Payel Pramanik

• Designation :  Assistant Professor
• Highest Qualification :  M.E in Computer Science & Engineering
• Subject Taught :  Computer Programming using C and Python, Data Structure, Design and Analysis of Algorithm, Compiler Design, Database Management System, Introduction to IT System, Computer Network
• Academic Experience (Recent to past) :
  • Assistant Professor in Computer Science, Department of Higher Education, Government of West Bengal, 2025 to Present
  • Lecturer in Computer Science & Technology, Department of Technical Education, Training and Skill Development, Government of West Bengal, 2021 to 2025
  • Assistant Professor, Department of Computer Science & Engineering, Future Institute of Technology, Garia, West Bengal, 2019 to 2021
• Number of Publications :
  • International Conference : 5
  • Journal : 8
  • Book Chapter : 1
  • Book : Nil
• Recent Publication Details (last 3 years) :
  • International Conference :
    • Roy, A., Pramanik, P., Ghosal, S., Valenkova, D., Kaplun, D., & Sarkar, R. (2024, July). GRU-Net: Gaussian Attention Aided Dense Skip Connection Based MultiResUNet for Breast Histopathology Image Segmentation. In Annual Conference on Medical Image Understanding and Analysis (pp. 300-313). Cham: Springer Nature Switzerland.
    • Chakraborty, S., Roy, A., Pramanik, P., Valenkova, D., & Sarkar, R. (2024, June). A Dual Attention-aided DenseNet-121 for Classification of Glaucoma from Fundus Images. In 2024 13th Mediterranean Conference on Embedded Computing (MECO) (pp. 1-4). IEEE.
    • Roy, A., Pramanik, P., Kaplun, D., Antonov, S., & Sarkar, R. (2024, May). AWGUNET: Attention-aided wavelet guided u-net for nuclei segmentation in histopathology images. In 2024 IEEE International Symposium on Biomedical Imaging (ISBI) (pp. 1-4). IEEE.
  • Journal :
    • Roy, A., Pramanik, P., & Sarkar, R. (2024). EU 2–Net: A Parameter Efficient Ensemble Model with Attention-aided Triple Feature Fusion for Tumor Segmentation in Breast Ultrasound Images. IEEE Transactions on Instrumentation and Measurement.
    • Pramanik, P., Roy, A., Cuevas, E., Perez-Cisneros, M., & Sarkar, R. (2024). DAU-Net: Dual attention-aided U-Net for segmenting tumor in breast ultrasound images. Plos one, 19(5), e0303670.
    • Pramanik, P., Pramanik, R., Schwenker, F., & Sarkar, R. (2023). DBU-Net: Dual branch U-Net for tumor segmentation in breast ultrasound images. Plos one, 18(11), e0293615.
    • Pramanik, R., Pramanik, P., & Sarkar, R. (2023). Breast cancer detection in thermograms using a hybrid of GA and GWO based deep feature selection method. Expert Systems with Applications, 219, 119643.
    • Pramanik, P., Mukhopadhyay, S., Mirjalili, S., & Sarkar, R. (2023). Deep feature selection using local search embedded social ski-driver optimization algorithm for breast cancer detection in mammograms. Neural Computing and Applications, 35(7), 5479-5499.
    • Majumdar, S., Pramanik, P., & Sarkar, R. (2023). Gamma function based ensemble of CNN models for breast cancer detection in histopathology images. Expert Systems with Applications, 213, 119022.
    • Bagchi, A., Pramanik, P., & Sarkar, R. (2022). A multi-stage approach to breast cancer classification using histopathology images. Diagnostics, 13(1), 126.
  • Book Chapter :
    • Pramanik, P., Pramanik, R., Naskar, A., Mirjalili, S., & Sarkar, R. (2024). U-WOA: an unsupervised whale optimization algorithm based deep feature selection method for cancer detection in breast ultrasound images. In Handbook of Whale Optimization Algorithm (pp. 179-191). Academic Press.
  • Book : Nil

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