UPLB ICS Peak One

As the number of overweight and obese individuals in the Philippines grows, the risk of premature death and health complications also increases. With this in mind, this study developed HealthyPeeps, an online platform designed to provide Filipinos, particularly those interested in healthier lifestyles, with a central place to access and share health and nutrition information. A survey was given to 15 respondents consisting of UPLB students, professors, and alumni to determine the usability of the application. Based on the results, HealthyPeeps achieved a System Usability Scale score of 82, indicating that the application is user-friendly and efficient. HealthyPeeps serves as more than an information hub - it’s a catalyst for positive health changes, encouraging better dietary habits and nutrition awareness among users. Offering culturally relevant content, it empowers users to make informed food choices, potentially improving the overall health of the Filipino community. In summary, HealthyPeeps holds promise as an impactful tool for promoting healthier lifestyles among Filipinos, blending cultural relevance with health promotion.

Datasets in crop image classification often suffer from limitations in quantity, balance, and fidelity to real-life conditions. Models trained using these datasets overfit and offer poor generalizability to actual samples of infected leaves. We evaluate the effectiveness of transformative and generative augmentation in enhancing a limited pest dataset to improve model accuracy. One control and two experimental setups were used. The control setup provided no augmentation to the limited pest dataset. The first experimental setup used random transforms in brightness, rotation, and color shift to expand the dataset. The second experimental setup utilized a generative adversarial network to generate fake images of pests to augment the limtied training set. Three separate models were trained for each setup, getting their accuracy scores as a measure of model skill. To gain a reliable score, the experiment was repeated 1000 times. At α = 0.05, the differences in the mean classification accuracy scores of the three experimental setups were significantly different. In particular, transformative augmentation performed the best in boosting classification accuracy (α = 0.033) as it helped reduce noise and biases in background, lighting, and angle.

This paper used the methods interpolation, extrapolation and regression for prediction of road traffic volume. The application aims to identify the status of traffic flow of vehicles and predict accurate road traffic volume using the 2012-2020 traffic data (AADT) by MMDA. The application gained an accuracy of 84.96% on the average in 2019 and 2020 traffic volume prediction using 2012-2018 traffic data.

Math-alino is a mobile-based math practice application targeted toward lower elementary students (specifically for grades 1 to 3). With the use of gamified lessons and assessments, it enables users to improve their mathematical abilities. The evaluation score for this app has an increase of 0.11 points from before the use of the application to during its use.

The PRacticum Activity eXpedition and Integration Software (PRAXIS) is a unified dashboard web-application that functions as a platform for conducting different tasks related to the CMSC 198 internship program. It includes support for task management, timekeeping, and messaging functionalities accessible to student interns, company representatives, and practicum advisers. Developed using the MERN stack (MongoDB, Express, React, and Node), the system received a mean S.U.S. score of 86 from twelve evaluators–evenly represented by University faculty members and students. Emphasis on UI/UX improvements is highly recommended for further system development.