Intelligence Performance in Students’ Absence System with Predicted Information by Data mining Algorithms

Ali Fattah Dakhil, Waffa Muhammad Ali, Asseel Jabbar Almahdi


There are many databases projects used by numerous number of organizations. However, embedding business intelligence, BI, technique is a qualitative impact and quite important factor to improve such project’s results and performance. Modern data mining algorithms have dramatically changed our business work, data model and the way we develop software projects. A database application that manages students’ attendance used in university classes is the objective scope that we adopt and work on in this paper. The main key of interest in this research is to improve such attendance system by participating one of data mining classification technique in which we then have a useful learned information and predicted reports about future students’ attendance. Beside this intelligent trait, our work would be crowned with pictorial analytic results that encourage us to have modern and well-mannered intelligent database application.  

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Jiawei H., Data Mining: Concepts and Techniques, 3rd ed.,Morgan Kaufmann Publishers, USA: 2012: pp 4-10.

Kalpesh A., Aditya G., Amiraj D., Rohit J., Vipul H., “PREDICTING STUDENTS’ PERFORMANCE USING ID3 AND C4.5 CLASSIFICATION ALGORITHMS”, International Journal of Data Mining & Knowledge Management Process (IJDKP), Vol.3, No.5, September

Wang X.,Wang L.,Li N., “An Application of Decision Tree Based on ID3”, 2012 International Conference on Solid State Devices and Materials Science, Physics Procedia 25 ( 2012 ) 1017 – 1021

Yuesheng T., Zhansheng Q., Jingyu W., “Applications of ID3 algorithms in computer crime forensics ”, 2011 International Conference on Multimedia Technology, 26-28 July 2011.

Ali D., Waffa A, Ali A, “Prioritizing Software Capabilities and Focal Points of MS Access and Excel in Perspective of Data Management”, Applied Computer Science, vol. 14, no. 3, pp. 15–30.

Mohammed J., Wagner M., Data Mining and Analysis: Fundamental Concepts and Algorithms, 1st ed., Cambridge University Press, NY USA, 2014: pp 3-30.

Kantardzic M., Data Mining: Concepts, Models, Methods, and Algorithms, 2nd ed., IEEE press, August 2018: pp 2-24.

Bhumika G., et al "Analysis of Various Decision Tree Algorithms for Classification in Data Mining", International Journal of Computer Applications, 2017: Vol. 163, No 8.

Rahman N., “Data Mining Problems Classification and Techniques”, International Journal of Big Data and Analytics in Healthcare, January-June 2018: Vol. 3 No. 1, p 37-58,

Brown L., Mues C., "An experimental comparison of classification algorithms for imbalanced credit scoring data sets", Expert Systems with Applications: February 2012, Volume 39, Issue 3, 15, Pages 3446-3453.

Rokach L., Maimon O., “data mining with decision trees: Theory and Applications”, 2014: 2nd. ed. World Scientific, London UK.

Dai Q., et al “Research of Decision Tree Classification Algorithm in Data Mining”, SERSC: IJDTA, 2016: Vol.9, No.5 pp.1-8.

J. Ross Quinlan, "C4.5: Programs for Machine Learning" 1993:6th ed.,morgan kaufmann publishers

Rupali Bhardwaj , Sonia Vatta "Implementation of ID3 Algorithm", International Journal of Advanced Research in Computer Science and Software Engineering. Vol 3, Issue 6, June 2013

B. Hitarthi, M. Shraddha, R. Lynette. "Use of ID3 Decision Tree Algorithm for Placement Prediction", International Journal of Computer Science and Information Technologies. Vol. 6 (5), 2015

Mathur, n.. et al “the base strategy for id3 algorithm of data mining using havrda and charvat entropy based on decision tree”, international journal of information and electronics engineering, , march 2012: vol. 2, no. 2.

Adhatrao k., et al “predicting students’ performance using id3 and c4.5 classification algorithms”, international journal of data mining & knowledge management process (ijdkp). september 2013: vol.3, no.5.

Christopher C. Brown, Denise Pan, and Gabrielle Wiersma, "Advanced Data Analysis: From Excel PivotTables to Microsoft Access" (2014). Proceedings of the Charleston Library Conference. p 571.

Rob J., Khandakar Y. “Automatic Time Series Forecasting: The forecast Package for R”, Journal of Statistical Software July 2008, Vol. 27, No. 3.



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