Mainak Chakraborty, Post Doc

E-mail:
mainak.chakraborty@mdu.se
Room:
U1-123
Phone:
+46 73 662 13 26
Division:
Division of Intelligent Future Technologies
Research group:
Heterogeneous systems - hardware software co-design
Web:
Linkedin page


Mainak Chakraborty (Senior Member, IEEE)  earned his B.Tech. and M.Tech. degrees in information technology and computer science & engineering from Maulana Abul Kalam Azad University of Technology, West Bengal, India, and later completed his Ph.D. in computer science & engineering from the Defence Institute of Advanced Technology, Pune, India. He was a recipient of AICTE-INAE Teachers Research Fellowship from the Indian National Academy of Engineering (INAE). He is currently working as a Postdoctoral Fellow at Mälardalen University, Sweden.

AWARDS:

  • Winner Smart India Hackathon-2019
  • Recipient of AICTE-INAE Teachers Research Fellowship 2018-21
  • Smart India Hackathon-2018 Finalist
  • 3rd in Smart India Hackathon-2017

Research interests:

Quantum AI, Frequency domain deep learning, Deep Learning, Machine Learning, Natural Language Processing, Human Activity Detection, and Medical Image Analysis.


[Google Scholar author page]

[1] Chakraborty M, Aryapoor M, Daneshtalab M. Frequency Domain Complex-Valued Convolutional Neural Network. Expert Systems with Applications. 2025 Jul 5:128893. (Q1/SCI)

[2] Saha, Mousumi, Mainak Chakraborty, Suchismita Maiti, and Deepanwita Das. "Breast-NET: a lightweight DCNN model for breast cancer detection and grading using histological samples." Neural Computing and Applications 36, no. 32 (2024): 20067-20087. (Q1/SCI)


[3] M. Chakraborty, H. C. Kumawat, S. V. Dhavale, and A. Arockia Bazil Raj. Diat-radharnet: A lightweight dcnn for radar based classification of human suspicious activities. IEEE Transactions on Instrumentation and Measurement, 71:1–10, 2022. (Q1/SCI)


[4] M. Chakraborty, H. C. Kumawat, S. V. Dhavale, and A. A. B. Raj. Diat-μradhar (micro-doppler signature dataset) amp; μradnet (a lightweight dcnn) - for human suspicious activity recognition. IEEE Sensors Journal, 22(7):6851–6858, 2022. (Q1/SCI)


[5] H. C. Kumawat, M. Chakraborty, and A. A. Bazil Raj. Diat-radsatnet-a novel lightweight dcnn architecture for micro-doppler based small unmanned aerial vehicle (suav) targets’ detection & classification. IEEE Transactions on Instrumentation and Measurement, 71:1–11, 2022. (Q1/SCI)


[6] H. C. Kumawat, M. Chakraborty, A. A. B. Raj, and S. V. Dhavale. Diat-μsat: Small aerial targets’ micro-doppler signatures and their classification using cnn. IEEE Geoscience and Remote Sensing Letters, 19:1–5, 2021. (Q1/SCI)


[7] M. Chakraborty, H. C. Kumawat, S. V. Dhavale, and A. B. Raj A. Application of dnn for radar micro-doppler signature-based human suspicious activity recognition. Pattern Recognition Letters,162:1-6, 2022. (Q1/SCI)


[8] M. M. Hasan, M. Chakraborty, H. C. Kumawat, S. V. Dhavale, and A. B. Raj A. A Hyper-parameters-tuned R-PCA+SVM Technique for sUAV Targets Classification using the Range-/Micro-Doppler Signatures. IEEE Transactions on Radar Systems,doi: 10.1109/TRS.2023.3322607, 2023. (Q1/SCI)


[9] M. Chakraborty, S. V. Dhavale, and J. Ingole. Corona-nidaan: lightweight deep convolutional neural network for chest x-ray based covid-19 infection detection. Applied Intelligence, 51(5):3026–3043, 2021. (Q1/SCI)


[10] M. Chakraborty, S. V. Dhavale, and J. Ingole. Two-stage deep learning architecture for chest x-ray-based covid-19 prediction. In Advances in Deep Learning for Medical Image Analysis, pages 19–37. CRC Press.


[11] M. Chakraborty, A. Pramanick, and S. V. Dhavale. Mobisamadhaan—intelligent vision-based smart city solution. In International Conference on Innovative Computing and Communications, pages 329–345, Springer, 2021.


[12] M. Chakraborty, A. Pramanick, and S. V. Dhavale. Two-stream mid-level fusion network for human activity detection. In International Conference on Innovative Computing and Communications, pages 331–343, Springer, 2021.


[13] S. Soni, M. Chakraborty, and A. A. B. Raj. AI based Small Unmanned Aerial Vehicle (SUAV) Targets Detection and Tracking Techniques. In International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), pages 43–49, IEEE,2022.

[14] Fard AS, Mashhadigholamali M, Zolfaghari S, Abedi H, Chakraborty M, Karmani S, Borzì L, Daneshtalab M, Shaker G. Fall Detection in Ambient-Assisted Living Environments Using FMCW Radars and Deep Learning. In2025 IEEE International Radar Conference (RADAR) 2025 May 3 (pp. 1-6). IEEE.

[15] Mashhadigholamali M, Fard AS, Zolfaghari S, Abedi H, Chakraborty M, Borzí L, Daneshtalab M, Shaker G. FMCW Radar-Based Human Activity Recognition: A Machine Learning Approach for Elderly Care. In2025 IEEE Wireless Communications and Networking Conference (WCNC) 2025 Mar 24 (pp. 1-6). IEEE. (CORE Conference)