Notice Board :

Call for Paper
Vol. 10 Issue 8

Submission Start Date:
Aug 01, 2018

Acceptence Notification Start:
Aug 10, 2018

Submission End:
Aug 15, 2018

Final MenuScript Due:
Aug 25, 2018

Publication Date:
Aug 30, 2018
                         Notice Board: Call for PaperVol. 10 Issue 8      Submission Start Date: Aug 01, 2018      Acceptence Notification Start: Aug 10, 2018      Submission End: Aug 15, 2018      Final MenuScript Due: Aug 25, 2018      Publication Date: Aug 30, 2018




Volume V Issue IV

Author Name
J Wu
Year Of Publication
2013
Volume and Issue
Volume 5 Issue 4
Abstract
- Computer Science offers a lot of different approaches for solving the problems of automatic facial expression recognition. This paper presents a new method for facial expression recognition (FER) using multi-feature fusion weighted principal component analysis(WPCA) and Linear Discriminate analysis(LDA) with support vector machines(SVMs). This paper employed WPCA for the dimensionality reduction and SVM for the classification. SVM has powerful generalization ability. It can solve non-linear problem with small samples and high dimension features. PCA and LDA are two powerful tools used for data reduction and feature extraction. The proposed method will produce more accurate results for Facial Expression Recognition.
PaperID
2013/EUSRM/05/04/1001

Author Name
R B Gallé
Year Of Publication
2013
Volume and Issue
Volume 5 Issue 4
Abstract
In this era of globalization, the software Personalization is highly demanding. So the software giants are being very sensitive toward user’s requirements and to customizing and deploying the product according to user’s mood/need. In this perspective this paper reflects the various Personalization and Information Extraction Techniques using Hidden Markov Model which helps to collect probabilities of the states and their transitions with various outcomes happens on the occurrence of certain Events initiated by user.
PaperID
2013/EUSRM/05/04/1013

Author Name
D Mosic
Year Of Publication
2013
Volume and Issue
Volume 5 Issue 4
Abstract
t-Pattern classification is data mining task which maps data into predefined groups or textit classes. It comes under supervised learning because the classes are determined before examining the data. All approaches to performing classification assume some knowledge of the data. For this a training set is used to develop the specific parameters required. The problems of prediction or classification can be solved by using neural networks (NN). An NN can be said to be a data processing system, consisting of a large number of simple, highly interconnected processing elements called as artificial neurons, in an architecture inspired by the structure of the cerebral cortex of the brain. The interconnected neural computing elements have the quality to learn and thereby acquire knowledge and make it available for use. In present work, three training algorithms of artificial feed forward neural networks namely: Back propagation Algorithm, Modified Back propagation Algorithm and Optical Back propagation Algorithm are compared on the basis of their error functions.
PaperID
2013/EUSRM/05/04/1019

Author Name
Y Nievergelt, P M Raj
Year Of Publication
2013
Volume and Issue
Volume 5 Issue 4
Abstract
This paper presents an impedance-source inverter fed (or Z-source converter) induction motor with its control characteristics. The results will compare with other traditional inverters. The z-source inverter employs a unique impedance network coupled with inverter and rectifier. It overcomes the conceptual barriers and limitations of the traditional voltage-source inverter such as V-source inverter and current-source inverter such as I-source inverter. By controlling the shoot-through duty cycle, the z-source inverter system provide ride-through capability during voltage sags, reduces line harmonics, improves power factor and extends output voltage range. Analysis and simulation results will presented to demonstrate these features.
PaperID
2013/EUSRM/05/04/1038