “things,” embedded with sensors, software, and other technologies that enable them to connect and exchange data with other devices and systems over the internet. The advantage of the Internet of Things (IoT) offers new opportunities to safety systems through real-time monitoring and remote alerts. By integrating IoT technology with LPG leak detection, it is possible to create a responsive and reliable safety system. This device focuses on the development of an IoT-based LPG tank leakage detection system with alarm and auto-off system, designed to detect leaks, sound an alarm, and automatically shut off the gas supply or even remotely turn off the valve through an android-based application to prevent accidents. The system uses IoT for real-time data transmission and remote monitoring, allowing users to receive instant notifications and take immediate action, even when away from the premises. The system uses gas sensors to continuously monitor LPG levels, triggering an immediate response in the event of a leak. Integrated with IoT technology, the system provides real-time alerts to users via android-based application, ensuring prompt action.