A New Approach to Identify Fingerprint Using Support Vector Machine

Texture recognition plays an important role in machine vision and signal processing. Over the last three decades, many texture analysis and recognition methods have been proposed, but most of them are sensitive to geometric distortion. This paper presents a new approach to scaling and rotation invariant texture classification using invariant moments based on radon transform. The radon transform is utilized to project the texture image onto projection space. Then the geometric invariant moments are calculated by the projection data. The support vector machine-based classifier is employed to implement the texture classification. Experimental results show the high classification accuracy of this approach.

Download Full-text

A new approach to digital protection of power transformer using support vector machine

2011 24th Canadian Conference on Electrical and Computer Engineering(CCECE) ◽

10.1109/ccece.2011.6030399 ◽

2011 ◽

Cited By ~ 1

Author(s):

A. M. Shah ◽

B. R. Bhalja

Keyword(s):

Support Vector Machine ◽

Power Transformer ◽

Support Vector ◽

New Approach ◽

Digital Protection

Download Full-text

A Business Classifier to Detect Readability Metrics on Software Games and Their Types

International Journal of E-Entrepreneurship and Innovation ◽

10.4018/ijeei.2013100104 ◽

2013 ◽

Vol 4 (4) ◽

pp. 47-57

Author(s):

Yahya M. Tashtoush ◽

Derar Darwish ◽

Motasim Albdarneh ◽

Izzat M. Alsmadi ◽

Khalid Alkhatib

Keyword(s):

Machine Learning ◽

Data Mining ◽

Support Vector Machine ◽

Software Metrics ◽

Machine Learning Algorithms ◽

Support Vector ◽

Mobile Environments ◽

New Approach ◽

Data Mining Tool ◽

Mining Tool

Readability metric is considered to be one of the most important factors that may affect games business in terms of evaluating games' quality in general and usability in particular. As games may go through many evolutions and developed by many developers, code readability can significantly impact the time and resources required to build, update or maintain such games. This paper introduces a new approach to detect readability for games built in Java or C++ for desktop and mobile environments. Based on data mining techniques, an approach for predicting the type of the game is proposed based on readability and some other software metrics or attributes. Another classifier is built to predict software readability in games applications based on several collected features. These classifiers are built using machine learning algorithms (J48 decision tree, support vector machine, SVM and Naive Bayes, NB) that are available in WEKA data mining tool.

Download Full-text