Dean (I&R)

Home Dean (I&R)

Dean (I&R)

Areas of Interest: Data Science, Algorithms and Pattern Recognition

Education: B.E., M.E., Ph.D.

E-mail: sanjay.garg@juet.ac.in

Contact No. : Ext. - 107

Prof. Sanjay Garg

Dr Sanjay Garg is B.E., M.E. and PhD in Computer Science and Engineering discipline. He served in various positions in his career spanning over 28 years and he joined JUET as Dean (Innovations and Research) and Professor (CSE). His academic and research interests include data science, algorithms and pattern recognition. He has published more than 70 research papers in journals and conferences of repute, supervised 10 PhD dissertations and over 30 M.Tech Dissertations. He has completed six funded research projects sponsored by ISRO under the RESPOND/AO-NISAR/MAHATRAM scheme and GUJCOST as Principal Investigator.

He has memberships in the following Professional Societies

Fellow: Institution of Engineers (India)
Senior Member: IEEE, ACM
Life Member: ISTE, CSI, SSI

He is a recipient of the "˜Best Engineering Teacher Award in Gujarat State by ISTE, New Delhi in 2016.

He is a member of various academic bodies for several universities and served as an expert in delivering talks as well as on Research Advisory Committees in several Institutions/universities.

Past Administrative/Academic Assignments:

  • Professor and Head(CSE), Institute of Technology, NIrma University, Gujarat
  • Dean(Academic Affairs) , DIT University, Dehradun
  • Pro Vice Chancellor, Indrashil University, Gujarat

He has played an instrumental role in the initiation and implantation of contemporary academic systems e.g. CBCS, and OBE in his serving institutes.

  1. S. Garg, S. Jain, N. Dube, and N. Varghese, Eds., Earth Observation Data Analytics Using Machine and Deep Learning: Modern tools, applications and challenges. IET Publishers, 2023. doi: 10.1049/pbpc056e.
  2. N. Sisodiya, S. Garg, N. Dube, P. Thakkar, A. Parmar, and S. Sharma, “Scalable clustering for EO data using efficient raster representation,” Multimedia Tools and Applications, vol. 82, no. 8, pp. 12303–12319, Sep. 2022, doi: 10.1007/s11042-022-13726-x.
  3. S. Jain et al., “Performance Evaluation of DL-based Models for LULC segmentation using SAR time series data of regions in Gujarat,” ResearchSquare, 2023.
  4. M. Patel, B. Gohil, S. Chaudhary, and S. Garg, “Smart offload chain: a proposed architecture for blockchain assisted fog offloading in smart city,” International Journal of Electrical and Computer Engineering (IJECE), vol. 12, no. 4, p. 4137, Aug. 2022, doi: 10.11591/ijece.v12i4.pp4137-4145.
  5. Anupama, J. P. Verma, S. H. Mankad, S. Garg, P. Bodani, and G. Sangar, “A New Approach for Processing Raster Geospatial Big Data in Distributed Environment,” in Lecture Notes in Networks and Systems, Springer Nature Singapore, 2022, pp. 83–93. doi: 10.1007/978-981-19-1018-0_8.
  6. P. Solanki, S. Garg, and H. Chhikaniwala, “Preserve Privacy on Streaming Data During the Process of Mining Using User Defined Delta Value,” in Innovative Data Communication Technologies and Application, Springer Nature Singapore, 2022, pp. 197–212. doi: 10.1007/978-981-16-7167-8_15.
  7. S. Garg and P. Khalpada, “Moral-Driven Planning in Simple Automated Narrative Generator,” American Journal of Science &amp\mathsemicolon Engineering, vol. 2, no. 3, pp. 15–23, Dec. 2021, doi: 10.15864/ajse.2303.
  8. A. Shrivastava, J. P. V. Verma, S. Jain, and S. Garg, “A deep learning based approach for trajectory estimation using geographically clustered data,” SN Applied Sciences, vol. 3, no. 6, May 2021, doi: 10.1007/s42452-021-04556-x.
  9. P. Khalpada and S. Garg, “Simple Automated Narrative Generator (SANG),” Jan. 2021. doi: 10.1109/ccwc51732.2021.9375954.
  10. J. V. Verma, S. Tanwar, S. Garg, and A. D. Rathod, “Crowdsourced Social Media Reaction Analysis for Recommendation,” International Journal of Knowledge and Systems Science, vol. 12, no. 1, pp. 1–19, Jan. 2021, doi: 10.4018/ijkss.2021010101.
  11. A. V. Kathiya, J. P. Verma, and S. Garg, “Leveraging Deep Learning Techniques on Remotely Sensing Agriculture Data,” Lecture Notes in Networks and Systems, vol. 204, pp. 955–965, 2021.
  12. S. H. Mankad and S. Garg, “On the performance of empirical mode decomposition-based replay spoofing detection in speaker verification systems,” Progress in Artificial Intelligence, vol. 9, no. 4, pp. 325–339, Aug. 2020, doi: 10.1007/s13748-020-00216-0.
  13. J. P. V. Verma, S. H. Mankad, and S. Garg, “GeoHash tag based mobility detection and prediction for traffic management,” SN Applied Sciences, vol. 2, no. 8, Jul. 2020, doi: 10.1007/s42452-020-2870-5.
  14. D. Vashi, H. B. Bhadka, K. Patel, and S. Garg, “An Efficient Hybrid Approach of Attribute Based Encryption For Privacy Preserving Through Horizontally Partitioned Data,” Procedia Computer Science, vol. 167, pp. 2437–2444, 2020, doi: 10.1016/j.procs.2020.03.296.
  15. N. Dalsaniya, S. H. Mankad, S. Garg, and D. Shrivastava, “Development of a Novel Database in Gujarati Language for Spoken Digits Classification,” in Communications in Computer and Information Science, Springer Singapore, 2020, pp. 208–219. doi: 10.1007/978-981-15-4828-4_18.
  16. V. Oza, Y. Thesia, D. Rasalia, P. Thakkar, N. Dube, and S. Garg, “Extreme Weather Prediction Using 2-Phase Deep Learning Pipeline,” in Communications in Computer and Information Science, Springer Singapore, 2020, pp. 266–282. doi: 10.1007/978-981-15-4018-9_25.
  17. S. Garg and S. H. Mankad, “Voice liveness detection under feature fusion and cross-environment scenario,” Multimedia Tools and Applications, vol. 79, no. 37–38, pp. 26951–26967, 2020.
  18. P. Khalpada and S. Garg, “Balancing Consistency and Plot Structure in Computational Storytelling,” Oct. 2019. doi: 10.1109/iemcon.2019.8936138.
  19. J. P. Verma, S. Tanwar, S. Garg, I. Gandhi, and N. H. Bachani, “Evaluation of Pattern Based Customized Approach for Stock Market Trend Prediction With Big Data and Machine Learning Techniques,” International Journal of Business Analytics, vol. 6, no. 3, pp. 1–15, Jul. 2019, doi: 10.4018/ijban.2019070101.
  20. K. J. Modi, S. Garg, and S. Chaudhary, “An Integrated Framework for RESTful Web Services Using Linked Open Data,” International Journal of Grid and High Performance Computing, vol. 11, no. 2, pp. 24–49, Apr. 2019, doi: 10.4018/ijghpc.2019040102.
  21. S. H. Mankad, S. Garg, M. Patel, and H. Adalja, “Investigating Feature Reduction Strategies for Replay Antispoofing in Voice Biometrics,” in Lecture Notes in Computer Science, Springer International Publishing, 2019, pp. 400–408. doi: 10.1007/978-3-030-34872-4_44.
  22. M. Patel, S. Chaudhary, and S. Garg, “vMeasure: Performance Modeling for Live VM Migration Measuring,” Lecture Notes in Networks and Systems, vol. 39, pp. 185–195, 2019.
  23. U. Shah, S. Garg, N. Sisodiya, N. Dube, and S. Sharma, “Rainfall prediction: Accuracy enhancement using machine learning and forecasting techniques,” PDGC 2018 - 2018 5th International Conference on Parallel, Distributed and Grid Computing, pp. 776–782, 2018.
  24. D. J. Prajapati, S. Garg, and N. C. Chauhan, “Interesting association rule mining with consistent and inconsistent rule detection from big sales data in distributed environment,” Future Computing and Informatics Journal, vol. 2, no. 1, pp. 19–30, Jun. 2017, doi: 10.1016/j.fcij.2017.04.003.
  25. R. Kotecha and S. Garg, “Preserving output-privacy in data stream classification,” Progress in Artificial Intelligence, vol. 6, no. 2, pp. 87–104, Feb. 2017, doi: 10.1007/s13748-017-0114-8.
  26. M. Patel, S. Chaudhary, and S. Garg, “Performance modeling of skip models for VM migration using Xen,” Proceeding - IEEE International Conference on Computing, Communication and Automation, ICCCA 2016, pp. 1256–1261, 2017.
  27. M. Patel, S. Chaudhary, and S. Garg, “Machine Learning Based Statistical Prediction Model for Improving Performance of Live Virtual Machine Migration,” Journal of Engineering (United States), vol. 2016, 2016.
  28. R. Kotecha and S. Garg, “Data streams and privacy: Two emerging issues in data classification,” Nov. 2015. doi: 10.1109/nuicone.2015.7449597.
  29. S. Surati, D. C. Jinwala, and S. Garg, “Evaluating the impact of fanout and dimension on the performance of a hybrid model for multidimensional indexing in peer-to-peer m-ary tree network,” International Journal of Communication Networks and Distributed Systems, vol. 14, no. 2, pp. 185–201, 2015.
  30. A. Misra, B. Kartikeyan, and S. Garg, “Wavelet based SAR data denoising and analysis,” Souvenir of the 2014 IEEE International Advance Computing Conference, IACC 2014, pp. 1087–1092, 2014.
  31. B. Prajapati, S. Garg, and N. C. Chauhan, “Some Investigations on Machine Learning Techniques for Automated Text Categorization,” International Journal of Computer Applications, vol. 71, no. 3, pp. 32–36, Jun. 2013, doi: 10.5120/12340-8617.
  32. S. D. Bhanderi and S. Garg, “Parallel frequent set mining using inverted matrix approach,” 3rd Nirma University International Conference on Engineering, NUiCONE 2012, 2012.
  33. R. Kotecha, V. Ukani, and S. Garg, “An empirical analysis of multiclass classification techniques in data mining,” 2011 Nirma University International Conference on Engineering: Current Trends in Technology, NUiCONE 2011 - Conference Proceedings, 2011.
  34. S. Garg and R. C. Jain, “Variations of k-mean algorithm: A study for high-dimensional large data sets,” Information Technology Journal, vol. 5, no. 6, pp. 1132–1135, 2006.

 

  1. "Design and Development of Spatio-Temporal Data Mining Techniques and Software Framework for Earth Observation Data" Sponsored by ISRO, Department of Space, Government of India, under RESPOND, mobilizing a grant of Rs. 10.80 lakh, May 2013-2015.
  2. "Object Detection and Surveillance System" Sponsored by the Department of Science and Technology, Gujarat State, GUJCOST MRP grant of Rs. 4.00 lakh, December 2015–2016.
  3. "Design and Development of Scalable Data Mining Techniques for Big Earth Data" Sponsored by ISRO, Department of Space, Government of India, under RESPOND, mobilizing a grant of Rs. 10.70 lakh, October 2016–2018.
  4. "Development of Comprehensive Technique for Soil Moisture Estimation using L & S Airborne Polarimetric SAR Data" Sponsored by ISRO, Department of Space, Government of India, under NISAR AO, mobilizing a grant of Rs. 21.80 lakh, January 2018–2021.
  5. "Design and Development of a Scalable Framework for Geospatial/Geoscience Data Ingestion, Ad-hoc Queries, and Analysis on Big Data Environment" Sponsored by ISRO, Department of Space, Government of India, under the RESPOND scheme, mobilizing a grant of Rs. 19.82 lakh, December 2019–2021 (Co-PI since March 2021).
  6. "Development of Advanced Algorithms for Land Use and Land Cover Classification using Deep Learning Techniques" In collaboration with and sponsored by SAC, ISRO, Department of Space, Government of India, under the MAHATRAM Scheme, mobilizing a grant of Rs. 27.09 lakh, May 2019–2023 (Co-PI since March 2021)
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