Applications of Computational Intelligence
Wu, Yue (editor)
Qin, Kai (editor)
Gong, Maoguo (editor)
Miao, Qiguang (editor)
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.
Keywordsartificial 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; opponent exploitation; no-limit Texas hold’em; neuroevolution; online update; reliable evaluation strategy; active–frozen memory model; visual tracking; evolutionary multitasking; particle swarm optimization; multipopulation optimization; computational intelligence; medical image segmentation; convolutional neural networks; weakly supervised segmentation; attention mechanism; HCNNs; progressive deep learning; disease screening; multi-target tracking; evolutionary optimization; random finite set; joint integrated probabilistic data association; few-shot learning; 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; circle chaotic map; Levy flight; nonlinear adaptive weight; tuna swarm optimization; 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; cross-modal learning network; graph neural network; propagation mechanism; data-driven method; gastrointestinal stromal tumor; semi-supervised learning; self-training; object detection
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Publication date and placeBasel, 2023
Information technology industries