M
Misbah Ahmad
Researcher at Information Technology Institute
Publications - 28
Citations - 658
Misbah Ahmad is an academic researcher from Information Technology Institute. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 10, co-authored 21 publications receiving 257 citations. Previous affiliations of Misbah Ahmad include Center for Excellence in Education.
Papers
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Journal ArticleDOI
A deep learning-based social distance monitoring framework for COVID-19.
TL;DR: Findings indicate that the developed framework successfully distinguishes individuals who walk too near and breaches/violates social distances; also, the transfer learning approach boosts the overall efficiency of the model.
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A Framework for Pandemic Prediction Using Big Data Analytics
TL;DR: In this paper, a health monitoring framework for the analysis and prediction of COVID-19 outbreak is presented, which takes advantage of big data analytics and IoT. The authors demonstrate a health monitor framework for analyzing and predicting the pandemic outbreak.
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Social distance monitoring framework using deep learning architecture to control infection transmission of COVID-19 pandemic.
TL;DR: In this paper, the authors presented a social distance framework based on deep learning architecture as a precautionary step that helps to maintain, monitor, manage, and reduce the physical interaction between individuals in a real-time top view environment.
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Comparison of Deep-Learning-Based Segmentation Models: Using Top View Person Images
TL;DR: This work explores widely used deep learning-based models for person segmentation using top view data set using Fully Convolutional Neural Network with Resnet-101 architecture, U-Net with Encoder-Decoder architecture, and a DeepLabV3 model with encoder-decoder architecture.
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Top view multiple people tracking by detection using deep SORT and YOLOv3 with transfer learning: within 5G infrastructure
TL;DR: Experimental results reveal that transfer learning improves the overall performance, detection accuracy, and reduces false positives of the detection model.