Steganalysis is the process of analyzing and predicting the presence of hidden information in images. Steganalysis would be most useful to predict whether the received images contain useful information. However, it is more difficult to predict the hidden information in images which is computationally difficult. In the existing research method, this is resolved by introducing the deep learning approach which attempts to perform steganalysis tasks in effectively. However, this research method does not concentrate the noises present in the images. It might increase the computational overhead where the error cost adjustment would require more iteration. This is resolved in the proposed research technique by introducing the novel research method called Non-Gaussian Noise Aware Auto Encoder Convolutional Neural Network (NGN-AEDNN). Classification technique provides a more flexible way for steganalysis where the multiple features present in the environment would lead to an inaccurate prediction rate. Here, learning accuracy is improved by introducing noise removal techniques before performing a learning task. Non-Gaussian Noise Removal technique is utilized to remove the noises before learning. Also, Gaussian noise removal is applied at every iteration of the neural network to adjust the error rate without the involvement of noisy features. This proposed work can ensure efficient steganalysis by accurate learning task. Matlab has been employed to implement the method by performing simulations from which it is proved that the proposed research technique NGN-AEDNN can ensure the efficient steganalysis outcome with the reduced computational overhead when compared with the existing methods.
In modern days, utility grids are facing major issues in the power system due to an uninterrupted load development and shortage of capital investment in transmission systems. Due to this, network is not capable to meet the estimated demand and transfer the produced power from the integrated power generation into the distribution network. This may generate high power losses in the distribution side that also disturbs the overall efficiency of the network. By employing an optimal placement of Distributed Generation (DG) in power systems is the only way to minimize this kind of problems in economical and practical way. DG has several benefits over centralized power generations that comprising system steadiness, improvement and dismissing transmission/distribution system bottleneck to meet the power demand. So in this paper, Binary Particle Swarm Optimization (BPSO) is used for optimal DG reconfiguration. BPSO iteratively moves the DG in each load bus of the test system that provides the smallest value of the objective function, which is considered as the finest candidate for DG connection. This method is simulated on IEEE 69-bus system that showed enhancement in voltage profile and decrease in power loss compared to other optimization methods.
This paper derives a child and adulthood classification technique by integrating the statistical and structural approaches. The structural approaches are derived on a 3 x 3 window based on Local binary pattern (LBP) approach. The proposed approach divides the LBP in to two structural patterns. The present paper derives two distinct patterns called Left Diagonal (LD) and Right Diagonal (RD) LBP's. The given image is converted into binary by comparing the average value of the 3 x 3 neighborhood with its neighbors. Then LD-LBP and RD-LBP codes are evaluated. The range of these code values will be 0 to 2 3 -1, since three pixels form the above proposed patterns. Based on LD and RD-LBP the present paper derived left and right diagonal-GLCM (LRD-GLCM) and features are evaluated.For efficient age classification chi-square distance method is used. To overcome the data dependency problem, the proposed method is implemented on three different facial namely FG-NET, Google and scanned images. The experimental results indicate a significant age classification rate over the existing methods.
To extract local features efficiently Jhanwar et al. proposed Motif Co-occurrence Matrix (MCM) [23] in the literature. The Motifs or Peano Scan Motifs (PSM) is derived only on a 2*2 grid. The PSM are derived by fixing the initial position and this has resulted only six PSM's on the 2*2 grid. This paper extended this approach by deriving Motifs on a 3*3 neighborhood. This paper divided the 3*3 neighborhood into cross and diagonal neighborhoods of 2*2 pixels. And on this cross and diagonal neighborhood complete Motifs are derived. The complete Motifs are different from initial Motifs, where the initial PSM positions are not fixed. This complete Motifs results 24 different Motifs on a 2*2 gird. This paper derived cross diagonal complete Motifs matrix (CD-CMM) that has relative frequencies of cross and diagonal complete Motifs. The GLCM features are derived on cross diagonal complete Motifs texture matrix for efficient face recognition. The proposed CD-CMM is evaluated face recognition rate on four popular face recognition databases and the face recognition rate is compared with other popular local feature based methods. The experimental results indicate the efficacy of the proposed method over the other existing methods.
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