Text Categorization (TC), also known as Text Classification, is the task of automatically classifying a set of text documents into different categories from a predefined set. If a document belongs to exactly one of the categories, it is a single-label classification task; otherwise, it is a multi-label classification task. TC uses several tools from Information Retrieval (IR) and Machine Learning (ML) and has received much attention in the last years from both researchers in the academia and industry developers. In this paper, we first categorize the documents using KNN based machine learning approach and then return the most relevant documents.

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In spite of recent advances, the interest in extracting knowledge hidden in the electroencephalogram(EEG) signals is rapidly growing, as well as their application in the computational neuro engineering field such as mobile robot control, wheelchair control and person identification using brainwaves. The large number of methods for EEG feature extraction demands a good feature for every task. Digging up the most unique feature would be worth for identification of individual using EEG signal. This research presents a novel approach for feature extraction of electroencephalogram (EEG) signal using the Empirical mode decomposition (EMD) and information-theoretic-method (ITM). The EMD technique is applied to decompose an EEG signal into set of intrinsic mode function (IMF). These decomposed signals are of the same length and in the same time domain as the Original Signal. Hence, EMD method preserves varying frequencies in time. To measure the performance of the features, we have used Hybrid learning for classification where we have selected Learning Vector Quantization Neural Network (LVQ-NN) with fuzzy algorithm. Furthermore, to investigate the performance and accuracy of each subject over the different cognitive tasks based on Cohen's kappa coefficient. The results are compared with past methods in literature for feature extraction and classification methods. Results confirm that proposed features present a satisfactory performance.