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Titlebook: Intelligent Computing Methodologies; 14th International C De-Shuang Huang,M. Michael Gromiha,Abir Hussain Conference proceedings 2018 Sprin

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发表于 2025-3-21 16:06:39 | 显示全部楼层 |阅读模式
书目名称Intelligent Computing Methodologies
副标题14th International C
编辑De-Shuang Huang,M. Michael Gromiha,Abir Hussain
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Intelligent Computing Methodologies; 14th International C De-Shuang Huang,M. Michael Gromiha,Abir Hussain Conference proceedings 2018 Sprin
描述.This book constitutes - in conjunction with the two-volume set LNCS 10954 and LNCS 10955 - the refereed proceedings of the 14th International Conference on Intelligent Computing, ICIC 2018, held in Wuhan, China, in August 2018. The 275 full papers and 72 short papers of the three proceedings volumes were carefully reviewed and selected from 632 submissions. .The papers are organized in topical sections such as Evolutionary Computation and Learning; Neural Networks; Pattern Recognition; Image Processing; Information Security; Virtual Reality and Human-Computer Interaction; Business Intelligence and Multimedia Technology; Biomedical Informatics Theory and Methods; Swarm Intelligence and Optimization; Natural Computing; Quantum Computing; Intelligent Computing in Computer Vision; Fuzzy Theory and Algorithms; Machine Learning; Systems Biology; Intelligent Systems and Applications for Bioengineering; Evolutionary Optimization: Foundations and Its Applications to Intelligent Data Analytics; Swarm Evolutionary Algorithms for Scheduling and Combinatorial Optimization; Swarm Intelligence and Applications in Combinatorial Qoptimization; Advances in Metaheuristic Optimization Algorithm; Adva
出版日期Conference proceedings 2018
关键词supervised learning; unsupervised learning; reinforcement learning; semi-supervised learning; data minin
版次1
doihttps://doi.org/10.1007/978-3-319-95957-3
isbn_softcover978-3-319-95956-6
isbn_ebook978-3-319-95957-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2018
The information of publication is updating

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发表于 2025-3-21 23:22:04 | 显示全部楼层
A Novel Many-Objective Optimization Algorithm Based on the Hybrid Angle-Encouragement Decomposition approaches, MOEA/AD-EBI is expected to effectively achieve a good balance between the convergence and the diversity when solving various kinds of MaOPs. Extensive experiments on some well-known benchmark problems validate the superiority of MOEA/AD-EBI over some state-of-the-art many-objective evolutionary algorithms.
发表于 2025-3-22 02:22:48 | 显示全部楼层
A Novel Multi-population Particle Swarm Optimization with Learning Patterns Evolved by Genetic Algoove diversity and exploration capabilities of populations. Meanwhile, the local search of PSO particles which start in the same position also evolved by GA independently maintains exploitation ability inside each sub population. Experimental results show that the accuracy is comparable and our method improves the convergence speed.
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Improved Sub-gradient Algorithm for Solving the Lower Bound of No-Wait Flow-Shop Scheduling with Seem is designed and an improved subgradient algorithm is proposed to solve this problem. The algorithm simulation experiment shows the effectiveness of the algorithm proposed by the article about flow shop scheduling problem and can calculate a tight lower bound.
发表于 2025-3-22 09:38:30 | 显示全部楼层
Cells Counting with Convolutional Neural Network,es to perform the final density prediction. We use three different cell counting benchmarks (MAE, MSE, GAME). Our method is tested on the cell pictures under microscope and shown to outperform the state of the art methods.
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A Question Answering System Based on Deep Learning,tional neural network is adopted to obtain a one-dimensional sentence vector, which is used to replace the keyword vector for the answer candidate. Experimental results show that the proposed method outperforms the traditional keyword vector method.
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