A Decision Support System for Restricted Movement with Semaphores During Lockdown in COVID-19 Pandemic

In the wake of COVID-19 pandemic in India, E-pass system was introduced to facilitate movements for emergency and was considered as an essential measure to curtail the spread of coronavirus, as it prohibits non-essential travel. This paper studies the optimization problem of sanctioning E-pass for restricted movements during COVID-19 pandemic. The paper arrives at the solution of using counting semaphores as an optimized solution for restricting the movements and as a result, curbing the spread of the pandemic. For any movement within the district and the state, our paper has proposed the count allowed to be in terms of counting semaphores and thus eliminating intermediaries to make illegal gains from it. In addition, the usage of semaphores in the E-pass system helps us with an upper limit of the movement permitted at a particular time. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Decision Support System for Scheduling Lockdown in COVID-19 Pandemic

COVID-19 has been the talk of the town for the past one and half year with lockdowns being implemented at various phases. The lockdown implementations have been at random and could have been handled better in a systematic approach. Our paper proposes an intelligent scheduling approach in dynamic time quantum round robin for scheduling lockdown in this pandemic by exploiting round robin scheduling algorithm with added intelligence. The systematic lockdown is essential because of the fact that the virus spreads through people mostly with either no symptoms or mild symptoms. The ability of persons with no symptoms to spread the disease can be decremented by our intelligent approach of lockdown by manipulating the quantum slice in the round robin scheduling algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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.