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Muhammad Hussain 0002
Person information
- affiliation: University of Huddersfield, School of Computing and Engineering, Department of Computer Science, UK
Other persons with the same name
- Muhammad Hussain (aka: Mohammad Hussain) — disambiguation page
- Muhammad Hussain 0001 (aka: Mohammad Hussain 0001) — King Saud University, Department of Computer Science, College of Computer and Information Sciences, Riyadh, Saudi Arabia (and 2 more)
- Muhammad Hussain 0003 — Northeast Normal University, Institute of Natural Disaster Research, School of Environment, Changchun, China
- Muhammad Hussain 0004 — COMSATS University Islamabad, Department of Mathematics, Lahore Campus, Pakistan (and 1 more)
- Muhammad Hussain 0005 — Bahria University, Department of Electrical Engineering, Karachi, Pakistan
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2020 – today
- 2024
- [j13]Muhammad Hussain:
YOLOv1 to v8: Unveiling Each Variant-A Comprehensive Review of YOLO. IEEE Access 12: 42816-42833 (2024) - [j12]Ali Raza, Mobeen Munir, Muhammad Hussain, Fikadu Tasgera:
A Spectrum-Based Approach to Network Analysis Utilizing Laplacian and Signless Laplacian Spectra to Torus Networks. IEEE Access 12: 52016-52029 (2024) - [j11]Rahima Khanam, Muhammad Hussain, Richard Hill, Paul Allen:
A Comprehensive Review of Convolutional Neural Networks for Defect Detection in Industrial Applications. IEEE Access 12: 94250-94295 (2024) - 2023
- [j10]Muhammad Hussain, Richard Hill:
Custom Lightweight Convolutional Neural Network Architecture for Automated Detection of Damaged Pallet Racking in Warehousing & Distribution Centers. IEEE Access 11: 58879-58889 (2023) - [j9]Muhammad Hussain, Hussain Al-Aqrabi, Muhammad Munawar, Richard Hill, Simon Parkinson:
Exudate Regeneration for Automated Exudate Detection in Retinal Fundus Images. IEEE Access 11: 83934-83945 (2023) - [j8]Benjamin Agbo, Hussain Al-Aqrabi, Tariq A. A. Alsboui, Muhammad Hussain, Richard Hill:
Imputation of Missing Clinical Covariates for Downstream Classification Problems. IEEE Access 11: 102935-102943 (2023) - [j7]Muhammad Hussain:
Exudate Detection: Integrating Retinal-Based Affine Mapping and Design Flow Mechanism to Develop Lightweight Architectures. IEEE Access 11: 125185-125203 (2023) - [j6]Muhammad Hussain:
When, Where, and Which?: Navigating the Intersection of Computer Vision and Generative AI for Strategic Business Integration. IEEE Access 11: 127202-127215 (2023) - [j5]Muhammad Hussain:
YOLO-v5 Variant Selection Algorithm Coupled with Representative Augmentations for Modelling Production-Based Variance in Automated Lightweight Pallet Racking Inspection. Big Data Cogn. Comput. 7(2): 120 (2023) - [j4]Damilola Animashaun, Muhammad Hussain:
Automated Micro-Crack Detection within Photovoltaic Manufacturing Facility via Ground Modelling for a Regularized Convolutional Network. Sensors 23(13): 6235 (2023) - [c2]Tariq A. A. Alsboui, Muhammad Hussain, Hussain Al-Aqrabi, Richard Hill, Mohammad Hijjawi:
A Scalable Decentralized and Lightweight Access Control Framework Using IOTA Tangle for the Internet of Things. IoTBDS 2023: 178-185 - 2022
- [j3]Muhammad Hussain, Tianhua Chen, Sofya Titrenko, Pan Su, Mufti Mahmud:
A Gradient Guided Architecture Coupled With Filter Fused Representations for Micro-Crack Detection in Photovoltaic Cell Surfaces. IEEE Access 10: 58950-58964 (2022) - [j2]Muhammad Hussain, Hussain Al-Aqrabi, Muhammad Munawar, Richard Hill, Tariq A. A. Alsboui:
Domain Feature Mapping with YOLOv7 for Automated Edge-Based Pallet Racking Inspections. Sensors 22(18): 6927 (2022) - [j1]Tariq A. A. Alsboui, Richard Hill, Hussain Al-Aqrabi, Hafiz Muhammad Athar Farid, Muhammad Riaz, Shamaila Iram, Hafiz Muhammad Shakeel, Muhammad Hussain:
A Dynamic Multi-Mobile Agent Itinerary Planning Approach in Wireless Sensor Networks via Intuitionistic Fuzzy Set. Sensors 22(20): 8037 (2022) - [c1]Muhammad Hussain, Tianhua Chen, Sofya Titarenko, Richard Hill, Hussain Al-Aqrabi:
Development of a Convolutional Neural Network Architecture for Production Based Photovoltaic Fault Detection. UKCI 2022: 415-426
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last updated on 2024-12-23 20:28 CET by the dblp team
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