Applied Computing and Artificial Intelligence
Applied computing and artificial intelligence methods have attracted a growing interest in recent years due to their effectiveness at solving technical problems. Recent developments in applied mathematics have largely led to benefits for industrial tasks in different fields, including the aerospace...
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Format: | Electronic Book Chapter |
Language: | English |
Published: |
Basel
MDPI - Multidisciplinary Digital Publishing Institute
2023
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Online Access: | DOAB: download the publication DOAB: description of the publication |
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100 | 1 | |a Li, Xiang |4 edt | |
700 | 1 | |a Zhang, Shuo |4 edt | |
700 | 1 | |a Zhang, Wei |4 edt | |
700 | 1 | |a Li, Xiang |4 oth | |
700 | 1 | |a Zhang, Shuo |4 oth | |
700 | 1 | |a Zhang, Wei |4 oth | |
245 | 1 | 0 | |a Applied Computing and Artificial Intelligence |
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520 | |a Applied computing and artificial intelligence methods have attracted a growing interest in recent years due to their effectiveness at solving technical problems. Recent developments in applied mathematics have largely led to benefits for industrial tasks in different fields, including the aerospace industry, manufacturing, transportation, energy, robotics, materials, informatics, etc. The objective of this Special Issue is to present advanced methods in applied computing and artificial intelligence to address the practical challenges in the related areas. The response of the scientific community has been remarkable, and a large number of papers have been submitted. After a careful peer-review process, 22 high-quality papers have been accepted and published. It is hoped that the papers will have an impact on international scholars in this field and promote further remarkable research in this area. | ||
540 | |a Creative Commons |f https://creativecommons.org/licenses/by/4.0/ |2 cc |4 https://creativecommons.org/licenses/by/4.0/ | ||
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650 | 7 | |a Mathematics & science |2 bicssc | |
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653 | |a scaling relationships | ||
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653 | |a parameter identification | ||
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653 | |a differential evolution | ||
653 | |a convolution operation | ||
653 | |a intelligent graphic design | ||
653 | |a brightness feature | ||
653 | |a traditional engraving graphics | ||
653 | |a fault diagnosis | ||
653 | |a domain adaptation | ||
653 | |a noisy label | ||
653 | |a deep learning | ||
653 | |a convolutional neural network | ||
653 | |a rotating machine | ||
653 | |a cuckoo search | ||
653 | |a adaptive method | ||
653 | |a spatial compression | ||
653 | |a photovoltaic model | ||
653 | |a integrated control and estimation | ||
653 | |a adaptive neuro fuzzy | ||
653 | |a noise | ||
653 | |a uncertainty | ||
653 | |a mathematical model | ||
653 | |a motor power curve | ||
653 | |a sucker rod pump | ||
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653 | |a delivery time | ||
653 | |a earliness | ||
653 | |a tardiness | ||
653 | |a number theory | ||
653 | |a Collatz conjecture | ||
653 | |a landslide displacement prediction | ||
653 | |a local mean decomposition | ||
653 | |a bidirectional long short-term memory | ||
653 | |a maximal information coefficient | ||
653 | |a single-machine scheduling | ||
653 | |a aging effect | ||
653 | |a group technology | ||
653 | |a athlete signal processing | ||
653 | |a balance control ability | ||
653 | |a multi-headed self-attention mechanism | ||
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653 | |a soil consolidation | ||
653 | |a excess pore water pressure | ||
653 | |a physics-informed neural network (PINN) | ||
653 | |a abstractive text summarization | ||
653 | |a semantic graph | ||
653 | |a semantic graph embedding | ||
653 | |a Arabic text summarization | ||
653 | |a autonomous vehicle | ||
653 | |a prognostic health management | ||
653 | |a sensor-based fault detection | ||
653 | |a data-driven approaches | ||
653 | |a perception sensors | ||
653 | |a composite materials | ||
653 | |a Convolutional AutoEncoder (CAE) | ||
653 | |a delamination | ||
653 | |a mechanoluminescent (ML) sensor | ||
653 | |a non-contact sensing | ||
653 | |a structural health monitoring | ||
653 | |a covariance matrix estimation | ||
653 | |a shrinkage transformations | ||
653 | |a rotation-invariant estimator | ||
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653 | |a PCA | ||
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653 | |a NAS | ||
653 | |a n/a | ||
856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/7545 |7 0 |z DOAB: download the publication |
856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/112434 |7 0 |z DOAB: description of the publication |