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

Remodeling

書(shū)目名稱Intelligent Computing Methodologies影響因子(影響力)




書(shū)目名稱Intelligent Computing Methodologies影響因子(影響力)學(xué)科排名




書(shū)目名稱Intelligent Computing Methodologies網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Intelligent Computing Methodologies網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Intelligent Computing Methodologies被引頻次




書(shū)目名稱Intelligent Computing Methodologies被引頻次學(xué)科排名




書(shū)目名稱Intelligent Computing Methodologies年度引用




書(shū)目名稱Intelligent Computing Methodologies年度引用學(xué)科排名




書(shū)目名稱Intelligent Computing Methodologies讀者反饋




書(shū)目名稱Intelligent Computing Methodologies讀者反饋學(xué)科排名

pacific

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.

補(bǔ)助

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.

手銬

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.

苦澀

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.

藕床生厭倦

HARD

世俗

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.

Muscularis

青石板