The week's pick
Random Articles
Reseach Article
Using Artificial Intelligence Techniques for Evaluating Practical Art Products for the Students in Art Education
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 182 - Number 15 |
| Year of Publication: 2018 |
| Authors: A. E. E. Elalfi, M. F. Elatawy, Nadia M. Mahmoud |
10.5120/ijca2018917831
|
A. E. E. Elalfi, M. F. Elatawy, Nadia M. Mahmoud . Using Artificial Intelligence Techniques for Evaluating Practical Art Products for the Students in Art Education. International Journal of Computer Applications. 182, 15 ( Sep 2018), 19-26. DOI=10.5120/ijca2018917831
Abstract
This paper develops a new technique to evaluate students’ performance in both designing and drawing subjects automatically without human intervention. This new technique leads to an accurate and quick evaluation. It is based on image processing. It differs from hand assessment in saving big time and effort for the faculty members and achieving credibility between students. Besides improving student grade with identifying points of strengthens and weaknesses, results evince that image processing method is excellently dependable in assessing art images.
References
- A.Q.Omar,”Evaluation of practical side in the lesson of art education of the preparatory stage from the point of view of its teachers”, 2012, PP.520.
- Serag Farag, “Tests and Examinations-Measuring abilities and performance”, 2007, PP.214.
- Donna M. Mertens,”Research and evaluation in education and psychology: integrating diversity with quantitative, qualitative, and mixed methods “, 2014, ISBN 978-1-4129-7190-4.
- Trisha Chakraborty et al., “Review of Various Image Processing Techniques for Currency Note Authentication”, International Journal of Computer Engineering In Research Trends, Vol. 3, Issue 3, 2016, PP. 119-122.
- Michael J. Timms,” Letting Artificial Intelligence in Education Out of the Box: Educational Cobots and Smart Classrooms”, International Journal of Artificial Intelligence in Education, 2016, Vol. 26, Issue 2, PP. 701–712.
- Mileta Zarkovic and Zlatan Stojkovic,” Analysis of artificial intelligence expert systems for power transformer condition monitoring and diagnostics”, Electric Power Systems Research, Vol. 149, 2017, PP. 125-136.
- A. Khandual et al.,” Evaluation of Fibre Migration Angle by Image Processing Using Economic USB Camera and Matlab: Demonstrated Example”, Vol. 2, Issues 4–5, 2015, PP. 2463-2471.
- A. Shams Nateri et al.,” Evaluation of yarn defects by image processing technique”, Optik - International Journal for Light and Electron Optics, Vol. 125, Issue 20, 2014, PP. 5998-6002.
- Malay Kishore Dutta et al.,” Image processing based method to assess fish quality and freshness”, Journal of Food Engineering, Vol. 177, 2016, PP. 50-58.
- J.Valença et al.,” Assessment of cracks on concrete bridges using image processing supported by laser scanning survey”, Construction and Building Materials, Vol. 146, 2017, PP. 668-678.
- Shiv Raj Singh and Shruti Kohli,” Enhanced CBIR using Color Moments, HSV Histogram, Color Auto Correlogram, and Gabor Texture”, International Journal of Computer Systems (ISSN: 2394-1065), Vol. 02, Issue 05, 2015.
- WWW.MathWorks.com, Monday, 2017.
- ZHI-CHUN HUANG et al.,” CONTENT-BASED IMAGE RETRIEVAL USING COLOR MOMENT AND GABOR TEXTURE FEATURE”, International Conference on Machine Learning and Cybernetics, 2010.
- Cristina Malegori et al.,” GLCM, an image analysis technique for early detection of biofilm”, Journal of Food Engineering, Vol. 185, 2016, PP. 48-55.
- H.M.R.B Herath, Y.P.R.D Yapa,” An Effective Combined Feature For Web Based Image Retrieval”, INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOL. 4, Issue 10, 2015.
- K. Sai Prasad et al., “Artificial Intelligence approach for Classifying Molecular Dataset using Density based technique with appropriate Euclidean Distance measure”,Vol.4, Issue 8, 2017, PP. 8827-8836.
- Shadi Aljawarneh, Monther Aldwairi and Muneer Bani Yasin “Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model”, 2017.
- Abir Ghosh et al.” Grid Color Moment Features in Glaucoma Classification”, International Journal of Advanced Computer Science and Applications, Vol. 6, No. 9, 2015.
- Sankhadeep Chatterjee et al., “Particle swarm optimization trained neural network for structural failure prediction of multi storied RC buildings”,2016.
Index Terms
Keywords