Advances In Combining Intelligent Methods

Complex problems usually cannot be solved by individual methods or techniques and require the synergism of more than one of them to be solved. This book presents a number of current efforts that use combinations of methods or techniques to solve complex problems in the areas of sentiment analysis, search in GIS, graph-based social networking, intelligent e-learning systems, data mining and recommendation systems. Most of them are connected with specific applications, whereas the rest are combinations based on principles. Most of the chapters are extended versions of the corresponding papers presented in CIMA-15 Workshop, which took place in conjunction with IEEE ICTAI-15, in November 2015. The rest are invited papers that responded to special call for papers for the book. The book is addressed to researchers and practitioners from academia or industry, who are interested in using combined methods in solving complex problems in the above areas.

This book presents a number of research efforts in combining AI methods or techniques to solve complex problems in various areas. The combination of different intelligent methods is an active research area in artificial intelligence (AI), since it is believed that complex problems can be more easily solved with integrated or hybrid methods, such as combinations of different soft computing methods (fuzzy logic, neural networks, and evolutionary algorithms) among themselves or with hard AI technologies like logic and rules; machine learning with soft computing and classical AI methods; and agent-based approaches with logic and non-symbolic approaches. Some of the combinations are already extensively used, including neuro-symbolic methods, neuro-fuzzy methods, and methods combining rule-based and case-based reasoning. However, other combinations are still being investigated, such as those related to the semantic web, deep learning and swarm intelligence algorithms. Most are connected with specific applications, while the rest are based on principles.

This book presents recent research on the hybridization of intelligent methods, which refers to combining methods to solve complex problems. It discusses hybrid approaches covering different areas of intelligent methods and technologies, such as neural networks, swarm intelligence, machine learning, reinforcement learning, deep learning, agent-based approaches, knowledge-based system and image processing. The book includes extended and revised versions of invited papers presented at the 6th International Workshop on Combinations of Intelligent Methods and Applications (CIMA 2016), held in The Hague, Holland, in August 2016. The book is intended for researchers and practitioners from academia and industry interested in using hybrid methods for solving complex problems.

Big data and data science are transforming our world today in ways we could not have imagined at the beginning of the twenty-first century. The accompanying wave of innovation has sparked advances in healthcare, engineering, business, science, and human perception, among others. The tremendous advances in computing power and intelligent techniques have opened many opportunities for managing data and investigating data in virtually every field, and the scope of data science is expected to grow over the next decade. These future research achievements will solve old challenges and create new opportunities for growth and development. Thus, the research presented in this book is interdisciplinary and covers themes embracing emotions, artificial intelligence, robotics applications, sentiment analysis, smart city problems, assistive technologies, speech melody, and fall and abnormal behavior detection. The book is directed to the researchers, practitioners, professors and students interested in recent advances in methodologies and applications of data science. An introduction to the topic is provided, and research challenges and future research opportunities are highlighted throughout.

This book forms an excellent basis for the development of intelligent manufacturing system for Industry 4.0, digital and distributed manufacturing, and factories for future. This book of new developments and advancement in intelligent control and optimization system for production engineering serves as a good companion to scholars, manufacturing companies, and RTO to improve the efficiency of production systems.

This book comprises select peer-reviewed papers presented at the International Conference on Advanced Engineering Optimization Through Intelligent Techniques (AEOTIT) 2018. The book combines contributions from academics and industry professionals, and covers advanced optimization techniques across all major engineering disciplines like mechanical, manufacturing, civil, automobile, electrical, chemical, computer and electronics engineering. Different optimization techniques and algorithms such as genetic algorithm (GA), differential evolution (DE), simulated annealing (SA), particle swarm optimization (PSO), artificial bee colony (ABC) algorithm, artificial immune algorithm (AIA), teaching-learning-based optimization (TLBO) algorithm and many other latest meta-heuristic techniques and their applications are discussed. This book will serve as a valuable reference for students, researchers and practitioners and help them in solving a wide range of optimization problems.

Although recommendation systems have become a vital research area in the fields of cognitive science, approximation theory, information retrieval and management sciences, they still require improvements to make recommendation methods more effective and intelligent. Intelligent Techniques in Recommendation Systems: Contextual Advancements and New Methods is a comprehensive collection of research on the latest advancements of intelligence techniques and their application to recommendation systems and how this could improve this field of study.

Computational Intelligence (CI) is a term corresponding to a new generation of algorithmic methodologies in artificial intelligence, which combines elements of learning, adaptation, evolution and approximate (fuzzy) reasoning to create programs that can be considered intelligent. Supply Chain Optimization, Design, and Management: Advances and Intelligent Methods presents computational intelligence methods for addressing supply chain issues. Emphasis is given to techniques that provide effective solutions to complex supply chain problems and exhibit superior performance to other methods of operations research.