| Book/Book Chapter: |
- Bhanja, S., & Das, A. (2021). Deep neural network for multivariate time-series forecasting. In Proceedings of international conference on frontiers in computing and systems (pp. 267-277). Springer, Singapore.
- Bhanja, S., & Das, A. (2021). A Deep Learning Framework to Forecast Stock Trends Based on Black Swan Events. In Proceedings of International Conference on Innovations in Software Architecture and Computational Systems (pp. 221-235). Springer, Singapore.
- Bhanja, S., & Das, A. (2021). Electrical Power Demand Forecasting of Smart Buildings: A Deep Learning Approach. In Proceedings of International Conference on Computational Intelligence, Data Science and Cloud Computing (pp. 71-82). Springer, Singapore.
- Bhanja, S., & Das, A. (2021). Deep Learning Approaches to Improve Effectiveness and Efficiency for Time Series Prediction. In Proceedings of International Conference on Computational Intelligence, Data Science and Cloud Computing (pp. 71-82). Springer, Singapore.
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| Journal : |
- Bhanja, S., & Das, A. (2019). Deep learning-based integrated stacked model for the stock market prediction. Int. J. Eng. Adv. Technol, 9(1), 5167-5174.
- Bhanja, S., & Das, A. (2021). A hybrid deep learning model for air quality time series prediction. Indonesian Journal of Electrical Engineering and Computer Science, 22(3), 1611-1618.
- Bhanja, S., & Das, A. (2022). A Black Swan event-based hybrid model for Indian stock markets trends prediction. Innovations in Systems and Software Engineering, 1-15.
- Bhanja, S., Metia, S., & Das, A. (2022). A hybrid neuro-fuzzy prediction system with butterfly optimization algorithm for PM2. 5 forecasting. Microsystem Technologies, 1-16.
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