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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which caused an outbreak of pneumonia in December 2019 in Wuhan, China, has spread rapidly throughout the world. This ongoing pandemic has resulted over 55.6 million cases of COVID-19 leading to 1.34 million deaths in more than 188 countries. However, it has been observed that the death rate is significantly in the lower side for the SAARC countries compared to the First World Nations. In this paper, the possible factors have been represented that determine this uneven distribution of COVID-19 deaths. The significance of the factors has been presented in this paper after the data analysis of the factors from 165 different countries. Based on the correlation of the factors and their critical impact towards the concerned countries death toll, the risk index of each factor has been labeled using analytical hierarchy process (AHP)-based MCDM, i.e., multiple criteria decision-making technique. The risk index of all the factors has been used to generate the susceptibility of COVID-19 for each of the countries in study, specifically the SAARC Nations. Finally, the hierarchical clustering was applied to visualize the death toll of the countries corresponding to their susceptibility index.

An unexpected outbreak of 2019 Coronavirus disease (COVID-19) in Wuhan, China, led to a massive catastrophe across the world. The majority of the COVID-19 patients are getting diagnosed with pneumonia in their early stages. Over 22,00,000 confirmed cases have shown various ranges of symptoms, but the most predominant set includes fever, cough, and shortness of breath. The predominant set of symptoms, coupled with other critical symptoms, a prediction process has been devised in this paper to check whether a person is infected with COVID-19 or not. Based on the crucial impact of the symptoms, we have applied the support vector machine classifier to classify the patient's condition in no infection, mild infection, and serious infection categories. We have achieved an accuracy of 87% in predicting the cases.

Before the advent of the app-based cab system, people used to spend a lot of time chasing down a taxi and waiting for a significant amount of time to get a cab. The online cab booking system not only saves this time for people but also picks them up from their doorstep and drops them to their exact destination. However, the demand for app cabs in the current scenario implies an even more improvement in the existing online cab booking system. Authors have studied the existing research papers related to online cab booking systems and tried to put all their proposed solutions in this paper. Different authors have proposed different ideas to make the existing system more feasible. Some of the notable features are fleet utilization, bargaining facility, trip recording, etc. The authors have also discussed all the possible implementations of these features. In addition to the above-mentioned features, we have also discussed the possible solutions to some existing problems like single point failure, low availability of cabs, etc. Technologies like distributive algorithm, PDPTW algorithm, adaptive scheduling algorithm are used to overcome these problems. This paper will help the reader to get clear information about all the research related to the online cab booking system and provides possible solutions in the context of the existing system in India.

The outbreak of pneumonia in December 2019 in Wuhan, China, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread rapidly throughout the world. With over 4.62 million confirmed cases of COVID-19 and 311,000 deaths in more than 188 countries, this ongoing pandemic has wreaked havoc all around the globe. However, the SAARC (South Asian Association for Regional Cooperation) countries, compared to the First World nations, have significantly low death rate. In this paper, the authors have determined this uneven distribution of COVID-19 deaths with the help of some possible factors, which are the prime cause of such variability among the different nations. This paper presents the significance of these factors through analysis of the data corresponding to each of these factors from 165 different countries. On the basis of the relationship between the factors and their significance on the concerned countries’ death toll, we have labelled each factor’s risk index using the multiple analytical hierarchy process (M-AHP), as it provides several experts’ views instead of a single expert’s opinion. The risk index of all the factors has been used to generate the susceptibility of COVID-19 for each of the countries in study, specifically the SAARC nations. Finally, we have applied a hierarchical clustering-based machine learning approach to visualize the countries’ death toll corresponding to their susceptibility index. This paper’s major findings are that authors holistically searched the root causes of why South Asian countries are less fatal concerning COVID-19.