Titlebook: Mathematics and Computing; Third International Debasis Giri,Ram N. Mohapatra,Mohammad S. Obaidat Conference proceedings 2017 Springer Natu

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書目名稱Mathematics and Computing影響因子(影響力)




書目名稱Mathematics and Computing影響因子(影響力)學(xué)科排名




書目名稱Mathematics and Computing網(wǎng)絡(luò)公開度




書目名稱Mathematics and Computing網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Mathematics and Computing被引頻次




書目名稱Mathematics and Computing被引頻次學(xué)科排名




書目名稱Mathematics and Computing年度引用




書目名稱Mathematics and Computing年度引用學(xué)科排名




書目名稱Mathematics and Computing讀者反饋




書目名稱Mathematics and Computing讀者反饋學(xué)科排名

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Accommodative FAS-FMG Multilevel Based Meshfree Augmented RBF-FD Method for Navier-Stokes Equations is based on accommodative FAS-FMG multigrid method. The axi-symmetric spherical polar Navier-Stokes equations are solved without using transformation. The non-linear convective terms are handled efficiently by considering upwind type of RBF nodes. The developed scheme saves around 34. of the CPU time than the usual RBF-FD method.

逃避現(xiàn)實

Finiteness of Criss-Cross Method in Complementarity Problems. We present a numerical illustration to show a comparison between criss-cross method and Lemke’s algorithm with respect to number of iterations before finding a solution. Finally we raise an open problem in the context of criss-cross method.

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1865-0929 nd selected from 129 submissions. They were organized in topical sections named: security and privacy; computing; applied mathematics; and pure mathematics.?.978-981-10-4641-4978-981-10-4642-1Series ISSN 1865-0929 Series E-ISSN 1865-0937

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Conference proceedings 2017 2017.?. The 35 papers presented in this volume were carefully reviewed and selected from 129 submissions. They were organized in topical sections named: security and privacy; computing; applied mathematics; and pure mathematics.?.

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On the Security of a Searchable Anonymous Attribute Based Encryption weakness and does not ensure anonymity feature, which is the?main claim of the scheme. We then present an improved scheme for mitigating the weaknesses of the scheme. The improved scheme retains the security claims of the original scheme [.] without adding any computation and communication overhead.

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A Deep Learning Based Artificial Neural Network Approach for Intrusion Detectionk as a classifier for the different types of intrusion attacks. A comparative study has also been carried out with Support Vector Machine (SVM). The experimental results show that the accuracy of intrusion detection using Deep Neural Network is satisfactory.

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