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Student academic performance prediction with MapReduce using the optimised deep belief network

Kamakshamma Vasepalli & Bharati Kodangalkar Fakeerappa

International Journal of Industrial and Systems Engineering2026https://doi.org/10.1504/ijise.2026.151041article
AJG 1
Weight
0.50

What the paper says

The emergence of learners count in the e-learning infrastructures has inspired researchers to carry out data-driven learning assessments for learning performance enhancement among students. A new technique, namely chicken squirrel search algorithm-based deep belief network (CSSA-DBN) is devised with MapReduce for predicting the student academic performance. Here, the details of students are attained, which performs pre-processing with log transformation to make it suitable for improved processing. Moreover, the mappers perform the selection of features with a correlation-based Tversky index wherein the Pearson correlation coefficient and Tversky index are integrated to choose imperative features. In addition, the reducers perform student performance prediction with a deep belief network (DBN), trained using the proposed chicken squirrel search algorithm (CSSA). The proposed CSSA is devised by blending chicken swarm optimisation (CSO) and squirrel search algorithm (SSA).

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https://doi.org/https://doi.org/10.1504/ijise.2026.151041

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@article{kamakshamma2026,
  title        = {{Student academic performance prediction with MapReduce using the optimised deep belief network}},
  author       = {Kamakshamma Vasepalli & Bharati Kodangalkar Fakeerappa},
  journal      = {International Journal of Industrial and Systems Engineering},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1504/ijise.2026.151041},
}

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Student academic performance prediction with MapReduce using the optimised deep belief network

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.