Notice Board :

Call for Paper
Vol. 5 Issue 12

Submission Start Date:
Dec 01, 2018

Acceptence Notification Start:
Dec 10, 2018

Submission End:
Dec 15, 2018

Final MenuScript Due:
Dec 30, 2018

Publication Date:
Jan 01, 2019
                         Notice Board: Call for PaperVol. 5 Issue 12      Submission Start Date: Dec 01, 2018      Acceptence Notification Start: Dec 10, 2018      Submission End: Dec 15, 2018      Final MenuScript Due: Dec 30, 2018      Publication Date: Jan 01, 2019




Volume III Issue IV

Author Name
Pooja A R, Raj Kumar T
Year Of Publication
2016
Volume and Issue
Volume 3 Issue 4
Abstract
The cloud is going to become the next computing environment in both data storage and computation, due to its pay-as-you-go and provision-as-you-go models. Cloud storage plays great role in several cases such as to back up desktop user data, used to host shared scientific data, used to store web application data and also used to serve web pages. But in today’s cloud stores there is an essential ingredient is missing that is data provenance. Provenance is the metadata of an object that describes the history of that particular object. With the use of provenance, data users can check the identity or authenticity of data of interest. Data provenance will play a significant role in cloud forensics investigation in future.
PaperID
2016/IJTRM/4/2016/6491

Author Name
Madhuri sahu, Pravin Patidar, A.C. Tiwari
Year Of Publication
2016
Volume and Issue
Volume 3 Issue 4
Abstract
We have study a framework for the de-noising of videos which is jointly corrupted by random noise and fixed-pattern noise. Our approach is based on motion-compensated 3-D spatiotemporal volumes, i.e. a sequence of 2-D square patches extracted along the motion trajectories of the noisy video. It realize using following steps: 3D transformation of 3D group (grouping similar 2D image blocks into 3D data arrays which we call "groups") and then the coefficients of the 3-D volume spectrum are shrunk using an adaptive 3-D threshold array. Such array depends on the particular motion trajectory of the volume, the individual power spectral densities of the random and fixed-pattern noise, and also the noise variances which are adaptively estimated in transform domain. Simulation result is obtained by using DST, DCT, and Hadamard transform.
PaperID
2016/IJTRM/4/2016/6492

Author Name
Sneha Ann Chandy, Anitha Jose
Year Of Publication
2016
Volume and Issue
Volume 3 Issue 4
Abstract
The number of malignant applications targeting internet banking transactions has incremented dramatically. This represents a challenge not only to the customers who utilize such facilities, but also to the institutions who offer them. These malignant applications make utilization of two kinds of assailment vector - local attacks which take place on the local computer, and remote attacks, which redirect the victim to a remote site. Keystroke capturing is one among such attacks. Evasive software keyloggers conceal their malicious behaviours to defeat run-time detection. This paper proposes an algorithm known as Dendritic Cell Algorithm (DCA) that uses an induction-correlation framework to detect the presence of Keyloggers. It also encrypts the log file which contains all the keystrokes captured making it useless when viewed by attacker thus providing added protection.
PaperID
2016/IJTRM/4/2016/6493

Author Name
Dipanti Marothiya, Prof. Abhishek Raghuvanshi
Year Of Publication
2016
Volume and Issue
Volume 3 Issue 4
Abstract
The data mining and their different applications are becomes more popular now in these days a number of large and small scale applications are developed with the help of data mining techniques i.e. predictors, regulators, weather forecasting systems and business intelligence. There are two kinds of model are available for namely supervised and unsupervised. The performance and accuracy of the supervised data mining techniques are higher as compared to unsupervised techniques therefore in sensitive applications the supervised techniques are used for prediction and classification. In this presented work the supervised learning based application is proposed and demonstrated. The proposed work is intended to demonstrate the data mining technique is disease prediction systems in medical domain. In order to perform this task the heart disease based data is selected for analysis and prediction.
PaperID
2016/IJTRM/4/2016/6494