Titlebook: Advances in Computing and Data Sciences; Second International Mayank Singh,P. K. Gupta,Tuncer ?ren Conference proceedings 2018 Springer Nat

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書目名稱Advances in Computing and Data Sciences影響因子(影響力)




書目名稱Advances in Computing and Data Sciences影響因子(影響力)學(xué)科排名




書目名稱Advances in Computing and Data Sciences網(wǎng)絡(luò)公開度




書目名稱Advances in Computing and Data Sciences網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Advances in Computing and Data Sciences被引頻次




書目名稱Advances in Computing and Data Sciences被引頻次學(xué)科排名




書目名稱Advances in Computing and Data Sciences年度引用




書目名稱Advances in Computing and Data Sciences年度引用學(xué)科排名




書目名稱Advances in Computing and Data Sciences讀者反饋




書目名稱Advances in Computing and Data Sciences讀者反饋學(xué)科排名

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Yi Han,Jie Li,Kun Jia,Yanxu Liu,Wenwu Zhaoor the actual solution to the computations. Secure multiparty computation computes a certain function without revealing their private secret information..In this paper, we presented a new secure and computationally efficient protocol utilizing multi cloud servers view. In our proposed protocol, encr

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Napoleon Kurantin,Bertha Z. Osei-Hwedie MANETs is considered as the major research area in wireless communication systems. Each node in the network functions as both a host and a router. Each intermediate node forwards data packets to the next node in the path independently. The greatest visible challenge in the design of wireless ad hoc

專橫

Global Disorder for the Post-COVID-19 Worldithin its defined perimeters is extremely important to protect the sanctity and fairness of the summative examination. In addition to the secrecy of question paper/answers-scripts, anonymity between students and examiners is also equally important for ensuring an unbiased evaluation. Anonymity and s

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Yong Xin Ruan,Charles T. L. Leungminative performance of ensemble learning model for diabetes prediction at an early stage. We have used different machine learning models and then ensemble it to improve the overall prediction accuracy. The dataset used is NHANES 2013-14 comprising of 10,172 samples and 54 feature variables for diab