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Background: COVID-19 (Coronavirus Disease-19), a disease caused by the SARS-CoV-2 virus, has been declared as a pandemic by the World Health Organization on March 11, 2020. Over 15 million people have already been affected worldwide by COVID-19, resulting in more than 0.6 million deaths. Protein-protein interactions (PPIs) play a key role in the cellular process of SARS-CoV-2 virus infection in the human body. Recently a study has reported some SARS-CoV-2 proteins that interact with several human proteins while many potential interactions remain to be identified., Method: In this article, various machine learning models are built to predict the PPIs between the virus and human proteins that are further validated using biological experiments. The classification models are prepared based on different sequence-based features of human proteins like amino acid composition, pseudo amino acid composition, and conjoint triad., Result: We have built an ensemble voting classifier using SVM Radial , SVM Polynomial , and Random Forest technique that gives a greater accuracy, precision, specificity, recall, and F1 score compared to all other models used in the work. A total of 1326 potential human target proteins of SARS-CoV-2 have been predicted by the proposed ensemble model and validated using gene ontology and KEGG pathway enrichment analysis. Several repurposable drugs targeting the predicted interactions are also reported., Conclusion: This study may encourage the identification of potential targets for more effective anti-COVID drug discovery., Competing Interests: Conflicts of interest The authors declare that they have no conflict of interest., (Copyright © 2020 Chang Gung University. Published by Elsevier B.V. All rights reserved.)

In the ever-evolving world of cricket, the T20 format has captured the imaginations of fans worldwide, intensifying the anticipation for match outcomes with each passing delivery. This study explores the realm of predictive analytics, leveraging the power of machine learning to alleviate the suspense by forecasting T20 cricket match winners before the first ball is bowled. Drawing on a rich dataset encompassing factors such as past team performance and rankings, a diverse ensemble of predictive models, including logistic regression, support vector machine (SVM), random forest, decision tree, and XGBoost, is meticulously employed. Among these, the random forest Classifier emerges as the standout performer, boasting an impressive prediction accuracy rate of 84.06%. To assess the real-world applicability of our predictive framework, a post-case study is conducted, focusing on the high-stakes World Cup T20 matches of 2022, where England emerges as the triumphant team. The dataset underpinning this study is meticulously curated from ESPN CricInfo, ensuring the robustness of our analysis. Moreover, this paper extends its contribution by offering a comprehensive comparative analysis, scrutinizing performance metrics such as accuracy, precision, recall, and the F1-score across benchmark machine learning models for cricket match prediction. This in-depth evaluation not only validates the efficacy of our models but also sheds light on their superior execution time and statistical robustness, further bolstering their utility in the realm of cricket outcome forecasting. [ABSTRACT FROM AUTHOR]

“Multiple Disease Prediction” is a system based on predictive modeling that will predict the user's disease based on the given symptoms. We have considered four diseases for now, that is Heart disease, Liver disease, Chronic Kidney Disease, and Diabetes. There are existing systems that can predict one disease at a time and perhaps with lower accuracy, which puts a patient’s health in danger. In this project, we aim to design a multiple disease prediction system by applying principles of machine learning, where our application incorporates two ways of predicting diseases for two different categories of users. For single users, the user must enter various parameters of the disease, and the system would display the output whether she/he has the disease or not. The project makes it easier for a multitude of users, such as a hospital management system, to anticipate diseases from the results of laboratory blood test reports. Blood analysis is a crucial disease indicator since it comprises several factors that are symptoms of diseases. This project not only emphasizes prediction precision but also opens the possibility of easily introducing new diseases. [ABSTRACT FROM AUTHOR]