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Titlebook: Neural Computing for Advanced Applications; First International Haijun Zhang,Zhao Zhang,Tianyong Hao Conference proceedings 2020 Springer

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发表于 2025-3-21 19:27:52 | 显示全部楼层 |阅读模式
书目名称Neural Computing for Advanced Applications
副标题First International
编辑Haijun Zhang,Zhao Zhang,Tianyong Hao
视频video
丛书名称Communications in Computer and Information Science
图书封面Titlebook: Neural Computing for Advanced Applications; First International  Haijun Zhang,Zhao Zhang,Tianyong Hao Conference proceedings 2020 Springer
描述This book presents refereed proceedings of the First International Conference on Neural Computing for Advanced Applications, NCAA 2020, held in July, 2020. Due to the COVID-19 pandemic the conference was held online. .The 36 full papers and 7 short papers were thorougly reviewed and selected from a total of 113 qualified submissions. The papers present resent research on such topics as neural network theory, and cognitive sciences, machine learning, data mining, data security & privacy protection, and data-driven applications, computational intelligence, nature-inspired optimizers, and their engineering applications, cloud/edge/fog computing, the Internet of Things/Vehicles (IoT/IoV), and their system optimization, control systems, network synchronization, system integration, and industrial artificial intelligence, fuzzy logic, neuro-fuzzy systems, decision making, and their applications in management sciences, computer vision, image processing, and their industrial applications, and natural language processing, machine translation, knowledge graphs, and their applications..
出版日期Conference proceedings 2020
关键词artificial intelligence; communication systems; computer hardware; computer networks; computer systems; c
版次1
doihttps://doi.org/10.1007/978-981-15-7670-6
isbn_softcover978-981-15-7669-0
isbn_ebook978-981-15-7670-6Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Nature Singapore Pte Ltd. 2020
The information of publication is updating

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Neural Computing for Advanced Applications978-981-15-7670-6Series ISSN 1865-0929 Series E-ISSN 1865-0937
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Adaptive Multiple-View Label Propagation for Semi-supervised Classification,e views rather than on the single view, and can exploit the complementarity of multiple views in the label prediction process. Moreover, MLP integrates the multi-view label propagation and the adaptive multiple graph weight learning into a unified model, where a linear transformation is used to enfo
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Container Damage Identification Based on Fmask-RCNN,was manually recorded, with hidden safety risks and inaccurate handling. To solve this problem, the paper proposes a Fmask-RCNN model. It is based on Mask-RCNN, introducing Res2Net101 framework and adding path fusion augmentation, multiple fully connected layers, fusion upsampling or enhancement. Fm
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Multiagent Reinforcement Learning for Combinatorial Optimization,roblems (CMOPs). In the multiagent system, each agent (grid) maintains at most one solution after the MARL-guided selection for local search. MARL adaptively adjusts the selection strategy for conducting better collaborative Pareto local search. In the experimental studies, the MARL-guided grid Pare
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Exploring Multi-scale Deep Encoder-Decoder and PatchGAN for Perceptual Ultrasound Image Super-Resolsors, transmission media and ultrasound characteristics, the quality of ultrasound imaging may be poor, especially its low spatial resolution. We incorporate, in this paper, a new multi-scale deep encoder-decoder structure into a PatchGAN (patch generative adversarial network) based framework for fa
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Reliable Neighbors-Based Collaborative Filtering for Recommendation Systems,s paper proposes a reliable neighbors-based collaborative filtering (RNCF) method for recommendation systems. RNCF first uses the item similarity to pre-estimated missing ratings to solve the issue of data sparsity, which enriches the sample data and reduces the impact of extremely sparse ratings on
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