This paper describes a gender classification scheme based on voice signals in which 16 different fuzzy models are proposed and optimized using four bio-inspired optimization algorithms and the quasi-Newton method. The classification scheme considers four data sets and five different voice features to define the input values of an algorithm in the optimization process. The inputs of each fuzzy model define the mean and variance of their Gaussian membership functions, and their fitness is evaluated by the input values of the algorithm and mean squared error as objective function to be minimized. A comparative analysis between models, algorithms and data sets is made to obtain conclusions according to the results of each optimized model.
This work is part of the research project “Encryption Models Based on Chaotic Attractors” institutionalized in the Research and Scientific Development Center of the Universidad Distrital Francisco José de Caldas. In this paper, a symmetric encryption method for surveillance videos is presented, based on reversible composite cellular automata developed for this purpose. This method takes advantage of reversible cellular automata and elementary rule 30 properties, for efficient regions of interest encryption in surveillance video frames, obtaining an algorithm which experimental results of security and performance are consistent with those reported in current literature. In addition, it allows decryption without loss of information through a fixed size key for each video frame.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.