Fish Disease Classification using Transfer Learning with ResNet50
[manuscript] 2026
Manuel Olalo Jr., Andrew Canlas, Jodd Villegas · Mapúa University
Developed an image-based classification system for detecting common
Tilapia diseases using a ResNet50 transfer learning approach, achieving
improved accuracy through image preprocessing, data augmentation, and
comparative model evaluation against a baseline CNN.
Computer Vision · Deep Learning · Transfer Learning · ResNet50 · Aquaculture
Manuel Olalo Jr., Daphnie Abano, John Lam, Kacey Vidal · Mapúa University
Presented WikaSaya, a gamified mobile learning app for children aged
2–8, designed to enhance Filipino language literacy and cultural
awareness. Using the Design Thinking Approach and feedback from parents
and teachers via the Six Thinking Hats method, the study highlights the
app's potential while addressing UX and screen-time considerations.
Educational Technology · Gamification · Mobile Learning · Design Thinking · Filipino Language · Early Childhood Education
Manuel Olalo Jr., Raymond Cruz, Dylan Magana, Paul Tuason · Mapúa University
Analyzed global video game sales using a 16,000+ row Kaggle dataset to
uncover sales patterns across regions and key factors influencing
success. The study provides actionable insights for publishers on
market strategies, game development, and product release planning.
Data Analysis · Video Games · Market Research · Business Intelligence · Gaming Industry · Kaggle Dataset
Manuel Olalo Jr., Paul Tuason, Andrew Canlas, Arjun Bali · Mapúa University
Applied AI and machine learning models, including Random Forest,
Logistic Regression, and deep learning, to analyze socioeconomic
indicators from the APIS dataset and predict poverty status among
Filipino households. Findings highlight critical factors and
demonstrate AI's potential to inform targeted policy.
Artificial Intelligence · Machine Learning · Socioeconomic Analysis · Poverty Prediction · Data-Driven Policy · Random Forest · Deep Learning