Nowadays, Vehicular ad hoc networks (VANETs) has received interest in the research because it is used to provide the information for drivers and passengers. In the urban VANET, security and safety is a main issue in recent days because of different kinds of attacks. From the attacks, Sybil attack can be considered as a very difficult for urban VANET networks. Hence, in this paper Emperor Penguin Optimization based Routing protocol (EPORP) is developed for detecting the Sybil attack as well as increasing the system performance. The main objective of the research is detecting the Sybil attack as well as improve the security in VANETS. The initial objective is achieved with the help of Rumour riding technique which detect the Sybil attack in the urban VANET.Similarly, the security of the system is achieved with the help of Split XOR (SXOR) operation. In the SXOR operation, the optimal key is generated with the assistance of Emperor Penguin Optimization (EPO). The proposed method is implemented in NS2 platform and performances are evaluated by metrics such as delay, throughput, delay, encryption time and decryption time. The proposed method is compared with existing methods such as Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO) and Firefly Algorithm (FA) respectively. While analyzing the delivery ratio, the proposed method has 0.96sec and the WOA, PSO and FA is 0.94, 0.92 and 0.90 respectively. From the analysis, the proposed method has the high delivery ratio value compared with the WOA, PSO and FA methods. Similarly, the other parameters are analyzed and compared with the existing methods.

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In the field of biomedicine, drug discovery is the cycle by which new and upcoming medicines are tested and invented to cure ailments. Drug discovery and improvement is an extensive, complex, and exorbitant cycle, settled in with a serious extent of vulnerability that a drug will really be successful or not. Developing new drugs have several challenges to enrich the current field of biomedicine. Among these ultimatums, predicting the reaction of the cell line to the injected or consumed drug is a significant point and this can minimize the cost of drug discovery in sophisticated fashion with a stress on the minimum computational time. Herein, the paper proposes a deep neural network structure as the Convolutional Neural Network (CNN) to detain the gene expression features of the cell line and then use the resulting abstract features as the input data of the XGBoost for drug response prediction. Dataset constituting previously identified molecular features of cancers associated to anti-cancer drugs are used for comparison with existing methods and proposed Hybrid CNNXGB model. The results evidently depicted that the predicted model can attain considerable enhanced performance in the prediction accuracy of drug efficiency.

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