Titlebook: Artificial Intelligence Applications and Innovations; 8th IFIP WG 12.5 Int Lazaros Iliadis,Ilias Maglogiannis,Harris Papadopo Conference pr

DIGN

书目名称Artificial Intelligence Applications and Innovations影响因子(影响力)




书目名称Artificial Intelligence Applications and Innovations影响因子(影响力)学科排名




书目名称Artificial Intelligence Applications and Innovations网络公开度




书目名称Artificial Intelligence Applications and Innovations网络公开度学科排名




书目名称Artificial Intelligence Applications and Innovations被引频次




书目名称Artificial Intelligence Applications and Innovations被引频次学科排名




书目名称Artificial Intelligence Applications and Innovations年度引用




书目名称Artificial Intelligence Applications and Innovations年度引用学科排名




书目名称Artificial Intelligence Applications and Innovations读者反馈




书目名称Artificial Intelligence Applications and Innovations读者反馈学科排名

词汇

Sang-Yun Han,Tschangho John Kims on the DTLZ benchmark functions using a set of well-known performance measures. We also propose a novel performance measure called unique hypervolume, which measures the volume of objective space dominated only by one or more solutions, with respect to a set of solutions. Based on our results, we

苍白

Ovulation

Conference proceedings 2012s, AIAI 2012, held in Halkidiki, Greece, in September 2012. The 44 revised full papers and 5 revised short papers presented were carefully reviewed and selected from 98 submissions. The papers are organized in topical sections on ANN-classification and pattern recognition, optimization - genetic alg

bourgeois

演绎

起皱纹

Applications to Urban Planning: An Overview. Then by knowing the system response of the test bench in the frequency domain, GA will be used again to fine tuning this parameterized signal. The result is then compared to those performances of using signal without fine tuning step. It is shown that after applying the fine tuning method, the resulted signal can achieve a better performance.

军械库

defeatist

decode

Multi-classify Hybrid Multilayered Perceptron (HMLP) Network for Pattern Recognition Applicationsed using a Modified Recursive Prediction Error (MRPE). This study uses three benchmark datasets in order to measure the capability of the network. The results show that the proposed Multi-Classify HMLP network provides a significant improvement over the conventional HMLP network for pattern recognition applications.