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

DIGN

書(shū)目名稱Artificial Intelligence Applications and Innovations影響因子(影響力)




書(shū)目名稱Artificial Intelligence Applications and Innovations影響因子(影響力)學(xué)科排名




書(shū)目名稱Artificial Intelligence Applications and Innovations網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Artificial Intelligence Applications and Innovations網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Artificial Intelligence Applications and Innovations被引頻次




書(shū)目名稱Artificial Intelligence Applications and Innovations被引頻次學(xué)科排名




書(shū)目名稱Artificial Intelligence Applications and Innovations年度引用




書(shū)目名稱Artificial Intelligence Applications and Innovations年度引用學(xué)科排名




書(shū)目名稱Artificial Intelligence Applications and Innovations讀者反饋




書(shū)目名稱Artificial Intelligence Applications and Innovations讀者反饋學(xué)科排名

詞匯

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

蒼白

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

演繹

起皺紋

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.

軍械庫(kù)

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.