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This cutting-edge Handbook offers a comprehensive introduction to the emerging research field of artificial intelligence (AI) in human resource management (HRM). Broadly mapping AI fields relevant for HR, it not only considers the more well-known areas of machine learning and natural language processing, but also lesser-known fields such as affective computing and robotic process automation.
This book showcases how EcoMechatronics can increase sustainability within engineering and manufacturing. It brings together material from experts in core mechatronics technologies, discussing the challenges related to moving towards more environmentally friendly methods, and presenting numerous case studies and examples of EcoMechatronics oriented applications. The book begins with an introduction to EcoMechatronics in the context of sustainability, before covering core conceptual, technical and design issues associated with EcoMechatronics. It then offers a series of case studies and examples of EcoMechatronics oriented applications and finally, a consideration of the educational issues associated with moving to a new generation of environmentally oriented mechatronic engineers. EcoMechatronics will be of interest to practicing engineers, researchers, system developers. and graduate students in the field of mechatronics and environmental engineering.
Those entrenched in academia often have daunting processes of formulating research questions, data collection, analysis, and scholarly paper composition. Artificial intelligence (AI) emerges as an invaluable ally, simplifying these processes and elevating the quality of scholarly output. Where the pursuit of knowledge meets the cutting edge of technology, Utilizing AI Tools in Academic Research Writing unfolds a transformative journey through the symbiotic relationship between AI and academic inquiry. It offers practical insights into the myriad ways AI can revolutionize academic pursuits. This book extends beyond theoretical discussions, delving into practical dimensions of AI integration, demonstrating how it facilitates topic identification, refines research design, empowers data analysis, and enriches literature reviews. Readers will explore AI's indispensable role in precise hypothesis development, enhancing the very foundation of academic inquiry. The book introduces AI-powered tools that streamline writing and editing, ensuring research papers meet the highest standards of clarity and correctness. Ethical considerations in AI-integrated research take center stage, emphasizing responsible and transparent practices. This book is ideal for doctoral candidates, master's students, undergraduates, or seasoned faculty members.
This book offers a comprehensive approach to the question of how artificial intelligence (AI) impacts politics, economy, and the society today. In this view, it is quintessential for understanding the complex nature of AI and its role in today’s world. The book has been divided into three parts. Part one is devoted to the question of how AI will be used for security and defense purposes, including combat in war zones. Part two looks at the value added of AI and machine learning for decision-making in the fields of politics and business. Part three consists of case studies—covering the EU, the USA, Saudi Arabia, Portugal, and Poland—that discuss how AI is being used in the realms of politics, security and defense. The discussion in the book opens with the question of the nature of AI, as well as of ethics and the use of AI in combat. Subsequently, the argument covers issues as diverse as the militarization of AI, the use of AI in strategic studies and military strategy design. These topics are followed by an insight into AI and strategic communication (StratCom), including disinformation, as well as into AI and finance. The case-studies included in part 3 of the book offer a captivating overview of how AI is being employed to stimulate growth and development, to promote data- and evidence-driven policy-making, to enable efficient and inclusive digital transformation and other related issues. Written by academics and practitioners in an academically sound, yet approachable manner, this volume queries issues and topics that form the thrust of processes that transform world politics, economics and society. As such, this volume will serve as the primer for students, researchers, lectures and other professionals who seek to understand and engage with the variety of issues AI implicates.
This two-volume set LNCS 12192 and 12193 constitutes the refereed proceedings of the 12th International Conference on Cross-Cultural Design, CCD 2020, held as part of HCI International 2020 in Copenhagen, Denmark in July 2020.The conference was held virtually due to the corona pandemic. The total of 1439 papers and 238 posters included in the 40 HCII 2020 proceedings volumes was carefully reviewed and selected from 6326 submissions. The regular papers of Cross-Cultural Design CCD 2020 presented in this volume were organized in topical sections named: Health, Well-being and Social Design Across Cultures, Culture, Learning and Communication, and Culture and Creativity.
This book addresses the growing need for machine learning and data mining in neuroscience. The book offers a basic overview of the neuroscience, machine learning and the required math and programming necessary to develop reliable working models. The material is presented in a easy to follow user-friendly manner and is replete with fully working machine learning code. Machine Learning for Neuroscience: A Systematic Approach, tackles the needs of neuroscience researchers and practitioners that have very little training relevant to machine learning. The first section of the book provides an overview of necessary topics in order to delve into machine learning, including basic linear algebra and Python programming. The second section provides an overview of neuroscience and is directed to the computer science oriented readers. The section covers neuroanatomy and physiology, cellular neuroscience, neurological disorders and computational neuroscience. The third section of the book then delves into how to apply machine learning and data mining to neuroscience and provides coverage of artificial neural networks (ANN), clustering, and anomaly detection. The book contains fully working code examples with downloadable working code. It also contains lab assignments and quizzes, making it appropriate for use as a textbook. The primary audience is neuroscience researchers who need to delve into machine learning, programmers assigned neuroscience related machine learning projects and students studying methods in computational neuroscience.
This book constitutes the refereed proceedings of the 4th Symposium of the Norwegian AI Society, NAIS 2022, held in Oslo, Norway, during May 31–June 1, 2022. The 11 full papers included in this book were carefully reviewed and selected from 17 submissions. They were organized in topical sections as follows: robotics and intelligent systems; ai in cyber and digital sphere; ai in biological applications and medicine; and towards new ai methods. This is an open access book.