Computational modeling and simulation has developed and expanded into a diverse range of fields such as digital signal processing, image processing, robotics, systems biology, and many more; enhancing the need for a diversifying problem solving applications in this area. Efficiency and Scalability Methods for Computational Intellect presents various theories and methods for approaching the problem of modeling and simulating intellect in order to target computation efficiency and scalability of proposed methods. Researchers, instructors, and graduate students will benefit from this current research and will in turn be able to apply the knowledge in an effective manner to gain an understanding of how to improve this field.

This book provides a systematic and comprehensive overview of AI and machine learning which have got the ability to identify patterns in large and complex data sets. A remarkable success has been experienced in the last decade by emulating the brain computer interface. It presents the cognitive science methods and technologies that have played an important role at the core of practical solutions for a wide scope of tasks between handheld apps, industrial process control, autonomous vehicles, environmental policies, life sciences, playing computer games, computational theory, and engineering development. The chapters in this book focuses on audiences interested in machine learning, cognitive and neuro-inspired computational systems, their theories, mechanisms, and architecture, which underline human and animal behaviour, and their application to conscious and intelligent systems. In the current version, it focuses on the successful implementation and step-by-step explanation of practical applications of the domain. It also offers a wide range of inspiring and interesting cutting-edge contributions on applications of machine learning and cognitive science such as healthcare products, medical electronics, and gaming.

The 2008 IEEE World Congress on Computational Intelligence (WCCI 2008), held during June 1–6, 2008 in Hong Kong, China, marked an important milestone in advancing the paradigms of the new fields of computational intelligence. As the fifth event in the series that has spanned the globe (Orlando-1994, Anchorage-1998, Honolulu-2002, Vancouver-2006), the congress offered renewed and refreshing focus on the progress in nature-inspired and linguistically motivated computation. Most of the congress’s program featured regular and special technical sessions that provided participants with new insights into the most recent developments in the field. As a tradition, in addition to the parallel technical sessions, WCCI holds a series of plenary and invited lectures which are not included in the congress proceedings. As its predecessors, at WCCI 2008, 20 expert speakers shared their expertise on broader, if not panoramic, topics spanning a diverse spectrum of computational intelligence in the areas of neurocomputing, fuzzy systems, evolutionary computation, and adjacent areas. Thanks to their time and expertise, we endeavored to offer this volume to attendees directly at the congress and the general public afterwards.

This book illustrates the design, development, and evaluation of personalized intelligent tutoring systems that emulate human cognitive intelligence by incorporating artificial intelligence. Artificial intelligence is an advanced field of research. It is particularly used in the field of education to increase the effectiveness of teaching and learning techniques. With the advancement of internet technology, there is a rapid growth in web based distance learning modality. This mode of learning is better known as the e-learning system. These systems present low intelligence because they offer a pre-identified learning frame to their learners. The advantage of these systems is to offer to learn anytime and anyplace without putting emphasis on a learner's needs, competency level, and previous knowledge. Every learner has different grasping levels, previous knowledge, and preferred mode of learning, and hence, the learning process of one individual may significantly vary from other individuals. This book provides a complete reference for students, researchers, and industry practitioners interested in keeping abreast of recent advancements in this field. It encompasses cognitive intelligence and artificial intelligence which are very important for deriving a roadmap for future research on intelligent systems.

The book is a collection of the best-selected research papers presented at the International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications (ICMISC 2023) held in September 2023 at the CMR Institute of Technology, Hyderabad, Telangana, India. This book will contain articles on current trends in machine learning, the internet of things, and smart city applications, emphasizing multi-disciplinary research in the area of artificial intelligence and cyberphysical systems. The book is a great resource for scientists, research scholars, and PG students to formulate their research ideas and find future directions in these areas. Further, this book serves as a reference work to understand the latest technologies used by practice engineers across the globe.

The two-volume set LNAI 9119 and LNAI 9120 constitutes the refereed proceedings of the 14th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2015, held in Zakopane, Poland in June 2015. The 142 revised full papers presented in the volumes, were carefully reviewed and selected from 322 submissions. These proceedings present both traditional artificial intelligence methods and soft computing techniques. The goal is to bring together scientists representing both areas of research. The first volume covers topics as follows neural networks and their applications, fuzzy systems and their applications, evolutionary algorithms and their applications, classification and estimation, computer vision, image and speech analysis, and the workshop: large-scale visual recognition and machine learning. The second volume has the focus on the following subjects: data mining, bioinformatics, biometrics and medical applications, concurrent and parallel processing, agent systems, robotics and control, artificial intelligence in modeling and simulation, and various problems of artificial intelligence.

Major tasks planned during the first period of the research project have been the analysis of data and evaluation of its statistical properties. Selection of feasible neural network architectures and their feasibility study have been pursued. Also, development of data manipulation software has been facilitated during the initial phase of the project work for the purpose of properly interfacing data with the neural network models.

This book discusses various machine learning & cognitive science approaches, presenting high-throughput research by experts in this area. Bringing together machine learning, cognitive science and other aspects of artificial intelligence to help provide a roadmap for future research on intelligent systems, the book is a valuable reference resource for students, researchers and industry practitioners wanting to keep abreast of recent developments in this dynamic, exciting and profitable research field. It is intended for postgraduate students, researchers, scholars and developers who are interested in machine learning and cognitive research, and is also suitable for senior undergraduate courses in related topics. Further, it is useful for practitioners dealing with advanced data processing, applied mathematicians, developers of software for agent-oriented systems and developers of embedded and real-time systems.