Diagnosis And Analysis Of Covid 19 Using Artificial Intelligence And Machine Learning Based Techniques
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Diagnosis and Analysis of COVID-19 using Artificial Intelligence and Machine Learning-Based Techniques
Diagnosis and Analysis of COVID-19 using Artificial Intelligence and Machine Learning-Based Techniques offers new insights and demonstrates how machine learning (ML), artificial intelligence (AI), and (Internet of Things (IoT) can be used to diagnose and fight COVID-19 infection. Sections also discuss the challenges we face in using these technologies. Chapters cover pathogenesis, transmission, diagnosis, and treatment strategies for COVID-19, Artificial Intelligence and Machine Learning, and Blockchain /IoT Blockchain technology, examining how AI can be applied as a tool for detection and containment of the spread of COVID-19, and on the socioeconomic and educational post-pandemic impacts of the disease. This is a multidisciplinary resource for those engaged in researching COVID-19 and how emerging technologies are being used as tools for detection, transmission and treatment strategies. - Describes the molecular basis of pathogenesis, epidemiology, transmission mechanism, diagnostic approaches, and the mutational landscape of SARS-CoV-2 - Provides insights into post COVID-19 symptoms and consequences - Demonstrates how machine learning, AI, and IoT is used to diagnose and fight COVID-19 infection - Examines the use of Blockchain technology/IoT and interpretation and validation of data obtained from artificial intelligence
Williams Manual of Hematology, Eighth Edition
Author: Marshall A. Lichtman
language: en
Publisher: McGraw Hill Professional
Release Date: 2011-05-31
A convenient full-color reference distilled from the world’s leading hematology text – perfect when you need answers in the office, clinic, or on hospital rounds. Williams Manual of Hematology, 8e is a concise and easy-to-navigate compilation of the pathogenic, diagnostic, and therapeutic essentials of blood cell and coagulation protein disorders. Referenced to the classic Williams Hematology, 8e, this handy, easily transported reference has been carefully edited to deliver only the most clinical point-of-care facts. Covering both common and uncommon blood disorders, this complete guide includes sections on: Disorders of red cells Disorders of granulocytes Disorders of monocytes and macrophages The clonal myeloid disorders The polyclonal lymphoid diseases The clonal lymphoid and plasma cell diseases Disorders of platelets and hemostasis Disorders of coagulation proteins Transfusion and hemapheresis Now in full color for the first time, Williams Manual of Hematology, 8e is the fastest and most convenient way to access the unmatched clinical authority of Williams Hematology, 8e.
Machine Learning and Deep Learning Techniques for Medical Science
The application of machine learning is growing exponentially into every branch of business and science, including medical science. This book presents the integration of machine learning (ML) and deep learning (DL) algorithms that can be applied in the healthcare sector to reduce the time required by doctors, radiologists, and other medical professionals for analyzing, predicting, and diagnosing the conditions with accurate results. The book offers important key aspects in the development and implementation of ML and DL approaches toward developing prediction tools and models and improving medical diagnosis. The contributors explore the recent trends, innovations, challenges, and solutions, as well as case studies of the applications of ML and DL in intelligent system-based disease diagnosis. The chapters also highlight the basics and the need for applying mathematical aspects with reference to the development of new medical models. Authors also explore ML and DL in relation to artificial intelligence (AI) prediction tools, the discovery of drugs, neuroscience, diagnosis in multiple imaging modalities, and pattern recognition approaches to functional magnetic resonance imaging images. This book is for students and researchers of computer science and engineering, electronics and communication engineering, and information technology; for biomedical engineering researchers, academicians, and educators; and for students and professionals in other areas of the healthcare sector. Presents key aspects in the development and the implementation of ML and DL approaches toward developing prediction tools, models, and improving medical diagnosis Discusses the recent trends, innovations, challenges, solutions, and applications of intelligent system-based disease diagnosis Examines DL theories, models, and tools to enhance health information systems Explores ML and DL in relation to AI prediction tools, discovery of drugs, neuroscience, and diagnosis in multiple imaging modalities Dr. K. Gayathri Devi is a Professor at the Department of Electronics and Communication Engineering, Dr. N.G.P Institute of Technology, Tamil Nadu, India. Dr. Kishore Balasubramanian is an Assistant Professor (Senior Scale) at the Department of EEE at Dr. Mahalingam College of Engineering & Technology, Tamil Nadu, India. Dr. Le Anh Ngoc is a Director of Swinburne Innovation Space and Professor in Swinburne University of Technology (Vietnam).