In this article we propose a text classification system using chi-value as feature selection method and SMO (sequential minimal optimization) algorithm as classifier. In addition, we use fuzzy model of fuzzy concept to describe documents' classified label and entropy to calculate the uncertainty of a document's classification result. Experimental results demonstrated that the proposed method can reach 87% or higher accuracy of text classification.
Big Data era is characterized by the explosive increase of image files on the Internet, massive image files bring great challenges to storage. It is required not only the storage efficiency of massive image files but also the accuracy and robustness of massive image file management and retrieval. To meet these requirements, distributed image file storage system based on cognitive theory is proposed. According to the human brain function, humans can correlate image files with thousands of distinct object and action categories and sorted store these files. Thus the authors proposed to sorted store image files according to different visual categories based on human cognition to resemble human memory. The experimental results demonstrate that the proposed distributed image file system (DIFS) based on cognition performs better than Hadoop Distributed File System (HDFS) and FastDFS.
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