Assistant Professor

Dr Debanjan Konar

Department of Computer Science and Engineering

Interests

  1. Quantum Machine Learning
  2. Deep Neural Networks
  3. Medical Image Analysis

Education

2010

University Institute of Technology, University of Burdwan, Burdwan, West Bengal
India
BE

2012

National Institute of Technical Teachers’ Training and Research (NITTTR), Kolkata
India
MTech

2021

Indian Institute of Technology Delhi, New Delhi
India
PhD

Experience

  • 01/9/2020-30/06/2021, Assistant Professor (Research) - Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Sikkim, India
  • 06/8/2012-31/08/2020, Assistant Professor - Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Sikkim, India

Research Interest

  • Quantum Machine Learning
  • Quantum-inspired Neural Networks
  • Deep Neural Networks
  • Medical Image Analysis

Awards & Fellowships

  • 2019 - The Young Scientist Nomination Award, awarded by International Science Festival (IISF) 2019 organized by Ministry of Science and Technology, Govt. of India and held in Kolkata during 05-08 November 2019.
  • Silver Medallist for secured 2nd rank in the National Institute of Technical Teachers’ Training and Research (NITTTR), Kolkata, awarded by West Bengal University of Technology Kolkata, India, 2017.
  • Graduate Aptitude Test Qualified in 2010 (Computer Science) andAwarded MHRD Scholarship (Fellowship) during M. Tech (2010-2012),awarded by Ministry of Human Resources and Development, Govt. of India, 2010-2012.
  • National scholarship qualified in 2003, awarded byawarded by Ministry of Human Resources and Development, Govt. of India, 2003.

Memberships

  • 21/9/2016-present, Institute of Electrical and Electronics Engineers (IEEE) Membership # 94028574
  • 01/10/2016-present Computer Society of India (CSI) CSI Membership # F8002120
  • 01/10/2018-present Association for Computing Machinery (ACM) Membership # 1283609,
  • 12/7/2016-present, Institute of Engineers, India (MIE) Membership #AM178316-8.

Publications

JOURNAL PAPERS
  • D. Konar, S. Bhattachryya, B. K. Panigrahi, and E. Behrman, “Qutrit-inspired Fully Self-supervised Shallow Quantum Learning Network for Brain Tumor Segmentation”, IEEE Transaction on Neural Networks and Learning Systems(Early Access), DOI: 10.1109/TNNLS.2021.3077188, 2021. (SCI/SCIE, Impact Factor: 8.793 (2020)).
  • D. Konar, S. Bhattacharyya, T. K. Gandhi, and B. K. Panigrahi, “A Quantum-Inspired Self-Supervised Network Model for Automatic Segmentation of Brain MR Images”, Journal of Applied Soft Computing,Elsevier,Volume 93, 2020, 106348, https://doi.org/10.1016/j.asoc.2020.106348 (SCI/SCIE, Impact Factor: 5.472 (2020)). [5]
  • D. Konar, B. K. Panigrahi, S. Bhattachryya,N. Dey, and R. Jiang, “Auto-diagnosis of COVID-19 using Lung CT Images with Semi-supervised Shallow Learning Network”, IEEE Access, vol. 9, pp. 28716-28728, 2021, 10.1109/ACCESS.2021.3058854 (SCI/SCIE, Impact Factor: 3.745 (2020)).[7]
  • D. Konar, S. Bhattacharyya, K. Sharma, S. Pradhan and S. Sharma, “An Improved Hybrid Quantum-Inspired Genetic Algorithm (HQIGA) for Scheduling of Real-Time Task in Multiprocessor System”, Applied Soft Computing, Elsevier, Volume 53, pp. 296–307, 2017 http://dx.doi.org/10.1016/j.asoc.2016.12.051 (SCI/SCIE, Impact Factor: 5.472 (2020)). [45]
  • D. Konar, S. Bhattacharyya, B. K. Panigrahi and K. Nakamatsu, “A quantum bi-directional self-organizing neural network (QBDSONN) architecture for binary object extraction from a noisy perspective”, Applied Soft Computing, Elsevier, Volume 46, pp. 731–752, 2016 http://dx.doi.org/10.1016/j.asoc.2015.12.040 (SCI/SCIE, Impact Factor: 5.472 (2020)). [15]
  • U. K. Chakraborty, D. Konar, S. Roy and S. Choudhury, “Intelligent fuzzy spelling evaluator for e-Learning systems”, Education and Information Technologies, Springer, Volume 21, pp. 171–184, 2014, DOI: 10.1007/s10639-014-9314-z (SCIMago/SSCI, Impact Factor: 2.1 (2019)). [8]
  • U. K. Chakraborty,D. Konar, S. Roy and S. Choudhury, “Automatic Short Answer Grading using Rough Concept Clusters”, International Journal of Advanced Intelligence Paradigms, vol. 14, Nos. 3/4, pp. 260–280, 2017, DOI: 10.1504/IJAIP.2019.103413.
JOURNALS (COMMUNICATED)
  • D. Konar, S. Bhattachryya, B. K. Panigrahi, and T. K. Gandhi, and R. Jiang, “3D Quantum-inspired Self-supervised Tensor Network for Volumetric Segmentation of Brain MR Images”, IEEE Transaction on Pattern Analysis and Machine Intelligence, Manuscript Id:TPAMI-2021-01-0029 (Under Review), TechRxiv. Preprint.https://doi.org/10.36227/techrxiv.12909860.v2.
  • D. Konar, S. Bhattachryya, S. Dey, and B. K. Panigrahi, “Optimized Activation for Quantum-Inspired Self-supervised Neural Network based Fully Automated Brain MR Image Segmentation”, Neural Computing and Applications, Manuscript Id: NCAA-D-20-03805 (Under Review), TechRxiv. Preprint. https://doi.org/10.36227/techrxiv.12909872.v1.
  • S. Chandra, M. K. Gourisaria, H. GM, D. Konar, M. Xu, and T. Wang, “Prolificacy Assessment of Spermatozoan via state-of-the-art Deep Learning Frameworks”, Computers in Biology and Medicine (Under Review), 2021.
  • S. Bhattacharyya, S. De, S. Gorbachev, D. Konar, I. Pan, L. Mrsic, and A. Mukherjee, “A quantitative assessment of rubrics using a soft computing approach”, CAAI Transactions on Intelligence Technology, Manuscript ID CIT-2021-02-0018 (Under Revision).
CONFERENCE PROCEEDINGS
  • D. Konar, Bhattachryya, S. Dey, and B. K. Panigrahi, “Opti-QIBDS Net: A Quantum Inspired Optimized Self-supervised Neural Network for Automatic Brain MR Image Segmentation”, Proc. IEEE Region 10 Conference (TENCON 2019), pp. 759-764, 2019, DOI: 10.1109/TENCON.2019.8929585, 2019. [3]
  • S. Bhattacharyya, S. Dey, and D. Konar, “A Novel Qutrit Based Quantum Ant Colony Optimization for Multi-level Thresholding”, Proc. IEEE Region 10 Conference (TENCON 2019), pp. 1375-1380, 2019, DOI: 10.1109/TENCON.2019.8929561.
  • D. Konar, S. Bhattacharyya, and B. K. Panigrahi, “QIBDS Net: A Quantum-Inspired Bi-Directional Self-supervised Neural Network Architecture for Automatic Brain MR Image Segmentation”, Proc. International Conference on Pattern Recognition and Machine Intelligence (PReMI 2019). Lecture Notes in Computer Science, vol 11942, pp. 87-95, 2019. Springer, https://link.springer.com/chapter/10.1007%2F978-3-030-34872-4_10. [3]
  • S. Bhattacharyya, V. Snasel, A. Dey, S. Dey, and D. Konar, “Quantum Spider Monkey Optimization (QSMO) Algorithm for Automatic Gray-Scale Image Clustering”, published in 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1869-1874, DOI: 10.1109/ICACCI.2018.8554872. [1]
  • S. Dey, S. De, D. Ghosh, and D. Konar, S. Bhattacharyya, J. Platos, “A Novel Quantum Inspired Sperm Whale Meta-heuristic for Image Thresholding”, 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP), ISBN: 978-1-5386-7989-0/19/$31.00 © 2019 IEEE, DOI: 10.1109/ICACCP.2019.8882905. [2]
  • D. Konar, K. Sharma, V. Sarogi, and S. Bhattacharyy, “A Multi-Objective Quantum-Inspired Genetic Algorithm (Mo-QIGA) for Real-Time Tasks Scheduling in Multiprocessor Environment”, published in 2018 The 8th International Congress of Information and Communication Technology (ICICT2018), China, Volume 131, 2018, pp 591-599, https://doi.org/10.1016/j.procs.2018.04.301. [13]
  • D. Konar, U. K. Chakraborty, S. Bhattacharyya, T. K. Gandhi and B. K. Panigrahi, “A quantum parallel bi-directional self-organizing neural network (QPBDSONN) architecture for extraction of pure color objects from noisy background” published in the proceedings of 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1912-1918, 2016, DOI: 10.1109/ICACCI.2016.773233. [4]
  • U. K. Chakraborty, D. Konar, S. Roy and S. Choudhury, “Intelligent Evaluation of Short Responses for e-Learning Systems” published in the proceedings of the First International Conference on Computational Intelligence and Informatics, pp. 365-372, 2016, DOI: 10.1007/978-981-10-2471-9_35.
  • U. K. Chakraborty, D. Konar, S. Roy, and S. Choudhury, “Rough Set based keyword selection and weighing for textual answer evaluation”, published in 2015 Annual IEEE India Conference (INDICON), 2015, DOI :10.1109/INDICON.2015.7443405.
  • D. Konar, S. Bhattacharyya, N. Das, and B. K. Panigrahi, “A Quantum Bi-Directional Self-Organizing Neural Network (QBDSONN) for Binary Image Denoising” published in the proceedings of 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1225-1230, 2015, DOI: 10.1109/ICACCI.2015.7275780. [5]
  • D. Konar, K. Sharma, S. Pradhan, and S. Sharma, “An Efficient Dynamic Scheduling Algorithm for Soft Real-Time Tasks in Multiprocessor System Using Hybrid Quantum-Inspired Genetic Algorithm” published in Proceedings of the 4th International Conference on Frontiers in Intelligent Computing: Theory and Applications (FICTA), pp. 3-11, 2015, DOI: 10.1007/978-81-322-2695-6_1. [2]
  • C. Kar, A. Kumar, D. Konar, and S. Bannerjee, “Automatic Region of Interest Detection of Tropical Cyclone Image by Center of Gravity and Distance Metrics”, Proceedings of 2019 Fifth International Conference on Image Information Processing (ICIIP), ISBN: 978-1-7281-0899-5, DOI: 10.1109/ICIIP47207.2019.8985860. [1]
BOOK CHAPTERS
  • D. Konar, and S. K. Kar, “An Efficient Handwritten Character Recognition using Quantum Multilayer Neural Network (QMLNN) Architecture: Quantum Multilayer Neural Network”, Quantum-Inspired Intelligent Systems for Multimedia Data Analysis, IGI Global, pp. 262-276, 2018, DOI: 10.4018/978-1-5225-5219-2.ch008. [3]
  • D. Konar, S. Bhattacharyya, B. K. Panigrahi and M. K. Ghose, “An efficient pure color image denoising using quantum parallel bidirectional self-organizing neural network architecture”, published in Quantum Inspired Computational Intelligence. Elsevier Book Chapter, pp. 149-205, http://dx.doi.org/10.1016/B978-0-12-804409-4.00005-X. [3]
  • D. Konar, R. Pradhan, T. Dey, T. Sapkota and P. Rai, “Predicting Students' Grades Using CART, ID3, and Multiclass SVM Optimized by the Genetic Algorithm (GA): A Case Study”, Recent Advances in Hybrid Metaheuristics for Data Clustering, John Wiley & Sons, Ltd, pp. 85-99, 2020, DOI: https://doi.org/10.1002/9781119551621.ch5.
  • S. Dey, S. De, D. Konar, S. Bhattacharyya, “An introductory illustration of medical image analysis”, Advanced Machine Vision Paradigms for Medical Image Analysis, pp. 1-9, 2021, https://doi.org/10.1016/B978-0-12-819295-5.00001-9.
AUTHORED BOOK
  • R. Althar, D. Samanta, D. Konar, and S. Bhattacharyya, “Statistical Modelling of Software Source Code”, Walter de Gruyter GmbH, Germany, 2021, https://www.degruyter.com/document/isbn/9783110703399/html.
EDITED BOOKS/CONFERENCE PROCEEDINGS
  • T. Gandhi, S. Bhattacharyya, S. De, D. Konar, and S. Dey, “Advanced Machine Vision Paradigms for Medical Image Analysis”, Hybrid Computational Intelligence for Pattern Analysis and Understanding, Elsevier, 2020, ISBN: 9780128192955, https://www.elsevier.com/books/advanced-machine-vision-paradigms-for-medical-image-analysis/gandhi/978-0-12-819295-5.
  • S. Bhattacharyya, D. Konar, J. Platos, C. Kar and K. Sharma, “Hybrid Machine Intelligence for Medical Image Analysis”, Studies in Computational Intelligence, ISBN: 978-981-13-8929-0, DOI: 10.1007/978-981-13-8930-6, https://www.springer.com/gp/book/9789811389290.[2]
  • M. Gupta, D. Konar, S. Bhattacharyya, and S. Biswas, “Computer Vision and Machine Intelligence in Medical Image Analysis”, Proceedings of International Symposium on ISCMM 2019, Volume: 992, Series: Advances in Intelligent Systems and Computing, ISBN: 978-981-13-8797-5, https://www.springer.com/gp/book/9789811387975, DOI: 10.1007/978-981-13-8798-2. [2]
  • S. Bhattacharyya, N. Chaki, D. Konar, U. K. Chakraborty, and C. T. Singh, “Advanced Computational and Communication Paradigms”, Proceedings of International Conference on ICACCP 2017, Volume 2 Series: Advances in Intelligent Systems and Computing, ISBN: 978-981-10-8236-8, http://www.springer.com/gp/book/9789811082368, DOI: 10.1007/978-981-10-8237-5. [1]
PATENTS
  • A Microcontroller Based Low-Cost Electronic Locking System Using 2-WayAuthentication, Patent number: 2021101384
    Inventors: S. Bhattacharyya, A. Basu, A. Roy, S. Sinha, P. Chakrabarti, S. De, D. Konar, D. Samanta, T. Dutta, S. Dey, and D. Mukhopadhyay
  • Automatic Violence Detection - A Tool for Woman’s Safety
    Application No.:202041006858, dated 18th February, 2020.
    Applicants:D. Konar, R. Rakshit, S. Dey, D. Samanata, C. Kar, H. Pal, K, Sharma, and S. Bhattacharyya;

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