Machine Learning, Computer Vision, Optimization Engineering, Medical Image Processing

Conference Publication

1. D. Das, R. Dash, and B. Majhi, “Optimization Based Feature Generation for Handwritten Odia-numeral Recognition,” in 2018 Fourteenth International Conference on Information Processing (ICINPRO), Bangalore, India: IEEE, Dec. 2018, pp. 1–5. doi: 10.1109/ICINPRO43533.2018.9096757. (21-23 Dec 2018)
2. D. Das and S. Dash, “Improving Performance of ELM with RAO Optimization for Brain CT Image Classification,” in 2021 4th International Conference on Recent Trends in Computer Science and Technology (ICRTCST), Jamshedpur, India: IEEE, Feb. 2022, pp. 325–328. doi: 10.1109/ICRTCST54752.2022.9782000.
3. M. Kaur, D. Das, and S. P. Mishra, “Survey and Evaluation of Extreme Learning Machine on TF- IDF Feature for Sentiment Analysis,” in 2022 International Conference on Machine Learning, Computer Systems and Security (MLCSS), Bhubaneswar, India: IEEE, Aug. 2022, pp. 247–252. doi: 10.1109/MLCSS57186.2022.00053. (Date of Conference: 05-06 August 2022)
4. D. Das, S. Prusty, B. Swain, and T. Sharma, “Evaluation of Optimal Feature Transformation Using Particle Swarm Optimization,” in Biologically Inspired Techniques in Many Criteria Decision Making, vol. 271, S. Dehuri, B. S. Prasad Mishra, P. K. Mallick, and S.-B. Cho, Eds., in Smart Innovation, Systems and Technologies, vol. 271. , Singapore: Springer Nature Singapore, 2022, pp. 211–219. doi: 10.1007/978-981-16-8739-6_19. (December 20-21, 2021)
5. Das, R. Dash, and B. Majhi, “Odia Compound Character Recognition Using Stroke Analysis,” in Computational Intelligence in Data Mining, vol. 556, H. S. Behera and D. P. Mohapatra, Eds., in Advances in Intelligent Systems and Computing, vol. 556. , Singapore: Springer Singapore, 2017, pp. 325–332. doi: 10.1007/978-981-10-3874-7_30.
6. D. Das and A. K. Nayak, “Investigation of Full-Reference Image Quality Assessment,” in Intelligent Computing, Communication and Devices, vol. 309, L. C. Jain, S. Patnaik, and N. Ichalkaranje, Eds., in Advances in Intelligent Systems and Computing, vol. 309. , New Delhi: Springer India, 2015, pp. 449–456. doi: 10.1007/978-81-322-2009-1_50.
7. S. S. Sahoo, S. Mohanty, and D. Das, “A Secure Privacy Preserving Authentication Schema for Remote Server Using Biometric,” in Computer Science and Education, vol. 1811, W. Hong and Y. Weng, Eds., in Communications in Computer and Information Science, vol. 1811. , Singapore: Springer Nature Singapore, 2023, pp. 324–336. doi: 10.1007/978-981-99-2443-1_29.

Journal Publication

1. D. Das, D. R. Nayak, R. Dash, and B. Majhi, “An empirical evaluation of extreme learning machine: application to handwritten character recognition,” Multimed Tools Appl, vol. 78, no. 14, pp. 19495–19523, Jul. 2019, doi: 10.1007/s11042-019-7330-0.
2. D. Das, D. R. Nayak, R. Dash, B. Majhi, and Y. Zhang, “H?WordNet: a holistic convolutional neural network approach for handwritten word recognition,” IET Image Processing, vol. 14, no. 9, pp. 1794–1805, Jul. 2020, doi: 10.1049/iet-ipr.2019.1398.
3. D. Das, D. R. Nayak, R. Dash, and B. Majhi, “MJCN: Multi-objective Jaya Convolutional Network for handwritten optical character recognition,” Multimed Tools Appl, vol. 79, no. 43–44, pp. 33023–33042, Nov. 2020, doi: 10.1007/s11042-020-09457-6.
4. A. M. Joshi, D. R. Nayak, D. Das, and Y. Zhang, “LiMS-Net: A Lightweight Multi-Scale CNN for COVID-19 Detection from Chest CT Scans,” ACM Trans. Manage. Inf. Syst., vol. 14, no. 1, pp. 1–17, Mar. 2023, doi: 10.1145/3551647.
5. D. R. Nayak, D. Das, R. Dash, S. Majhi, and B. Majhi, “Deep extreme learning machine with leaky rectified linear unit for multiclass classification of pathological brain images,” Multimed Tools Appl, vol. 79, no. 21–22, pp. 15381–15396, Jun. 2020, doi: 10.1007/s11042-019-7233-0.
6. D. R. Nayak, D. Das, B. Majhi, S. V. Bhandary, and U. R. Acharya, “ECNet: An evolutionary convolutional network for automated glaucoma detection using fundus images,” Biomedical Signal Processing and Control, vol. 67, p. 102559, May 2021, doi: 10.1016/j.bspc.2021.102559.
7. D. R. Nayak, Y. Zhang, D. S. Das, and S. Panda, “MJaya-ELM: A Jaya algorithm with mutation and extreme learning machine based approach for sensorineural hearing loss detection,” Applied Soft Computing, vol. 83, p. 105626, Oct. 2019, doi: 10.1016/j.asoc.2019.105626.
8. S. S. Sahoo and D. Das, “Design of Key Agreement Authentication Scheme With IDS Using OS-RVFL Neural Network for Secure Fog Computing,” IEEE Internet Things J., pp. 1–1, 2025, doi: 10.1109/JIOT.2025.3566962.

Book Chapters

1. D. Das, D. R. Nayak, R. Dash, and B. Majhi, “A Multi-Stage Hybrid Model for Odia Compound Character Recognition,” in Applied Intelligent Decision Making in Machine Learning, 1st ed., H. Das, J. K. Rout, S. C. Moharana, and N. Dey, Eds., CRC Press, 2020, pp. 53–70. doi: 10.1201/9781003049548-3. (eBook ISBN : 9781003049548)
2. D. R. Nayak, D. Das, R. Dash, and B. Majhi, “Automated Detection of Brain Abnormalities Using Multi-Directional Features and Randomized Learning: A Comparative Study,” in Advances in Healthcare Information Systems and Administration, D. S. Sisodia, R. B. Pachori, and L. Garg, Eds., IGI Global, 2020, pp. 20–41. doi: 10.4018/978-1-7998-2120-5.ch002. (ISBN13: 9781799821205 | ISBN10: 179982120X | ISBN13 Softcover: 9781799821212 | EISBN13: 9781799821229)

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