Readwell: A Book Recommender Android Application with a Point-of-Sales System

The COVID-19 pandemic has given people much free time. With this, the researchers want to encourage these people to read instead of scrolling through social media. A barrier to reading for many people is not knowing what to read and disinterest in popular books that they would find when they search online. The existing websites that encourage book reading rely on social networking for their recommendations, while the collaborative filtering algorithms applied to books do not exist in the mobile application form. Readwell is a book recommender Android app with a Point-of-Sales System created using Java, Python, and SQLite databases. The information regarding the books was web scraped from the Goodreads website. It aims to apply the more efficient collaborative filtering algorithm to an accessible mobile application that allows users to directly buy the books they are interested in, thus encouraging the reading and buying of books. The researchers created unit test cases to validate the different functionalities of the application. © 2022 IEEE.

Classifying Chest X-Ray Images using Residual Neural Network

This unprecedented time of the COVID-19 outbreak challenged the status-quo whether it is on business operation, political leadership, scientific capability, engineering implementation, data analysis, and strategic thinking, in terms of resiliency, agility, and innovativeness. Due to some identified constraints, while addressing the issue of global health, human ingenuity has proven again that in times of crisis, it is our best asset. Constraints like limited testing capacity and lack of real-time information regarding the spread of the virus, are the highest priority in the mitigation process, aside from the development of vaccines and the pushing through of vaccination programs. Using the available Chest X-Ray Images dataset and an AI-Computer Vision Technique called Convolutional Neural Network, features of the images were extracted and classified as COVID-19 positive or not. This paper proposes the usage of the 18-layer Residual Neural Network (ResNet-18) as an architecture instead of other ResNet with a higher number of layers. The researcher achieves the highest validation accuracy of 99.26%. Moving forward, using this lower number of layers in training a model classifier, resolves the issue of device constraints such as storage capacity and computing resources while still assuring highly accurate outputs. © 2022 IEEE.

Social Distancing Monitoring and Contact Tracing on CCTV Footage

Social distancing is one of the most effective measures to prevent the spread of the COVID-19 disease. Most methods of enforcing this in the Philippines resort to manual methods. As such, a video-based social distancing monitoring tool can help ensure constant enforcement of social distancing due to the availability and up-time of CCTV cameras in various areas. This can be achieved by using object detection and tracking techniques. Object detection can be used to detect people within an area, and tracking can be used to watch people who get into close contact with one another. Contact tracing can also be performed by processing the social distancing measurements and tracking information. This information can be stored to keep a record of who has a high risk of infection based on who they came into contact with and for how long. We introduce a social distancing monitoring and contact tracing framework using the EfficientDet object detector and DeepSORT tracker. This framework is used to monitor social distancing violations and keep a record of violations associated to the tracked people. © 2022 IEEE.