Md Jobair Hossain Faruk, Ph.D.

Md Jobair Hossain Faruk, Ph.D.

Tenure-Track Assistant Professor in Computer Science & Engineering at the University of Central Arkansas. Research areas include quantum machine learning, health informatics, cybersecurity, and software engineering.

Publications

Research contributions in health informatics, quantum machine learning, cybersecurity, software engineering, and intelligent systems.

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Journal Articles

  • Raihan, M. M., M. J. H. Faruk, et al. (2022). A comparative study on HIPAA technical safeguards assessment of android mHealth applications. Smart Health, Elsevier.

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  • Siam, M. K., Varela, A., M. J. H. Faruk, et al. (2026). Benchmarking large language models on the United States medical licensing examination for clinical reasoning and medical licensing scenarios. Scientific Reports.

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Conference Papers

  • M. J. H. Faruk, S. Trivedi, M. Masum, M. Valero, H. Shahriar, and S. I. Ahamed (2022). A Novel IoT-based Framework for Non-Invasive Human Hygiene Monitoring using Machine Learning Techniques. IEEE International Conference on Health Informatics (ICHI). Best Paper Award.

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  • M. J. H. Faruk, H. Shahriar, M. Valero, S. Sneha, S. I. Ahamed, and M. Rahman (2021). Towards Blockchain-Based Secure Data Management for Remote Patient Monitoring. IEEE International Conference on Digital Health (ICDH). Special Paper Award.

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  • M. J. H. Faruk, et al. (2025). Multi-Class Deep Learning Model in Whole Slide Images (WSIs): Vision Transformers to Distinguish Cancer, Tumor, Mitosis, Red Blood Cells, and Karyorrhexis. IEEE/ACM CHASE 2025.
  • M. J. H. Faruk, et al. (2026). A Hybrid Classical-Quantum Machine Learning Framework for Tumor Mutation Classification: A Comprehensive Study of Quantum Optimizers in Medical Image Analysis. IEEE International Conference on Health Informatics (ICHI), 2026.
  • M. J. H. Faruk, et al. (2026). Reinforcement Learning-Guided Adaptive Fusion Model: Hybrid-Parametrized Quantum Circuits for Robust Histopathological Tumor Classification. Submitted to IEEE International Conference on Health Informatics (ICHI), 2026.
  • M. J. H. Faruk, et al. (2022). Project-based Learning in Software Engineering Education: Integrating Blockchain-Oriented Repositories in SE Curriculum and Coursework. IEEE/ACIS SERA 2022.
  • M. J. H. Faruk, et al. (2022). Software Engineering Process and Methodology in Blockchain-Oriented Software Development: A Systematic Study. IEEE/ACIS SERA 2022.
  • Mohammad, M., M. J. H. Faruk, et al. (2022). Quantum Machine Learning for Software Supply Chain Attacks: How Far Can We Go? IEEE COMPSAC 2022.
  • Mst. Shapna, M. J. H. Faruk, et al. (2022). Software Supply Chain Vulnerabilities Detection in Source Code: Performance Comparison between Traditional and Quantum Machine Learning Algorithms. IEEE TrustCom 2022.
  • M. J. H. Faruk, et al. (2022). Blockchain-Based Decentralized Verifiable Credentials: Leveraging Smart Contracts for Privacy-Preserving Authentication Mechanisms to Enhance Data Security in Scientific Data Access. IEEE Big Data 2022.
  • M. J. H. Faruk (2023). Malware Classification and Detection using Quantum Neural Network (QNN). ACM SIGCSE 2023.
  • M. J. H. Faruk, et al. (2022). Authentic Learning of Machine Learning in Cybersecurity with Portable Hands-on Labware: Neural Network Algorithms for Network Denial of Service (DoS) Detection. IEEE Big Data 2022.
  • M. J. H. Faruk, et al. (2022). Object Detection and Tracking: Deep Learning based Novel Tools to Generate Robust Human and Machine-Annotated Ground Truth Data for Training AI Models. IEEE STI 2022.
  • M. J. H. Faruk, et al. (2024). A Deep Learning Framework for Object Detection and Tracking: CNN-Based Parcel Tracking on Conveyor Belts. IEEE STI 2024.
  • M. J. H. Faruk, M. Valero, and H. Shahriar (2021). An investigation on non-invasive brain-computer interfaces: Emotiv EpoC+ neuroheadset and its effectiveness. IEEE COMPSAC 2021.

Book Chapter

  • M. J. H. Faruk, et al. (2022). AI-oriented Software Engineering: Challenges, Opportunities and New Directions. IEEE/Springer.

Under Review / In Progress

  • M. J. H. Faruk, et al. (2026). A Vision Transformer-based Framework: Deep Learning-Based Prediction of MYCN Amplification from Neuroblastic Tumors Whole-Slide Images. Submitted to Nature Cancer.
  • M. J. H. Faruk, et al. (2025). Deep Learning-based Novel Model for Cancer Classification in Pediatric Neuroblastoma and Ganglioneuroblastoma: Tumor Mutation Prediction using Nodular-Type WSIs Datasets. In progress.
  • M. J. H. Faruk, et al. Deep Learning Framework for Object Detection and Tracking in Logistics: Automated Annotation and Optimized Package Identification Using CNN and Hungarian Algorithm with Multi-Dataset Analysis. In progress.