@inproceedings{HOSSAIN10647844,
    author={M.S. Hossain and A.K.M.M. Rahman and M.A. Amin and A.A. Ali},
    booktitle={2024 IEEE International Conference on Image Processing (ICIP)}, 
    title={Lightweight Recurrent Neural Network for Image Super-Resolution}, 
    year={2024},
    month={10},
    pages={1567-1573},
    location={Abu Dhabi, United Arab Emirates},
    keywords={Performance evaluation;Recurrent neural networks;Costs;Convolution;Computational modeling;Superresolution;Transformers;Single Image Super-Resolution;Recurrent Neural Networks;Efficient Super-Resolution},
    doi={10.1109/ICIP51287.2024.10647844},
    url={https://ieeexplore.ieee.org/document/10647844},
    code={https://github.com/mirsazzathossain/LiteSRNet},
    poster={https://mirsazzathossain.me/files/super-resolution-icip2024-poster.pdf}
}


@article{HOSSAIN2023601,
    title = {Morphological classification of Radio Galaxies using Semi-Supervised Group Equivariant CNNs},
    journal = {Procedia Computer Science},
    volume = {222},
    issue = {C},
    pages = {601-612},
    year = {2023},
    note = {International Neural Network Society Workshop on Deep Learning Innovations and Applications (INNS DLIA 2023)},
    issn = {1877-0509},
    doi = {https://doi.org/10.1016/j.procs.2023.08.198},
    url = {https://www.sciencedirect.com/science/article/pii/S1877050923009638},
    author = {M.S. Hossain and S. Roy and K.M.B. Asad and A. Momen and A.A. Ali and M.A. Amin and A.K.M.M. Rahman},
    keywords = {Radio Galaxy, Fanaroff-Riley, G-CNN, SimCLR, BYOL, Semi-supervised Learning},
    abstract = {Out of the estimated few trillion galaxies, only around a million have been detected through radio frequencies, and only a tiny fraction, approximately a thousand, have been manually classified. We have addressed this disparity between labeled and unlabeled images of radio galaxies by employing a semi-supervised learning approach to classify them into the known Fanaroff-Riley Type I (FRI) and Type II (FRII) categories. A Group Equivariant Convolutional Neural Network (G-CNN) was used as an encoder of the state-of-the-art self-supervised methods SimCLR (A Simple Framework for Contrastive Learning of Visual Representations) and BYOL (Bootstrap Your Own Latent). The G-CNN preserves the equivariance for the Euclidean Group E(2), enabling it to effectively learn the representation of globally oriented feature maps. After representation learning, we trained a fully-connected classifier and fine-tuned the trained encoder with labeled data. Our findings demonstrate that our semi-supervised approach outperforms existing state-of-the-art methods across several metrics, including cluster quality, convergence rate, accuracy, precision, recall, and the F1-score. Moreover, statistical significance testing via a t-test revealed that our method surpasses the performance of a fully supervised G-CNN. This study emphasizes the importance of semi-supervised learning in radio galaxy classification, where labeled data are still scarce, but the prospects for discovery are immense.},
    code = {https://github.com/mirsazzathossain/radio-galaxy-classifier-v1},
    poster = {https://mirsazzathossain.me/files/rgc-iub-poster.pdf},
    slides = {https://mirsazzathossain.me/files/rgc-inns-dlia-2023-presentation.pdf}
}

@article{iftee2025slomo,
  title={SloMo-Fast: Slow-Momentum and Fast-Adaptive Teachers for Source-Free Continual Test-Time Adaptation},
  author={M.A.K. Iftee and M.S. Hossain and R.H. Rajib and A.K.M.M. Rahman and T. Iqbal and M.M. Islam and M.A. Amin and A.K.M.M. Rahman},
  journal={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2025},
  pages={To appear},
  address={Nashville, TN, USA},
  url={https://mirsazzathossain.me/files/slomo-fast.pdf},
  code={https://github.com/mirsazzathossain/test-time-adaptation},
  note={This paper is under review.}
}

@article{adnan2025relation,
  title={Investigating the Relation Between Environment and Internal Structure of Massive Elliptical Galaxies Using Strong Lensing},
  author={S.M. Rafee Adnan and M.J. Hasan and A. Al-Imtiaz and S.H. Robin and F.R. Shwadhin and A.J. Shajib and M.H. Nahid and M.H. Tanver and T. Akter and N. Jahan and Z. Jafar and M. Rashid and A. Biswas and A.A. Chowdhury and J. Feardous and A. Rahaman and M. Ridwan and R.D. Sharma and Z. Chowdhury and M.S. Hossain},
  journal={Astronomy & Astrophysics (A&A)},
  year={2025},
  volume={To appear},
  pages={To appear},
  note={This paper is under review.},
  url={https://arxiv.org/pdf/2412.00361},
  code={https://github.com/mirsazzathossain/BDLensing},
}

@article{HOSSAIN2023512,
    title = {RGC: a radio galaxy classifier based on artificial neural networks: I. Classifying the VLA images of bent radio AGNs},
    journal = {Astronomy & Astrophysics (A&A)},
    month = {11},
    year = {2024},
    volume = {To appear},
    pages = {To appear},
    note = {In Preparation},
    author = {M.S. Hossain and K.M.B. Asad and P. Saikia, and F. Akter and A. Momen and A.A. Ali and J.K. Ghosh and M.A. Amin and A.K.M.M. Rahman},
    code = {https://github.com/mirsazzathossain/radio-galaxy-classifier},
    url = {https://mirsazzathossain.me/files/rgc.pdf},
}

@inproceedings{rajib2025fedctta,
  title={FedCTTA: A Collaborative Approach to Continual Test-Time Adaptation in Federated Learning},
  author={R.H. Rajib and M.A.K. Iftee and M.S. Hossain and A.K.M.M. Rahman and S. Mistry and M.A. Amin and A.K.M.M. Rahman},
  year={2025},
  booktitle={To appear},
  code={https://github.com/mirsazzathossain/fedctta},
  url={https://mirsazzathossain.me/files/fedctta.pdf},
  note={In Preparation},
}