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Machine Learning And Data Science Fundamentals And Applications
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Machine Learning and Data Science Fundamentals and ApplicationsEdited byPrateek AgrawalCharu GuptaAnand SharmaVishu MadaanandNisheeth JoshiEngineering high-quality systems requires mastering advanced machinelearning and data science concepts and dealing with large and complexdata. There is growing realization in the system development communitythat we can learn useful system properties from large data by analysis with machine learning and data science.Machine learning and data science are currently a very active topicwith an extensive scope, both in terms of theory and applications. Theyhave been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. Simultaneously, their applications provide important challenges that can often be addressed only with innovative machine learning and data science algorithms.Those algorithms encompass the larger areas of artificial intelligence,data analytics, machine learning, pattern recognition, natural languageunderstanding, and big data manipulation. They also tackle related newscientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation.This book encompasses all aspects of research and development in MLand Data Science, including but not limited to data discovery, computervision, natural language processing (NLP), intelligent systems, neuralnetworks, AI-based software engineering, and their applications in theareas of engineering, business and social sciences. It also covers a broadspectrum of applications in the community, from industry, government, and academia. This book brings together thought leaders, researchers,industry practitioners, and potential users of machine learning, data science and analytics, to develop the field, discuss new trends and opportunities, exchange ideas and practices, and promote interdisciplinary and cross-domain collaborations.Prateek AgrawalCharu GuptaAnand SharmaVishu MadaanNisheeth Joshi

Introduction Coronavirus comes from the family of RNA viruses of the Nidovirales order. The corona prefix comes from the Latin word for crown, named for the crown-like appearance of virus. The human coronavirus causes respiratory and gastrointestinal tract infections. Human epidemics can occur when it transmits from one species to another. There are various types of coronaviruses, including alpha, beta, gamma, and delta viruses. Human coronaviruses are alpha and beta coronaviruses. People who are suffering from an organ transplants, cancer, severe asthma, weak immune system, serious heart condition, old age, high blood pressure, diabetes, etc. are of high risk for COVID19. RT-PCR testing is the method for diagnosing COVID19 as positive or negative. HRCT (Chest City Scans) is a more reliable way to find and diagnose the COVID19 affected patient. The initial common symptoms of COVID19 shown in Figure 7.1 help to diagnose the COVID19 disease.
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This chapter provides all the information regarding the COVID19 pandemic. The research provides whether the patient needs to go for the Common symptoms Fever After 2 to 7 days, develop a dry cough Mild breathing difficulties at the outset Gastrointestinal issues Diarrhea General body aches Severe symptoms High fever (100.4F or higher) Pneumonia Kidney failure Death Transmission Coughs or sneezes from infected person or touching contaminated objects. Figure 7.1 COVID19 diagnosis.
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COVID19 Detection, Prevention Measures, and Prediction 111 COVID19 test or not. The results are experimentally evaluated using the symptoms as inputs and applying various Machine Learning algorithms like Naive Bayes, random forest, j48 decision tree, logistic regression, and AdaBoost and deep learning algorithms like multilayer perceptron and voted perceptron for prediction. This paper will be a one-stop destination where people can get information about COVID19, its symptoms, avoidance methods, precautions, and prevention techniques. Also, for researchers, this paper will be helpful to study the COVID19 disease properly. 7.2 Literature Review
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The Novel Corona-virus SARS-CoV-2 (COVID19) diseases first case was detected in the last month of 2019 in Wuhan City, China. It has spread worldwide and has created a major pandemic situation across the country. It is desirable to limit Humanto-Human broadcasts to scale back secondary infections among close contacts and healthcare-workers and to avoid transmission amplification events and further international spread from the country of China . The new coronavirus, COVID19, developed a global-health-emergency owed to the very high-risk of spread and effect of COVID19. There are not any exact drugs or vaccines against COVID19, thus operative antiviral agents are still immediately needed to combat this virus. Herein, the FEP (Free Energy Perturbation)-based screening policy is freshly derived as a rapid protocol to accurately reposition potential agents against COVID19 by targeting the viral proteinase Mpro. This virus has also infected all the continents and over 180 other countr
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