Long queues in supermarkets are one of major problems for consumers and retail establishments around the globe. Thus, self-checkouts using barcode scanning have been developed in pursuit of shorter checkout times. However, some customers find these systems to be tedious to use. Furthermore, there is a gap in implementation when it comes to non-barcoded items which require weighing and manual code entry. Thus, deviating from the barcode system and eliminating the need for manual barcode scans may simplify the checkout process by eliminating the need for packaging, weighing, and labeling in advance. This research aims to develop a semi-automated self-checkout system for shopping item classification and price calculation of non-barcode items such as fresh proteins and produce. This was done by developing a computer vision module using YOLOv8 trained on a custom dataset, a timebased digital scale module for real-time weight reading, a price calculation module, and a hardware configuration.

This paper presents ADMUNAV, a mobile navigation system developed to assist pedestrian wayfinding within Ateneo de Manila University. Addressing the challenges of navigating a large, congested campus, ADMUNAV utilizes geospatial datasets and Dijkstra's algorithm to compute optimal walking routes in real time. The application features an offline-capable Realm database, intuitive Android user interface, multi-entrance building handling, and dynamic path recalculations with alternative route suggestions. Performance testing demonstrated 100 % routing accuracy, an average computation time of 98.9 ms, and minimal memory usage across multiple Android devices. By offering a lightweight, infrastructure-independent navigation solution tailored for university environments, ADMUNAV contributes toward enhancing campus mobility and supports Sustainable Development Goal 9 on innovation and infrastructure.

The research was dedicated to devising and implementing a comprehensive monitoring system tailored specifically for the e-jeeps operating within the confines of Ateneo de Manila University. Leveraging advanced GPS sensors, this system enabled the real-time tracking of e-jeepneys while simultaneously providing crucial insights into their speed and estimated time of arrival at various points across the campus. the research unearthed intriguing findings regarding the relationship between passenger count and environmental conditions. While a minimal correlation was observed, it was noted to be contingent upon the specific time of day, indicating potential variations in passenger behavior influenced by diurnal factors. In order to streamline data management and visualization, the researchers capitalized on the robust infrastructure provided by various Google applications and cloud services. This integration facilitated the seamless uploading and synthesis of sensorderived data, empowering the creation of a dynamically updated dashboard providing comprehensive insights into e-jeepney operations The study represents a significant milestone in the realm of transportation management, successfully culminating in the development and deployment of a sophisticated monitoring system.

Diabetes is increasing in both prevalence and incidence worldwide. The International Diabetes Federation (IDF) showed that in 2021, 15.51 percent of the total adult population have diabetes. Current invasive and costly DM detection methods raise the need for more available and accessible non-invasive detection systems for diabetes given the prevalence of diabetes in Filipino adults. The research focused on the development of a mobile application employing Facial Texture Analysis as a novel method for detection of diabetes among Filipino adults, ages 21–40. The mobile application, which was built using Python, allows the user to upload a facial image which the application processes. The photo will undergo resizing, facial block extraction, texture feature extraction using the Gabor filter, and diabetes classification using the Support Vector Machine model, trained with a 70:30 training-to-testing ratio, which was stored in a web server.

Vision impairment is a global issue and a daily struggle for those affected. Activities requiring access to information, particularly shopping, are a major hindrance for people with vision impairment. This study aimed to bridge the accessibility gap for people with vision impairment or PVI by developing assistive technology to aid them in shopping. The developed device used the Raspberry Pi with the YOLOv8 object detection model in eyeglasses form factor to detect shopping items using a custom dataset with Philippine shopping items and output the detected item description as audio feedback.