Jaypee Institute of Information Technology

About: Jaypee Institute of Information Technology is a education organization based out in Noida, Uttar Pradesh, India. It is known for research contribution in the topics: Computer science & Cluster analysis. The organization has 2136 authors who have published 3435 publications receiving 31458 citations. The organization is also known as: JIIT Noida.

A bug Mining tool to identify and analyze security bugs using Naive Bayes and TF-IDF

Abstract: Bug report contains a vital role during software development, However bug reports belongs to different categories such as performance, usability, security etc. This paper focuses on security bug and presents a bug mining system for the identification of security and non-security bugs using the term frequency-inverse document frequency (TF-IDF) weights and naive bayes. We performed experiments on bug report repositories of bug tracking systems such as bugzilla and debugger. In the proposed approach we apply text mining methodology and TF-IDF on the existing historic bug report database based on the bug s description to predict the nature of the bug and to train a statistical model for manually mislabeled bug reports present in the database. The tool helps in deciding the priorities of the incoming bugs depending on the category of the bugs i.e. whether it is a security bug report or a non-security bug report, using naive bayes. Our evaluation shows that our tool using TF-IDF is giving better results than the naive bayes method.

Fourier-Based Feature Extraction for Classification of EEG Signals Using EEG Rhythms

Abstract: In this paper, we propose a method for the analysis and classification of electroencephalogram (EEG) signals using EEG rhythms. The EEG rhythms capture the nonlinear complex dynamic behavior of the brain system and the nonstationary nature of the EEG signals. This method analyzes common frequency components in multichannel EEG recordings, using the filter bank signal processing. The mean frequency (MF) and RMS bandwidth of the signal are estimated by applying Fourier-transform-based filter bank processing on the EEG rhythms, which we refer intrinsic band functions, inherently present in the EEG signals. The MF and RMS bandwidth estimates, for the different classes (e.g., ictal and seizure-free, open eyes and closed eyes, inter-ictal and ictal, healthy volunteers and epileptic patients, inter-ictal epileptogenic and opposite to epileptogenic zone) of EEG recordings, are statistically different and hence used to distinguish and classify the two classes of signals using a least-squares support vector machine classifier. Experimental results, with 100 % classification accuracy, on a real-world EEG signals database analysis illustrate the effectiveness of the proposed method for EEG signal classification.

Visual Vehicle Egomotion Estimation using the Fourier-Mellin Transform

Abstract: This paper is concerned with the problem of estimating the motion of a single camera from a sequence of images, with an application scenario of vehicle egomotion estimation. Egomotion estimation has been an active area of research for many years and various solutions to the problem have been proposed. Many methods rely on optical flow or local image features to establish the spatial relationship between two images. A new method of egomotion estimation is presented which makes use of the Fourier-Mellin Transform for registering images in a video sequence, from which the rotation and translation of the camera motion can be estimated. The Fourier-Mellin Transform provides an accurate and efficient way of computing the camera motion parameters. It is a global method that takes the contributions from all pixels into account. The performance of the proposed approach is compared to two variants of optical flow methods and results are presented for a real-world video sequence taken from a moving vehicle.