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State of health (SOH) and remaining useful life (RUL) are two major key parameters which plays a major role in battery management system. In recent years, various machine learning approaches have been proposed to estimate SOH and RUL effectively for establishing the battery conditions. In the proposed work establishes an effective method to predict the battery aging process with accurate battery health estimation with real time simulations and hardware approach. This paper effectively exhibits a process to estimate SOH and RUL of a Li-Ion 18650 cell which are based on various factors like state of charge, discharge voltage transfers characteristics, internal resistance and capacity. To identify an optimal SOH and RUL machine learning based estimation approach, various battery’s statistical models are developed and implemented on a standalone hardware platform. The experimental results in this real time application shows that SOH is predicted by deep neural network approach which are found to be within the accepted error rate of ±5% and long short time memory neural network model estimates a battery’s RUL effectively with an accuracy of ±10 cycles. This approach exhibits various machine learning models in an realistic hardware platform which establishes optimal battery life.

Cloud computing provides the computational machines as a support of the clients utilizing cloud organize. In cloud computing, the user inputs are executed with required machines to convey the administrations. Numerous task scheduling methods are utilized to plan the client tasks to the machines. In this paper, another successful hybrid task scheduling is proposed to minimize the total execution time using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms. In hybrid Genetic Algorithm - Particle Swarm Optimization (GA-PSO) algorithm, PSO helped GA to obtain better results compare to a standard genetic algorithm, Min-Min, and Max-Min algorithms results.

Steganography is the insertion of secret data inside a normal cover media such as digital image, audio, video or text. The inserted message must not be damaged when processing is applied on the cover media. Several image processing operations such as format conversion, lossy and lossless compression, noise removal and other enhancement. This paper presents multi layer image encryption and decryption using random puzzle based method to embed secret data into the cover image. This approach enhances the security and provides robust embedding. In this work, a 8×8 Sudoku puzzle which has a 64×64 reference matrix is used. The proposed method gives a superior average capacity of 5 bits per pixel. The message is embedded at the sender using random permutation puzzle. At the Receiver end, the user has to rearrange the image thereby solving the puzzle. The attacker lacks knowledge of original image order and is unable to solve the puzzle. Our outcome indicate that the recommended approach provides high steganographic security while providing high embedding capacity of about 5 bits per pixel. A brief analysis of the performance and security aspects of the proposed method are presented.

Microwaves ad-hoc feeler groups are spatially distributed self governing sensors to examine physical or environmental conditions and for conveyance purposes. Besides all the comforts of the life wireless network spouses serious security threats. Here we implement a simulation based model by using both dynamic threshold algorithm and recursive algorithm and hidden terminal process is used to transfer the data packets to the outer region nodes without any hazards. The proposed method detects the attack and restrain from the attacker node in the network based on CS (Cognitive Sensing) node.