PFCMVO: Political fractional competitive multi-verse optimization enabled deep neuro fuzzy network for student performance estimation in spark environment

Abstract

Student performance calculation is an essential process in online learning scheme, which intends to afford students along with admittance to active learning. Student performance forecast is most concerning problem in education and training field, particularly in educational data mining (EDM). The prediction process provisions the students to choose courses and intend suitable training strategies for themselves. Furthermore, student performance calculation permits lecturers and educational supervisors to designate which students should be observed and maintained to finish their plans with finest outcomes. These provisions can decrease the official notices and exclusions from universities because of students’ poor performance. In this paper, Political Fractional Competitive Multi-verse Optimization enabled Deep Neuro fuzzy network (PFCMVO enabled DNFN) uses spark framework for student performance calculation. Moreover, Yeo–Johnson transformation is applied for transforming the input data for effectual student performance prediction. In addition, Damerau–Levenshtein (DL) distance is applied to select appropriate features. The DNFN classifier is utilized to execute student performance prediction where the classifier is trained by PFCMVO algorithm. The developed student performance prediction model outperforms than the other existing techniques with respect to Precision, Recall, [Formula: see text]-measure, and Prediction accuracy of 0.9259, 0.9321, 0.9290, and 0.9372 for dataset-1 and 0.9126, 0.9271, 0.9198, and 0.9248 for dataset-2, respectively.