A Robust Development of Superficial Learning Model for Employee Layoff Prediction using Artificial Intelligence Paradigm

In today’s business environment, predicting employee layoffs is a challenging task to maintain both operational efficiency and employee morale. Traditional methods proved insufficient in terms of precision and dependability, this is the reason why new predictive models have been forged. The proposed Employee Layoff Prediction model uses Hybrid Neuro Classifier (HNC), combining the advantages of convolutional neural networks (CNNs) and artificial neural networks (ANNs) to improve prediction accuracy. The proposed HNC model extends LeNet CNN to perform automatic capturing of complex patterns and spatial hierarchies in the data using its deep feature extraction capabilities. The extracted features are then fed into cascaded ANNs where refinements produced with the aid of learning deep intricate dependencies enable delicate representations. Since this hybrid approach combines the refinement needed to tune sentences applied in grounded experiments and additionally can effectively generalize over large amounts accurately labeled data, it offers predictive accuracy. The implementation of the HNC model was built and trained models with Python using libraries. Experimental results show that the proposed method can accurately predict employee layoffs with $\mathbf{9 7. 8 1 \%}$ accuracy.