Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning (Advances in Computational Intelligence and Robotics)

Product Description 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. About the Author Dr. Richard S. Segall is a Professor of Computer & Information Technology in the College of Business at Arkansas State University in Jonesboro, AR and also teaches in the Master of Engineering Management (MEM) Program in the College of College of Agriculture, Engineering & Technology. He is also Affiliated Faculty at the University of Arkansas at Little Rock (UALR) where he serves on thesis committees. He holds a Bachelor of Science and Master of Science in Mathematics as well as a Master of Science in Operations Research and Statistics from Rensselaer Polytechnic Institute in Troy, New York. He also holds a PhD in Operations Research form University of Massachusetts at Amherst, He has served on the faculty of Texas Tech University, University of Louisville, University of New Hampshire, University of Massachusetts-Lowell, and West Virginia University. His research interests include data mining, text mining, web mining, database management, Big Data, and mathematical modeling. Dr. Segall's publications have appeared in numerous journals including International Journal of Information Technology and Decision Making (IJITDM), International Journal of Information and Decision Sciences (IJIDS), Applied Mathematical Modelling (AMM), Kybernetes: The International Journal of Cybernetics, Systems and Management Sciences, Journal of the Operational Research Society (JORS) and Journal of Systemics, Cybernetics and Informatics (JSCI) . He has published book chapters in Encyclopedia of Data Warehousing and Mining, Handbook of Computational Intelligence in Manufacturing and Production Management, Handbook of Research on Text and Web Mining Technologies, Encyclopedia of Information Science & Technology, and Encyclopedia of Business Analytics & Optimization . Dr. Segall is a member of the Arkansas Center for Plant-Powered-Production (P3), and on the Editorial Board of the International Journal of Data Mining, Modelling and Management (IJDMMM) and International Journal of Data Science (IJDS), and served as Local Arrangements Chair of the MidSouth Computational Biology & Bioinformatics Society (MCBIOS) Conference that was hosted at Arkansas State University. His research has been funded by National Research Counci