Determining the Optimal Parameter for Education Surveillance System

Sageemas Na Wichian; Suwimon Wongwanich; Patharawut Saengsiri

doi:10.4028/www.scientific.net/AMR.403-408.3709

Paper Titles

Combining Algorithms for Recommendation System on Twitter
p.3688

PM₁₀ Prediction Model by Support Vector Regression Based on Particle Swarm Optimization
p.3693

Improve Abstract Data with Feature Selection for Classification Techniques
p.3699

Web Based Application Maintenance Cost Estimation Modeling Using Bayesian SEM
p.3704

Determining the Optimal Parameter for Education Surveillance System
p.3709

AGRIX: An Ontology Based Agricultural Expertise Retrieval Framework
p.3714

Classifying Business Types on Twitter Based on User Influential Analysis
p.3719

Quality Assessment in Vocational Education Based on a Hierarchical Classification Technique
p.3724

Health Index Evaluation on Acoustic Emission Signal for Concrete Structure by Intensity Analysis Method
p.3729

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 403-408Determining the Optimal Parameter for Education...

Determining the Optimal Parameter for Education Surveillance System

Abstract:

Education Surveillance System is designed for predicting the state of education based on form of alarm signal using Incremental Leaning based on Mahalanobis Distance (ILM). However, ILM need to define two crucial parameters (co-variance matrix and distance threshold) it is not only very difficult for determining by general user but also depend on dataset property. This research proposed GAILM algorithm based on Ordinary National Education Test (Bangkok) dataset for finding approximate parameter and predicting. The result of experiment is represent GAILM technique discovering proximate co-variance matrix (0.91) and distance threshold parameter (0.44) and also high accuracy rate as 90.91% and 92.07%, in the year 2007 to 2008 respectively. This result was higher than the accuracy rate of traditional technique by K-Means algorithm and Cobweb.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 403-408)

Pages:

3709-3713

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.403-408.3709

Citation:

Cite this paper

Online since:

November 2011

Authors:

Sageemas Na Wichian, Suwimon Wongwanich, Patharawut Saengsiri

Keywords:

Determining, Education, Genetics, Incremental Learning

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Andreas Antoniou and Wu-Sheng Lu: Practical Optimization Algorithms and Engineering Applications, Springer Science+Business Media, LLC, (2007), pp.1-4.

Google Scholar

[2] Mitsuo Gen and Runwei Cheng: Genetic Algorithm & Engineering Optimization, John Wiley & Sons, Inc., (2000), pp.2-4.

Google Scholar

[3] Hussein A. Abdou: Genetic programming for credit scoring: The case of Egyptian public sector banks, Expert Systems with Applications, Vol. 36 (2009), pp.11402-11417.

DOI: 10.1016/j.eswa.2009.01.076

Google Scholar

[4] G. G. Yen and P. Meesad: An effective neuro-fuzzy paradigm for machinery condition health monitoring, Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions, Vol. 31 (2001), pp.523-536.

DOI: 10.1109/3477.938258

Google Scholar

[5] M. Komkhao, S. Sodsee, and P. Meesad: An Incremental Learning Algorithm Based-on Mahalanobis Distance for Unsupervised Learning, 3th National Conference on National Computer on Computing and Information Technology, (2007), pp.20-25.

Google Scholar

[6] H. O. Myint and P. Meesad: Incremental Learning Algorithm based on Support Vector Machine with Mahalanobis distance (ISVMM) for intrusion prevention, in 6th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, (2009).

DOI: 10.1109/ecticon.2009.5137129

Google Scholar