The knowledge of subnuclear localization in eukaryotic cells is essential for understanding the life function of nucleus. Developing prediction methods and tools for proteins subnuclear localization become important research fields in protein science for special characteristics in cell nuclear. In this study, a novel approach has been proposed to predict protein subnuclear localization. Sample of protein is represented by Pseudo Amino Acid (PseAA) composition based on approximate entropy (ApEn) concept, which reflects the complexity of time series. A novel ensemble classifier is designed incorporating three AdaBoost classifiers. The base classifier algorithms in three AdaBoost are decision stumps, fuzzy K nearest neighbors classifier, and radial basis-support vector machines, respectively. Different PseAA compositions are used as input data of different AdaBoost classifier in ensemble. Genetic algorithm is used to optimize the dimension and weight factor of PseAA composition. Two datasets often used in published works are used to validate the performance of the proposed approach. The obtained results of Jackknife cross-validation test are higher and more balance than them of other methods on same datasets. The promising results indicate that the proposed approach is effective and practical. It might become a useful tool in protein subnuclear localization. The software in Matlab and supplementary materials are available freely by contacting the corresponding author.

For video summarization and retrieval, one of the important modules is to group temporal-spatial coherent shots into high-level semantic video clips namely scene segmentation. In this paper, we propose a novel scene segmentation and categorization approach using normalized graph cuts(NCuts). Starting from a set of shots, we first calculate shot similarity from shot key frames. Then by modeling scene segmentation as a graph partition problem where each node is a shot and the weight of edge represents the similarity between two shots, we employ NCuts to find the optimal scene segmentation and automatically decide the optimum scene number by Q function. To discover more useful information from scenes, we analyze the temporal layout patterns of shots, and automatically categorize scenes into two different types, i.e. parallel event scenes and serial event scenes. Extensive experiments are tested on movie, and TV series. The promising results demonstrate that the proposed NCuts based scene segmentation and categorization methods are effective in practice.

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