Cyberattacks have given rise to several phenomena and have raised concerns among users and power system operators. When they are built to bypass state estimation bad data recognition methods executed in the convention...
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Cyberattacks have given rise to several phenomena and have raised concerns among users and power system operators. When they are built to bypass state estimation bad data recognition methods executed in the conventional grid system control room, False Data Injection Attacks (FDIA) pose a significant security threat to the operation of power systems. Therefore, real-time monitoring becomes inevitable with the quick implementation of renewables within the grid operator. The state estimation algorithm plays a major role in defining the grid's operating scenarios. FDIA creates a significant risk to these estimation strategies by adding malicious information to the measurement obtained. Real-time recognition of these attack classes improves grid resiliency and ensures a secure grid operation. This study introduces a novel Attribute Reduction with a Deep Learning-based False Data Injection Attack Recognition (ARDL-FDIAR) technique. The primary goal of the ARDL-FDIAR technique is to improve security via the FDIA detection process. The ARDL-FDIAR technique uses Z-score normalization to scale the input data. The attribute reduction process gets invoked using the modified Lemrus optimizationalgorithm (MLOA) to choose optimal feature sets. Moreover, the FDIA detection process is performed by modelling an improved deep belief network (IDBN) model. Furthermore, the performance of the IDBN model is improved by the cetacean optimization algorithm (COA)-based hyperparameter tuning process. A series of experiments were performed to ensure the enhancement of the ARDL-FDIAR technique. The results indicated the enhanced security performance of the ARDL-FDIAR technique compared to other DL approaches.
Conventional preprocessing methods in medical imaging can effectively reduce the combination of Gaussian and Impulsive noise, while still maintaining the crucial features such as edges within the images. However, thes...
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Conventional preprocessing methods in medical imaging can effectively reduce the combination of Gaussian and Impulsive noise, while still maintaining the crucial features such as edges within the images. However, these techniques are found to lag in the optimizing the certain image parameters which play a crucial rule in enhancing the input images and thus may lead to failing in preserving the edges that may adversely affect the early diagnosis of diseases. An attempt has been made to enhance the contrast of cardiac MR (Medical Resonance) images using cetacean optimization algorithm (COA) for improving disease diagnosis. Input cardiac images have been acquired from the free online available databases viz., SCMR consensus and AMRG Atlas. The methodology includes integration of the cost function with contrast measure based on certain metrics to create a newer transformation function. It results in enhanced search pattern using a transformation function in spatial domain and improves the pixel intensity to increase the resolution of an image. The developed algorithm’s performance was evaluated by computing various parameters including peak signal-to-noise ratio (PSNR), structural similarity index measurement (SSIM), mean square error (MSE), and normalized absolute error (NAE). Analysis of the results reveals that the proposed method achieves the highest PSNR (72 dB) and SSIM (0.99) values, along with the lowest NAE (0.16%) and MSE (0.22%) compared to both the particle swarm optimized texture-based histogram equalization (PSOTHE) method and the modified sunflower optimization (MSFO) method. The results indicate that the suggested strategy might improve the intricate details of the input cardiac MRI images. This enhancement can contribute to the efficient identification of cardiac disorder-specific biomarkers.
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