Abstract: This paper gives a complete analysis of Skype Traffic. Based on the analysis of Skype Traffic, we proposed a heuristic based detection method which classified the Skype Signaling and Skype Media Traffic. We properly categorized the Skype Media traffic as audio or video conversation. In this paper, we also propose a novel approach to identify VoIP Network Traffic in the first few seconds of initial state of communication. The proposed classifier works with Machine Learning Techniques based on the statistical features. The experimental results show that the proposed method can achieve over 99% accuracy for all testing dataset.
Keywords: Skype; Network Traffic Analysis; Traffic Classification; Machine Learning
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Abstract: Liver is an important vital organ. In these days medical professionals utilize CT scan abdominal images for the diagnosis of liver disorders. There arise the problem of liver segmentation and image processing for recapturing the matching image of predetermined image of liver ill state. In this present work a new way of methodology has been introduced for the pulling out liver image in a large dataset of abdominal scanned images. Further the segmented liver images are preprocessed for the feature extractions of Shape, Intensity and Texture. An automatic system of Least Distance Method (LDM) is used for the recalling of image is run into the system. There is a significant speed and accuracy have been notified by this LDM. The above results are discussed with earlier related works and concluded with the application in clinical practice.
Keywords: Automatic retrieval technique, Digital image processing, Liver Segmentation, Least Distance method for recapturing matching image.
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