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Zahangir Alom

Research Engineer

Staff

Contact

Email: Zahangir Alom
Phone: 937-232-5118
visionlab.udayton.edu
Kettering Labs Room 461

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Bio

Dr. Zahangir Alom is a research engineer at the University of Dayton, Ohio, USA. He received his B.S. and M.S. degrees in computer engineering from the University of Rajshahi, Bangladesh, and Chonbuk National University, South Korea, in 2008 and 2012 respectively. He received his Ph.D. in electrical and computer engineering from the University of Dayton in 2018. His research interests include machine learning, deep learning, medical imaging, and computational pathology. He is a student member of IEEE, member of International Neural Network Society (INNS) and member of Digital Pathology Association (DPA), USA.

Degrees

  • Ph.D. in Electrical and Computer Engineering, University of Dayton, Ohio, USA
  • M.E. in Computer Engineering, Chonbuk National University, Jeonju, South Korea
  • B.Sc. in Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh

Selected Journals

  1. Alom, M. Z., Yakopcic, C., Taha, T. M., & Asari, V. K. (2019). Breast cancer classification from histopathological images with inception recurrent residual convolutional neural network. Journal of Digital Imaging (JDI), Springer .
  2. Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Hasan, M., Van Esesn, B. C., Awwal, A. A. S., & Asari, V. K. (2018). The history began from AlexNet: A comprehensive survey on deep learning approaches. arXiv preprint arXiv:1803.01164, Journal of Electronics MDPI.
  3. Alom, M. Z., Hasan, M., Yakopcic, C., Taha, T. M., & Asari, V. K. (2018). Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. SPIE Journal of Medical Imaging.
  4. Alom, M. Z., Aspiras, T., Taha, T. M., Asari, V. K., & Bowen, T. J. (2018). Advanced deep convolutional neural network approaches for digital pathology image analysis: A comprehensive evaluation with different use cases. Journal of Pathology Informatics (JPI).
  5. Alom, M. Z., Hasan, M., Yakopcic, C., Taha, T. M., & Asari, V. K. (2018). Improved inception-residual convolutional neural network for object recognition. Neural Computing and Applications, doi: 10.1007/s00521-018-3627-6, 1-15. (Springer)
  6. Alom, M. Z., Sidike, P., Hasan, M., Taha, T. M., & Asari, V. K. (2018). Handwritten Bangla character recognition using the state-of-the-art deep convolutional neural networks. Computational Intelligence and Neuroscience.
  7. Sidike, P., Krieger, E., Alom, M. Z., Asari, V. K., & Taha, T. M. (2017). A fast-single image super-resolution via directional edge guided regularized extreme learning regression. Signal, Image and Video Processing (SIVP), 1-8.
  8. Alom, M. Z., Sidike, P., Hasan, M., Taha, T. M., & Asari, V. K. (2016). State preserving extreme learning machine: A monotonically increasing learning approach. Neural Processing Letters, 1-23.
  9. Alom, M. Z., Bontupalli, V. R., & Taha, T. M. (2015). Intrusion detection using deep belief network and extreme learning machine. International Journal of Monitoring and Surveillance Technologies Research (IJMSTR), ACM Digital Library, 03(02), 35-56.
  10. Alom, M. Z., Karim, N. T., Rozario, S. P., Hoque, M. R., Bin, M. R., & Ashraf, S. L. R. (2014). Computer vision-based employee activities analysis. International Journal of Computer and Information Technology, 03(05). 
  11. Alom, M. Z., Sarwar, S. S., Biswas, R., Mostakim, M., & Lee, H. J. (2013). Real time object detection system based on color and spatial information. International Journal of Computer and Information Technology (IJCIT), 02(01).
  12. Alom, M. Z., Bhuiyan, F. H., & Lee, H. J. (2013). Implementation of real time dress-up system based on image blending. International Journal of Computer Applications (0975 – 8887), 75(01).
  13. Alom, M. Z., et al. (2012). Optimized facial features-based age classification. International Journal of Computer, Electrical, Automation, Control and Information Engineering, 6(03).
  14. Alom, M. Z., et al. (2012). Facial expressions recognition from complex background using face context and adaptively weighted sub-pattern PCA. International Journal of Computer, Electrical, Automation, Control and Information Engineering, 06(03).
  15. Alom, M. Z., & Lee, H. J. (2011). A comparative study of different color space for paddy disease segmentation. Journal of the Institute of Electronics Engineers of Korea (IEEK), 48(03), 90-98.

Submitted Journals

  1. Alom, M. Z., et al. (submitted). Microscopic nuclei classification, segmentation and detection with improved deep convolutional neural network (DCNN) approaches. arXiv preprint arXiv:1811.03447, Journal of Pathology Informatics (JPI).
  2. Alom, M. Z., Hasan, M., Yakopcic, C., & Taha, T. M. (submitted). Inception recurrent convolutional neural network for object recognition. arXiv preprint arXiv:1704.07709, Computer Vision and Image Understanding, Elsevier.

Selected Publications of Peer-Reviewed International Conferences

  1. Alom, M. Z., Aspiras, T., Taha, T. M., Asari, V. K., & Bowen, T. J. (2018, November). Advanced deep convolutional neural network approaches for digital pathology image analysis: A comprehensive evaluation with different use cases. Pathology Visions 2018, San Diego, Calif., USA. (DPA: Digital Pathology Association)
  2. Alom, M. Z., Yakopcic, C., Taha , T. M., & Asari, V. K. (2018, July). Nuclei segmentation with recurrent residual convolutional neural networks-based U-Net (R2U-Net). 2018 IEEE National Aerospace & Electronics Conference, Dayton, Ohio, USA. (NAECON)
  3. Alom, M. Z., Yakopcic, C., Taha , T. M., & Asari, V. K. (2018, July). Microscopic blood cell classification using Inception recurrent residual convolutional neural networks. 2018 IEEE National Aerospace & Electronics Conference, Dayton, Ohio, USA, 23 - 26 July 2018. (NAECON)
  4. Alom, M. Z., Moody, A. T., Maruyama, N., Van Essen, B. C., & Taha, T. M. (2017). Effective quantization approaches for recurrent neural networks. Neural Networks (IJCNN), 2017 International Joint Conference, 3922-3929. (IEEE)
  5. Alom, M. Z., Yakopcic, C., Rahman, N., & Taha, T. M. (2017). Deep versus wide network for object recognition on neuromorphic computing system. Neural Networks (IJCNN), 2017 International Joint Conference, 3922-3929. (IEEE)
  6. Alom, M. Z., Awwal, A. A. S., Lowe-Webb, R., & Taha, T. M. (2017). Optical beam classification using deep learning: a comparison with rule-and feature-based classification. Optics and Photonics for Information Processing XI, 10395, 103950O. International Society for Optics and Photonics.
  7. Alom, M. Z., & Taha, T. M. (2017). Network intrusion detection for cyber security on neuromorphic computing system. Neural Networks (IJCNN), 2017 International Joint Conference, 3830-3837. (IEEE)
  8. Alom, M. Z., Van Essen, B., Moody, A. T., Widemann, D. P., & Taha, T. M. (2017). Quadratic unconstrained binary optimization (QUBO) on neuromorphic computing system. Neural Networks (IJCNN), 2017 International Joint Conference, 3922-3929. (IEEE)
  9. Alom, M. Z., Van Essen, B., Moody, A. T., Widemann, D. P., & Taha, T. M. (2017). Convolutional sparse coding on neurosynaptic cognitive system. Neural Networks (IJCNN), 2017 International Joint Conference, 3609-3616. (IEEE)
  10. Alom, M. Z., Alam, M., Taha, T. M., & Iftekharuddin, K. M. (2017). Object recognition using cellular simultaneous recurrent networks and convolutional neural network. In 2017 International Joint Conference on Neural Networks (IJCNN), 2873-2880. (IEEE)
  11. Alom, M. Z., & Taha, T. M. (2017). Network intrusion detection for cyber security using unsupervised deep learning approaches. In Aerospace and Electronics Conference (NAECON), 2017 IEEE National, 63-69. (IEEE)
  12. Alom, M. Z., & Taha, T. M. (2017). Robust multi-view pedestrian tracking using neural networks. In Aerospace and Electronics Conference (NAECON), 2017 IEEE National, 17-22. (IEEE)
  13. Yakopcic, C., Alom, M. Z., & Taha, T. M. Extremely parallel memristor crossbar architecture for convolutional neural network implementation. In Neural Networks (IJCNN), 2017 International Joint Conference, 1696-1703. (IEEE)
  14.  Yakopcic, C., Alom, M. Z., & Taha, T. M. (2016). Memristor crossbar deep network implementation based on a convolutional neural network.Proceeding of the International Joint Conference on Neural Networks (IJCNN), 963-970.
  15. Sidike, , Alom, M. Z., Asari V. K., & Taha, T. M. (2016, February). Non-regularized state preserving extreme learning machine for natural scene classification. Proceeding of International Conference on Computer Vision and Image Processing (CVIP), 1. (book chapter in Springer)