As libraries transition into the digital age, they encounter a pressing challenge: outdated information systems hinder their ability to meet the diverse needs of patrons. Traditional library management systems struggle to cope with the demands of modern users, resulting in inefficient resource allocation, limited accessibility, and disjointed user experiences. This disconnect between antiquated systems and evolving user expectations poses a significant barrier to libraries striving to remain relevant in an increasingly digital world. Improving Library Systems with AI: Applications, Approaches, and Bibliometric Insights presents a comprehensive solution to this pressing problem. By integrating modern digital tools and technologies, libraries can revolutionize their information systems, enhancing accessibility, efficiency, and user satisfaction. This book offers practical insights and strategies for modernizing library services and operations, from digitizing physical resources to implementing advanced search algorithms and data analytics. Librarians, administrators, and technology providers will find invaluable guidance on navigating the complexities of digital transformation and maximizing the impact of their efforts.

Due to changes in the learning and research environment, changes in the behavior of library users, and unique global disruptions such as the COVID-19 pandemic, libraries have had to adapt and evolve to remain up-to-date and responsive to their users. Thus, libraries are adding new, digital resources and services while maintaining most of the old, traditional resources and services. New areas of research and inquiry in the field of library and information science explore the applications of machine learning, artificial intelligence, and other technologies to better serve and expand the library community. The Handbook of Research on Knowledge and Organization Systems in Library and Information Science examines new technologies and systems and their application and adoption within libraries. This handbook provides a global perspective on current and future trends concerning library and information science. Covering topics such as machine learning, library management, ICTs, blockchain technology, social media, and augmented reality, this book is essential for librarians, library directors, library technicians, media specialists, data specialists, catalogers, information resource officers, administrators, IT consultants and specialists, academicians, and students.

In an increasingly globalized world, migration has become prevalent, with individuals seeking better opportunities in new environments. However, migrants often face numerous challenges, including racism, language barriers, and unemployment, which can hinder their integration and well-being in the host country. These obstacles require a deeper understanding of migrants' experiences and the factors influencing their integration process. Principles of Conducting Qualitative Research in Multicultural Settings takes a comprehensive approach to understanding and addressing the complexities of migration and multiculturalism. This book offers a detailed exploration of qualitative research design, data collection techniques, and data analysis methods tailored for studying multicultural populations. By incorporating diverse research methods such as participant observation and visual documentation, the book provides a holistic understanding of immigrant experiences, enriching policy recommendations and interventions.

Build a solid foundation in surgical AI with this engaging, comprehensive guide for AI novices Machine learning, neural networks, and computer vision in surgical education, practice, and research will soon be de rigueur. Written for surgeons without a background in math or computer science, Artificial Intelligence in Surgery provides everything you need to evaluate new technologies and make the right decisions about bringing AI into your practice. Comprehensive and easy to understand, this first-of-its-kind resource illustrates the use of AI in surgery through real-life examples. It covers the issues most relevant to your practice, including: Neural Networks and Deep Learning Natural Language Processing Computer Vision Surgical Education and Simulation Preoperative Risk Stratification Intraoperative Video Analysis OR Black Box and Tracking of Intraoperative Events Artificial Intelligence and Robotic Surgery Natural Language Processing for Clinical Documentation Leveraging Artificial Intelligence in the EMR Ethical Implications of Artificial Intelligence in Surgery Artificial Intelligence and Health Policy Assessing Strengths and Weaknesses of Artificial Intelligence Research Finally, the appendix includes a detailed glossary of terms and important learning resources and techniques―all of which helps you interpret claims made by studies or companies using AI.

This book presents a compilation of the most recent implementation of artificial intelligence methods for solving different problems generated by the COVID-19. The problems addressed came from different fields and not only from medicine. The information contained in the book explores different areas of machine and deep learning, advanced image processing, computational intelligence, IoT, robotics and automation, optimization, mathematical modeling, neural networks, information technology, big data, data processing, data mining, and likewise. Moreover, the chapters include the theory and methodologies used to provide an overview of applying these tools to the useful contribution to help to face the emerging disaster. The book is primarily intended for researchers, decision makers, practitioners, and readers interested in these subject matters. The book is useful also as rich case studies and project proposals for postgraduate courses in those specializations.

This text provides a comprehensive vision of the future of health technology by looking at the ways to advance medical technologies, health information infrastructure and intellectual leadership. It also explores technology creations, adoption processes and the impact of evolving technologies.

This book probes the dynamics of academic research and scholarship evaluation. Readers will learn about scholarly metric evolution, impact factors, disruptive technologies, and a myriad of forces affecting policy development at institutions through an examination of widely-used measurements and growing concerns about their influence.

In the rapidly evolving landscape of industrial activities, artificial intelligence (AI) has emerged as a powerful force driving transformative change. Among its many applications, AI has proven to be instrumental in reducing processing costs associated with optimization challenges. The intersection of AI with optimization and multi-criteria decision making (MCDM) techniques has led to practical solutions in diverse fields such as manufacturing, transportation, finance, economics, and artificial intelligence. Using Traditional Design Methods to Enhance AI-Driven Decision Making delves into a wide array of topics related to optimization, decision-making, and their applications. Drawing on foundational contributions, system developments, and innovative techniques, the book explores the synergy between traditional design methods and AI-driven decision-making approaches. The book is ideal for higher education faculty and administrators, students of higher education, librarians, researchers, graduate students, and academicians. Contributors are invited to explore a wide range of topics, including the role of AI-driven decision-making in leadership, trends in AI-driven decision-making in Industry 5.0, applications in various industries such as manufacturing, transportation, healthcare, and banking services, as well as AI-driven optimization in mechanical engineering and materials.

This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.

Library Services in Theory and Context provides a theoretical framework for considering the nature of library services. This book is organized into four parts encompassing 16 chapters that focus on the management of library services. The first part presents some basic problems in librarianship, along with the scope and probable nature of appropriate theory. The second part considers the five key aspects and use of library services, including inquiries, retrieval, the process of becoming informed, the demand for library services, and the allocation of resources to and within library services. The third part deals with the connection and extensions of library services, specifically the problem of achieving internal consistency and the generalizability of conceptual framework to information services. The fourth part reconsiders some of the basic conceptual problems in library services. This book will be of great value to school administrators and librarians.