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This book provides a comprehensive exploration of Computational Intelligence (CI) techniques and their applications, offering valuable insights into advanced information processing, Machine Learning concepts, and their impact on agile manufacturing systems.
Computational Intelligence presents a new concept for advanced information processing. Computational Intelligence (CI) is the principle, architecture, implementation, and growth of Machine Learning concepts that are physiologically and semantically inspired. Computational Intelligence methods aim to develop an approach to evaluating and creating flexible processing of human information, such as sensing, understanding, learning, recognizing, and thinking. The Artificial Neural Network simulates the human nervous system's physiological characteristics and has been implemented numerically to non-linear mapping. Fuzzy Logic Systems simulate the human brain's psychological characteristics and have been used for linguistic translation through membership functions and bioinformatics. The Genetic Algorithm simulates computer evolution and has been applied to solve problems with optimization algorithms for improvements in diagnostic and treatment technologies for various diseases. To expand the agility and learning capacity of manufacturing systems, these methods play essential roles. This book will express the computer vision techniques that make manufacturing systems more flexible, efficient, robust, adaptive, and productive by examining many applications and research into Computational Intelligence techniques concerning the main problems in design, making plans, and manufacturing goods in agile manufacturing systems.
Chapter 1 encompasses computing paradigms inspired by nature and cognition, rooted in evolution, fuzzy systems, and neural networks. Computational Intelligence (CI) underpins potent AI systems, notably Deep Learning, a cornerstone of modern AI technology. It is the bedrock of highly effective AI systems, driving advancements like video games and cognitive development.
Chapter 2 handles merging evolutionary computation, neural networks, and fuzzy systems decades ago. Nature-inspired algorithms have evolved, proving more adaptable for optimization. Various types of these algorithms, particularly those centered on self-organizing natural communities, are actively developed. This article compares state-of-the-art optimization techniques with established gradient-based and gradient-free methods. It also identifies unresolved issues in optimization and meta-heuristics for future research.
Chapter 3 demotes AI’s significance in modern industries for its robust computing capabilities, which process extensive data, yielding valuable insights and aiding decision-making. AI customizes experiences through user data analysis. Computational intelligence research benefits individuals and society, enhancing data processing for intelligent AI systems.
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Chapter 16 revolutionized AI in marketing, providing innovative ways to engage customers. It processes vast data, offering insights for tailored marketing strategies. Social media and placement automation streamlines operations, saving time and enhancing plans. AI-powered chatbots handle basic inquiries, freeing marketers. Predictive analytics use client data to forecast behavior and refine products and services