ABSTRACT
With the recent global COVID-19 pandemic and lockdowns, accreditation delays have become inevitable in lieu of the strict travel restrictions. The usual accreditation inspection process conducted face-To-face was affected. Organizations are shifting to a reliance on technology to adapt to the national emergency. The study aims to bridge the gap by digitalization Professional Regulation Commission's (PRC) monitoring and accreditation system to conduct a virtual inspection and monitoring. With all of these said, the specific objectives of the researchers and developers are to develop an efficient digitized system that captures the original one. In developing the proposed accreditation and monitoring system and document management system (website) for PRC, the group will adapt and take inspiration from the Agile Development Lifecycle methodology, which will help the modification and other functionality of the system by using the iterative style in the development of the system. The proposed digital monitoring system undergoes a cross-browser test, and performance test, i.e., Requirements Traceability Matrix (RTM). These tests show that the proposed system passed the compatibility for commonly used browsers like Chrome, Edge, Mozilla, and many more. The Final Test in Performance Testing showed that the system RTM functions had passed all final testing. © 2023 IEEE.
ABSTRACT
Due to the rise of severe and acute infections called Coronavirus 19, contact tracing has become a critical subject in medical science. A system for automatically detecting diseases aids medical professionals in disease diagnosis to lessen the death rate of patients. To automatically diagnose COVID-19 from contact tracing, this research seeks to offer a deep learning technique based on integrating a Bayesian Network and K-Anonymity. In this system, data classification is done using the Bayesian Network Model. For privacy concerns, the K-Anonymity algorithm is utilized to prevent malicious users from accessing patients' personal information. The dataset for this system consisted of 114 patients. The researchers proposed methods such as the K-Anonymity model to remove personal information. The age group and occupations were replaced with more extensive categories such as age range and numbers of employed and unemployed. Further, the accuracy score for the Bayesian Network with k-Anonymity is 97.058%, which is an exceptional accuracy score. On the other hand, the Bayesian Network without k-Anonymity has an accuracy score of 97.1429%. These two have a minimal percent difference, indicating that they are both excellent and accurate models. The system produced the desired results on the currently available dataset. The researchers can experiment with other approaches to address the problem statements in the future by utilizing other algorithms besides the Bayesian one, observing how they perform on the dataset, and testing the algorithm with undersampled data to evaluate how it performs. In addition, researchers should also gather more information from various sources to improve the sample size distribution and make the model sufficiently fair to generate accurate predictions. © 2022 IEEE.
ABSTRACT
The onset of the COVID-19 pandemic delayed certain operations and paved the way to strict travel restrictions. This also meant that the traditional face-to-face process for accreditation inspection was affected. With this, to adapt to the situation, many institutions switched to relying on technology. This study aims to address the digitalization of the Professional Regulation Commission of the Philippines' monitoring and accreditation system. Hence, the objective of the study is to develop an effective digital system for the Professional Regulation Commission of the Philippines. The Agile Development Lifecycle will be implemented in constructing the system. Mobile Application Test and Performance Test are tests designed to evaluate the system's capabilities and usefulness. The outcomes met the study's goal, which led to the tests being successful. © 2022 ACM.