Artificial intelligence (AI) is taking an increasingly important role in our society. From cars, smartphones, airplanes, consumer applications, and even medical equipment, the impact of AI is changing the world around us. The ability of machines to demonstrate advanced cognitive skills in taking decisions, learn and perceive the environment, predict certain behavior, and process written or spoken languages, among other skills, makes this discipline of paramount importance in today's world. Although AI is changing the world for the better in many applications, it also comes with its challenges. This book encompasses many applications as well as new techniques, challenges, and opportunities in this fascinating area.

Though an individual can process a limitless amount of information, the human brain can only comprehend a small amount of data at a time. Using technology can improve the process and comprehension of information, but the technology must learn to behave more like a human brain to employ concepts like memory, learning, visualization ability, and decision making. Emerging Trends and Applications in Cognitive Computing is a fundamental scholarly source that provides empirical studies and theoretical analysis to show how learning methods can solve important application problems throughout various industries and explain how machine learning research is conducted. Including innovative research on topics such as deep neural networks, cyber-physical systems, and pattern recognition, this collection of research will benefit individuals such as IT professionals, academicians, students, researchers, and managers.

Within the healthcare sector, a pressing need for transformative changes is growing. From chronic diseases to complex diagnostic procedures, the industry stands at the crossroads of technological innovation and a burgeoning demand for more efficient, precise interventions. Patient expectations are soaring, and the deluge of medical data is overwhelming traditional healthcare systems. It is within this challenging environment that AI-Driven Innovations in Digital Healthcare: Emerging Trends, Challenges, and Applications emerges as a beacon of insight and practical solutions. The traditional healthcare framework is struggling to keep pace with the diverse demands of patients and the ever-expanding volume of medical data. As diseases become more intricate, attempts to provide timely identification and precise treatment of ailments become increasingly elusive. The urgency for a paradigm shift in healthcare delivery is emphasized by the critical need for early interventions, particularly in disease prediction. This challenge necessitates a holistic approach that harnesses the power of artificial intelligence (AI) and innovative technologies to steer healthcare toward a more responsive and patient-centric future.

With the emergence of smart technology and automated systems in today’s world, big data is being incorporated into many applications. Trends in data can be detected and objects can be tracked based on the real-time data that is utilized in everyday life. These connected sensor devices and objects will provide a large amount of data that is to be analyzed quickly, as it can accelerate the transformation of smart technology. The accuracy of prediction of artificial intelligence (AI) systems is drastically increasing by using machine learning and other probability and statistical approaches. Big data and geospatial data help to solve complex issues and play a vital role in future applications. Emerging Trends, Techniques, and Applications in Geospatial Data Science provides an overview of the basic concepts of data science, related tools and technologies, and algorithms for managing the relevant challenges in real-time application domains. The book covers a detailed description for readers with practical ideas using AI, the internet of things (IoT), and machine learning to deal with the analysis, modeling, and predictions from big data. Covering topics such as field spectra, high-resolution sensing imagery, and spatiotemporal data engineering, this premier reference source is an excellent resource for data scientists, computer and IT professionals, managers, mathematicians and statisticians, health professionals, technology developers, students and educators of higher education, librarians, researchers, and academicians.

"This book focuses on e-collaboration technologies that enable group-based interaction, and the impact that those technologies have on group work"--Provided by publisher.

The increase in computing power and sensor data has driven Information Technology on end devices, such as smart phones or automobiles. The widespread application of IT across the globe includes manufacturing, engineering, retail, e-commerce, health care, education, financial services, banking, space exploration, politics (to help predict the sentiments of voter demographics), etc. The papers in this conference proceeding examine and discuss various interdisciplinary researches that could accelerate the advent of Information Technology.

The confluence of Artificial Intelligence of Things (AIoT) and Semantic Web technologies is nothing short of revolutionary. The profound impact of this synergy extends far beyond the realms of industry, research, and society; it shapes the very fabric of our future. Semantic Web Technologies and Applications in Artificial Intelligence of Things is a meticulously crafted reference that not only acknowledges this significance but also serves as a guide for those navigating the complexities of Industry 4.0 and AIoT. This curated compendium of cutting-edge technologies acts as a veritable knowledge base for future developments. As academics, scholars, and industry professionals, the ideal audience of this book, will find meticulously curated content that caters to their diverse interests and expertise, covering topics ranging from smart agriculture, manufacturing, industry, health sciences, and government. Seasoned academics, students, and visionary industry leaders, will find this book to be an indispensable guide that paves the way for innovation and progress.

The book proposes techniques, with an emphasis on the financial sector, which will make recommendation systems both accurate and explainable. The vast majority of AI models work like black box models. However, in many applications, e.g., medical diagnosis or venture capital investment recommendations, it is essential to explain the rationale behind AI systems decisions or recommendations. Therefore, the development of artificial intelligence cannot ignore the need for interpretable, transparent, and explainable models. First, the main idea of the explainable recommenders is outlined within the background of neuro-fuzzy systems. In turn, various novel recommenders are proposed, each characterized by achieving high accuracy with a reasonable number of interpretable fuzzy rules. The main part of the book is devoted to a very challenging problem of stock market recommendations. An original concept of the explainable recommender, based on patterns from previous transactions, is developed; it recommends stocks that fit the strategy of investors, and its recommendations are explainable for investment advisers.