Covid-19 Forecasting Using CNN Approach With A Halbinomial Distribution And A Linear Decreasing Inertia Weight-Based Cat Swarm Optimization

In recent times, the COVID-19 epidemic has spread to over 170 nations. Authorities all around the world are feeling the strain of COVID-19 since the total of infected people is rising as well as they does not familiar to handle the problem. The majority of current research effort is thus being direc...

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Published in:International journal of computers, communications & control Vol. 18; no. 1
Main Authors: Murugesan, Rajesh Khanna, Madhu, Karthikeyan, Sambandam, Jayalakshmi, Malliga, Lakshmanan
Format: Journal Article
Language:English
Published: Oradea Agora University of Oradea 01.02.2023
Subjects:
Algorithms
Artificial intelligence
Artificial neural networks
Binomial distribution
Coronaviruses
COVID-19
Forecasting
Inertia
Optimization
ISSN:1841-9836, 1841-9844
Online Access:Get full text
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Summary:In recent times, the COVID-19 epidemic has spread to over 170 nations. Authorities all around the world are feeling the strain of COVID-19 since the total of infected people is rising as well as they does not familiar to handle the problem. The majority of current research effort is thus being directed on the analysis of COVID-19 data within the framework of the machines learning method. Researchers looked the COVID 19 data to make predictions about who would be treated, who would die, and who would get infected in the future. This might prompt governments worldwide to develop strategies for protecting the health of the public. Previous systems rely on Long Short- Term Memory (LSTM) networks for predicting new instances of COVID-19. The LSTM network findings suggest that the pandemic might be over by June of 2020. However, LSTM may have an over-fitting issue, and it may fall short of expectations in terms of true positive. For this issue in COVID-19 forecasting, we suggest using two methods such as Cat Swarm Optimization (CSO) for reducing the inertia weight linearly and then artificial intelligence based binomial distribution is used. In this proposed study, we take the COVID-19 predicting database as an contribution and normalise it using the min-max approach. The accuracy of classification is improved with the use of the first method to choose the optimal features. In this method, inertia weight is added to the CSO optimization algorithm convergence. Death and confirmed cases are predicted for a certain time period throughout India using Convolutional Neural Network with Partial Binomial Distribution based on carefully chosen characteristics. The experimental findings validate that the suggested scheme performs better than the baseline system in terms of f-measure, recall, precision, and accuracy.
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ISSN:1841-9836
1841-9844
DOI:10.15837/ijccc.2023.1.4396