Applications of Computational Intelligence, Volume 2

doi:10.3390/books978-3-7258-1911-9

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https://mdpi.com/books/pdfview/book/9757

Contributor(s)

Wu, Yue (editor)

Qin, Kai (editor)

Gong, Maoguo (editor)

Miao, Qiguang (editor)

Language

English

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Abstract

Computational Intelligence (CI) is the theory, design, application, and development of biologically and linguistically motivated computational paradigms. CI mainly includes three parts: neural networks, fuzzy systems, and evolutionary computation. CI has been widely used to solve complex problems in various domains, such as image processing, point cloud processing, and classification. Due to the great advantages of CI in dealing with practical application problems, more and more researchers have paid attention to the theoretical research and application of CI in recent years. This reprint reports the latest research on applications of computational intelligence. Many novel and interesting methods are introduced, which provide guiding significance for the further development of computational intelligence.

URI

https://directory.doabooks.org/handle/20.500.12854/143722

Keywords

knowledge graph; knowledge graph embedding; link prediction; quaternion; relation pattern; cryptographic medical images; convolutional neural network; DarkNet53; migration learning; DCT; NasNet-Mobile network; chaotic encryption; zero watermarking; Industrial Internet of Things; network security situation assessment; Analytic Hierarchy Process; data sampling; emergency material scheduling; gray prediction; ant colony algorithm; demand forecasting; multi-objective optimization; perception design; image super resolution; generative adversarial network; artifact suppression; intelligent computing; micro-expression spotting; multi-scale; optical flow; computational intelligence; neural networks; building footprint extraction; attention mechanism; remote-sensing images; artificial intelligence; LSTM model; deep learning; applications of computational intelligence; gas turbine; combustion chamber; artificial neural network; multiple co-existing ALM routing trees; node congestion; one-off optimization; DAFSA