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

This book presents social, cognitive and neuroscientific approaches to the study of self-control, connecting recent work in cognitive and social psychology with recent advances in cognitive and social neuroscience. In bringing together multiple perspectives on self-control dilemmas from internationally renowned researchers in various allied disciplines, this is the first single-reference volume to illustrate the richness, depth, and breadth of the research in the new field of self control.

While online learning was an existing practice, the COVID-19 pandemic greatly accelerated its capabilities and forced educational organizations to swiftly introduce online learning for all units. Though schools will not always be faced with forced online learning, it is apparent that there are clear advantages and disadvantages to this teaching method, with its usage in the future cemented. As such, it is imperative that methods for measuring and assessing the effectiveness of online and blended learning are examined in order to improve outcomes and future practices. Measurement Methodologies to Assess the Effectiveness of Global Online Learning aims to assess the effectiveness of online teaching and learning in normal and pandemic situations by addressing challenges and opportunities of adoption of online platforms as well as effective learning strategies, investigating the best pedagogical practices in digital learning, questioning how to improve student motivation and performance, and managing and measuring academic workloads online. Covering a wide range of topics such as the future of education and digital literacy, it is ideal for teachers, instructional designers, curriculum developers, educational software developers, academics, researchers, and students.

This book constitutes the refereed proceedings of the 13th International Conference on Brain Informatics, BI 2020, held in Padua, Italy, in September 2020. The conference was held virtually due to the COVID-19 pandemic. The 33 full papers were carefully reviewed and selected from 57 submissions. The papers are organized in the following topical sections: cognitive and computational foundations of brain science; investigations of human information processing systems; brain big data analytics, curation and management; informatics paradigms for brain and mental health research; and brain-machine intelligence and brain-inspired computing.