Disordered regions of proteins are highly abundant in various biological processes, involving regulation and signaling and also in relation with cancer, cardiovascular, autoimmune diseases and neurodegenerative disorders. Hence, recognizing disordered regions in proteins is a critical task. In this paper, we presented a new feature encoding technique built from physicochemical properties of residues selected as per the chaotic structure of related protein sequence. Our feature vector has been tested with various classification algorithms on an up-to-date data set and also compared to other methods. The proposed method shows better classification performance than many methods in terms of accuracy, sensitivity and specificity. Our results suggest that the new method that links the residues and their physicochemical properties using Lyapunov exponents is highly effective in recognition of disordered regions.
We investigate the use of delay vector variance-based features for recorded speech steganalysis. Considering that data hiding within a speech signal distorts the properties of the original speech signal, we design a steganalyzer that uses surrogate data based delay vector variance (DVV) features to detect the existence of a stego-signal. We evaluate the performance of the proposed DVV features as steganalyzer with numerical results.
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