The Development of HP Based ML Model for Reliable Computational Intelligence System

• Published in: 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) pp. 733 - 738
• Main Authors: Vahidhabanu, Y., Ramkumar, K., Bhatt, Chandradeep, Srinivasan, M.L., Sasirekha, S.P., Saravanakumar, S.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 14-05-2024
• Subjects: Analytical models; and weka; Benchmark testing; cnn segmentation; Computational modeling; graduation rates; j48 compressor; Knowledge discovery; knowledge discovery mining; Machine learning; Particle separators; Reliability engineering
• DOI: 10.1109/ICACITE60783.2024.10617272

many educational institutions employ data mining to keep track of their students' records, particularly their academic achievements, which is more significant. To improve their achievements, students' academic performances, as well as the institutions' overall results, must be evaluated. Furthermore, the prediction of students' academic accomplishment educational data mining has been a growing research area machine learning techniques are used to gather information from educational warehouses. As a result, this research proposes the hybrids pedagogical statistical model, a novel approach for analyzing student performance, to improve educational quality for students. The suggested methodology assesses student performance using diverse parameters that result in suitable outcomes. In addition, for obtaining the findings and accurately categorising student performance, the framework illustrates the synergy between the j 48 separator and the enn-based segmentation method. The model is put to the test using the weka environment's benchmark schooling dataset., which is available online. The findings suggest that the proposed model outperforms previous studies in assessing edm student performance.