Artificial Intelligence in Healthcare


Book Description

Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data




Handbook of Research on Computational Intelligence Applications in Bioinformatics


Book Description

Developments in the areas of biology and bioinformatics are continuously evolving and creating a plethora of data that needs to be analyzed and decrypted. Since it can be difficult to decipher the multitudes of data within these areas, new computational techniques and tools are being employed to assist researchers in their findings. The Handbook of Research on Computational Intelligence Applications in Bioinformatics examines emergent research in handling real-world problems through the application of various computation technologies and techniques. Featuring theoretical concepts and best practices in the areas of computational intelligence, artificial intelligence, big data, and bio-inspired computing, this publication is a critical reference source for graduate students, professionals, academics, and researchers.







Handbook of Machine Learning for Computational Optimization


Book Description

Technology is moving at an exponential pace in this era of computational intelligence. Machine learning has emerged as one of the most promising tools used to challenge and think beyond current limitations. This handbook will provide readers with a leading edge to improving their products and processes through optimal and smarter machine learning techniques. This handbook focuses on new machine learning developments that can lead to newly developed applications. It uses a predictive and futuristic approach, which makes machine learning a promising tool for processes and sustainable solutions. It also promotes newer algorithms that are more efficient and reliable for new dimensions in discovering other applications, and then goes on to discuss the potential in making better use of machines in order to ensure optimal prediction, execution, and decision-making. Individuals looking for machine learning-based knowledge will find interest in this handbook. The readership ranges from undergraduate students of engineering and allied courses to researchers, professionals, and application designers.




Integer Programming


Book Description




Computational Learning Approaches to Data Analytics in Biomedical Applications


Book Description

Computational Learning Approaches to Data Analytics in Biomedical Applications provides a unified framework for biomedical data analysis using varied machine learning and statistical techniques. It presents insights on biomedical data processing, innovative clustering algorithms and techniques, and connections between statistical analysis and clustering. The book introduces and discusses the major problems relating to data analytics, provides a review of influential and state-of-the-art learning algorithms for biomedical applications, reviews cluster validity indices and how to select the appropriate index, and includes an overview of statistical methods that can be applied to increase confidence in the clustering framework and analysis of the results obtained. - Includes an overview of data analytics in biomedical applications and current challenges - Updates on the latest research in supervised learning algorithms and applications, clustering algorithms and cluster validation indices - Provides complete coverage of computational and statistical analysis tools for biomedical data analysis - Presents hands-on training on the use of Python libraries, MATLAB® tools, WEKA, SAP-HANA and R/Bioconductor










Gas Dynamics with Applications in Industry and Life Sciences


Book Description

This proceedings volume gathers selected contributions presented at two instances of the "JSPS/SAC Seminar: On Gas Kinetic/Dynamics and Life Science", held by the Chalmers University of Technology and University of Gothenburg, Sweden, on March 25-26, 2021 (virtual) and March 17-18, 2022 (virtual). Works in this book provide a concise approach to the theoretical and numerical analysis of kinetic type equations that arise, for example, in modeling industrial, medical, and environmental problems. Readers will find some of the most recent theoretical results, newly developed numerical methods in the field, and some open problems. Possible application areas encompass fission/fusion energy, electromagnetics, nuclear science and engineering, medical service, radiation oncology, and plants growth conditions, to name a few. The JSPS/SAC seminars are jointly organized by JSPS (Japan Society for the Promotion of Science)—Stockholm Office and the Department of Mathematical Sciences, Chalmers University of Technology & University of Gothenburg, Sweden. These seminars foster discussions on the mathematical theory, industrial and life science applications, and numerical analysis of non-linear hyperbolic partial differential equations modeling collision-less plasma and charged particles. Chapter 4 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com. Chapter 11 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.