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20th International Learning and Technology Conference, L and T 2023 ; : 120-127, 2023.
Article in English | Scopus | ID: covidwho-2316285
Computer Journal ; 65(8):2146-2163, 2022.
Article in English | Scopus | ID: covidwho-2312430
2nd International Conference on Electronic Information Engineering and Computer Technology, EIECT 2022 ; : 292-295, 2022.
Article in English | Scopus | ID: covidwho-2306226
2nd International Conference on Electronics and Renewable Systems, ICEARS 2023 ; : 961-967, 2023.
Article in English | Scopus | ID: covidwho-2303023
International Journal of Imaging Systems and Technology ; 2023.
Article in English | Scopus | ID: covidwho-2300790
5th International Conference on Contemporary Computing and Informatics, IC3I 2022 ; : 1895-1901, 2022.
Article in English | Scopus | ID: covidwho-2293164
11th EAI International Conference on Context-Aware Systems and Applications, ICCASA 2022 ; 475 LNICST:102-111, 2023.
Article in English | Scopus | ID: covidwho-2292310
19th IEEE India Council International Conference, INDICON 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2261610
2022 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2259998
4th International Conference on Artificial Intelligence and Speech Technology, AIST 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2248173
4th International Conference on Electrical Engineering and Control Technologies, CEECT 2022 ; : 349-353, 2022.
Article in English | Scopus | ID: covidwho-2288625
2022 International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2022 ; : 563-569, 2022.
Article in English | Scopus | ID: covidwho-2283637
7th International Conference on Parallel, Distributed and Grid Computing, PDGC 2022 ; : 176-180, 2022.
Article in English | Scopus | ID: covidwho-2283508
11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022 ; : 1199-1203, 2022.
Article in English | Scopus | ID: covidwho-2281688
6th World Conference on Smart Trends in Systems, Security and Sustainability, WS4 2022 ; 579:461-468, 2023.
Article in English | Scopus | ID: covidwho-2276423
4th International Conference on Circuits, Control, Communication and Computing, I4C 2022 ; : 95-102, 2022.
Article in English | Scopus | ID: covidwho-2273413
CMES - Computer Modeling in Engineering and Sciences ; 135(3):2047-2064, 2023.
Article in English | Scopus | ID: covidwho-2238483
2nd International Conference on Signal and Information Processing, IConSIP 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2233270


As a result of the COVID-19 pandemic, medical examinations (RTPCR, X-ray, CT-Scan, etc.) may be required to make a medical decision. COVID-19's SARS-CoV-2 virus infects and spreads in the lungs, which can be easily recognized by chest X-rays or CT scans. However, along with COVID-19 instances, cases of another respiratory ailment known as Pneumonia began to rise. As a result, clinicians are having difficulty distinguishing between COVID-19 and Pneumonia. So, more tests were required to identify the condition. After a few days, the COVID-19 SARS-CoV-2 virus multiplied in the lungs, causing pneumonia and COVID-19 named Novel Corona virus infected Pneumonia. We employ Machine Learning and Deep Learning models to predict diseases such as COVID-19 Positive, COVID-19 Negative, and Viral Pneumonia in this research. A dataset of data is used in a Machine Learning model. A dataset of 120 images was used in the Machine Learning model. By extracting eight statistical elements from an image texture, we calculated accuracy. Adaboost, Decision Tree & Naive Bayes have overall accuracy of 88.46%, 86.4% and 80%, respectively. When we compared the algorithms, Adaboost algorithm performs the best, with overall accuracy of 88.46%, sensitivity of 84.62%, specificity of 92.31%, F1-score of 88% and Kappa of 0.8277. VGG16 Architecture is used in CNN model for 838 images in Deep Learning model. The model's total accuracy is 99.17 %. © 2022 IEEE.

2022 International Conference on Smart Applications, Communications and Networking, SmartNets 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2231539


The COVID-19 Coronavirus (SARS-CoV-2), has caused destruction all around the world, since December 2019. It is still managing to grow at an unprecedented scale. It was declared as a health emergency for the entire globe by the World Health Organization (WHO) in January 2022. The virus continues to impact the lives of millions of people. An early detection system warning about the repercussions of the virus at a county level can be favorable for the residents as well and aid the government to enforce appropriate safety measures. This research aims at modeling such a warning system which predicts the positivity rate of COVID-19 for a geographical location. The proposed solution uses supervised machine learning techniques such as Random Forest, Linear Regression, Naive Bayes, and Gradient Boosting Regression. The prediction is made based on the analysis of the past data in each time frame with temporal input such as the population of the area, number of tests conducted, number of positive tests, reported cases in that area among others. The Gradient Boosting algorithm outperforms all the other algorithms used in this research. Machine learning based recommendation system for COVID-19 spread can help the public and government to take necessary precautions for suppressing its effect. The proposed modeling approach provides a reliable tool to predict COVID-19 transmission with an accuracy of 99.4%. © 2022 IEEE.