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Fuzzy Logic is a multi-valued logic where the truth values lies between zero and one. In any system there are two phases namely the training (learning) phase and the testing phase. In the training or learning phase the data samples are given as input to the system for training the fuzzy system, to classify the inputs according to the characteristics of the problem. In the testing phase the data instances are given as input to check whether the system classifies correctly. Considering the above two phases the training phase consumes a large amount of time. In order to classify the input samples, this paper proposes the Fuzzy C-Means (FCM) for classification purpose. The FCM has number of clusters equal to the number of classes to which the input samples are to be classified, where the membership function plays a vital role in converging the number of iterations. The training time of the fuzzy system is improved through the adaptive skipping method. This paper focuses on the Human Activity Recognition (HAR) in which the human activities are to be classified.

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Computational biology is the research area that contributes to the analysis of biological information. The selection of the subset of cancer-related genes is one amongst the foremost promising clinical research of gene expression data. Since a gene can take the role of various biological pathways that in turn can be active only under specific experimental conditions, the stacked denoising auto-encoder(SDAE) and the genetic algorithm were combined to perform biclustering of cancer genes from huge dimensional microarray gene expression data. The Genetic-SDAE proved superior to recently proposed biclustering methods and better to determine the maximum similarity of a set of biclusters of gene expression data with lower MSR and higher gene variance. This work also assesses the results with respect to the discovered genes and spot that the extracted set of biclusters are supported by biological evidence, such as enrichment of gene functions and biological processes.