Titlebook: Intelligent Computing Theories and Application; 12th International C De-Shuang Huang,Vitoantonio Bevilacqua,Prashan Pre Conference proceedi

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Introduction

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A Variable Neighborhood Search Approach for the Capacitated m-Ring-Star Probleme maximum capacity. The goal is to minimize the visiting and allocation cost. For solving the problem, we propose a VNS approach based on random perturbation for escaping from the local optimal solutions. Our method reached the optimal solution in a reasonable amount of time in a set of instances from the literature.

CRP743

Convolutional Neural Network Application on Leaf Classificationmostly are green leaves. This system is trained by 1500 leaves to classify 50 kinds of plants. Compared to other research, our net use RGB images for input. And in convolutional neural network, we use PReLU instead of traditional ReLU. The experimental result shows that our method for classification gives accuracy of 94.8 %.

Endearing

短程旅游

spondylosis

Stereo Matching with Improved Radiometric Invariant Matching Cost and Disparity Refinementare small radiometric distortions. In addition, we also develop a disparity refinement method with computational complexity invariant to the disparity range. Experimental results on Middlebury datasets show those artifacts near object boundaries are reduced using the proposed disparity refinement method.

Armory

SIPSO: Selectively Informed Particle Swarm Optimization Based on Mutual Information to Determine SNPon structure, and different learning strategies as its interaction modes, considering the heterogeneity of particles. Experiments are performed on both simulation and real data sets, which show that SIPSO is promising in inferring SNP-SNP interactions, and might be an alternative to existing methods. The software package is available online at ..

Aprope

A Clustering Based Feature Selection Method Using Feature Information Distance for Text Dataders the diversity between features. Unlike the incremental search algorithm mRMR, it avoids prematurely falling into local optimum. Experimental results show that the features selected by the proposed algorithm can gain better classification accuracy.

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