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A group communication needs high security in cloud due to the security issues like group forward and backward secrecy. Many works have addressed those problems, but still they have some lack in their solutions. To address this issue, we propose a new deep learning-based key transmission (DLKT) protocol. This protocol gives the secure group communication in cloud by using autoencoder-based generative adversarial network framework which allows users to transmit their key or message in a secure manner. Both hash and reverse hash techniques are used in this protocol to perform secure session establishment process. A novelty is making by applying deep learning technique into the key transmission protocol for improving the efficiency and security level of the proposed method. The outcome of the work proves that our DLKT protocol reduces the operation time, increases the attack detection rate and accuracy of attack detection compared with existing works and provides very high security.

The key focus of this venture is to evaluate the calibration of classifiers built on rules, trees, and functions by exploring the uncertain information that exists in the Cardiotocography (CTG) dataset. Classification is imperative in diagnosing the health of the foetus and new born specifically in critical cases. It facilitates the obstetricians in acquiring the information of foetal well-being in pregnancy, substantially for the woman with complications. The research aims to classify the CTG data points into normal, suspicious and pathologic. Rules, trees, and function-based classifiers are applied in machine learning for predicting the health of the new born. Particle Swarm Optimization (PSO) is used in pre-processing for selecting the relevant features. Rough set approximations are exploited in extracting the uncertain information from the data set. The result reveals the importance of useful information present in the uncertain data during classification. In this paper, the overall highest accuracy is displayed by Random Forest classifier with 99.57% and a tree-based approach has shown its supremacy over other approaches.

In a bacterial population, the cell-to-cell communication using chemical molecule as a signal in order to perform a collective task is called as quorum sensing. This communicating mechanism regulates the gene expression depending on the population density and dictates the fate of a cell. Quorum sensing regulates gene expression of important traits such as the production of extracellular polysaccharides, enzymes, antibiotics, siderophores, and pigments, virulence, motility, biofilm formation, bio-luminescence, competence, conjugation, sporulation and symbiosis. In order to control these physiological activities that are harmful to the host like humans, animals and plants, understanding of how the quorum sensing phenomenon regulates the gene expression is essential. In this review paper, we have discussed about the mathematical modelling framework used to capture quorum sensing phenomenon. These frameworks are based on the consideration of various factors such as spatial constrains, production of common good and population dynamics. These framework can be simulated using either deterministic modelling approach when average behavior of the population is sufficient to study quorum sensing or stochastic modelling approach when variation in quorum sensing among the individuals in a population occurs.

Wireless Sensor Network were deployed in a complex environment where the wide range of complex application is mandatory for the services. Such application includes military, agriculture, healthcare, defense, monitoring, surveillance etc. In general sensor nodes were spatially distributed and deployed in remote fashion, usually they are powered up by batteries. These battery powered sensor nodes are pruned to failure due to its power constrained nature. This led many researchers to explore energy efficient context aware routing for Wireless Sensor Networks. Hence a novel energy harvesting based efficient routing scheme is desirable to address the above stated problem. The key idea is to harvest the energy source from the deployed environment. The proposed routing scheme is tested and validated in MATLAB based simulation test bed. The experimental results shows that the proposed routing scheme is robust and meet all the requirements of routing and promising results for energy usage.

Aviation safety management system is a vital component of the aviation industry. Aviation safety inspectors apply a broad knowledge about aviation industry, aviation safety, and the central laws and regulations, and strategies affecting aviation. In addition, they put on severe technical knowledge and skill in the operation and maintenance of aircraft. Data mining methods also have been successfully applied in aviation safety management system. Aviation industry accumulates large amount of knowledge and data. This paper proposes a method that applied data mining technique on the accident reports of the Federal Aviation Administration (FAA) accident/incident data system database which contains accident data records for all categories of civil aviation between the years of 1919 and 2014. In this study, we have investigated the application of several data mining methods on the accidents reports, to arrive at new inferences that could help aviation management system. Moreover correlation based feature selection (CFS) with Oscillating Search Technique is used to select the number of prominent attributes that are potential factors causing maximum number of accidents in aircraft. The principle of this work is to find out the effective attributes in order to reduce the number of the accidents in the aviation industry. This proposed novel idea named "improved oscillated correlation feature selection (IOCFS)" is evaluated against the conventional classifiers like Naive bayes, support vector machine (SVM), artificial neural network (ANN), k-nearest neighbor (k-NN), Multiclass classifier and decision tree (J48). The selected features are tested in terms of their accuracy, running time and reliability as in terms of true positive rate, false positive rate, precision, recall-measure and ROC. The results are seen to be the best for k-NN classifier on comparing with other conventional classifiers, with the value of k = 5.

Security is a critical issue when sensor nodes are deployed in a hostile environment. This paper proposes a novel Polynomial based Q composite random scheme for the establishment of triple key among communicating nodes in a network. The proposed scheme combines the robustness of Q-Composite Key Generation Scheme with the Polynomial Pool-Based Schemethat enables a secure communication between wireless sensor nodes. The security analysis shows that our scheme assures a high probability of secure communication with a low communication overhead. The simulation results prove that our scheme performs better in terms of network resilience against node capture attacks with a high probability of connectivity. The proposed scheme shows an improvement in compromised data links between non compromised sensors.Our scheme also provides high resilience in terms of the fraction of unaffected communication links against the compromised nodes even in the presence of a large number of malicious nodes.

Electrolytes of alkaline zincate, acidified zinc sulphate, zinc-ammonia, zinc-ammonia-EDTA, zinc-EDTA/NaOH have been examined in order to verify their applicability for electrodeposition (ED) of zinc on stainless steel (SS) disc at ppm level. Operating parameters suitable for ED of zinc at 90% deposition (amount of zinc removed from plating solution) on SS disc using a total 1 ppm zinc in a 5 cm3 bath volume were determined. It helped to prepare radioactive source (65Zn) using inactive Zn as carrier. Speciation of zinc in a Zn-oxalate-EDTA-ammonia bath has been computed.