Journal of Computer Sciences and Applications

Genetic algorithm plays a significant role, as search techniques for handling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. The genetic processes on the natural evolution principles of populations have been fairly successful at solving problems and produce optimized solution from generation to generation. This is applied in students’ quantitative data analysis to identify the most impact factor in their performance in their curriculum. The results will help the educational institutions to improve the quality of teaching after evaluating the marks achieved by the students’ in academic career. This student analysis model considers the quantitative factors such as theoretical, mathematical, practical, departmental and other departmental marks to find the most impacting factor using real genetic algorithm.

Journal of Computer Sciences and Applications. 2013, 1(4), 75-79. DOI: 10.12691/jcsa-1-4-3
Pub. Date: June 07, 2013

Full-Text PDF

23711 Views7986 Downloads34 Likes4 Citations

Prosodic Boundary Prediction for Greek Speech Synthesis

by Panagiotis Zervas

Original Research

In this article, we evaluate features and algorithms for the task of prosodic boundary prediction for Greek. For this purpose a prosodic corpus composed of generic domain text was constructed. Feature contribution was evaluated and ranked with the application of information gain ranking and correlation-based feature selection filtering methods. Resulted datasets were applied to C4.5 decision tree, one-neighbour instance based learner and Bayesian learning methods. Models performance exploitation led as to the construction of a practically optimal feature set whose prediction effectiveness was evaluated with two prosodic databases. In terms of total accuracy and F-measure, evaluation results established the decision tree effectiveness in learning rules for prosodic boundary prediction.

Journal of Computer Sciences and Applications. 2013, 1(4), 61-74. DOI: 10.12691/jcsa-1-4-2
Pub. Date: May 26, 2013

Full-Text PDF

31347 Views13476 Downloads34 Likes

A Comparative Study on Fingerprint Matching Algorithms for EVM

by D. Ashok Kumar and T. Ummal Sariba Begum

Original Research

In biometric system, the fingerprint recognition has been researched for the long period of time and it has shown the most promising future in the real world application. However, because of the complex distortions among the different impression of the same finger in real life, fingerprint recognition is still a challenging problem. Matching two fingerprints can be unsuccessful due to various reasons and also depends upon the method that is being used for matching. Electronic Voting Machine (EVM) is a simple electronic device used to record votes in place of ballot papers and boxes which were used earlier in conventional voting system. Because biometric identifiers cannot be easily misplaced, forged, or shared, they are considered more reliable for person recognition than traditional token or knowledge based methods. In this paper, the authors are interested to compare three fingerprint matching algorithms by conducting the election using novel EVM. Based on the election result in terms of matching accuracy, time taken for matching, the best algorithm is found for novel EVM. The three matching techniques are direct matching, minutiae matching and matching based on Ratios of distance. We conducted the evaluation on the FVC-2000 datasets and the results were observed by conducting election with the help of these matching techniques and the best matching technique is found for novel EVM.

Journal of Computer Sciences and Applications. 2013, 1(4), 55-60. DOI: 10.12691/jcsa-1-4-1
Pub. Date: May 19, 2013

Full-Text PDF

44057 Views11264 Downloads43 Likes14 Citations