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This paper derives new results for the complete synchronization of 4D identical Rabinovich hyperchaotic systems by using two strategies: active and nonlinear control. Nonlinear control strategy is considered as one of the powerful tool for controlling the dynamical systems. The stabilization results of error dynamics systems are established based on Lyapunov second method. Control is designed via the relevant variables of drive and response systems. In comparison with previous strategies, the current controller (nonlinear control) focuses on convergence speed and the minimum limits of relevant variables. Better performance is to achieve full synchronization by designing the control with fewer terms. The proposed control has certain significance for reducing the time and complexity for strategy implementation.

A large amount of data being generated from different sources and the analyzing and extracting of useful information from these data becomes a very complex task. The difficulty of dealing with big data arises from many factors such as the high number of features, existence of lost data, and variety of data. One of the most effective solutions that used to overcome the huge amount of big data is the feature reduction process. In this paper, a set of hybrid and efficient algorithms are proposed to classify the datasets that have large feature size by merging the genetic algorithms with the artificial neural networks. The genetic algorithms are used as a prestep to significantly reduce the feature size of the analyzed data before handling that data using machine learning techniques. Reducing the number of features simplifies the task of classifying the analyzed data and enhances the performance of the machine learning algorithms that are used to extract valuable information from big data. The proposed algorithms use a new gene-weight mechanism that can significantly enhance the performance and decrease the required search time. The proposed algorithms are applied on different datasets to pick the most relative and important features before applying the artificial neural networks algorithm, and the results show that our proposed algorithms can effectively enhance the classifying performance over the tested datasets.

This work derives new results for the anti-synchronization of 4D identical Rabinovich hyperchaotic systems by using two strategies: active and nonlinear control. The stabilization results of error dynamics systems are established based on Lyapunov second method. Control is designed via the relevant variables of drive and response systems.  In comparison with previous strategies, the current controller (Nonlinear control) focused on the minimum possible limits for relevant variables. The better performance is realizing the anti- synchronization by designing a control with low terms. After obtaining analytical results of the proposed controller, numircal simulation is carried out using Matlab. The graphical results prove validity and applicability of proposed control without know any parameter.The proposed control has certain significance for reducing the time and complexity for strategy implementation.

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