ABSTRACT
Everybody, around the globe, is aware that their kids, relatives, and family are suffering from the pandemic COVID-19. S everal people are still facing post-COVID-19 issues. During COVID-19's second wave, mucormycosis, sometimes known as "black fungus, " plagued people, especially those who had previously been infected with the virus. The clinical manifestations of mucormycosis are quite varied, the disease affects the skin, subcutaneous fatty tissue, and visceral organs such as the eyes and brain. This paper surveys the Mucormycosis-affected eye diseases due to post-COVID-19 complications and leverages the Machine learning model to differentiate it from other eye diseases. COVID-19-associated Mucormycosis carries a very high mortality rate and timely detection that can assist people in starting therapy at an early stage of the disease, increasing their chances of recovery. Though it was evaluated for a specific disease (COVID-19-associated mucormycosis) we ended up developing a framework that can detect other eye diseases. Thus, the goal of this research is to distinguish Mucormycosis from other eye diseases such as Bulging Eyes, Cataracts, Crossed Eyes, Glaucoma, and Uveitis. This study implies Deep learning techniques with a Convolutional Neural Network based on the TensorFlow and Keras model to detect and make use of computer vision to accurately classify eye diseases. We achieved a precision of 70% in this study by developing a webpage using the trained model for an eye diseases evaluation. © 2022 IEEE
ABSTRACT
Analysis of the physical-chemical characteristics of chemical structures requires the incorporation of topological indices into account. A structure-based approach that concentrates antiviral treatment on the main protease of the SARS-COV-2 virus. One degree-based measure that is a little comparable to the Zagreb index and Lanzhou index. This paper analyzed the effectiveness of the chemical compounds pyroxene, amphibole, detour-saturated plants, and several antiviral medications listed for the Lanzhou index. The obtained results demonstrate a significant relationship between the Lanzhou index under investigation and the physicochemical features of potential antiviral medications. The Lanzhou index for antioxidant drugs for M polynomial is enumerated. © 2023 Taylor & Francis Group, LLC.
ABSTRACT
The whole world faces an uncommon situation in its history due to the spread of the novel coronavirus (COVID-19). First impacted its existence during December 2019 in Wuhan City, Hubei Province, China. However, the spread of the disease is marginally visible and resulting in an epidemic distribution across capital cities of India. As of June 15, 2020, in India, 368705 are the confirmed cases, and 12280 people have deceased their lives. Collecting the statistics of daily infections, deaths and recovery data and predicting epidemic trends of COVID-19 in India has the most significant importance for developing and measuring the impacts of public intervention strategies. Based on India and Tamil Nadu's initial 105 days of COVID-19 statistics of (one of its states), we built the logistic growth model and compared their accuracy with the R2 coefficient measure. Based on the lockdown periods and severe protection measures, a scenario-based analysis of four different SIR models predicts the confirmed cases. This proposed scenario-based analysis is helpful to pre-estimate the maximum infection rate and maximum peak day of infection with the total percentage of the population being infected by the COVID-19 outbreak in India. This analysis suggests that the severe control measures are working well in India, despite the exponential growth of the outbreak situation. © 2022 Author(s).