Applications of Computational Intelligence

doi:10.3390/books978-3-0365-7039-6

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Applications of Computational Intelligence

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April 2023

332 pages

ISBN978-3-0365-7038-9 (Hardback)
ISBN978-3-0365-7039-6 (PDF)

https://doi.org/10.3390/books978-3-0365-7039-6

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This book is a reprint of the Special Issue Applications of Computational Intelligence that was published in

Computer Science & Mathematics

Engineering

Physical Sciences

Summary

Computational Intelligence (CI) is the theory, design, application, and development of biologically and linguistically motivated computational paradigms. Traditionally, the three main pillars of CI have been neural networks, fuzzy systems, and evolutionary computation. However, in time, many nature-inspired computing paradigms have evolved. Thus, CI is an evolving field, and, at present, in addition to the three main constituents, it encompasses computing paradigms such as ambient intelligence, artificial life, cultural learning, artificial endocrine networks, social reasoning, and artificial hormone networks. CI plays a major role in developing successful intelligent systems, including games and cognitive developmental systems. Over the last few years, there has been an explosion of research on deep learning, specifically deep convolutional neural networks, and deep learning has become the core method for artificial intelligence. In fact, some of the most successful AI systems today are based on CI. Therefore, this reprint focuses on the theoretical study of computational intelligence and its applications.

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Keywords

artificial intelligence; deep learning; AlphaZero; NoGo games; reinforcement learning; evolutionary algorithm; convolutional neural network; transfer learning; image classification; large-scale multiobjective optimization; sparse unmixing; hyperspectral image; evolutionary algorithm; opponent exploitation; no-limit Texas hold’em; neuroevolution; reinforcement learning; online update; reliable evaluation strategy; active–frozen memory model; visual tracking; evolutionary multitasking; particle swarm optimization; multipopulation optimization; computational intelligence; sparse unmixing; medical image segmentation; computational intelligence; convolutional neural networks; weakly supervised segmentation; attention mechanism; computational intelligence; image classification; HCNNs; progressive deep learning; disease screening; multi-target tracking; evolutionary optimization; random finite set; joint integrated probabilistic data association; few-shot learning; computational intelligence; medical image classification; spatial attention; crop insect pest identification; convolutional neural network (CNN); capsule network (CapsNet); multi-scale convolution-capsule network (MSCCN); image retrieval; Transformer; self-attention; knowledge distillation; hashing learning; Kuroshio Extension Observatory; sound speed profile; self-organizing map; artificial intelligence; circle chaotic map; Levy flight; nonlinear adaptive weight; tuna swarm optimization; computational intelligence; quality of experience; human perception; electroencephalogram; crop disease leaf image segmentation (CDLIS); U-Net; dilated convolution; lightweight multi-scale dilated U-Net (LWMSDU-Net); crystal structure algorithm; golden sine algorithm; levy flight; engineering optimization problems; people counting; CSI; knowledge distillation; cross-modal learning network; computational intelligence; graph neural network; propagation mechanism; data-driven method; deep learning; gastrointestinal stromal tumor; semi-supervised learning; self-training; object detection; computational intelligence