Deep Learning based Task Prediction and Neural Network Analytics for Employees


  • Vidya Dwi Amalia Zati, Syaifuddin, Sofiyan, Salam Faris


Deep learning, task prediction, neural network analytics, employee productivity, efficiency, recurrent neural networks (RNNs), convolutional neural networks (CNNs),


In today's dynamic work environments, accurately predicting tasks and optimizing employee performance are crucial for organizational success. Traditional methods often fall short in handling the complexity and variability of modern workplaces. This paper proposes a deep learning-based approach to task prediction and neural network analytics for enhancing employee productivity and efficiency.Our methodology leverages deep learning architectures such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformers to model the intricate relationships between various factors influencing task assignments and employee performance. By integrating diverse data sources including historical task assignments, employee profiles, project requirements, and performance metrics, our model learns complex patterns and dependencies, enabling accurate task predictions and insightful analytics.Key components of our approach include data preprocessing to handle noise and missing values, feature engineering to extract relevant information, and model training using large-scale datasets. We explore techniques such as attention mechanisms to capture salient features and interpret model predictions. Additionally, we employ transfer learning to leverage pre-trained models and adapt them to specific organizational contexts, facilitating faster convergence and improved performance.


Download data is not yet available.


Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 5998-6008).

Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1). MIT press Cambridge.

Kim, Y. (2014). Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882.

Ruder, S. (2019). Transfer learning in natural language processing. arXiv preprint arXiv:1801.06146.

Li, R., & Hoi, S. C. (2020). Towards better transfer learning for personalized task recommendation in project management. Information Sciences, 510, 462-480.

Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence, 35(8), 1798-1828.

Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).

Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., ... & Desmaison, A. (2019). PyTorch: An imperative style, high-performance deep learning library. In Advances in neural information processing systems (pp. 8024-8035).

Cobbinah, A. F. O. E. (2021). High-Performance Work Practices and Turnover Intention : Investigating the Mediating Role of Employee Morale and the Moderating Role of Psychological Capital. Original Research, 1–22.

Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.

Liu, Z., Luo, P., Wang, X., & Tang, X. (2016). Deep learning face attributes in the wild. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3730-3738).

Jaderberg, M., Mnih, V., & Czarnecki, W. M. (2016). Reinforcement learning with unsupervised auxiliary tasks. arXiv preprint arXiv:1611.05397.

Zhang, W., & Wang, H. (2017). Learning task group hierarchies from user sequential data with hierarchical deep learning. In Proceedings of the 26th International Conference on World Wide Web Companion (pp. 1491-1499).

Kohavi, R., & Tang, D. (2014). Understanding the effectiveness of big data personalization: Big vs small data, issues in user modeling, and priors. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1266-1275).

Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to forget: Continual prediction with LSTM. Neural computation, 12(10), 2451-2471.

Zhou, J., Cui, Z., Zhang, Z., Yang, C., Liu, Z., Wang, L., ... & Yang, H. (2018). Graph neural networks: A review of methods and applications. arXiv preprint arXiv:1812.08434.

Cheng, H., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., ... & Dean, J. (2016). Wide & deep learning for recommender systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems (pp. 7-10).

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).




How to Cite

Syaifuddin, Sofiyan, Salam Faris, V. D. A. Z. . (2024). Deep Learning based Task Prediction and Neural Network Analytics for Employees . International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1587–1591. Retrieved from



Research Article