Employability Prediction of Lateral Entry Engineering Students: A Deep Learning Based Inductive Reasoning and Interpretable Framework


  • Mousoomi Bora, Rupam Baruah


- Time series dataset, Gated Recurrent Neural Network, Attention mechanism, Interpretability framework, Lambda layer, Performance metrics


Numerous technology advancements have contributed significantly to the nation's economic stimulation. Proactive steps have been taken by the technical education industry to boost the employment rate of young engineers. Higher Education Institutions (HEI) have given equal opportunities to the Lateral Entry (LE) engineering students to compete and survive in the competitive job market as Regular Engineering (RE) students. A Deep Learning (DL) based inductive reasoning framework has been developed to forecast the employability rate of LE students after their graduation. A detailed time-series analysis is conducted to examine the trend of academic performance in terms of employability. An interpretable module is integrated with the model to interpret the major contributing features in the overall prediction process. The proposed framework outperforms the existing models in terms of Mean Square Error (MSE) and Mean Absolute Error (MAE).


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How to Cite

Mousoomi Bora. (2024). Employability Prediction of Lateral Entry Engineering Students: A Deep Learning Based Inductive Reasoning and Interpretable Framework. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 3523 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6063



Research Article