As technology spreads globally, researchers and scientists continue to develop and study the strategy behind creating artificial life. This research field is ever expanding, and it is essential to stay current in the contemporary trends in artificial life, artificial intelligence, and machine learning. This an important topic for researchers and scientists in the field as well as industry leaders who may adapt this technology. The Handbook of Research on New Investigations in Artificial Life, AI, and Machine Learning provides concepts, theories, systems, technologies, and procedures that exhibit properties, phenomena, or abilities of any living system or human. This major reference work includes the most up-to-date research on techniques and technologies supporting AI and machine learning. Covering topics such as behavior classification, quality control, and smart medical devices, it serves as an essential resource for graduate students, academicians, stakeholders, practitioners, and researchers and scientists studying artificial life, cognition, AI, biological inspiration, machine learning, and more.

Research on artificial life is critical to solving various dynamic obstacles individuals face on a daily basis. From electric wheelchairs to navigation, artificial life can play a role in improving both the simple and complex aspects of civilian life. The Handbook of Research on Investigations in Artificial Life Research and Development is a vital scholarly reference source that examines emergent research in handling real-world problems through the application of various computation technologies and techniques. Examining topics such as computational intelligence, multi-agent systems, and fuzzy logic, this publication is a valuable resource for academicians, scientists, researchers, and individuals interested in artificial intelligence developments.

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.

Computational science, in collaboration with engineering, acts as a bridge between hypothesis and experimentation. It is essential to use computational methods and their applications in order to automate processes as many major industries rely on advanced modeling and simulation. Computational science is inherently interdisciplinary and can be used to identify and evaluate complicated systems, foresee their performance, and enhance procedures and strategies. Applications of Computational Science in Artificial Intelligence delivers technological solutions to improve smart technologies architecture, healthcare, and environmental sustainability. It also provides background on key aspects such as computational solutions, computation framework, smart prediction, and healthcare solutions. Covering a range of topics such as high-performance computing and software infrastructure, this reference work is ideal for software engineers, practitioners, researchers, scholars, academicians, instructors, and students.

Optimized linear algebra (LA) libraries that are able to exploit the underlying hardware are always of interest in the high-performance computing community. The implementation of LA software has evolved along with computer architecture, while the specification remains unaltered almost from the beginning. It is important to differentiate between the specification of LA libraries and their implementation. Because LA libraries pursue high performance, the implementation for a given architecture needs to be optimized for it specifically. However, the type of operations included in the libraries, the input/output parameters, and the data types to be handled are common to all of them. This is why, while the specification remains constant, the implementation evolves with the creation of new architectures. Developing Linear Algebra Codes on Modern Processors: Emerging Research and Opportunities presents the main characteristics of LA libraries, showing the differences between the standards for sparse and dense versions. It further explores relevant linear algebra problems and shows, in a clear and understandable way, how to solve them using different computer architectures. Covering topics such as programming models, batched computing, and distributed memory platforms, this premier reference source is an excellent resource for programmers, computer scientists, engineers, students and faculty of higher education, librarians, researchers, and academicians.

The academic community is currently facing the challenge of navigating the complexities of swarm robotics. This field demands understanding the design, control, and coordination of autonomous robotic swarms. The intricacies of developing algorithms that facilitate communication, cooperation, and adaptation among simple individual agents remain a formidable obstacle. Addressing issues like task allocation, formation control, path planning, and decentralized decision-making are pivotal to unlocking the true potential of swarm robotics. Bio-inspired Swarm Robotics and Control: Algorithms, Mechanisms, and Strategies immerses readers in the cutting-edge realm of swarm robotics, a discipline inspired by the intricate choreography observed in biological systems like insect colonies, bird flocks, and fish schools. Encompassing a rich array of bio-inspired algorithms, mechanisms, and strategies, the text elucidates how robots can communicate, cooperate, and adapt within dynamic environments. The book propels robotics, automation, and artificial intelligence advancements by fostering interdisciplinary connections and charting a course toward more efficient and resilient multi-robot systems. This book is ideal for biologists, engineers, and computer scientists to join forces in unlocking the full potential of swarm robotics.

Due to the rise of new applications in electronic appliances and pervasive devices, automated hand gesture recognition (HGR) has become an area of increasing interest. HGR developments have come a long way from the traditional sign language recognition (SLR) systems to depth and wearable sensor-based electronic devices. Where the former are more laboratory-oriented frameworks, the latter are comparatively realistic and practical systems. Based on various gestural traits, such as hand postures, gesture recognition takes different forms. Consequently, different interpretations can be associated with gestures in various application contexts. A considerable amount of research is still needed to introduce more practical gesture recognition systems and associated algorithms. Challenges and Applications for Hand Gesture Recognition highlights the state-of-the-art practices of HGR research and discusses key areas such as challenges, opportunities, and future directions. Covering a range of topics such as wearable sensors and hand kinematics, this critical reference source is ideal for researchers, academicians, scholars, industry professionals, engineers, instructors, and students.

Smart systems are rapidly evolving and finding ways to influence different aspects of human life, industry, and the environment. Smart systems based on available data should have the ability to predict and be adaptive, which leads to performing reliable, smart actions. Smartness and learning capabilities are essential characteristics describing smart systems besides connectivity and digital virtual cloudification technologies. Perspectives and Considerations on the Evolution of Smart Systems discusses the latest edge development that informs and facilitates the next level of development. It highlights how the evolving technologies and techniques are going to impact the developments in the field considering climate, environment, circular economy, and ecosystems. Covering topics such as dynamic difficulty adjustment, intelligent control, and serious games, this premier reference source is an excellent resource for engineers, computer scientists, IT professionals, developers, data analysts, students and educators of higher education, librarians, researchers, and academicians.

This two-volume set LNCS 12239-12240 constitutes the refereed proceedings of the 6th International Conference on Artificial Intelligence and Security, ICAIS 2020, which was held in Hohhot, China, in July 2020. The conference was formerly called “International Conference on Cloud Computing and Security” with the acronym ICCCS. The total of 142 full papers presented in this two-volume proceedings was carefully reviewed and selected from 1064 submissions. The papers were organized in topical sections as follows: Part I: Artificial intelligence and internet of things. Part II: Internet of things, information security, big data and cloud computing, and information processing.

An authoritative, up-to-date survey of the state of the art in artificial intelligence, written for non-specialists.