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Eye gaze patterns or scanpaths of subjects looking at art while answering questions related to the art have been used to decode those tasks with the use of certain classifiers and machine learning techniques. Some of these techniques require the artwork to be divided into several Areas or Regions of Interest. In this paper, two ways of clustering the static visual stimuli - k-means and the density...
Epilepsy is a neurological disorder which affects persons of all age. The brain waves are studied for epilepsy detection. The Electroencephalogram (EEG) is the simplest diagnostic technique available for brain wave analysis. In this paper, we investigate the performance of KNN classifier and K-means clustering for the classification of epilepsy risk level from EEG signals. To identify the non linearity...
The corpus callosum is one of the most important structures in human brain. Most of the neurological disorders reflect directly or indirectly on the morphological features of Corpus Callosum. The mid-sagittal brain Magnetic Resonance images fully describe the anatomical structure of corpus callosum. Often considered challenging task of segmenting Corpus Callosum from Magnetic Resonance images has...
This paper proposes a method to cluster documents of variable length. The main idea is to apply (a) automatic identification of 1, 2, and 3 grams (To reduce the dependency on huge background vocabulary support or learning or complex probabilistic approach), (b) order them by some measure of relevance, which is developed with the help of Tf-Idf and Term-Weighting approach, and finally (c) use them...
The 3 most important issues for anomaly detection based intrusion detection systems by using data mining methods are: feature selection, data value normalization, and the choice of data mining algorithms. In this paper, we study primarily the feature selection of network traffic and its impact on the detection rates. We use KDD CUP 1999 dataset as the sample for the study. We group the features of...
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