Deep learning neural network derivation and testing to distinguish acute poisonings

Mehrpour, Omid; Hoyte, Christopher; Masud, Abdullah Al; Biswas, A. K.; Schimmel, Jonathan; Nakhaee, Samaneh; Nasr, Mohammad Sadegh; Delva-Clark, Heather; Goss, Foster R.

doi:10.1080/17425255.2023.2232724

Cited by 2 publications

(1 citation statement)

References 39 publications

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“…Deep learning techniques can be used to predict employee turnover by analyzing employee feedback and information. Deep learning techniques have succeeded in applications in various domain, such as healthcare [15], finance [16], cloud detection [17] and human resource, specifically employee turnover [18,19,20,21].It can learn feature hierarchies from higher levels in the hierarchy generated by a combination of lower-level characteristics [22]. Deep learning techniques which rely on representation-learning to extract multiple levels of representation.…”

Section: Introductionmentioning

confidence: 99%

Hybrid GA–DeepAutoencoder–KNN Model for Employee Turnover Prediction

LIM,

MALIK,

KHAW

et al. 2023

Stat., optim. inf. comput.

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Organizations strive to retain their top talent and maintain workforce stability by predicting employee turnover and implementing preventive measures. Employee turnover prediction is a critical task, and accurate prediction models can help organizations take proactive measures to retain employees and reduce turnover rates. Therefore, in this study, we propose a hybrid genetic algorithm–autoencoder–k-nearest neighbor (GA–DeepAutoencoder–KNN) model to predict employee turnover. The proposed model combines a genetic algorithm, an autoencoder, and the KNN model to enhance prediction accuracy. The proposed model was evaluated and compared experimentally with the conventional DeepAutoencoder–KNN and k-nearest neighbor models. The results demonstrate that the GA–DeepAutoencoder–KNN model achieved a significantly higher accuracy score (90.95\%) compared to the conventional models (86.48% and 88.37% accuracy, respectively). Our findings are expected to assist HR teams identify at-risk employees and implement targeted retention strategies to improve the retention rate of valuable employees. The proposed model can be applied to various industries and organizations, making it a valuable tool for HR professionals to improve workforce stability and productivity.

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