Mohammad Rahman Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
PhD Candidate
grad_student
Research Areas
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Biography and Research Information
OverviewAI-generated summary
Mohammad Rahman's research focuses on the application of machine learning and deep learning techniques, particularly within the domain of hyperspectral imaging and industrial automation. His recent work includes a review of machine learning applications integrated with digital twin and edge AI for intelligent industrial automation. He has investigated data-efficient deep learning algorithms for hyperspectral band selection, such as BSDR, which utilizes discrete relaxation. Rahman has also explored adaptive downsampling of hyperspectral bands for applications like soil organic carbon estimation, and addressed limitations in attention-based hyperspectral band selection algorithms. He collaborates with researchers at the University of Arkansas at Little Rock, including Kamran Iqbal, Ali A. Abushaiba, and Md Farhan Shahrior, with whom he has co-authored multiple publications. His scholarly output includes 9 publications with 31 citations and an h-index of 4.
Metrics
- h-index: 2
- Publications: 8
- Citations: 15
Selected Publications
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Comparative Evaluation of DC–DC Converter Topologies for Electric Vehicle Chargers (2025)
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Enabling Intelligent Industrial Automation: A Review of Machine Learning Applications with Digital Twin and Edge AI Integration (2025)
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Enabling Intelligent Industrial Automation: A Review of Machine Learning Applications with Digital Twin and Edge AI Integration (2025)
Collaboration Network
Top Collaborators
- BSDR: A Data-Efficient Deep Learning-Based Hyperspectral Band Selection Algorithm Using Discrete Relaxation
- Addressing Limitations of Common Methods in Attention-Based Hyperspectral Band Selection Algorithms
- Deep Learning-Based Adaptive Downsampling of Hyperspectral Bands for Soil Organic Carbon Estimation
- BSDR: A Data-Efficient Deep Learning-Based Hyperspectral Band Selection Algorithm Using Discrete Relaxation
- Addressing Limitations of Common Methods in Attention-Based Hyperspectral Band Selection Algorithms
- Deep Learning-Based Adaptive Downsampling of Hyperspectral Bands for Soil Organic Carbon Estimation
- BSDR: A Data-Efficient Deep Learning-Based Hyperspectral Band Selection Algorithm Using Discrete Relaxation
- Addressing Limitations of Common Methods in Attention-Based Hyperspectral Band Selection Algorithms
- Deep Learning-Based Adaptive Downsampling of Hyperspectral Bands for Soil Organic Carbon Estimation
- BSDR: A Data-Efficient Deep Learning-Based Hyperspectral Band Selection Algorithm Using Discrete Relaxation
- Addressing Limitations of Common Methods in Attention-Based Hyperspectral Band Selection Algorithms
- Deep Learning-Based Adaptive Downsampling of Hyperspectral Bands for Soil Organic Carbon Estimation
- Enabling Intelligent Industrial Automation: A Review of Machine Learning Applications with Digital Twin and Edge AI Integration
- Enabling Intelligent Industrial Automation: A Review of Machine Learning Applications with Digital Twin and Edge AI Integration
- Enabling Intelligent Industrial Automation: A Review of Machine Learning Applications with Digital Twin and Edge AI Integration
- Enabling Intelligent Industrial Automation: A Review of Machine Learning Applications with Digital Twin and Edge AI Integration
- Enabling Intelligent Industrial Automation: A Review of Machine Learning Applications with Digital Twin and Edge AI Integration
- Enabling Intelligent Industrial Automation: A Review of Machine Learning Applications with Digital Twin and Edge AI Integration
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