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Ibrar Hussain on Muck Rack

Ibrar Hussain

Pakistan
As seen in: Medium, Nature, arXiv, PLOS ONE, BMJ Open, Frontiers, MDPI, DEV Community, PeerJ, EPJ and
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Articles

An attention-infused deep convolutional paradigm for multi-label classification of thoracic pathologies in chest radiographs

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By Khalil Ullah, Shah Khalid, Ibrar Hussain, Sergei Romanov, Shah Imran, Evgeny Karpulevich, Yury Markin
| Nature Verified
Abstract This research proposes an empirical benchmarking study of an attention-infused deep convolutional framework for multi-label thoracic pathology classification in chest radiographs, designed to emulate how radiologists selectively focus on suspicious regions. Existing CNN models process entire images uniformly, often missing fine-grained or subtle abnormalities that require localized visual emphasis.
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Resistance of Quartzite, Granodiorite, and Gabbronorite Aggregates to Freeze–Thaw Weathering of Concrete | Journal of Materials in Civil Engineering | Vol 38, No 1

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By Ibrar Hussain, Muhammad Sajid, Muhammad Khan, Muhammad Shoaib
| ascelibrary.org
Get full access to this article View all available purchase options and get full access to this article. Data Availability Statement All data, models, and code generated or used during the study appear in the published article. Acknowledgments The authors are grateful to their parents for their long-lasting support.
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Novel deep neural network architecture fusion to simultaneously predict short-term and long-term energy consumption

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By Abrar Ahmed, Safdar Ali, ALi RaZa, Ali Raza, Ibrar Hussain
| PLOS ONE
Loading metrics Open Access Peer-reviewed Research Article Citation: Ahmed A, Ali S, Raza A, Hussain I, Bilal A, Fitriyani NL, et al. (2025) Novel deep neural network architecture fusion to simultaneously predict short-term and long-term energy consumption. PLoS ONE 20(1): e0315668. https://doi.org/10.1371/journal.pone.0315668 Editor: Jinran Wu, Australian Catholic University, AUSTRALIA Received: July 24, 2024; Accepted: November 29, 2024; Published: January 7, 2025 Copyright: © 2025 Ahmed et al.
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