Envergan faculty’s research collaboration notches another publication

Through research collaboration with other esteemed universities, two Envergan faculty members produced another research publication at the 2nd International Conference on Information Technology Research and Innovation (ICITRI) in August 2023.

The research, "Wind Speed Prediction Using Gaussian Process Regression: A Machine Learning Approach," had Dr. Pedro Jose De Castro, a faculty member of the College of Arts and Sciences, and Dr. Ronaldo Maano, a faculty member of the College of Engineering, as co-authors. The main author is Pitz Gerald Lagrason, while the other co-authors are Ace Lagman, Dr. Marmelo Abante, John Heland Jasper Ortega, Roland Calderon, and Manuel Garcia, who all came from different universities in the country.

Their study delved into Philippine climate patterns using data gathered from PAGASA. The information was employed to train and test a machine-learning model for wind speed prediction.

The research event was organized by Universitas Nusa Mandiri and sponsored by the Institute of Electrical and Electronic Engineers (IEEE) Indonesia Section, and adopted a hybrid format, allowing the researchers to present their study via live online conferencing.

Dr. De Castro shared his insights from the experience he gained.  “This series of conferences functioned as a platform for the exchange of knowledge and research in the field of computer and information science. It provided an opportunity for researchers and professionals from academic and industrial backgrounds to come together and discuss the latest advancements in the field,” Dr. De Castro reported.

“This implies that the research skills of the University brought honor not only to the country but also on a global scale,” Dr. De Castro added.

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• SDG 4: Quality Education - The publication and presentation of the research at the ICITRI conference showcase the advanced research capabilities of Enverga University faculty members, highlighting the institution's commitment to quality education and research excellence.
• SDG 9: Industry, Innovation, and Infrastructure - The study on wind speed prediction using machine learning represents a significant contribution to technological and scientific innovation, reflecting advancements in industry practices and infrastructure.
• SDG 13: Climate Action - By analyzing Philippine climate patterns and employing data from PAGASA to improve wind speed prediction, the research contributes to climate action and better understanding and managing climate-related challenges.
• SDG 17: Partnerships for the Goals - The collaborative effort with various universities and the involvement of international organizations such as IEEE Indonesia Section exemplify the importance of partnerships in advancing research and sharing knowledge on a global scale.