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

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Neural Computing for Advanced Applications978-981-15-7670-6Series ISSN 1865-0929 Series E-ISSN 1865-0937

痛苦一生

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

ADORN

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

ANTH

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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LIKEN

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

Palter

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