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Implement of Students Result by Using Genetic Algorithm

Abhishek Katiyar1 , Anil Pandey2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-12 , Page no. 51-56, Dec-2019


Online published on Dec 31, 2019

Copyright © Abhishek Katiyar, Anil Pandey . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Abhishek Katiyar, Anil Pandey, “Implement of Students Result by Using Genetic Algorithm,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.12, pp.51-56, 2019.

MLA Style Citation: Abhishek Katiyar, Anil Pandey "Implement of Students Result by Using Genetic Algorithm." International Journal of Computer Sciences and Engineering 7.12 (2019): 51-56.

APA Style Citation: Abhishek Katiyar, Anil Pandey, (2019). Implement of Students Result by Using Genetic Algorithm. International Journal of Computer Sciences and Engineering, 7(12), 51-56.

BibTex Style Citation:
author = {Abhishek Katiyar, Anil Pandey},
title = {Implement of Students Result by Using Genetic Algorithm},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {12 2019},
volume = {7},
Issue = {12},
month = {12},
year = {2019},
issn = {2347-2693},
pages = {51-56},
url = {},
doi = {}
publisher = {IJCSE, Indore, INDIA},

RIS Style Citation:
DO = {}
UR -
TI - Implement of Students Result by Using Genetic Algorithm
T2 - International Journal of Computer Sciences and Engineering
AU - Abhishek Katiyar, Anil Pandey
PY - 2019
DA - 2019/12/31
SP - 51-56
IS - 12
VL - 7
SN - 2347-2693
ER -

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The artificial intelligence technique such as Genetic algorithm plays a significant role for handling in many fields such as artificial intelligence, engineering, robotic, etc. This is the technique to evaluate the new populations from natural population and provide the best result generation to generation. This is applied in students’ quantitative data analysis to identify the most impact factor in their performance in their curriculum. The results will help the educational institutions to improve the quality of teaching after evaluating the marks achieved by the students’ in academic career. This student analysis model considers the quantitative factors such as compiler, automata, data structure and other departmental marks to find the most impacting factor using genetic algorithm.

Key-Words / Index Term

students’ performance, quantitative factors, genetic algorithm, influencing parameter, student’s evaluation results


[1] P ramya, M, Mahesh kumar,”Student Performance Analysis using Educational Data Mining”, International Journal of Computer science and Information Security (IJCSIS), ISSN NO 1947-5500, vol-14, pp:69-76, 2016.
[2] Chew li sa, Emmy Dehlana Hossain, “Student Performance Analysis System (SPAS)”, JANUARY 2015.
[3] Saddam Khan, Sunny Gupta,”A Study of Data Mining Techniques and Genetic Algorithm in education sector”, International Journal of Computer Science and Mobile Computing,ISSN 2320-088X, vol 4, march, 2015, pg 681-683.
[4] Somnath mazi,”genetic algorithm with different crossover for new analysis model of students performance”international journal of advanced research and development, ISSN: 2455-4030, march-2018, volume:3,pp:67-72.

[5] A. Martin, V. Prasanna Venkatesan et al, “To find the most impact financial features for bankruptcy model using genetic algorithm”, International Conference on Advances in Engineering and Technology, (ICAET-2011), May 27-28, 2011.
[6] Ramjeet Singh Yadav, “Modeling Academic Performance Evaluation Using Soft Computing Techniques: A Fuzzy Logic Approach”, International Journal on Computer Science and Engineering (IJCSE), Vol. 3 No. 2 Feb 2011.
[7] O.K Chaudhari, P.G khot, “Soft computing model for academic performance of teachers using fuzzy lo logic”, British journal of applied science and technology, 2(2):213-226, 2012.
[8] V.O. Oladokun, “Predicting Students’ Academic Performance using Artificial Neural Network: A Case Study of an Engineering Course”, The Pacific Journal of Science and Technology, Volume9. Number 1. May-June 2008 (Springer).
[9] Osman Taylan, Bahattin Karagozog, “An adaptive neuro-fuzzy model for prediction of student’s academic performance computers & Industrial Engineering”, 57 (2009) 732-741.

[10] Emmanuel N. Ogor “Student Academic Performance Monitoring and Evaluation Using Data Mining Techniques” published on Fourth Congress of Electronics, Robotics and Automotive Mechanics, 2009