Dr. Shom Prasad Das
- Ph.D. (Computer Science & Engineering), Biju Patnaik University of Technology, Rourkela, Odisha.
- M.Tech. (Computer Science & Engineering), Biju Patnaik University of Technology, Rourkela, Odisha.
- B.Tech. (Electronics & Communication Engineering), Berhampur University, Berhampur, Odisha.
What subject you teach at SSU ?
Programming Methodology, Data Science, Machine Learning, Artificial Intelligence
- INSA Teacher Summer Fellowship 2020 jointly by Indian Academy of Sciences – Bengaluru, Indian National Science Academy -New Delhi, and The National Academy of Sciences- Prayagraj.
- University Foundation Day Research Award 2019 (based on the research paper publication from 2017 to 2019 in Computer Science & Engineering) by Biju Patnaik University of Technology, Rourkela, Odisha.
- SAP Certified Trainer in SAP Business Intelligence from SAP Asia Pacific Japan.
- SAP Certified Trainer in SAP Functional from SAP Asia Pacific Japan.
- SUN Certified Web Component Developer for J2EE Platform (SCWCD) from Sun Microsystems Inc., USA.
- SUN Certified Java Programmer (SCJP) from Sun Microsystems Inc., USA.
JOURNAL PUBLICATIONS: (INTERNATIONAL/ NATIONAL)
- Das, S. P., & Padhy, S. Selection of a machine learning based model for prediction of commodity futures index. International Journal of Research in Electronics and Computer Engineering (2018) 6(3), 1463-1476.
- Das, S. P., & Padhy, S. A novel hybrid model using teaching–learning-based optimization and a support vector machine for commodity futures index forecasting. International Journal of Machine Learning and Cybernetics (2018) 9, 97-111. (https://doi.org/10.1007/s13042-015-0359-0). (SCI Impact Factor: 3.844)
- Das, S. P., & Padhy, S. A new hybrid parametric and machine learning model with homogeneity hint for European-style index option pricing. Neural Computing and Applications (2017) 28: 4061-4077. (https://doi.org/10.1007/s00521-016-2303-y). (SCI Impact Factor: 4.664)
- Das, S. P., & Padhy, S. Unsupervised extreme learning machine and support vector regression hybrid model for predicting energy commodity futures index. Memetic Computing (2017) 9, 333-346. (https://doi.org/10.1007/s12293-016-0191-4). (SCI Impact Factor: 2.674)
- Das, S. P., Acharya N. S., & Padhy, S. Novel hybrid SVM-TLBO forecasting model incorporating dimensionality reduction techniques. Applied Intelligence (2016) 45, 1148-1165. (https://doi.org/10.1007/s10489-016-0801-3). (SCI Impact Factor: 1.904)
- Kumar, M. S., Das, S. P., & Reza, M. (2013). Effect of Return and Volatility Calculation on Option Pricing: Using BANKNIFTY. International Journal of Innovation, Management and Technology, 4(4), 443.
- Das, S. P., Tripathy, P. K., & Panda, U. K. (2012). Developing a Computational Model for Pricing Index Future. International Journal of Computer Applications, 55(5).
- Das, S. P., & Padhy, S. (2012). Support vector machines for prediction of futures prices in Indian stock market. International Journal of Computer Applications, 41(3).
BOOK PUBLICATIONS/ EDITED: (INTERNATIONAL/ NATIONAL)
- Das, S.K., Das, S.P., Dey, N., Hassanien, A.-E. (Eds.) (2020). Machine Learning Algorithms for Industrial Applications. Springer Nature Switzerland. (https://doi.org/10.1007/978-3-030-50641-4)
CONFERENCE PUBLICATIONS: (INTERNATIONAL/ NATIONAL)
- S. P. Das, V. Laharika and N. S. Achray, “Improved short-term electricity load forecasting using extreme learning machines,” 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC), Chirala, 2017, pp. 5-10. (doi: 10.1109/ICBDACI.2017.8070800)
- Kumar, G., Kumar, M.S., Das, S. P., & Reza, M. (2013, January). Performance analysis of parallel computation for option pricing on OpenMP. National Conference on High Performance Computing & Simulation (NCHPCS -2013).
- Kumar, M. S., Das, S. P., & Reza, M. (2012, October). A comparison between analytic and numerical solution of linear Black-Scholes equation governing option pricing: Using BANKNIFTY. World Congress on Information and Communication Technologies (WICT) Trivandrum, 2012 (pp. 437-441). IEEE. (DOI:10.1109/WICT.2012.6409117)