Md Sadman Haque | Computer Science | Best Researcher Award

Published on by Forensic Scientist Awards

Best Researcher Award

Md Sadman Haque
United International University, Bangladesh

Md Sadman Haque
Affiliation United International University
Country Bangladesh
Google Scholar ID 3fMbSl8AAAAJ
Documents 5
Citations 11
h-index 2
Subject Area Computer Science
Event International Forensic Scientist Awards
ORCID 0009-0001-0471-8197

Md Sadman Haque is a computer science researcher affiliated with United International University, Bangladesh. His documented research output includes work spanning interpretable machine learning, computer vision datasets, laboratory equipment recognition, and human–computer interaction involving large language models. His publication record reflects an applied orientation toward machine learning methods and datasets designed for practical research and educational contexts. Current bibliometric information records five documents, 11 citations, and an h-index of 2. [1]

Abstract

Md Sadman Haque’s research profile is characterized by applied computer science studies using machine learning and data-driven approaches. Recent publications address interpretable prediction of financial market stress, large-scale image datasets for physics laboratory equipment, chemistry laboratory apparatus recognition, and student perspectives on artificial intelligence-assisted academic search. These works collectively indicate engagement with machine learning, computer vision, dataset development, and human-centered artificial intelligence. [2] [3]

Keywords

Computer Science; Machine Learning; Explainable AI; Deep Learning; Computer Vision; Image Datasets; Artificial Intelligence; Human–Computer Interaction; Financial Analytics.

Introduction

The research record associated with Haque demonstrates the application of contemporary computational techniques to problems in finance, laboratory education, visual recognition, and information-seeking behavior. Rather than being limited to a single application domain, the publications show a common methodological emphasis on machine learning and structured data. Such cross-domain applications are relevant to the broader development of practical and interpretable artificial intelligence systems. [4]

Research Profile

A principal feature of the profile is the use of machine learning for prediction and recognition tasks. The 2026 study on firm-level market stress in the Dhaka Stock Exchange emphasizes interpretability, connecting predictive modeling with the need to understand model outputs. [2] Related research develops image datasets for identifying laboratory equipment, supporting computer vision research and potentially automated educational environments. [3]

Research Contributions

  • Development of an interpretable machine learning framework for next-day firm-level market-stress prediction.
  • Contribution to large-scale image dataset development for physics laboratory equipment detection.
  • Creation of a real-world chemistry laboratory image dataset covering 25 apparatus categories.
  • Investigation of university students’ use of LLMs and traditional search engines for academic problem solving.

Publications

“An interpretable machine learning framework for predicting next day firm level market stress in the Dhaka Stock Exchange” was published in Discover Artificial Intelligence on 6 September 2026. [2] Other recent works include the Scientific Data article on physics laboratory equipment detection, the chemistry laboratory image dataset study, and the HCI+NLP conference paper examining LLMs and traditional search. [3] [4] [5]

Research Impact

The available bibliometric record lists five documents, 11 citations, and an h-index of 2. [1] The broader significance of the research can also be considered through its application-oriented scope, particularly the development of reusable datasets and interpretable computational methods. Publications in Scientific Data further position dataset construction as a research contribution in its own right. [3]

Award Suitability

The profile is relevant to the Best Researcher Award within the International Forensic Scientist Awards based on its documented publication activity and demonstrated engagement with contemporary computer science research. The combination of explainable machine learning, computer vision, dataset development, and human-centered AI provides a substantive basis for academic recognition, while the assessment remains grounded in the documented research record and bibliometric indicators.

Conclusion

Md Sadman Haque presents an emerging computer science research profile centered on practical applications of machine learning and artificial intelligence. His recent publications demonstrate methodological breadth across financial prediction, computer vision datasets, laboratory equipment recognition, and academic information-seeking. The available record supports consideration for research recognition based on these documented contributions.

References

  1. Elsevier. (n.d.). Google Scholar author details: Md Sadman Haque, Author ID 3fMbSl8AAAAJ.
    https://scholar.google.com/citations?user=3fMbSl8AAAAJ&hl=en
  2. Haque, M. S., et al. (2026). An interpretable machine learning framework for predicting next day firm level market stress in the Dhaka Stock Exchange. Discover Artificial Intelligence.
    https://doi.org/10.1007/s44163-026-02065-7
  3. Haque, M. S., et al. (2026). Large-Scale Image Dataset for Physics Laboratory Equipment Detection Using Deep Learning Models. Scientific Data.
    https://doi.org/10.1038/s41597-026-07839-3
  4. Haque, M. S., et al. (2025). Real-world chemistry lab image dataset for equipment recognition across 25 apparatus categories. Scientific Data.
    https://doi.org/10.1038/s41597-025-05952-3
  5. Haque, M. S., et al. (2025). Rethinking Search: A Study of University Students’ Perspectives on Using LLMs and Traditional Search Engines in Academic Problem Solving. Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing.
    https://doi.org/10.18653/v1/2025.hcinlp-1.5
  6. ORCID. (n.d.). Md Sadman Haque — ORCID record.
    https://orcid.org/0009-0001-0471-8197

Yuan Rao | Computer Science | Best Researcher Award

Published on by Forensic Scientist Awards

Dr. Yuan Rao | Computer Science | Best Researcher Award

Lecturer | School of Artificial Intelligence, Guangzhou University | China

Yuan Rao is a dedicated researcher and lecturer specializing in media forensics and AI security. She has contributed significantly to the field of image forgery detection, focusing on self-supervised learning and domain adaptation techniques. Currently a Lecturer at the School of Artificial Intelligence, Guangzhou University, Yuan Rao combines academic excellence and practical expertise, earning recognition through various awards and high-impact publications.

Profile

Scopus

Google Scholar

Strengths for the Awards

  • Outstanding Research Contributions 
    • Yuan Rao has published numerous high-impact research articles in leading journals and conferences such as:
      • IEEE Transactions on Pattern Analysis and Machine Intelligence
      • Pattern Recognition
      • IEEE International Conference on Computer Vision (ICCV)
    • Her work on JPEG-resistant image forgery detection and AI security vulnerabilities reflects groundbreaking innovation in media forensics.
  • Leadership in Projects 
    • She has successfully undertaken four research projects as Principal Investigator (PI), funded by prestigious bodies such as:
      • National Natural Science Foundation of China
      • Guangdong Basic and Applied Research Foundation
      • Science and Technology Foundation of Guangzhou

🎓 Education

  • PhD in Information and Communication Engineering (2014.9 – 2021.6)
    • Sun Yat-sen University
    • Supervisor: Professor Ni Jiangqun
  • Master’s in Communication and Information Systems (2011.9 – 2014.6)
    • Jinan University
    • Supervisor: Professor Junkai Huang
  • Bachelor’s in Telecommunications Engineering and Management (2007.9 – 2011.6)
    • Beijing University of Posts and Telecommunications

 Work Experience

  • Lecturer (2021.6 – Present)
    • School of Artificial Intelligence, Guangzhou University
    • Yuan Rao conducts research, supervises projects, and inspires students in the evolving fields of AI and media forensics.

🔍 Research Interests On Computer Science

Yuan Rao’s primary research interests focus on:

  • Media Forensics: Detecting and localizing image forgeries using deep learning and self-supervised techniques.
  • AI Security: Exploring vulnerabilities and robustness of AI models in practical scenarios.

🏆 Awards

  • 🥇 Champion and 3rd Place in “Guangzhou Pazhou Algorithm Competition” – Algorithm Safety Track, 2023
  • 🥇 First Prize in the National “Challenge Cup” Black Science and Technology Special Competition, 2023
  • 🏅 6th Rank out of 1471 in “Secure AI Challenger Program: Tamper Detection for Forged Images,” 2020

📚 Publications

1. A Deep Learning Approach to Detection of Splicing and Copy-Move Forgeries in Images

  • Authors: Y. Rao, J. Ni
  • Publication Year: 2016
  • Citations: 590

2. Deep Learning Local Descriptor for Image Splicing Detection and Localization

  • Authors: Y. Rao, J. Ni, H. Zhao
  • Publication Year: 2020
  • Citations: 104

3. Multi-semantic CRF-based Attention Model for Image Forgery Detection and Localization

  • Authors: Y. Rao, J. Ni, H. Xie
  • Publication Year: 2021
  • Citations: 53

4. Block-based Convolutional Neural Network for Image Forgery Detection

  • Authors: J. Zhou, J. Ni, Y. Rao
  • Publication Year: 2017
  • Citations: 46

5. Self-supervised Domain Adaptation for Forgery Localization of JPEG Compressed Images

  • Authors: Y. Rao, J. Ni
  • Publication Year: 2021
  • Citations: 29

6. Towards JPEG-Resistant Image Forgery Detection and Localization via Self-Supervised Domain Adaptation

  • Authors: Y. Rao, J. Ni, W. Zhang, J. Huang
  • Publication Year: 2022
  • Citations: 13

7. High-accuracy Current Sensing Circuit with Current Compensation Technique for Buck–Boost Converter

  • Authors: Y. Rao, W.L. Deng, J.K. Huang
  • Publication Year: 2015
  • Citations: 5

8. A Trigger-Perceivable Backdoor Attack Framework Driven by Image Steganography

  • Authors: W. Tang, J. Li, Y. Rao, Z. Zhou, F. Peng
  • Publication Year: 2024
  • Citations: – (Recent publication, citation data unavailable)

9. Dig a Hole and Fill in Sand: Adversary and Hiding Decoupled Steganography

  • Authors: W. Tang, H. Yang, Y. Rao, Z. Zhou, F. Peng
  • Publication Year: 2024
  • Citations: – (Recent publication, citation data unavailable)

10. Exploring the Vulnerability of Self-supervised Monocular Depth Estimation Models

  • Authors: R. Hou, K. Mo, Y. Long, N. Li, Y. Rao
  • Publication Year: 2024
  • Citations: – (Recent publication, citation data unavailable)

11. MKD: Mutual Knowledge Distillation for Membership Privacy Protection

  • Authors: S. Huang, Z. Liu, J. Yu, Y. Tang, Z. Luo, Y. Rao
  • Publication Year: 2023
  • Citations: – (Recent publication, citation data unavailable)

12. Deep Multi-image Hiding with Random Key

  • Authors: W. Zhang, W. Tang, Y. Rao, B. Li, J. Huang
  • Publication Year: 2023
  • Citations: – (Recent publication, citation data unavailable)

Research Projects

Yuan Rao has led multiple projects as Principal Investigator (PI):

  1. National Natural Science Foundation of China: Research on Self-supervised Transfer Learning Based Image Forgery Forensics.
  2. Guangdong Basic and Applied Basic Research Foundation: Image Forgery Localization Combining Self-supervised Learning and Knowledge Distillation.
  3. Science and Technology Foundation of Guangzhou: Verifiable Robustness of Object Detection Models Using Domain Knowledge.
  4. Science and Technology Foundation of Guangzhou: Short-term Heavy Precipitation Forecasting with Super-resolution Reconstruction.

📝 Conclusion

Yuan Rao is a prominent researcher and educator in media forensics and AI security. With a robust academic background, impressive research achievements, and numerous accolades, she is committed to advancing technologies that ensure the integrity and security of digital media.