During these uncertain and turbulent times, intelligent technologies including artificial neural networks (ANN) and machine learning (ML) have played an incredible role in being able to predict, analyze, and navigate unprecedented circumstances across a number of industries, ranging from healthcare to hospitality. Multi-factor prediction in particular has been especially helpful in dealing with the most current pressing issues such as COVID-19 prediction, pneumonia detection, cardiovascular diagnosis and disease management, automobile accident prediction, and vacation rental listing analysis. To date, there has not been much research content readily available in these areas, especially content written extensively from a user perspective.

Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning is designed to cover a brief and focused range of essential topics in the field with perspectives, models, and first-hand experiences shared by prominent researchers, discussing applications of artificial neural networks (ANN) and machine learning (ML) for biomedical and business applications and a listing of current open-source software for neural networks, machine learning, and artificial intelligence. It also presents summaries of currently available open source software that utilize neural networks and machine learning. The book is ideal for professionals, researchers, students, and practitioners who want to more fully understand in a brief and concise format the realm and technologies of artificial neural networks (ANN) and machine learning (ML) and how they have been used for prediction of multi-disciplinary research problems in a multitude of disciplines.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

Applications of Machine Learning Methods
Applications of Neural Networks and Machine Learning to COVID-19 Predictions
Artificial Neural Networks
Cardiovascular Applications of Artificial Intelligence
Deep Learning Models
Deep Neural Networks
Image Identification and Damage Estimation Through Convolutional Neural Network
Machine Learning Methods Comparison for Unemployment Rate Prediction
Machine Learning Techniques
Multilayer Perceptron Neural Network
Neural Networks
Open Source Software
Protein-Protein Interactions via Deep Neural Network

Reviews & Statements

"The book Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning edited by Dr. Richard S. Segall and Dr. Gao Niu meets the challenges in the era of big data and is timely for utilizing Artificial Intelligence (AI) and machine learning techniques to advance biomedical and business data analytics. This book keeps pace with the rapid advancements in machine learning, business analytics, data tools and infrastructure, and practices that can help solve real-world problems. It is perceivably beneficial for data scientists, biomedical researchers and industry users to understand the latest state-of-art algorithms and apply these techniques to facilitate business management and operation, and biomedical research and clinical applications. The book covers a wide spectrum of machine learning and AI algorithms and open-source software in chapter 1, biomedical applications in chapters 2-7, and business applications in chapters 8-13. In particular, chapter 2 and chapter 6 present AI and deep-learning models for COVID-19 prediction and diagnosis, which is just-in-time to tackle the critical challenges posed by COVID-19 pandemic that currently have created profound impacts on human health and economic growth worldwide. I believe a broad range of researchers, professionals and students will find this book useful. I highly recommend this book to the communities of machine learning, biomedical research, data analytics and business intelligence. "

– Mary Yang, Ph.D., Professor of Engineering and Information Science Director of MidSouth Bioinformatics Center and Joint Bioinformatics Graduate Programs of University of Arkansas at Little Rock (UALR); University of Arkansas for Medical Sciences (UAMS)

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Editor/Author Biographies

Dr. Richard S. Segall is Professor of Information Systems and Business Analytics in Neil Griffin College of Business at Arkansas State University in Jonesboro, AR where he also taught for ten years in the College of Engineering & Computer Science Master of Engineering Management (MEM) Program and is Affiliated Faculty of the Environmental Sciences Program and Center for No-Boundary Thinking (CNBT). He is also an Affiliated Faculty at the University of Arkansas at Little Rock (UALR) where he serves on thesis committees. His research interests include data mining, text mining, web mining, database management, Big Data, and mathematical modeling. His research has been funded by National Research Council (NRC), U.S. Air Force (USAF), National Aeronautical and Space Administration (NASA), Arkansas Biosciences Institute (ABI), and Arkansas Science & Technology Authority (ASTA). His publications have appeared in IGI Global journals of: International Journal of Fog Computing (IJFC), International Journal of Open Source Software and Processes (IJOSP), and International Journal of Big Data and Analytics in Healthcare (IJBDAH).

Gao Niu is an Assistant Professor in Actuarial Science and Program Coordinator of Actuarial Math Program at Bryant University. He also serves as the Faculty Consultant of the Janet & Mark L Goldenson Center for Actuarial Research at the University of Connecticut. He has a doctorate in actuarial science from the University of Connecticut, is an Associate of the Casualty Actuarial Society and a Member of the American Academy of Actuaries. Dr. Niu has years of experience in academic actuarial research and consulting practice. His research area includes but not limited to the following: big data analytics application in insurance industry, property and casualty insurance practice, predictive modeling, agent-based modeling, financial planning, life insurance and health insurance pricing, reserving and data mining.

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