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All Journal International Journal of Informatics and Communication Technology (IJ-ICT)
Ayuba John
Federal University Dutse
Computer Science & IT

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Comparative analysis on different software piracy prevention techniques Ahmad Mohammad Hassan; Ayuba John
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 10, No 1: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v10i1.pp1-8

Abstract

Numerous type of software piracy known today, have several prevention techniques which has been established against them. Although, different software piracy techniques have been established, but the choice of which one should be the best to develop any software is the challenge for most software developers. Consequently, example of the types of piracy in software development can be categorise as follows: cracks and serials, softlifting and hard disk loading, internet piracy and software forging, mischaneling, reverse engineering, and tampering. We have several types of prevention techniques which aimed to resolved piracy in software development, although the methods have been wrecked. In this work a critical analysis has been carryout on different software piracy techniques and a simple model software was designed using the best technique to validate the results of the analysis.
Classifiers ensemble and synthetic minority oversampling techniques for academic performance prediction Abdulazeez Yusuf; Ayuba John
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 8, No 3: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (424 KB) | DOI: 10.11591/ijict.v8i3.pp122-127

Abstract

The increasing need for data driven decision making recently has resulted in the application of data mining in various fields including the educational sector which is referred to as educational data mining. The need for improving the performance of data mining models has also been identified as a gap for future researcher. In Nigeria, higher educational institutions collect various students’ data, but these data are rarely used in any decision or policy making to improve the academic performance of students. This research work, attempts to improve the performance of data mining models for predicting students’ academic performance using stacking classifiers ensemble and synthetic minority over-sampling techniques. The research was conducted by adopting and evaluating the performance of J48, IBK and SMO classifiers. The individual classifiers models, standard stacking classifier ensemble model and stacking classifiers ensemble model were trained and tested on 206 students’ data set from the faculty of science federal university Dutse. Students’ specific previous academic performance records at Unified Tertiary Matriculation Examination, Senior Secondary Certificate Examination and first year Cumulative Grade Point Average of students are used as data inputs in WEKA 3.9.1 data mining tool to predict students’ graduation classes of degrees at undergraduate level. The result shows that application of synthetic minority over-sampling technique for class balancing improves all the various models performance with the proposed modified stacking classifiers ensemble model outperforming the various classifiers models in both performance accuracy and RSME values making it the best model.
Co-Authors Abdulazeez Yusuf Ahmad Mohammad Hassan