Open Access
Issue
BIO Web Conf.
Volume 97, 2024
Fifth International Scientific Conference of Alkafeel University (ISCKU 2024)
Article Number 00108
Number of page(s) 8
DOI https://doi.org/10.1051/bioconf/20249700108
Published online 05 April 2024
  • Brijesh Kumar, Baradwaj and Saurabh Pal. (2011). “Mining Educational Data to Analyze Students Performance”. International Journal of Advanced Computer Science and Applications, 2(6): 64–39. [Google Scholar]
  • Mahendra Tiwari, Randhir Singh and Neeraj Vimal. (2013). “An Empirical Study of Applications of Data Mining Techniques for Predicting Student Performance in Higher Education”. International Journal of Computer Science and Mobile Computing., 2(2): 53–57. [Google Scholar]
  • Sherine Dominick and Abdul Razak, T. (2014). “Analyzing the Student Performance using Classification Techniques to find the better Suited Classifier”. International Journal of Computer Applications., 104(4): 1–3. [CrossRef] [Google Scholar]
  • WEI Yong, L.I. AN and Yi-Feng, “A Network Security Situational Awareness Model Based on Log Audit and Performance Correction [J]”, Chinese Journal of Computers, vol. 4, pp. 763–764, 2009. [Google Scholar]
  • Abdullah, A.L.-Malaise, Areej Malibari and Alkhozae. (2014). “Students’ Performance Prediction System Using Multi Agent Data Mining Technique”. International Journal of Data Mining and Knowledge Management Process., 4(5): 1–20. [Google Scholar]
  • Tribhuvan, A.P., Tribhuvan, P.P. and Gade, J.G. (2015). “Applying Naive Bayesian Classifier for Predicting Performance of a Student Using Weka”. Advances in Computational Research., 7(1): 239–242. [Google Scholar]
  • Humera Shaziya, Raniah Zaheer and Kavitha, G. (2015). “Prediction of Students Performance in Semester Exams using a Naïve bayes Classifier”. International Journal of Innovative Research in Science, Engineering and Technology., 4(10): 9823–9829. [Google Scholar]
  • Bass, T. “Intrusion detection systems & multisensor data fusion: Creating Cyberspace Situational Awareness [J]”. Communications of the ACM, 2000, 43 (4): 992105. [CrossRef] [Google Scholar]
  • Yin Xiaoxin, Yurcik, W., Treaster, M., et al. VisFlowConnect: “Net Flow visualizations of link relationships for security situational awareness [C] PPProc of the 2004 ACM Workshop on Visualization and Data Mining for Computer Security”. New York: ACM, 2004: 26234. [Google Scholar]
  • Lakkaraju, K., Yurcik, W., Lee, A.J. NVisionl, P.: “Net Flow visualizations of system state for security situational awareness [C] PPProc of the 2004 ACM Workshop on Visualization and Data Mining for Computer Security”. New York: ACM, 2004: 65272. [Google Scholar]
  • Jyoti Bansode Shah. (2016). “Mining Educational Data to Predict Student ‘s Academic Performance”. International Journal on Recent and Innovation Trends in Computing and Communication., 4(1): 01–05. [Google Scholar]
  • Tismy Devasia, Vinushree, T.P. and Vinayak Hegde, “Prediction of Students Performance using Educational Data Mining”. Proc. International Conference on Data Mining and Advanced Computing, Ernakulam, India, pp. 91–95, 2016. ISBN: 9781467385954 [Google Scholar]
  • Mashael, A. Al-Barrak and Muna Al-Razgan. (2016). “Predicting Students Final, G.P.A Using Decision Trees”: A Case Study. International Journal of Information and Education Technology., 6(7):528–533. [CrossRef] [Google Scholar]
  • Chen Xiuzhen, Zheng Qinghua, Guan Xiaohong, et al. “Quantitative hierarchical threat evaluation model for network security [J]”. Journal of Software, 2006, 17 (4): 8852897. [Google Scholar]
  • Zhang Haixia, Su Purui, Feng Dengguo. “A Network Security Analysis Model Based on the Increase in Attack Ability [J]”. Journal of Computer Research and Development, 2007, 44 (12): 201222019. [Google Scholar]
  • Abu Zohair, L.M. (2019) “Prediction of Student’s performance by modelling small dataset size”. International Journal of Educational Technology in Higher Education., 16(27):1–18. [CrossRef] [Google Scholar]
  • Micheline Apolinar Gotardo. (2019). “Using Decision Tree Algorithm to Predict Student Performance”. Indian Journal of Science and Technology., 12(5): 1–8. [CrossRef] [Google Scholar]
  • Ramanathan, L., Angelina Geetha, Khalid and Swarnalatha. (2016). “Student Performance Prediction Model Based on Lion-Wolf Neural Network”. International Journal of Intelligent Engineering and System., 10(1): 114–123. [Google Scholar]
  • Oyerinde, O. D and Chia, P.A. (2017). “Predicting Students’ Academic Performances -A Learning Analytics Approach using Multiple Linear Regression”. International Journal of Computer Applications., 157(4): 37–44. [Google Scholar]
  • Fumiya Okubo., Yamashita, T., Shimada, A. and Ogata, H. “A Neural Network Approach for Students' Performance Prediction”. Proc. Seventh International Learning Analytics and Knowledge Conference, Vancouver, British Columbia, Canada, 2017, pp. 598–599. [CrossRef] [Google Scholar]

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