ABSTRACT The proportion of COVID-19 patients is significantly expanding around the world. Treatment with serious consideration has become a significant problem. Identifying clinical indicators of succession towards severe conditions is desperately required to empower hazard stratification and optimise resource allocation in the pandemic of COVID-19. Consequently, the classification of severity level is significant for the patient’s triaging. It is required to categorise the severity level as mild, moderate, severe, and critical based on the patients’ symptoms. Various symptomatic parameters may encourage the evaluation of infection seriousness. Likewise, with the rapid spread and transmissibility of COVID-19 patients, it is crucial to utilise telemonitoring schemes for COVID-19 patients. Telemonitoring mediation encourages remote data and information exchange among medicinal services, suppliers, and patients, furthermore, risk mitigation and provision of appropriate medical facilities. This paper provides explorative data analysis of symptoms, comorbidities, and other parameters, comparing different machine learning algorithms for case severity detection. This paper also provides a system (based on the degree of truthfulness) for case severity detection that might be utilised to stratify risk levels for anticipated moderate and severe COVID-19 patients. Finally, we provide a telemonitoring model of COVID-19 patients to ensure the remote and continuous monitoring of case severity progression and appropriate risk mitigation strategies.

Exploratory data analysis, classification, comparative analysis, case severity detection, and internet of things in COVID-19 telemonitoring for smart hospitals

QLattice Environment and Feyn QGraph Models—A New Perspective Toward Deep Learning

In the Current Scenario, when the whole world is going witnessing, it becomes a matter of paramount importance to control the spread of the disease. For the same, the governing authorities have been advising various regulations and guidelines. Among various guidelines, the most promising preventing measure is to cover the face with a mask to minimize the spread of COVID –19. For the same, various models are being developed by different researchers to automate the process of mask detection that will help to ensure that maximum people adhere to the advisories issued by leading health organizations. Here, in this paper, the authors propose an automated system that detects the mask. The proposed model identifies faces through facial features through technology such as video cameras. The proposed Face Mask Recognition System employs Deep Learning, a type of Artificial Intelligence (AI), to detect the person with or without a mask. The proposed model can be connected with any surveillance system installed at your premises. Further, the authorities can identify the person who is not wearing a mask to initiate any action if required. The system also sends an alert message to the administrator if someone enters the premises without a face mask. The proposed model has been tested to establish its efficiency and effectiveness. The accuracy of the proposed model is obtained to be in the range of 95% to 99% depending on the digital capabilities, establishing the supremacy of the proposed model.

An AI and Computer Vision-based Face Mask Recognition & Detection System

Employing Machine Learning Approaches for Predictive Data Analytics in Retail Industry

Machine Learning–Based Data Analysis


 During this pandemic outbreak of COVID-19, the whole world is getting severely affected in respect of population health and economy. This novel virus has brought the whole world including the most developed countries to a standstill in a very short span like never before. The prime reason for this unexpected outburst of COVID-19 is lack of effective medicine and lack of proper understanding of the influencing factors. Here, authors aim to find the effect of epidemiological factors that influence its spread using a fuzzy approach. For the same, a total of 9 factors have been considered which are classified into Risk and Preventive factors. This fuzzy model supports to understand and evaluate the impact of these factors on the spread of COVID-19. Also, the model establishes a basis for understanding the effect of risk factors on preventive factors and vice versa. It is worth mentioning that this is the first attempt to analyze the effect of Clinical and Epidemiological factors with respect to COVID-19 using a fuzzy approach.

Fuzzy Modelling of Clinical and Epidemiological Factors for COVID-19

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Fuzzy Modelling of Clinical and Epidemiological Factors for COVID-19