Discovering COVID-19 Death Patterns from Deceased Patients: A Case Study in Saudi Arabia

COVID-19 is a serious infection that cause severe injuries and deaths worldwide. The COVID-19 virus can infect people of all ages, especially the elderly. Furthermore, elderly who have co-morbid conditions (e.g., chronic conditions) are at an increased risk of death. At the present time, no approach exists that can facilitate the characterization of patterns of COVID-19 death. In this study, an approach to identify patterns of COVID-19 death efficiently and systematically is applied by adapting the Apriori algorithm. Validation and evaluation of the proposed approach are based on a robust and reliable dataset collected from Health Affairs in the Makkah region of Saudi Arabia. The study results show that there are strong associations between hypertension, diabetes, cardiovascular disease, and kidney disease and death among COVID-19 deceased patients © 2023, International Journal of Advanced Computer Science and Applications.All Rights Reserved.

Implementation of Association Rules with Apriori Algorithm in Determining Customer Purchase Patterns

Micro, Small, and Medium Enterprises in Indonesia Usaha Mikro, Kecil, Menengah (UMKM) have been affected by the COVID-19 pandemic. The barcode scanning system currently only helps support the buying and selling process and cannot determine the provision of stock or the creation of promotional packages. Website application development using the Association Rule Method with the Apriori Algorithm is the solution offered to produce a pattern of relationships between products that buy by customers. The goods relationship is the basis for making decisions by shop owners to determine the stock of interconnected goods or making promotional packages with the association method by calculating the value of support and confidence. The system was built using the PHP programming language and 4820 transaction data. The results of data analysis through the website using the Antoni store dataset show the results of association rules. Based on 50 experiments conducted by researchers, if the Antoni shop wants to produce two directions, it is better to use minimum confidence of 10% or 12% with minimum support of 2% or 4%. However, if you want to produce 1 rule, you should use minimum confidence of 14%, 16%, or 18% with minimum support of 2%, 4%, or 6%. The lift ratio value of each minimum belief and the recommended support are more significant than 1. Therefore, the combination of association rules results is solid and valid. It can be used that this algorithm is suitable for a collection of related items, so it is appropriate to be used in analyzing product sales patterns at Antoni Stores. © 2022 IEEE.

Machine Learning Approach: Consumer Buying Behavior Analysis

The rise of multiple company competitors during the COVID-19 outbreak resulted in fierce competition among competing firms for new clients and the retention of current ones. As a result of the foregoing, exceptional customer service is required, regardless of the size of the organization. Furthermore, any company's ability to know each of its customers' desires will provide it an advantage when it comes to providing specialized customer care and establishing customized marketing plans for them. The term 'Consumer Buying Behavior Analysis' refers to a comprehensive assessment of the company's ideal clients/customers. In this project, we're utilizing the K-Means Algorithm to divide clients into two groups: 'Highly Active Customers' and 'Least Active Customers.' Then, utilizing the Apriori Algorithm, we use Association Rule Mining to recommend the best goods to clients based on their purchasing history and associations. We take one step further and use Logistic Regression to validate our Clustering operation by doing Binary Classification with our clusters as the label, resulting in accuracy and an F1 score of 91%. © 2022 IEEE.

WiFi-Crowd Spy: A novel crowd-counting system

Currently, there is a requirement in many countries to keep public and work spaces safe due to COVID-19. In fact, indoor spaces must be monitored to control the allowed capacity, which can vary depending on the alert level of a city at a given time. This has motivated some researchers to investigate several technologies to implement methods and strategies to enable the reopening of these spaces in a safe manner. In this paper, we propose a crowd counting detection system that this paper, we propose a crowd counting detection system that addresses the problem of controlling the indoor capacity of offices inside buildings. The proposed solution uses an existing communication technology such as WiFi in order to determine the crowd counting for the indoor environment. In particular, the existing infrastructure consists of two Wireless LAN Controllers (WLC) and several APs deployed in a building, which allows us to estimate the number of people based on the access to Wireless Access Points (APs). Thus, the proposed system takes into account when a mobile device connects/disconnects to the AP to increase or decrease the number of people in a particular office and sends the respective alert to the system administrator when this capacity is about to be exceeded or already surpassed. © 2022 IEEE.

Novel Approach for Predicting COVID-19 Symptoms using ARM based APRIORI Algorithm

Based on several pre-defined standard symptoms, a model that can determine the coronavirus illness as positive is developed. Guidelines for these symptoms have been issued by the World Health Organization (WHO) and India's Ministry of Health and Family Welfare. In this model the various symptoms of the illnesses is given to the system. It allows users to discuss their symptoms, with the algorithm predicting a condition based on factual information. This factual information is then evaluated using the ARM based Apriori algorithm to get the most accurate results. Other conventional models such as Support Vector Machine (SVM), Artificial Neural Networks (ANNs), and Random Forests (RF) are considered and have analyzed the predictions and have found that the proposed algorithm predicts a higher accuracy score. © 2022 IEEE.