In today's modernized world, the field of healthcare has seen significant practical innovations with the implementation of computational intelligence approaches and soft computing methods. These two concepts present various solutions to complex scientific problems and imperfect data issues. This has made both very popular in the medical profession. There are still various areas to be studied and improved by these two schemes as healthcare practices continue to develop. Computational Intelligence and Soft Computing Applications in Healthcare Management Science is an essential reference source that discusses the implementation of soft computing techniques and computational methods in the various components of healthcare, telemedicine, and public health. Featuring research on topics such as analytical modeling, neural networks, and fuzzy logic, this book is ideally designed for software engineers, information scientists, medical professionals, researchers, developers, educators, academicians, and students.

Biostatistics Manual for Health Research: A Practical Guide to Data Analysis is a guide for researchers on how to apply biostatistics on different types of data. The book approaches biostatistics and its application from medical and health researcher's point-of-view and has real and mostly published data for practice and understanding. The interpretation and meaning of the statistical results, reporting guidelines and mistakes are taught with real world examples. This is a valuable resource for biostaticians, students and researchers from medical and biomedical fields who need to learn how to apply statistical approaches to improve their research.

Computational Methods in Drug Discovery and Repurposing for Cancer Therapy provides knowledge about ongoing research and computational approaches for drug discovery and repurposing for cancer therapy. The book provides detailed descriptions about target molecules and pathways and their inhibitors for easy understanding and applicability. Users will find discussions on tools and techniques such as integrated bioinformatics approaches, systems biology tools, molecular docking, computational chemistry, artificial intelligence, machine learning, structure-based virtual screening, biomarkers and transcriptome which are discussed in the context of different cancer types, such as colon, glioblastoma, endometrial and retinoblastoma, amongst others. This book will be a valuable resource for researchers, students and member of the biomedical and medical fields who want to learn more about the use of computational modeling to better tailor treatments for cancer patients.

Though in the past online learning was considered of poorer professional quality than classroom learning, it has become a useful and, in some cases, vital tool for promoting the inclusivity of education. Some of its benefits include allowing greater accessibility to educational resources previously unattainable by those in rural areas, and in current times, it has proven to be a critical asset as universities shut down due to natural disasters and pandemics. Examining the current state of distance learning and determining online assessment tools and processes that can enhance the online learning experience are clearly crucial for the advancement of modern education. The Handbook of Research on Determining the Reliability of Online Assessment and Distance Learning is a collection of pioneering investigations on the methods and applications of digital technologies in the realm of education. It provides a clear and extensive analysis of issues regarding online learning while also offering frameworks to solve these addressed problems. Moreover, the book reviews and evaluates the present and intended future of distance learning, focusing on the societal and employer perspective versus the academic proposals. While highlighting topics including hybrid teaching, blended learning, and telelearning, this book is ideally designed for teachers, academicians, researchers, educational administrators, and students.

The Easy Introduction to Machine Learning (Ml) for Nontechnical People--In Business and Beyond Artificial Intelligence for Business is your plain-English guide to Artificial Intelligence (AI) and Machine Learning (ML): how they work, what they can and cannot do, and how to start profiting from them. Writing for nontechnical executives and professionals, Doug Rose demystifies AI/ML technology with intuitive analogies and explanations honed through years of teaching and consulting. Rose explains everything from early "expert systems" to advanced deep learning networks. First, Rose explains how AI and ML emerged, exploring pivotal early ideas that continue to influence the field. Next, he deepens your understanding of key ML concepts, showing how machines can create strategies and learn from mistakes. Then, Rose introduces current powerful neural networks: systems inspired by the structure and function of the human brain. He concludes by introducing leading AI applications, from automated customer interactions to event prediction. Throughout, Rose stays focused on business: applying these technologies to leverage new opportunities and solve real problems. Compare the ways a machine can learn, and explore current leading ML algorithms Start with the right problems, and avoid common AI/ML project mistakes Use neural networks to automate decision-making and identify unexpected patterns Help neural networks learn more quickly and effectively Harness AI chatbots, virtual assistants, virtual agents, and conversational AI applications