Advanced computational intelligence techniques have been designed and developed in recent years to cope with various big data challenges and provide fast and efficient analytics that assist in making critical decisions. With the rapid evolution and development of internet-based services and applications, this technology is receiving attention from researchers, industries, and academic communities and requires additional study. Convergence of Big Data Technologies and Computational Intelligent Techniques considers recent advancements in big data and computational intelligence across fields and disciplines and discusses the various opportunities and challenges of adoption. Covering topics such as deep learning, data mining, smart environments, and high-performance computing, this reference work is crucial for computer scientists, engineers, industry professionals, researchers, scholars, practitioners, academicians, instructors, and students.

Digital technology has enabled a number of internet-enabled devices that generate huge volumes of data from different systems. This large amount of heterogeneous data requires efficient data collection, processing, and analytical methods. Deep Learning is one of the latest efficient and feasible solutions that enable smart devices to function independently with a decision-making support system. Convergence of Deep Learning and Internet of Things: Computing and Technology contributes to technology and methodology perspectives in the incorporation of deep learning approaches in solving a wide range of issues in the IoT domain to identify, optimize, predict, forecast, and control emerging IoT systems. Covering topics such as data quality, edge computing, and attach detection and prediction, this premier reference source is a comprehensive resource for electricians, communications specialists, mechanical engineers, civil engineers, computer scientists, students and educators of higher education, librarians, researchers, and academicians.

With the development of innovative technologies, the study of industrial automation is obtaining extraordinary attention from academia, governments, researchers, and various industrial communities. The technical innovations in advanced technologies such as deep learning, blockchain, artificial intelligence of things (AIOT), and more have unlocked the potential for bringing intelligent automation and efficiency to the industries to control their various operations. These new technologies provide an efficient and secure way for industries to manage and develop products intelligently. Revolutionizing Industrial Automation Through the Convergence of Artificial Intelligence and the Internet of Things includes the recent advancements in exploring and developing AIOT-powered strategies and mechanisms for future industrial automation and transforming industrial functions and architectures to help improve various industrial operations. Covering topics such as convolutional neural networks, smart urban logistics, and industrial automation, this premier reference source is an excellent resource for computer scientists, IT managers, engineers, business leaders and executives, logistics managers, students and faculty of higher education, libraries, researchers, and academicians.

Emotional intelligence has emerged as an important area of research in the artificial intelligence field as it covers a wide range of real-life domains. Though machines may never need all the emotional skills that people need, there is evidence to suggest that machines require at least some of these skills to appear intelligent when interacting with people. To understand how deep learning-based emotional intelligence can be applied and utilized across industries, further study on its opportunities and future directions is required. Multidisciplinary Applications of Deep Learning-Based Artificial Emotional Intelligence explores artificial intelligence applications, such as machine and deep learning, in emotional intelligence and examines their use towards attaining emotional intelligence acceleration and augmentation. It provides research on tools used to simplify and streamline the formation of deep learning for system architects and designers. Covering topics such as data analytics, deep learning, knowledge management, and virtual emotional intelligence, this reference work is ideal for computer scientists, engineers, industry professionals, researchers, scholars, practitioners, academicians, instructors, and students.

In recent decades, there has been an increasing interest in using machine learning and, in the last few years, deep learning methods combined with other vision and image processing techniques to create systems that solve vision problems in different fields. There is a need for academicians, developers, and industry-related researchers to present, share, and explore traditional and new areas of computer vision, machine learning, deep learning, and their combinations to solve problems. The Handbook of Research on Computer Vision and Image Processing in the Deep Learning Era is designed to serve researchers and developers by sharing original, innovative, and state-of-the-art algorithms and architectures for applications in the areas of computer vision, image processing, biometrics, virtual and augmented reality, and more. It integrates the knowledge of the growing international community of researchers working on the application of machine learning and deep learning methods in vision and robotics. Covering topics such as brain tumor detection, heart disease prediction, and medical image detection, this premier reference source is an exceptional resource for medical professionals, faculty and students of higher education, business leaders and managers, librarians, government officials, researchers, and academicians.

Recent advances in socio-cognitive and affective computing require further study as countless benefits and opportunities have emerged from these innovative technologies that may be useful in a number of contexts throughout daily life. In order to ensure these technologies are appropriately utilized across sectors, the challenges and strategies for adoption as well as potential uses must be thoroughly considered. Principles and Applications of Socio-Cognitive and Affective Computing discusses several aspects of affective interactions and concepts in affective computing, the fundamentals of emotions, and emerging research and exciting techniques for bridging the emotional disparity between humans and machines, all within the context of interactions. The book also considers problem and solution guidelines emerging in cognitive computing, thus summarizing the roadmap of current machine computational intelligence techniques for affective computing. Covering a range of topics such as social interaction, robotics, and virtual reality, this reference work is crucial for scientists, engineers, industry professionals, academicians, researchers, scholars, practitioners, instructors, and students.

Robotics plays a pivotal role in many domains such as industry and medicine. Robots allow for increased safety, production rates, accuracy, and quality; however, robots must be well designed and controlled to achieve the required performance. The design and control of robotics involve many varying disciplines, such as mechanical engineering, electronics, and automation, and must be further studied to ensure the technology is utilized appropriately. Design and Control Advances in Robotics considers the most recent applications and design advances in robotics and highlights the latest developments and applications within the field of robotics. Covering key topics such as deep learning, machine learning, programming, automation, and control advances, this reference work is ideal for engineers, computer scientists, industry professionals, academicians, practitioners, scholars, researchers, instructors, and students.

In recent years, most applications deal with constraint decision-making systems as problems are based on imprecise information and parameters. It is difficult to understand the nature of data based on applications and it requires a specific model for understanding the nature of the system. Further research on constraint decision-making systems in engineering is required. Constraint Decision-Making Systems in Engineering derives and explores several types of constraint decisions in engineering and focuses on new and innovative conclusions based on problems, robust and efficient systems, and linear and non-linear applications. Covering topics such as fault detection, data mining techniques, and knowledge-based management, this premier reference source is an essential resource for engineers, managers, computer scientists, students and educators of higher education, librarians, researchers, and academicians.

Artificial intelligence (AI) and robotics have boomed in the 21st century. These emerging and disruptive technologies are immersed in our lives, from apps in mobile devices, the purchases we make on the internet streaming platforms, and even court decisions and predictive policing. Together with science and certain needs, relevant implementations of AI and robotics arise, related to its transparency, resulting in biases, the kinds of applications that can be implemented, and the degree of workforce replacement in decision-making assistance. It is essential to analyze the widely used AI techniques, the application of these technologies in different sectors, the implications of AI and robotics on society and welfare, and more. The Handbook of Research on Applied Artificial Intelligence and Robotics for Government Processes presents state-of-the-art research on AI and robotics in different fields of knowledge, its benefits, applications, and implications. It features chapters containing theoretical and practical research that analyzes the transparency and expandability of AI in different fields, as well as the analysis of unexpected results, biases, and cases of discrimination. Covering topics such as criminal intelligence, artificial intelligence-based chatbots, and gender violence, this major reference work is an excellent resource for government officials, practitioners in the public sector, business administrators and managers, IT professionals, law enforcement, federal agencies, students and faculty of higher education, researchers, and academicians.

Humans have the most advanced method of communication, which is known as natural language. While humans can use computers to send voice and text messages to each other, computers do not innately know how to process natural language. In recent years, deep learning has primarily transformed the perspectives of a variety of fields in artificial intelligence (AI), including speech, vision, and natural language processing (NLP). The extensive success of deep learning in a wide variety of applications has served as a benchmark for the many downstream tasks in AI. The field of computer vision has taken great leaps in recent years and surpassed humans in tasks related to detecting and labeling objects thanks to advances in deep learning and neural networks. Deep Learning Research Applications for Natural Language Processing explains the concepts and state-of-the-art research in the fields of NLP, speech, and computer vision. It provides insights into using the tools and libraries in Python for real-world applications. Covering topics such as deep learning algorithms, neural networks, and advanced prediction, this premier reference source is an excellent resource for computational linguists, software engineers, IT managers, computer scientists, students and faculty of higher education, libraries, researchers, and academicians.