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Rathore, Ravi Kumar
- Student Prediction System for Placement Training Using Fuzzy Inference System
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Authors
Affiliations
1 School of Computer Science and Engineering, Lovely Professional University, IN
1 School of Computer Science and Engineering, Lovely Professional University, IN
Source
ICTACT Journal on Soft Computing, Vol 7, No 3 (2017), Pagination: 1443-1446Abstract
Proposed student prediction system is most vital approach which may be used to differentiate the student data/information on the basis of the student performance. Managing placement and training records in any larger organization is quite difficult as the student number are high; in such condition differentiation and classification on different categories becomes tedious. Proposed fuzzy inference system will classify the student data with ease and will be helpful to many educational organizations. There are lots of classification algorithms and statistical base technique which may be taken as good assets for classify the student data set in the education field. In this paper, Fuzzy Inference system has been applied to predict student performance which will help to identify performance of the students and also provides an opportunity to improve to performance. For instance, here we will classify the student's data set for placement and non-placement classes.Keywords
Classification, Fuzzy Inference System, MATLAB.
References
- Indriana Hidayah, Adhistya Erna Permanasari and Ning Ratwastuti, “Student Classification for Academic Performance Prediction using Neuro Fuzzy in a Conventional Classroom”, Proceedings of IEEE Conference Information Technology and Electrical Engineering, pp. 1-5, 2013.
- Satwanti Devi, Sanjay Kumar and Govind Singh Kushwaha, “An Adaptive Neuro Fuzzy Inference System for Prediction of Anxiety of Students”, Proceedings of 8th International Conference on Advanced Computational Intelligence, pp. 7-13, 2016.
- M. Alkhozae, “Students Performance Prediction System using Multi Agent Data Mining”, International Journal of Data Mining and Knowledge Management Process, Vol. 4, No. 5, pp. 1-20, 2014.
- Q. Qiu and G. Sapiro, “Learning Transformations for Clustering and Classification”, Journal of Machine Learning Research, Vol. 16, pp. 187-225, 2015.
- M. Sharma, “Data Mining : A Literature Survey”, International Journal of Emerging Research in Management and Technology, Vol. 9359, No. 2, pp. 1-4, 2014.
- Mashael A. Al-Barrak and Muna Al-Razgan, “Predicting Students Final GPA Using Decision Trees: A Case Study”, International Journal of Information and Education Technology, Vol. 6, No. 7, pp. 528-533, 2016.
- S.K. Yadav, “Data Mining : A Prediction for Performance Improvement of Engineering Students using Classification”, World of Computer Science and Information Technology Journal, Vol. 2, No. 2, pp. 51-56, 2012.
- B. Minaei-bidgoli, D.A. Kashy, G. Kortemeyer and W.F. Punch, “Predicting Student Performance: An Application of Data Mining Methods with An Educational Web-Based System”, Proceedings of 33rd Annual Frontiers in Education, pp. 1-6, 2003.
- Bashir Khan, Malik Sikandar Hayat and Muhammad Daud Khattak, “Final Grade Prediction of Secondary School Student using Decision Tree”, International Journal of Computer Applications, Vol. 115, No. 21, pp. 32-36, 2015.