Profile
Assistant Professor in the field of Computer Science & Engineering
- Ph.D.
IIT Patna
Ph.D. with 8.38 (CGPA), Computer Science and Engineering from IIT Patna. Thesis title: Explorations in Metric Learning with Applications to clustering and classification. Duration:2016-2019 - MTech.
SoIT, RGPV Bhopal.
M.Tech. with 8.43 (CGPA), Computer Technology and Application from SoIT, RGPV Bhopal. Duration:2012-2014 - B.E.
NIIST Bhopal
B.E. with 75.31 (%), Computer Science and Engineering from NIIST, Bhopal. Duration:2007-2011
- Dec-2024-Present Assistant Professor (Level-11)
Assistant Professor (Level-11), Department of Computer Science and Engi neering, IIITDM Jabalpur.
- Dec-2021-Dec-2024 Assistant Professor (Level-11)
Assistant Professor (Level-11), Department of Computer Science and Engineering, IIIT Sri City.
- Dec-2020-Dec-2021 Assistant Professor(Level-10)
Assistant Professor(Level-10), Department of Computer Science and Engineering, IIIT Sri City
- Feb-2020-Dec-2020 Research Assistant Professor
Research Assistant Professor, Department of Electrical Engineering, NTUT Taiwan
Research
Areas or Specialisation / Project Activities / Publications / Books
Machine Learning, Deep Learning, Robotics Intelligence, AI/ML Applications
- Development of Novel Unsupervised Domain Adaptation Framework for Image Classification
PI-CRG,SERB (2023-2026)
- Development of a novel real-time adaptive deep domain transfer framework for underwater marine object recognition.
PI-:MoES (2024-2026)
- Advanced methods and algorithms for automatic information extraction for (online/offline)processing and analysis of images/data from various multi-source data.
Co-PI-ISRO (2023-2026)
- Jounal-1. O. Gilo, J. Mathew, S. Mondal, and R. K. Sanodiya, “Subdomain adaptation via correlation alignment with entropy minimization for unsupervised domain adaptation,” Pattern Analysis and Applications, vol. 27, no. 1, p. 13, 2024. doi: 10.1007/s10044-024-01232-9.
- Jounal-2. R. R. P. Karn and R. K. Sanodiya, “Pso-based unified framework for unsupervised domain adaptation in image classification,” Applied Intelligence, pp. 1–27, 2024. doi: 10.1007/s10489-024-05706-5.
- Jounal-3. P. Kumar, R. K. Sanodiya, J. Mathew, T. Setty, and B. Prakash, “Zero shot plant disease classification with semantic attributes.,” Artificial Intelligence Review, 2024, issn: 2169-3536. doi: 10.1007/s10462-024-10950-9
- Jounal-4. R. Lekshmi, B. R. Jose, J. Mathew, and R. K. Sanodiya, “Mnemonic: Multikernel contrastive domain adaptation for time-series classification,” Engineering Applications of Artificial Intelligence, vol. 133, p. 108 255, 2024. doi: 10.1016/j.engappai.2024.108255
- Jounal-5. S. R. Singh, R. R. Yedla, S. R. Dubey, R. K. Sanodiya, and W.-T. Chu, “Frequency disentangled residual network,” Multimedia Systems, vol. 30, no. 1, pp. 1–13, 2024
- Jounal-6. A. Devika, R. K. Sanodiya, B. R. Jose, and J. Mathew, “Visual domain adaptation through locality information,” Engineering Applications of Artificial Intelligence, vol. 123, p. 106 172, 2023, issn: 0952-1976. doi: 10.1016/j.engappai.2023.106172
- Jounal-7. O. Gilo, J. Mathew, S. Mondal, and R. K. Sanodiya, “Rdaot: Robust unsupervised deep sub-domain adaptation through optimal transport for image classification,” IEEE Access, 2023
- Jounal-8. O. Gilo, J. Mathew, S. Mondal, and R. K. Sanodiya, “Unsupervised sub-domain adaptation using optimal transport,” Journal of Visual Communication and Image Representation, p. 103 857, 2023, issn: 1047-3203. doi: 10.1016/j.jvcir.2023.103857.
- Jounal-9. R. R. P. Karn, R. K. Sanodiya, and P. Bajpai, “A unified framework for visual domain adaptation with covariance matching,” Knowledge-Based Systems, p. 110 894, 2023. doi: 10.1016/j.knosys.2023.110894.
- Jounal-10. R. K. Lakshmi Sanodiya, B. R. Jose, and J. Mathew, “Kernelized global-local discriminant information preservation for unsupervised domain adaptation,” Applied Intelligence, pp. 1–23, 2023. doi: 10.1007/s10489-023-04706-1.
- Jounal-11. S. Mishra and R. K. Sanodiya, “A novel angular based unsupervised domain adaptation framework for image classification,” IEEE Transactions on Artificial Intelligence, 2023, issn: 2691-4581. doi: 10.1109/TAI.2023.3293077.
- Jounal-12. R. S. R. Singh and R. K. Sanodiya, “Zero-shot transfer learning framework for plant leaf disease classification,” IEEE Access, 2023.
- Jounal-13. R. K. Sanodiya, S. Mishra, P. Arun, et al., “Manifold embedded joint geometrical and statistical alignment for visual domain adaptation,” Knowledge-Based Systems, vol. 257, p. 109 886, 2022, issn: 0950-7051. doi: 10.1016/j.knosys.2022.109886.
- Jounal-14. R. K. Sanodiya, J. Mathew, R. Aditya, A. Jacob, and B. Nayanar, “Kernelized unified domain adaptation on geometrical manifolds,” Expert Systems with Applications, vol. 167, p. 114 078, 2021, issn: 0957-4174. doi: 10.1016/j.eswa.2020.114078
- Jounal-15. R. K. Sanodiya and L. Yao, “Discriminative information preservation: A general framework for unsupervised visual domain adaptation,” Knowledge-Based Systems, vol. 227, p. 107 158, 2021, issn: 0950-7051. doi: 10.1016/j.knosys.2021.107158.
- Jounal-16. R. K. Sanodiya, J. Mathew, S. Saha, and P. Tripathi, “Particle swarm optimization based parameter selection technique for unsupervised discriminant analysis in transfer learning framework,” Applied Intelligence, vol. 50, pp. 3071–3089, 2020. doi: 10.1007/s10489-020-01710-7.
- Jounal-17. R. K. Sanodiya, S. Saha, and J. Mathew, “Semi-supervised orthogonal discriminant analysis with relative distance: Integration with a moo approach,” Soft Computing, vol. 24, pp. 1599–1618, 2020, issn: 1432-7643. doi: 10.1007/s00500-019-03990-9.
- Jounal-18. R. K. Sanodiya, M. Tiwari, J. Mathew, S. Saha, and S. Saha, “A particle swarm optimization-based feature selection for unsupervised transfer learning,” Soft Computing, vol. 24, pp. 18 713–18 731, 2020, issn: 1432-7643. doi: 10.1007/s00500-020-05105-1.
- Jounal-19. R. K. Sanodiya and L. Yao, “A subspace based transfer joint matching with laplacian regularization for visual domain adaptation,” Sensors, vol. 20, no. 16, p. 4367, 2020, issn: 1424-8220. doi: 10.3390/s20164367.
- Jounal-20. R. K. Sanodiya and L. Yao, “Linear discriminant analysis via pseudo labels: A unified framework for visual domain adaptation,” IEEE Access, vol. 8, pp. 200 073–200 090, 2020, issn: 2169-3536. doi: 10.1109/ACCESS.2020.3035422.
- Jounal-21. R. K. Sanodiya and L. Yao, “Unsupervised transfer learning via relative distance comparisons,” IEEE Access, vol. 8, pp. 110 290–110 305, 2020, issn: 2169-3536. doi: 10.1109/ACCESS.2020.3002666.
- Jounal-22. R. K. Sanodiya and J. Mathew, “A framework for semi-supervised metric transfer learning on manifolds,” Knowledge-Based Systems, vol. 176, pp. 1–14, 2019, issn: 0950-7051. doi: 10.1016/j.knosys.2019.03.021.
- Jounal-23. R. K. Sanodiya and J. Mathew, “A novel unsupervised globality-locality preserving projections in transfer learning,” Image and Vision Computing, vol. 90, p. 103 802, 2019, issn: 0262-8856. doi: 10.1016/j.imavis.2019.08.006.
- Jounal-24. R. K. Sanodiya, J. Mathew, B. Paul, and B. A. Jose, “A kernelized unified framework for domain adaptation,” IEEE Access, vol. 7, pp. 181 381–181 395, 2019, issn: 2169-3536. doi: 10.1109/ACCESS.2019.2958736.
- Jounal-25. R. K. Sanodiya, J. Mathew, S. Saha, and M. D. Thalakottur, “A new transfer learning algorithm in semi-supervised setting,” IEEE Access, vol. 7, pp. 42 956–42 967, 2019, issn: 2169-3536. doi: 10.1109/ACCESS.2019.2907571.
- Jounal-26. R. K. Sanodiya, S. Saha, and J. Mathew, “A kernel semi-supervised distance metric learning with relative distance: Integration with a moo approach,” Expert Systems with Applications, vol. 125, pp. 233–248, 2019, issn: 0957-4174. doi: 10.1016/j.eswa.2018.12.051.
- Conference-1. A. Nigam, R. K. Sanodiya, P. Joshi, et al., “Generalized visual path following on jetbot using normalization with reinforcement learning,” in 2024 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO), IEEE, 2024, pp. 247–252
- Conference-2. A. Nigam, R. K. Sanodiya, S. Saha, and Subhangi, “Optimizing training speed with novel adaptive exploration technique in simulation and real-world robotics for visual path following,” in Neural Information Processing: 31th International Conference, ICONIP 2024, Auckland, New Zealand, December 02-6, 2024, Springer, 2024
- Conference-3.S. Jangala and R. K. Sanodiya, “A novel framework for multi-source domain adaptation with discriminative feature learning,” in 2023 International Joint Conference on Neural Networks (IJCNN), IEEE, 2023, pp. 1–7. doi: 10.1109/IJCNN54540.2023.10191410.
- Conference-4.A. S. Namboodiri, R. K. Sanodiya, and P. Arun, “Remote sensing cloud removal using a combination of spatial attention and edge detection,” in 2023 11th International Symposium on Electronic Systems Devices and Computing (ESDC), IEEE, vol. 1, 2023, pp. 1–6. doi: 10.1109/ESDC56251.2023.10149875.
- Conference-5.N. R. Nandyala and R. K. Sanodiya, “Underwater object detection using synthetic data,” in 2023 11th International Symposium on Electronic Systems Devices and Computing (ESDC), IEEE, vol. 1, 2023, pp. 1–6. doi: 10.1109/ESDC56251.2023.10149870.
- Conference-6.J. Prakash, M. Ghorai, and R. Sanodiya, “Transfer learning: Kernel-based domain adaptation with distance-based penalization,” in International Conference on Pattern Recognition and Machine Intelligence, Springer, 2023, pp. 189–198
- Conference-7.B. Y. Reddy, S. R. Dubey, R. K. Sanodiya, and R. R. P. Karn, “Context unaware knowledge distillation for image retrieval,” in Computer Vision and Machine Intelligence: Proceedings of CVMI 2022, Springer, 2023, pp. 65–77. doi: 10.1007/978-981-19-7867-8_6.
- Conference-8.R. Sidibomma and R. K. Sanodiya, “Learning semantic representations and discriminative features in unsupervised domain adaptation,” in 2023 11th International Symposium on Electronic Systems Devices and Computing (ESDC), IEEE, vol. 1, 2023, pp. 1–6. doi: 10.1109/ESDC56251.2023.10149872.
- Conference-9.S. Suryavardan, V. Pulabaigari, and R. K. Sanodiya, “Unsupervised domain adaptation supplemented with generated images,” in Neural Information Processing: 29th International Conference, ICONIP 2022, Virtual Event, November 22–26, 2022, Proceedings, Part IV, Springer, 2023, pp. 659–670. doi: 10.1007/978-981-99-1639-9_55.
- Conference-10.P. Bajpai and R. K. Sanodiya, “A unified framework for covariance adaptation with multiple source domains,” in 2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), IEEE, 2022, pp. 1–6. doi: 10.1109/UPCON56432.2022.9986432.
- Conference-11.R. R. P. Karn, R. K. Sanodiya, E. S. Chandaluri, S. Suryavardan, L. R. Reddy, and L. Yao, “Virtual try-on using style transfer,” in Responsible Data Science: Select Proceedings of ICDSE 2021, Springer, 2022, pp. 131–139. doi: 10.1007/978-981-19-4453-6_9.
- Conference-12.R. R. P. Karn, R. K. Sanodiya, T. Sharma, et al., “A feature and parameter selection approach for visual domain adaptation using particle swarm optimization,” in 2022 IEEE Congress on Evolutionary Computation (CEC), IEEE, 2022, pp. 1–7. doi: 10.1109/CEC55065.2022.9870263.
- Conference-13.R. Lekshmi, R. K. Sanodiya, B. R. Jose, and J. Mathew, “Joint cross-domain preserving and distribution adaptation for heterogeneous domain adaptation,” in 2022 IEEE 19th India Council International Conference (INDICON), IEEE, 2022, pp. 1–6. doi: 10.1109/INDICON56171.2022.10039779.
- Conference-14.S. Mishra and R. K. Sanodiya, “Scatter matrix normalization for unsupervised domain adaptation,” in 2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), IEEE, 2022, pp. 1–6. doi: 10.1109/UPCON56432.2022.9986396.
- Conference-15.R. Satya Rajendra Singh, R. K. Sanodiya, and P. Arun, “Joint geometrical and statistical alignment using triplet loss for deep domain adaptation,” in Responsible Data Science: Select Proceedings of ICDSE 2021, Springer, 2022, pp. 119–130. doi: 10.1007/978-981-19-4453-6_8.
- Conference-16.R. Lekshmi, R. K. Sanodiya, R. Linda, B. R. Jose, and J. Mathew, “Kernelized transfer feature learning on manifolds,” in Neural Information Processing: 28th International Conference, ICONIP 2021, Sanur, Bali, Indonesia, December 8–12, 2021, Proceedings, Part II 28, Springer, 2021, pp. 297–308. doi: 10.1007/978-3-030-92270-2_26.
- Conference-17.R. K. Sanodiya, V. V. Gottumukkala, L. D. Kurugundla, P. R. Dhansri, R. R. P. Karn, and L. Yao, “A novel multi-source domain learning approach to unsupervised deep domain adaptation,” in Neural Information Processing: 28th International Conference, ICONIP 2021, Sanur, Bali, Indonesia, December 8–12, 2021, Proceedings, Part V 28, Springer, 2021, pp. 64–72.
- Conference-18.M. Tiwari, R. K. Sanodiya, J. Mathew, and S. Saha, “A particle swarm optimization based feature selection approach for multi-source visual domain adaptation,” in Neural Information Processing: 28th International Conference, ICONIP 2021, Sanur, Bali, Indonesia, December 8–12, 2021, Proceedings, Part V 28, Springer, 2021, pp. 701–709. doi: 10.1007/978-3-030-92307-5_82
- Conference-19.M. Tiwari, R. K. Sanodiya, J. Mathew, and S. Saha, “Multi-source based approach for visual domain adaptation,” in 2021 International Joint Conference on Neural Networks (IJCNN), IEEE, 2021, pp. 1–7. doi: 10.1109/IJCNN52387.2021.9534305.
- Conference-20.L. Yao, S. Prasad, R. K. Sanodiya, and D. Paul, “Statistical and geometrical alignment for unsupervised deep domain adaptation,” in Proceedings of International Conference on Machine Intelligence and Data Science Applications: MIDAS 2020, Springer, 2021, pp. 433–444. doi: 10.1007/978-981-33-4087-9_37.
- Conference-21.R. K. Sanodiya, P. Kumar, M. Tiwari, L. Yao, and J. Mathew, “A modified joint geometrical and statistical alignment approach for low-resolution face recognition,” in Neural Information Processing: 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 23–27, 2020, Proceedings, Part I 27, Springer, 2020, pp. 88–100. doi: 10.1007/978-3-030-63830-6_8.
- Conference-22.R. K. Sanodiya, A. Mathew, J. Mathew, and M. Khushi, “Statistical and geometrical alignment using metric learning in domain adaptation,” in 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, 2020, pp. 1–8. doi: 10.1109/IJCNN48605.2020.9206877
- Conference-23.R. K. Sanodiya, D. Paul, L. Yao, J. Mathew, and A. Juhi, “A feature selection approach to visual domain adaptation in classification,” in Neural Information Processing: 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 23–27, 2020, Proceedings, Part II 27, Springer, 2020, pp. 77–89. doi: 10.1007/978-3-030-63833-7_7.
- Conference-24.R. K. Sanodiya, S. Saha, J. Mathew, M. D. Thalakottur, and U. Aadya, “Multi-objective approach for semi-supervised discriminant analysis with relative distance,” in 2019 IEEE Congress on Evolutionary Computation (CEC), IEEE, 2019, pp. 2808–2815. doi: 10.1109/CEC.2019.8790027.
- Conference-25.R. K. Sanodiya, C. Sharma, and J. Mathew, “Unified framework for visual domain adaptation using globality-locality preserving projections,” in Neural Information Processing: 26th International Conference, ICONIP 2019, Sydney, NSW, Australia, December 12–15, 2019, Proceedings, Part I 26, Springer, 2019, pp. 340–351. doi: 10.1007/978-3-030-36708-4_28.
- Conference-26.R. K. Sanodiya, M. D. Thalakottur, J. Mathew, and M. Khushi, “Semi-supervised regularized coplanar discriminant analysis,” in Neural Information Processing: 26th International Conference, ICONIP 2019, Sydney, NSW, Australia, December 12–15, 2019, Proceedings, Part V 26, Springer, 2019, pp. 198–205. doi: 10.1007/978-3-030-36802-9_22.
- Conference-27.R. K. Sanodiya, S. Saha, and J. Mathew, “A multi-kernel semi-supervised metric learning using multi-objective optimization approach,” in Neural Information Processing: 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13–16, 2018, Proceedings, Part II 25, Springer, 2018, pp. 530–541. doi: 10.1007/978-3-030-04179-3_47.
- Conference-28.R. K. Sanodiya, S. Saha, J. Mathew, and P. Bangwal, “Semi-supervised transfer metric learning with relative constraints,” in Neural Information Processing: 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13–16, 2018, Proceedings, Part III 25, Springer, 2018, pp. 230–241. doi: 10.1007/978-3-030-04182-3_21.
- Conference-29.R. K. Sanodiya, S. Saha, J. Mathew, and A. Raj, “Supervised and semi-supervised multi-task binary classification,” in Neural Information Processing: 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, Proceedings, Part IV 25, Springer, 2018, pp. 380–391. doi: 10.1007/978-3-030-04212-7_33.
M. Tech./M.Des.
| Roll no | Name | Status | Year | Specialization | Co-guide |
|---|---|---|---|---|---|
| S20201010004 | MidhunV | Completed | 2022 | Domain Adaptation for Semi-supervised Semantic Segmentation. |
Ph. D.
Contact me
Feel free to contact
Rakesh Kumar Sanodiya
rakesh.s@iiitdmj.ac.in
rakesh.s@iiitdmj.ac.in
+ 91 8770120278
(Fax) 91-
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