M
Mst Shamima Nasrin
Researcher at University of Dayton
Publications - 6
Citations - 1770
Mst Shamima Nasrin is an academic researcher from University of Dayton. The author has contributed to research in topics: Deep learning & Convolutional neural network. The author has an hindex of 5, co-authored 6 publications receiving 995 citations.
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Journal ArticleDOI
A State-of-the-Art Survey on Deep Learning Theory and Architectures
Zahangir Alom,Tarek M. Taha,Chris Yakopcic,Stefan Westberg,Paheding Sidike,Mst Shamima Nasrin,Mahmudul Hasan,Brian Van Essen,Abdul A. S. Awwal,Vijayan K. Asari +9 more
TL;DR: This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network and goes on to cover Convolutional Neural Network, Recurrent Neural Network (RNN), and Deep Reinforcement Learning (DRL).
Posted Content
The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches.
Md. Zahangir Alom,Tarek M. Taha,Christopher Yakopcic,Stefan Westberg,Paheding Sidike,Mst Shamima Nasrin,Brian Van Essen,Abdul A. S. Awwal,Vijayan K. Asari +8 more
TL;DR: This report presents a brief survey on development of DL approaches, including Deep Neural Network (DNN), Convolutional neural network (CNN), Recurrent Neural network (RNN) including Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL).
Journal ArticleDOI
Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network
TL;DR: The IRRCNN model provides superior classification performance in terms of sensitivity, area under the curve (AUC), the ROC curve, and global accuracy compared to existing approaches for both datasets.
Posted Content
COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches
TL;DR: A fast and efficient way to identify COVID-19 patients with multi-task deep learning methods and a novel quantitative analysis strategy is proposed to determine the percentage of infected regions in X-ray and CT images.
Proceedings ArticleDOI
Bangla License Plate Recognition Using Convolutional Neural Networks (CNN)
TL;DR: In this paper, the authors have implemented CNNs based Bangla license plate recognition system with better accuracy that can be applied for different purposes including roadside assistance, automatic parking lot management system, vehicle license status detection and so on.