Abstract
There are numerous methods for extracting useful information from data. This paper describes a method for predicting performance of students. This method modifies the basic particle swarm optimization (PSO) algorithm using a set of rules. An attribute is selected from a set of performance attributes of the students. This attribute is used to frame rules. These rules determine the value of a modifying factor. This factor changes the mathematical expression of the function used in PSO for finding the solution. These rules are based on number of students in a particular shift. Other attributes are assigned different indexes. These indexes indicate number of students deviating from average value. The modified PSO algorithm takes the values of these indexes as inputs and generates a solution set which minimizes the values of indexes. A comparison of the solution set given by modified PSO and the solution set with unmodified PSO is presented. A brief outline of the modified PSO is given. The selection of the modifying factor and design of rules is described. These rules are based on the number of students in a particular shift. The different possible classes for the shift attribute are given. Thus, a decision strategy for predicting performance is described.
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References
Pandey, U.K., Pal, S.: Data mining: a prediction of performer or underperformer using classification. Int. J. Comput. Sci. Inform. Technol. 2(2), 686–690 (2011)
Ahmed, A.B.E.D., Elaraby, I.S., Data mining: a prediction for student’s performance using classification method. World J. Comput. Appl. Technol. 2(2), 43–47 (2014)
Tair, M.M.A., El-Halees, A.M.: Mining educational data to improve student’s performance: a case study. Int. J Inform. Commun. Technol. Res. 2(2) (2012)
Lee, K.Y., El-Sharkawi, M.A.: Modern Heuristic Optimization Technique, Theory and Application to Power System. IEEE press, Willey-Interscience publication. (4), pp. 71–86
Sembiring, S., Zarlis, M., Hartama, D., Ramliana, S., Wani, E.: Prediction of students academic performance by an application of data mining technique. In: International Conference on management and artificial intelligence, IPDER, vol. 6, IACSIT Press, Bali, Indonesia (2011)
Sousa, T., Nevas, A., Silva, A., Swarm optimisation as a new Tool for data Mining. In: Proceeding of the International Parallel and Distributed Processing Symposium (IPDPS03). IEEE Computer Society
Ali, M.M.: Role of Data Mining in Education Sector. Int. J. Comput. Sci. Mob. Comput. IJCSMC 2(4), 374–383 (2013) (ISSN 2320-088X)
Oskouei, R.J.: Analysing Different Aspect of Social Network Usage on Students Behaviors and Academic Performance, IEEE (2010)
Yadav, S.K., Bhardwaj, B., Pal, S.: Data mining applications: a comparative study for predicting student’s performance. Int. J. Innov. Technol. Creative Eng. 1(12) (2012) (ISSN: 2045-711)
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Srivastava, S. (2018). Novel Method for Predicting Academic Performance of Students by Using Modified Particle Swarm Optimization (PSO). In: Panigrahi, B., Hoda, M., Sharma, V., Goel, S. (eds) Nature Inspired Computing. Advances in Intelligent Systems and Computing, vol 652. Springer, Singapore. https://doi.org/10.1007/978-981-10-6747-1_21
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DOI: https://doi.org/10.1007/978-981-10-6747-1_21
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