The emergence of deep learning has impacted numerous machine learning based applications and research. The reason for its success lies in two main advantages: 1) it provides the ability to learn very complex non-linear relationships between features and 2) it allows one to leverage information from unlabeled data that does not belong to the problem being handled. This paper presents a transfer learning procedure for cancer classification, which uses feature selection and normalization techniques in conjunction with stacked sparse auto-encoders on gene expression data. While classifying any two tumor types, data of other tumor types were used in unsupervised manner to improve the feature representation. The performance of our algorithm was tested on 36 two-class benchmark datasets from the GEMLeR repository. On performing statistical tests, it is clearly ascertained that our algorithm statistically outperforms several generally used cancer classification approaches. The deep learning based molecular disease classification can be used to guide decisions made on the diagnosis and treatment of diseases, and therefore may have important applications in precision medicine.
Noise is inevitably common in digital images, leading to visual image deterioration. Therefore, a suitable filtering method is required to lessen the noise while preserving the image features (edges, corners, etc.). This paper presents the efficient type-2 fuzzy weighted mean filter with an adaptive threshold to remove the SAP noise. The present filter has two primary steps: The first stage categorizes images as lightly, medium, and heavily corrupted based on an adaptive threshold by comparing the M-ALD of processed pixels with the upper and lower MF of the type-2 fuzzy identifier. The second stage eliminates corrupted pixels by computing the appropriate weight using GMF with the mean and variance of the uncorrupted pixels in the filter window. Simulation results vividly show that the obtained denoised images preserve image features, i.e., edges, corners, and other sharp structures, compared with different filtering methods. The code and experimented data of the AT-2FF is available on the GitHub platform: https://github.com/vikkyak/Image-Denoising.
This paper presents a systematic procedure for designing a wide-area centralized Takagi-Sugeno fuzzy controller to improve the angular stability of a multimachine power system. The proposed fuzzy controller is robust and designed by satisfying certain linear matrix inequality conditions, to stabilize the system at multiple operating points. The bilinear matrix inequality problem, encountered in Lyapunov-based stability criterion, has been converted into a convex optimization problem to eliminate iterative solution. The input-output control signals are selected in the proposed control scheme by defining joint model controllability and observability index applying geometric approach. The proposed wide-area control scheme employs a global signal from the centralized controller to damp out the interarea mode of oscillations, apart from the local controllers, which are assumed to be present to damp out the local mode of oscillations. The proposed control scheme has been implemented on three test systems. The effectiveness of the proposed control scheme has been compared with a robust wide area control scheme based on mixed H 2 /H ∞ output feedback synthesis and with a conventional power system stabilizer control scheme.Index Terms-Interarea modes of oscillation, linear matrix inequality, power system stabilizer, TS fuzzy controller.
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