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Software Engineering pp 193–201Cite as

Advances in EDM: A State of the Art

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 731))

Abstract

Potentials of data mining in academics have been discussed in this paper. To enhance the Educational Institutional services along with the improvement in student’s performance by increasing their grades, retention rate, maintain their attendance, giving prior information about their eligibility whether they can give examination or not based on attendance, evaluating the result using the marks, predicting how many students have enrolled in which course and all other aspects like this can be analyzed using various fields of Data Mining. This paper discusses one of this aspect in which the distinction has been predicted based on the marks scored by the MCA students of Bharati Vidyapeeth Institute of Computer Applications and Management, affiliated to GGSIPU using various machine learning algorithms, and it has been observed that “Boost Algorithm” outperforms other machine learning models in the prediction of distinction.

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References

  1. Ayesha, S., Mustafa, T., Sattar, A., Khan, I.: Data mining model for higher education system. Eur. J. Sci. Res. 43(1), 24–29 (2010)

    Google Scholar 

  2. Shovon, Md.H.I.: Prediction of student academic performance by an application of K-means clustering algorithm. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 2(7) (2012)

    Google Scholar 

  3. Sharma, T.C.: WEKA approach for comparative study of classification algorithm (IJARCCE). Int. J. Adv. Res. Comput. Commun. Eng. 2(4) (2013)

    Google Scholar 

  4. Kumar, V., Chadha, A.: An empirical study of the applications of data mining techniques in higher education. Int. J. Adv. Comput. Sci. Appl. 2(3), 80–84 (2011)

    Google Scholar 

  5. Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)

    Google Scholar 

  6. Keerthi, S.S., Gilbert, E.G.: Convergence of a generalized SMO algorithm for SVM classifier design. Mach. Learn. 46(1), 351–360 (2002)

    Article  Google Scholar 

  7. Fernandez Caballero, J.C., Martinez, F.J., Hervas, C., Gutierrez, P.A.: Sensitivity versus accuracy in multiclass problem using Memetic Pareto evolutionary neural networks. IEEE Trans. Neural Netw. 21, 750–770 (2010)

    Article  Google Scholar 

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Authors and Affiliations

  1. Bharati Vidyapeeth Institute of Computers Application and Management (BVICAM), New Delhi, India

    Manu Anand

Authors
  1. Manu Anand
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Corresponding author

Correspondence to Manu Anand .

Editor information

Editors and Affiliations

  1. Bharati Vidyapeeth’s Institute of Computer Applications and Management (BVICAM), New Delhi, Delhi, India

    M. N. Hoda

  2. Department of Computer Engineering, YMCAUST, Faridabad, Haryana, India

    Naresh Chauhan

  3. Department of Computer Science, University of Kashmir, Srinagar, Jammu and Kashmir, India

    S. M. K. Quadri

  4. Department of Information Technology and Systems, Indian Institute of Management Rohtak, Rohtak, Haryana, India

    Praveen Ranjan Srivastava

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Anand, M. (2019). Advances in EDM: A State of the Art. In: Hoda, M., Chauhan, N., Quadri, S., Srivastava, P. (eds) Software Engineering. Advances in Intelligent Systems and Computing, vol 731. Springer, Singapore. https://doi.org/10.1007/978-981-10-8848-3_19

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  • DOI: https://doi.org/10.1007/978-981-10-8848-3_19

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8847-6

  • Online ISBN: 978-981-10-8848-3

  • eBook Packages: EngineeringEngineering (R0)

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