Research Publications

Journal Articles1

1.  Konar, D., Neerav, S., Jinag, R., & Aggarwal, V. (2025). Alz-QNet: A quantum regression network for studying alzheimer’s gene interactions. Computers in Biology and Medicine, 196(110837). doi:https://doi.org/10.1016/j.compbiomed.2025.110837

2. Konar, D., Peddireddy, D., & Aggarwal, V. (2025). Tensor ring optimized quantum-enhanced tensor neural networks. Quantum Machine Intelligence, 7(57), 1–22. doi:https://doi.org/10.1007/s42484-025-00281-5

3.  Konar, D., Bhattacharyya, S., Gandhi, T. K., Panigrahi, B. K., & Jiang, R. (2023). 3D quantum-inspired self-supervised tensor network for volumetric segmentation of medical images. IEEE Transactions on Neural Networks and Learning Systems, 1–14. doi:10.1109/TNNLS.2023.3240238.

4. Konar, D., Bhattacharyya, S., Sarma, A., Das, Bhandary, S., Cangi, A., & Aggarwal, V. (2023). A shallow hybrid classical-quantum spiking feedforward neural network for noise-robust image classification. Applied Soft Computing, 136, 110099.  doi:https://doi.org/10.1016/j.asoc.2023.110099

5. Konar, D., Bhattacharyya, S., Panigrahi, B. K., & Behrman, E. C. (2022). Qutrit-inspired fully self-supervised shallow quantum learning network for brain tumor segmentation. IEEE Transactions on Neural Networks and Learning Systems, 33 (11), 6331-6345 . doi:10.1109/TNNLS.2021.3077188.

6. Konar, D., Gelenbe, E., Bhandary, S., Sarma, A. D., & Cangi, A. (2022). Random quantum neural networks (RQNN) for noisy image recognition. doi:https://arxiv.org/abs/2203.01764.

7.  Chandra, S., Gourisaria, M. K., Harshvardhan, G., Konar, D., Gao, X., Wang, T., & Xu, M. (2022). Prolificacy assessment of spermatozoan via state-of-the-art deep learning frameworks. IEEE Access, 10, 13715–13727. doi:10.1109/ACCESS.2022.3146334.

8. 3Konar, D., Bhattacharyya, S., Dey, S., & Panigrahi, B. K. (2022). Optimized activation for quantum-inspired self-supervised neural network based fully automated brain lesion segmentation. Applied Intelligence. doi:https://doi.org/10.1007/s10489-021-03108-5.

9. 4Vemula, D. R., Konar, D., Satheesan, S., Kalidasu, S. M., & Cangi, A. (2022). A scalable 5,6-qubit grover’s quantum search algorithm. arXiv. doi:10.48550/ARXIV.2205.00117.

10. 5Konar, D., Bhattacharyya, S., Gandhi, T. K., Panigrahi, B. K., & Jiang, R. (2021). 3D Quantum-inspired self-supervised tensor network for volumetric segmentation of medical images. doi:10.36227/techrxiv.12909860.v3.

11.  Konar, D., Panigrahi, B. K., Bhattacharyya, S., Dey, N., & Jiang, R. (2021). Auto-diagnosis of Covid-19 using lung CT images with semi-supervised shallow learning network. IEEE Access, 9, 28716–28728. doi:10.1109/ACCESS.2021.3058854. 

12.  Konar, D., Bhattacharyya, S., Gandhi, T. K., & Panigrahi, B. K. (2020). A quantum-inspired self-supervised network model for automatic segmentation of brain MR images. Applied Soft Computing, 93, 106348. doi:https://doi.org/10.1016/j.asoc.2020.106348.

13.  Chakraborty, U. K., Konar, D., Roy, S., & Choudhury, S. (2019). Automatic short answer grading using rough concept clusters. International Journal of Advanced Intelligence Paradigms, 14(3-4), 260–280. doi:10.1504/IJAIP.2019.103413. 

14.  Konar, D., Bhattacharyya, S., Sharma, K., Sharma, S., & Pradhan, S. R. (2017). An improved hybrid quantum-inspired genetic algorithm (HQIGA) for scheduling of real-time task in multiprocessor system. Applied Soft Computing, 53, 296–307. doi:https://doi.org/10.1016/j.asoc.2016.12.051.

15.  Chakraborty, U. K., Konar, D., Roy, S., & Choudhury, S. (2016). Intelligent fuzzy spelling evaluator for e-learning systems. Education and Information Technologies, 21(1), 171–184. doi:https://doi.org/10.1007/s10639-014-9314-z.

16.  Konar, D., Bhattacharyya, S., Panigrahi, B. K., & Nakamatsu, K. (2016). A quantum bi-directional self-organizing neural network (QBDSONN) architecture for binary object extraction from a noisy perspective. Applied Soft Computing, 46, 731–752. doi:https://doi.org/10.1016/j.asoc.2015.12.040.

Conference Proceedings

1.  Bhattacharyya, S., Dey, S., & Konar, D. (2019). A novel qutrit based quantum ant colony optimization for multi-level thresholding. In Tencon 2019-2019 IEEE Region 10 Conference (TENON) (pp. 1375–1380). IEEE. doi:10.1109/TENCON.2019.8929561

2.  Dey, S., De, S., Ghosh, D., Konar, D., Bhattacharyya, S., & Platos, J. (2019). A novel quantum-inspired sperm whale meta-heuristic for image thresholding. In 2019 Second international conference on advanced computational and communication paradigms (icaccp) (pp. 1–7). IEEE. doi:10.1109/ICACCP.2019.8882905

3.  Konar, D., Bhattacharyya, S., Dey, S., & Panigrahi, B. K. (2019). Opti-QIBDS Net: A quantum-inspired optimized bi-directional self-supervised neural network architecture for automatic brain mr imagesegmentation. In Tencon 2019-2019 IEEE Region 10 Conference (TENON) (pp. 761–766). IEEE. doi:10.1109/TENCON.2019.8929585

4.  Konar, D., Bhattacharyya, S., & Panigrahi, B. K. (2019). QIBDS Net: A quantum-inspired bi-directional self-supervised neural network architecture for automatic brain mr image segmentation. In International conference on pattern recognition and machine intelligence (pp. 87–95). Springer. doi: https://doi.org/10.1007/978-3-030-34872-4_10

5.  Bhattacharyya, S., Snasel, V., Dey, A., Dey, S., & Konar, D. (2018). Quantum spider monkey optimization (QSMO) algorithm for automatic gray-scale image clustering. In 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 1869–1874). IEEE. doi:10.1109/ICACCI.2018.8554872

6.  Konar, D., Sharma, K., Sarogi, V., & Bhattacharyya, S. (2018). A multi-objective quantum-inspired genetic algorithm (Mo-QIGA) for real-time tasks scheduling in multiprocessor environment. (vol. 131, pp. 591–599). doi:https://doi.org/10.1016/j.procs.2018.04.301

7. Bhattacharyya, S., Chaki, N., Konar, D., Chakraborty, U. K., & Singh, C. T. (2017). Advanced computational and communication paradigms. In Proceedings of international conference on ICACCP. Springer. doi:10.1007/978-981-10-8240-5

8.  U. Chakraborty, Konar, D., Roy, S., Choudhury, S. et al. (2017). Intelligent evaluation of short responses for e-learning systems. In Proceedings of the First International conference on computational intelligence and informatics (pp. 365–372). Springer. doi:https://doi.org/10.1007/978-981-10-2471-9_35

9.  Konar, D., Chakraborty, U. K., Bhattacharyya, S., Gandhi, T. K., & Panigrahi, B. K. (2016). A quantum parallel bi-directional self-organizing neural network (qpbdsonn) architecture for extraction of pure color objects from noisy background. In 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 1912–1918). IEEE. doi:10.1109/ICACCI.2016.7732330

10.  Konar, D., Sharma, K., Pradhan, S. R., & Sharma, S. (2016). An effiffifficient dynamic scheduling algorithm for soft real-time tasks in multiprocessor system using hybrid quantum-inspired genetic algorithm. In Proceedings of the 4th International conference on frontiers in intelligent computing: Theory and applications (FICTA) 2015 (pp. 3–11). Springer. doi:https://doi.org/10.1007/978-81-322-2695-6_1

11.  Chakraborty, U. K., Konar, D., Roy, S., & Choudhury, S. (2015). Rough set based keyword selection and weighing for textual answer evaluation. In 2015 Annual IEEE India conference (INDICON) (pp. 1–6). IEEE. doi:10.1109/INDICON.2015.7443405

12.  Konar, D., Bhattacharyya, S., Das, N., & Panigrahi, B. K. (2015). A quantum bi-directional self-organizing neural network (QBDSONN) for binary image denoising. In 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 1225–1230). IEEE. doi:10.1109/ICACCI.2015.7275780

Books and Chapters

1. Althar, R. R., Samanta, D., Konar, D., & Bhattacharyya, S. (2021). Software source code: Statistical modeling. Walter de Gruyter GmbH & Co KG.

2.  Dey, S., Konar, D., De, S., & Bhattacharyya, S. (2021a). Chapter 1 - an introductory illustration of medical image analysis. In T. Gandhi, S. Bhattacharyya, S. De, D. Konar, & S. Dey (Eds.), Advanced machine vision paradigms for medical image analysis (pp. 1–9). doi:https://doi.org/10.1016/B978-0-12-819295-5.00001-9

3.  Dey, S., Konar, D., De, S., & Bhattacharyya, S. (2021b). Chapter 11 - conclusion and future research directions. In T. Gandhi, S. Bhattacharyya, S. De, D. Konar, & S. Dey (Eds.), Advanced machine vision paradigms for medical image analysis (pp. 273–277). doi:https://doi.org/10.1016/B978-0-12-819295-5.00011-14

4.  Gandhi, T., Bhattacharyya, S., De, S., Konar, D., & Dey, S. (Eds.). (2021). Advanced machine vision paradigms for medical image analysis. doi:https://doi.org/10.1016/C2018-0-05420-1

5.  Konar, D., Bhattacharyya, S., De, S., Das, A., Platos, J., Gorbachev, S. V., & Muhammad, K. (2021). Early prediction of coronavirus epidemic outbreak using stacked long short-term memory networks. (p. 81). CRC Press.

6.  Bhattacharyya, S., Konar, D., Platos, J., Kar, C., & Sharma, K. (2020). Hybrid machine intelligence for medical image analysis. doi:10.1007/978-981-13-8930-6

7.  Gupta, M., Konar, D., Bhattacharyya, S., & Biswas, S. (2020). Computer vision and machine intelligence in medical image analysis. doi:10.1007/978-981-13-8798-2

8.  Konar, D., Pradhan, R., Dey, T., Sapkota, T., & Rai, P. (2020). Predicting students’ grades using cart, id3, and multiclass svm optimized by the genetic algorithm (GA): A case study. (pp. 85–99). doi:https://doi.org/10.1002/9781119551621.ch5

9.  Kar, C., Kumar, A., Konar, D., & Banerjee, S. (2019). Automatic region of interest detection of tropical cyclone image by center of gravity and distance metrics. In 2019 Fifth International conference on image information processing (ICIIP) (pp. 141–145). doi:10.1109/ICIIP47207.2019.8985860

10.  Konar, D., & Kar, S. K. (2018). An effiffifficient handwritten character recognition using quantum multilayer neural network (qmlnn) architecture: Quantum multilayer neural network. In Quantum-inspired intelligent systems for multimedia data analysis (pp. 262–276). doi:10.4018/978-1-5225-5219-2.ch008

Patents

1. March 2021: A Microcontroller Based Low-Cost Electronic Locking System Using 2-Way Authentication, Patent#: 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.

2. February 2020 Automatic Violence Detection - A Tool for Woman’s Safety, Application#202041006858, Applicants: D. Konar, R. Rakshit, S. Dey, D. Samanata, C. Kar, H. Pal, K. Sharma and S. Bhattacharyya.