Students Classification With Adaptive Neuro Fuzzy
Автор: Mohammad Saber Iraji, Majid Aboutalebi, Naghi. R. Seyedaghaee, Azam Tosinia
Журнал: International Journal of Modern Education and Computer Science (IJMECS) @ijmecs
Статья в выпуске: 7 vol.4, 2012 года.
Бесплатный доступ
Identifying exceptional students for scholarships is an essential part of the admissions process in undergraduate and postgraduate institutions, and identifying weak students who are likely to fail is also important for allocating limited tutoring resources. In this article, we have tried to design an intelligent system which can separate and classify student according to learning factor and performance. a system is proposed through Lvq networks methods, anfis method to separate these student on learning factor . In our proposed system, adaptive fuzzy neural network(anfis) has less error and can be used as an effective alternative system for classifying students.
Adaptive neuro fuzzy, Neural network, Students classification, Lvq
Короткий адрес: https://sciup.org/15014468
IDR: 15014468
Список литературы Students Classification With Adaptive Neuro Fuzzy
- N. M Rusli, Z. Ibrahim, R. M. Janor ,“Predicting Students’ Academic Achievement: Comparison between Logistic Regression, Artificial Neural Network, and Neuro-Fuzzy”, IEEE,2008
- O. taylan , b. karagozoglu ,”an adaptive neuro –fuzzy model for prediction of students’ academic performance ” , computer & industrial engineering 57,2009,p732-741
- V.O. Oladokun, A. T. Adebanjo, O.E. Charles Owaba, “Predicting Students’ Academic Performance using Artificial Neural Network: A Case Study of an Engineering Course”, The Pacific Journal of Science and Technology, Volume 9. Number 1. May-June 2008 (Spring)
- S.Mitra, Y.Hayashi. Neuro-fuzzy rule generation: Survey in soft computing framework. IEEE Transactions on Neural Networks,2000, 11(3), 748–768.
- H.R.Berenji, P.Khedkar, P.” Learning and tuning fuzzy logic controllers through reinforcements”. IEEE Transactions on Neural Networks, 1992, 3, 724–740.
- O. Cordon, F. Gomide, F.Herrera, F.Hofmann, L. Magdalna. “Ten years of genetic fuzzy systems: Current framework and new trends”.2004, Fuzzy Sets and Systems, 141, 5–31.
- H.J. Walberg, "Improving educational productivity," Philadelphia: Laboratory for School Success. Retrieved April 2005, from httpJ/www.temple.edu/lss/pdf/publ icatiol1s / pubs2003-I.pdf, 2003
- C.C. Bakare. “Some Psychological Correlates of Academic Success and Failure”. African Journal of Educational Research. 1975.
- m. Lage. and m. Tregelia. “The Impact Of Integrating Scholarship On Women Into Introductory Economics: Evidence From One Institution”. Journal Of Economic Education. 1996,27:26-36.
- K. Dynan. and c.Rouse. “The Under Utilization Of Women In Economics: A Study OfUndergraduate Economics Students”. Journal OfEconomic Education. 1997. 28:50-68.
- O.B. Adedeji. “A Study Of The Relationship Between Students UME Results And Their Undergraduate Performance”. Department Of Industrial And Production Engineering, University Of Ibadan. Unpublished Bsc Project,2001.
- B. Kosko “Neural Networks and Fuzzy Systems, A dynamic systems approach to machine intelligence”, Prentice Hall, 1992.
- J. M. Byers, and S. L. DesJardins, Artificial neural networks: A new approach to predicting application behavior. Research in Higher Education, 43(2),2002, pp. 235-258.
- K. Barker, T. Trafalis, and T. R. Rhoads, Learning from Student Data, Proceedings of the 2004 IEEE Symposium on Systems and information Engineering Design, April 2004, pp. 79-86.
- Merry McDonald, Brian Dorn, and Gary McDonald, A Statistical Analysis of Student Performance in Online Computer Science Courses. Proc. 35th SIGCSE Technical Symposium on Computer Science Education, Norfolk, Virgina, USA, 2004, 71-74.
- Bijayananda Naik & Srinivasan, Using Neural Network to Predict MBA Student Success, College Student Journal, March 2004, 1-4.
- Y.Norazah,”Student Learning Assessment Model Using Hybrid Method”. Universiti Kebangsaan Malaysia.2005, PhD Thesis.
- Spiros V. Ioannou, Amaryllis T. Raouzaiou, Vasilis A.Tzaouvaras, and et. al, “Emotion Recognition Through Facial Expression Analysis Based on a Neurofuzzy Network”,NeuralNetwork, Elsevier, 2005, vol. 18, pp 423-435.
- Xiang-Jie Liu, Felipe Lara-Rosano, C.W. Chan,“Neurofuzzy Modelling and Control of Steam Pressurein 300 MW Steam-Boiler System”, Engineering Application of Artificial Intelligence, Elsevier, 2003, Vol. 16, pp 431-440.
- Tran, C., Abraham, A., and Jain, L, “A concurrentfuzzy-neural network approach for decision supportsystems”. FUZZ apos;03. The 12th IEEE International Conference, vol 2, 25-28 May 2003, pp. 1092 – 1097.
- Barai S. V. and Nair R. S, “Neuro-Fuzzy Models For Constructability Analysis”. ITcon, vol 9, 2004, pp 65-73.
- Kosko B., Neural Networks and Fuzzy Systems, ADynamic Systems Approach to Machine Intelligence’, Prentice Hall, 1992.
- James R. Nolan, A Prototype Application of FuzzyLogic and Expert Systems in Education Assessment,Proceedings Fifteenth National Conference on ArtificialIntelligence (AAAI-98), Menlo Par, CA, USA, 1998, 1134-9.
- James R. Nolan, An Expert Fuzzy Classification System for Supporting the Grading Student Writing Samples, Expert Systems with Applications, 1998, Vol. 15, Iss. 1, 59-68.
- Jian Ma and Duanning Zhou, Fuzzy Set Approach to the Assessment of Student-Centered Learning, IEEE Transaction on Education, 2000, Vol. 43, No. 2.
- Echauz, J. R. and Vachtesvanos, G. J., Fuzzy Grading System, IEEE Transaction of Education, 1995, Vol. 38, No.2, 158-164.
- Ranjit, B., An Application of Fuzzy Set in StudentsEvaluation”, Fuzzy Sets and Systems, 1995, Vol. 74, pp 187-194.
- Mir Sadique Ali, Ashok A. Ghatol, An Adaptive Neuro Fuzzy Inference System For Student Modeling In Web-Based Intelligent Tutoring Systems, 2005.
- Marian B. Gorzalczany, Computational Intelligence Systems and Applications, Springer Verlag, 2002.
- James R. Nolan, “A prototype Application of FuzzyLogic and Expert Systems in Education Assessment”,Proceedings Fifteenth National Conference on ArtificialIntelligence (AM-98). Tentli Coiference on InnovativeApplications of Artificial Intelligence. AAA1 PressMITPress, Menlo Park, CA, USA. pp. 1134-9 1998.
- James R. Nolan ”An expert fuzzy classification system for supporting the grading pf student writing samples”,Expert Systems with Applications. Vol. 15, Iss. 1, pp. 59-68; July 1998.
- Jian Ma and Duanning Zhou, “Fuzzy Set Approach to the assessment of Student-Centered Learning”, IEEE transaction on Education, Val, 43, No. 2, May 2000.
- J.R. Echauz and G. J. Vachtesvanos, “Fuzzy grading system”, IEEE trans. Educ., vol. 38, no.2, pp. 158-364.1995.
- B. Ranjit, “An application of fizy set in students evaluation”, Fuzy sets and systems, vol. 74, pp. 187-194, 1995.
- S.Becker,”Unsupervised learning procedures for neural networks”,intr.journal of natr.sys.vol.2,1991
- N.N.,"Fuzzy Logic Benchmarks for MCUs" , http:// www.fuzzytech.com/e_ftedbe.htm (1998).
- Ashish Ghosh, B. Uma Shankar, Saroj K. Meher ,“A novel approach to neuro-fuzzy classification”,Neural Networks, Volume 22, Issue 1, January 2009, Pages 100-109
- K.Fong-ReyLiu,Jia-Shen Chen, “Prediction and assessment of student learning outcomes in structural mechanics “,2010 2end international conference on education technology and computer(ICETC) ,2010 IEEE
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