Recently, several fog computing applications have been developed like IoT-based healthcare, 5G, blockchains, autonomous driving, and mobile wireless applications. They also address challenges such as data management, scalability, regulations, interoperability, device network human interfaces, security, and privacy. Further study on these applications is required to ensure this technology is utilized appropriately. Multi-Disciplinary Applications of Fog Computing: Responsiveness in Real-Time focuses on fog computing problems and solutions for various applications and covers the new approaches, architecture, and theoretical foundations in the fog paradigm of storage, communication, and computing. The book explores recent trends and challenges that lead to a potential course for the ideas, practices, norms, and strategies related to fog computing. Covering key topics such as data privacy, data analytics, and the internet of things, this reference work is ideal for computer scientists, policymakers, researchers, scholars, practitioners, instructors, and students.

The world is approaching a point where big data will start to play a beneficial role in many industries and organizations. Today, analyzing data for new insights has become an everyday norm, increasing the need for data analysts to use efficient and appropriate tools to provide quick and valuable results to clients. Existing research in the field currently lacks a full coverage of all essential algorithms, leaving a knowledge void for practical implementation and code in Python with all needed libraries and links to datasets used. Advanced Interdisciplinary Applications of Machine Learning Python Libraries for Data Science serves as a one-stop book to help emerging data scientists gain hands-on skills needed through real-world data and completely up-to-date Python code. It covers all the technical details, from installing the needed software to importing libraries and using the latest data sets; deciding on the right model; training, testing, and evaluating the model; and including NumPy, Pandas, and matplotlib. With coverage on various machine learning algorithms like regression, linear and logical regression, classification, support vector machine (SVM), clustering, k-nearest neighbor, market basket analysis, Apriori, k-means clustering, and visualization using Seaborne, it is designed for academic researchers, undergraduate students, postgraduate students, executive education program leaders, and practitioners.

Artificial intelligence has been utilized in a diverse range of industries as more people and businesses discover its many uses and applications. A current field of study that requires more attention, as there is much opportunity for improvement, is the use of artificial intelligence within literary works and social media analysis. The Handbook of Research on Artificial Intelligence Applications in Literary Works and Social Media presents contemporary developments in the adoption of artificial intelligence in textual analysis of literary works and social media and introduces current approaches, techniques, and practices in data science that are implemented to scrap and analyze text data. This book initiates a new multidisciplinary field that is the combination of artificial intelligence, data science, social science, literature, and social media study. Covering key topics such as opinion mining, sentiment analysis, and machine learning, this reference work is ideal for computer scientists, industry professionals, researchers, scholars, practitioners, academicians, instructors, and students.

Artificial Intelligence based Solutions for Industrial Applications aims to examine the utilization of artificial intelligence (AI) technologies to tackle difficult industrial issues and offers readers a thorough understanding of how these technologies are being employed to address intricate industrial challenges and to stimulate innovation. This book explores the fundamental principles of artificial intelligence (AI) and its practical use in industrial environments. This book improves understanding of core concepts, the present state of the art and real-time implementation of AI in many industrial applications. This book describes the detailed implementation of AI in the industrial sector as well as related case studies for in-depth understanding. Basic concepts, related work reviews, illustrations, empirical results, and tables are integrated within each chapter to give the readers the opportunity to gain maximum knowledge and to easily understand the methodology and results presented. This book introduces a variety of smart algorithms to help in filtering important information and to solve problems in the application domains. Application of machine learning and deep learning in the industry demonstrates the capabilities by which it may be used to solve practical problems in the 'Fourth Industrial Revolution', and it equips readers with the necessary knowledge and tools to design solutions by themselves with the help of theory and practical examples dealt with. The fourth industrial revolution and its consequences on society and organizations are discussed in this book. Features: Detailed understanding of the industrial application of AI. Discussion of core concepts of different machine learning and deep learning techniques such as artificial neural networks, support vector machines, K –nearest neighbour, decision tree, logistic regression, and many more. Detailed study on various industrial applications of machine learning and deep learning in healthcare, education, entertainment, share market, manufacturing, and many more. Case studies on industrial application of AI Summataion of the fourth industrial revolution and its consquences on society and organizations. This book is primarily written for graduate students, engineers, and academic researchers, industrial practitioners, and anyone who wants to optimize production processes, explore AI technology, or stay ahead in the industrial field. It covers the complexities of AI in industrial contexts from core basic understanding to complex implementation.

The rapid growth and capability of artificial intelligence, digital twin, and the internet of things are unlocking incredible opportunities to overcome some of the greatest environmental and social impact challenges currently facing the global community, such as feeding a growing population, safety, affordable housing, and environmental sustainability. The Handbook of Research on Applications of AI, Digital Twin, and Internet of Things for Sustainable Development provides an interdisciplinary platform encompassing research on the potential opportunities and risks of reaching sustainable development using artificial intelligence, digital twin, and the internet of things. Covering key topics such as big data, environmental protection, and smart cities, this major reference work is ideal for computer scientists, industry professionals, researchers, scholars, academicians, librarians, policymakers, practitioners, educators, and students.

Communication based on the internet of things (IoT) generates huge amounts of data from sensors over time, which opens a wide range of applications and areas for researchers. The application of analytics, machine learning, and deep learning techniques over such a large volume of data is a very challenging task. Therefore, it is essential to find patterns, retrieve novel insights, and predict future behavior using this large amount of sensory data. Artificial intelligence (AI) has an important role in facilitating analytics and learning in the IoT devices. Applying AI-Based IoT Systems to Simulation-Based Information Retrieval provides relevant frameworks and the latest empirical research findings in the area. It is ideal for professionals who wish to improve their understanding of the strategic role of trust at different levels of the information and knowledge society and trust at the levels of the global economy, networks and organizations, teams and work groups, information systems, and individuals as actors in the networked environments. Covering topics such as blockchain visualization, computer-aided drug discovery, and health monitoring, this premier reference source is an excellent resource for business leaders and executives, IT managers, security professionals, data scientists, students and faculty of higher education, librarians, hospital administrators, researchers, and academicians.

Enhancing healthcare quality and fostering patient engagement are pivotal in healthcare management. As global healthcare systems face challenges, from rising costs to various patient outcomes, innovations in technology transform patient care techniques. From electronic health record systems that streamline data management to telemedicine platforms to expand access to care, the integration of technology improves efficiency, accuracy, and patient satisfaction. Achieving healthcare quality also demands more research into effective management strategies that combine technological innovations with patient-centric care models. Improving Healthcare Quality and Patient Engagement: Management and Technology Insights explores key insights into the convergence of healthcare management and technology. It outlines the integration of healthcare quality and patient care for improved patient outcomes and reshaped healthcare services. This book covers topics such as digital technology, sustainable development, and geriatric care, and is a useful resource for medical workers, healthcare professionals, business owners, sociologists, computer engineers, data scientists, researchers, and academicians.