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230515 ||| eng |
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|a books978-3-0365-7039-6
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|a 9783036570389
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|a 9783036570396
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| 100 |
1 |
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|a Wu, Yue
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| 245 |
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|a Applications of Computational Intelligence
|h Elektronische Ressource
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| 260 |
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|a Basel
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2023
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| 300 |
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|a 1 electronic resource (332 p.)
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| 653 |
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|a particle swarm optimization
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| 653 |
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|a visual tracking
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| 653 |
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|a cross-modal learning network
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| 653 |
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|a NoGo games
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| 653 |
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|a sound speed profile
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| 653 |
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|a crop insect pest identification
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| 653 |
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|a U-Net
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| 653 |
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|a multi-target tracking
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| 653 |
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|a Kuroshio Extension Observatory
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| 653 |
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|a data-driven method
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| 653 |
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|a joint integrated probabilistic data association
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| 653 |
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|a dilated convolution
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| 653 |
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|a weakly supervised segmentation
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| 653 |
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|a Transformer
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| 653 |
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|a active-frozen memory model
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| 653 |
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|a deep learning
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| 653 |
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|a levy flight
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| 653 |
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|a hashing learning
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| 653 |
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|a people counting
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| 653 |
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|a propagation mechanism
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| 653 |
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|a progressive deep learning
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| 653 |
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|a few-shot learning
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| 653 |
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|a CSI
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| 653 |
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|a neuroevolution
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| 653 |
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|a online update
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| 653 |
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|a gastrointestinal stromal tumor
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| 653 |
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|a evolutionary optimization
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| 653 |
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|a image retrieval
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| 653 |
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|a object detection
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| 653 |
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|a self-training
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| 653 |
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|a transfer learning
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| 653 |
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|a evolutionary algorithm
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| 653 |
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|a reinforcement learning
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| 653 |
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|a Information technology industries / bicssc
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| 653 |
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|a crystal structure algorithm
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| 653 |
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|a attention mechanism
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| 653 |
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|a crop disease leaf image segmentation (CDLIS)
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| 653 |
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|a opponent exploitation
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| 653 |
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|a human perception
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| 653 |
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|a graph neural network
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| 653 |
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|a lightweight multi-scale dilated U-Net (LWMSDU-Net)
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| 653 |
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|a random finite set
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| 653 |
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|a quality of experience
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| 653 |
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|a reliable evaluation strategy
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| 653 |
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|a disease screening
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| 653 |
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|a multipopulation optimization
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| 653 |
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|a large-scale multiobjective optimization
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| 653 |
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|a hyperspectral image
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| 653 |
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|a nonlinear adaptive weight
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| 653 |
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|a Computer science / bicssc
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| 653 |
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|a knowledge distillation
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| 653 |
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|a convolutional neural networks
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| 653 |
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|a evolutionary multitasking
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| 653 |
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|a self-attention
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| 653 |
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|a sparse unmixing
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| 653 |
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|a medical image segmentation
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| 653 |
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|a Levy flight
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| 653 |
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|a HCNNs
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| 653 |
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|a multi-scale convolution-capsule network (MSCCN)
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| 653 |
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|a semi-supervised learning
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| 653 |
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|a artificial intelligence
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| 653 |
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|a capsule network (CapsNet)
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| 653 |
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|a no-limit Texas hold'em
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| 653 |
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|a convolutional neural network (CNN)
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| 653 |
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|a image classification
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| 653 |
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|a computational intelligence
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| 653 |
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|a golden sine algorithm
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| 653 |
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|a tuna swarm optimization
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| 653 |
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|a convolutional neural network
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| 653 |
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|a circle chaotic map
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| 653 |
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|a engineering optimization problems
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| 653 |
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|a self-organizing map
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| 653 |
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|a electroencephalogram
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| 653 |
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|a spatial attention
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| 653 |
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|a medical image classification
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| 653 |
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|a AlphaZero
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| 700 |
1 |
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|a Qin, Kai
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| 700 |
1 |
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|a Gong, Maoguo
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| 700 |
1 |
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|a Miao, Qiguang
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
|
| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 024 |
8 |
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|a 10.3390/books978-3-0365-7039-6
|
| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/100013
|z DOAB: description of the publication
|
| 856 |
4 |
0 |
|u https://www.mdpi.com/books/pdfview/book/7106
|7 0
|x Verlag
|3 Volltext
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|a 000
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|a 610
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|a 576
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|a 600
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|a 620
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|a 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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