Samuel Odoom | Information Technology | Innovative Research Award

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Innovative Research Award

Samuel Odoom

Mampong Technical College of Education, Ghana

Samuel Odoom
Affiliation Mampong Technical College of Education
Country Ghana
Scopus ID 6507729911
Documents 21
Citations 286
h-index 9
Subject Area Information Technology
Event Top Teachers Awards
ORCID 0009-0003-3551-5370
Google Scholar cqeBuLYAAAAJ

Samuel Odoom is a researcher and educator specializing in Artificial Intelligence, Machine Learning, and Data-Driven Decision Support Systems. His work focuses on applying advanced predictive modeling techniques, including ensemble learning, explainable AI, and psychometric analytics, to address critical challenges in healthcare and education. He has contributed to research on disease risk prediction, student success modeling through emotional intelligence and behavioral traits, and intelligent systems for evidence-based decision-making. Odoom is committed to developing ethical, interpretable, and impactful AI solutions that bridge research, policy, and practice for sustainable societal advancement.[1]

Abstract

This article presents the research and scholarly contributions of Samuel Odoom, nominated for the Innovative Research Award in recognition of his work in artificial intelligence, machine learning, and data-driven decision support. His research addresses pressing challenges in healthcare and education, with a focus on predictive modeling and explainable AI. With 21 documents, 286 citations, and an h-index of 9, Odoom’s work demonstrates measurable academic impact and practical relevance. His commitment to ethical and interpretable AI aligns with the goals of the Top Teachers Awards, making him a strong candidate for this recognition.[1]

Keywords

Artificial Intelligence, Machine learning, Data Analytics, Public Health, Predictive Modeling, Explainable AI, Educational Technology.

Introduction

The integration of artificial intelligence and machine learning into critical sectors such as healthcare and education has opened new frontiers for evidence-based decision-making. Samuel Odoom has been at the forefront of this movement, leveraging predictive analytics and explainable AI to address real-world problems in Ghana and beyond. His work spans disease risk prediction, student success modeling through emotional intelligence, and intelligent systems design. This article provides a comprehensive overview of his research profile, contributions, and impact, with a focus on his suitability for the Innovative Research Award.[1][2]

Research Profile

Samuel Odoom is currently pursuing a PhD in Information Technology at Ghana Communication Technology University, building on an MPhil from Kwame Nkrumah University of Science and Technology and a BSc in Information Technology Education from the University of Education, Winneba-Kumasi. His academic training is complemented by extensive leadership experience, including roles as Form Master at Islamic Senior High School, Wa, and Editorial Board Chairman of INFOTESS. Odoom’s research is characterized by interdisciplinary collaboration, with publications that span healthcare outcomes, HIV self-esteem assessment, and traditional medicine training, often employing data mining and machine learning techniques.[1][2]

Research Contributions

Samuel Odoom’s contributions are rooted in the application of AI to address systemic challenges in healthcare and education. His work on tuberculosis treatment outcomes provided critical insights into factors affecting patient recovery in the Ashanti region of Ghana[3]. In the domain of public health, he co-authored a study on self-esteem among adolescents living with HIV, highlighting psychological dimensions of care[4]. More recently, his collaboration on training traditional bonesetters in fracture management demonstrates a commitment to integrating modern medical principles with local practices[5]. These projects reflect his ability to translate complex data into actionable insights, a hallmark of his research philosophy.

Publications

Samuel Odoom has authored or co-authored 21 documents indexed in Scopus, with 286 citations and an h-index of 9. His key publications include a retrospective study on tuberculosis treatment outcomes in Ghana[3], an assessment of self-esteem among adolescents living with HIV[4], and a proof-of-concept study on training traditional bonesetters in fracture treatment[5]. These works demonstrate his interdisciplinary approach and commitment to improving health outcomes through data-driven research.

Samuel Odoom has authored or co-authored 21 documents indexed in Scopus, with 286 citations and an h-index of 9. Key publications include:

  • Agyare, S. A., Osei, F. A., Odoom, S. F., Mensah, N. K., Amanor, E., et al. (2021). Treatment outcomes and associated factors in tuberculosis patients at Atwima Nwabiagya district, Ashanti region, Ghana: a ten‐year retrospective study. Tuberculosis Research and Treatment, 2021, 9952806.
  • Agyemang, E. O., Dapaah, J. M., Osei, F. A., Appiah, S. C. Y., Mensah, N. K., Odoom, S. F. (2020). Self-esteem assessment among adolescents living with HIV and seeking healthcare at Komfo Anokye Teaching Hospital-Kumasi, Ghana. Journal of the International Association of Providers of AIDS Care, 19, 2325958220976828.
  • Konadu-Yeboah, D., Yempabe, T., Buunaaim, A. D. B., Konadu, P., Owusu, R., Odoom, S. F. (2023). Training traditional bonesetters in basic principles of fracture treatment: a proof of concept in Ghana. JBJS, 105(24), 1995-2001.

Research Impact

The impact of Samuel Odoom’s work is evidenced by 286 citations across his 21 documents, reflecting the relevance of his research to both academic and practitioner communities. His studies on tuberculosis treatment outcomes and HIV self-esteem have informed public health policies in Ghana, while his recent work on bonesetter training bridges traditional and modern healthcare. In education, his focus on emotional intelligence and behavioral traits in student success modeling has implications for personalized learning interventions. Samuel Odoom’s research is regularly presented at seminars, including the Microsoft Elevate for Educators and the Research Clinic at KNUST, demonstrating his engagement with the broader scholarly community.[1][2]

Award Suitability

Samuel Odoom exemplifies the qualities of an innovative researcher: technical excellence, interdisciplinary collaboration, and a clear commitment to societal impact. His work aligns with the United Nations Sustainable Development Goals, particularly in health (SDG 3) and education (SDG 4). The Top Teachers Awards recognize educators who go beyond the classroom, and Samuel Odoom’s research-driven approach to improving healthcare and educational outcomes makes him an ideal candidate. His leadership roles, including course representation and editorial board chairmanship, further demonstrate his capacity to influence and mentor peers.[1][2]

Conclusion

This article has provided a comprehensive overview of Samuel Odoom’s research profile, contributions, and impact. His work in artificial intelligence, machine learning, and data-driven decision support has addressed critical challenges in healthcare and education, with measurable outcomes in citations and practical applications. Odoom’s commitment to ethical, interpretable, and impactful AI positions him as a leader in his field. His nomination for the Innovative Research Award is well-supported by his scholarly output, interdisciplinary collaborations, and dedication to bridging research, policy, and practice.[1][2]

References

  1. Elsevier. (n.d.). Scopus author details: Samuel Odoom, Author ID 6507729911. Scopus. https://www.scopus.com/pages/authors/6507729911
  2. Google Scholar. (n.d.). Samuel Odoom – Google Scholar Profile. https://scholar.google.com/citations?user=cqeBuLYAAAAJ&hl=en&oi=sra
  3. Agyare, S. A., Osei, F. A., Odoom, S. F., Mensah, N. K., Amanor, E., et al. (2021). Treatment outcomes and associated factors in tuberculosis patients at Atwima Nwabiagya district, Ashanti region, Ghana: a ten‐year retrospective study. Tuberculosis Research and Treatment, 2021, 9952806. https://doi.org/10.1155/2021/9952806
  4. Agyemang, E. O., Dapaah, J. M., Osei, F. A., Appiah, S. C. Y., Mensah, N. K., Odoom, S. F. (2020). Self-esteem assessment among adolescents living with HIV and seeking healthcare at Komfo Anokye Teaching Hospital-Kumasi, Ghana. Journal of the International Association of Providers of AIDS Care (JIAPAC), 19, 2325958220976828. https://doi.org/10.1177/2325958220976828
  5. Konadu-Yeboah, D., Yempabe, T., Buunaaim, A. D. B., Konadu, P., Owusu, R., Odoom, S. F. (2023). Training traditional bonesetters in basic principles of fracture treatment: a proof of concept in Ghana. Journal of Bone and Joint Surgery, 105(24), 1995-2001. https://doi.org/10.2106/jbjs.22.01304

Dehui Du | Computer Science | Innovative Research Award

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Innovative Research Award

Dehui Du
East China Normal Universty, China

Dehui Du
Affiliation East China Normal Universty
Country China
Scopus ID 14044898400
Documents 68
Citations 504
h-index 11
Subject Area Computer Science
Event Top Teachers Awards

The Innovative Research Award recognizes scholars whose research activities demonstrate originality, methodological rigor, and measurable contributions to the advancement of scientific knowledge. Dehui Du of East China Normal Universty has established a research profile in computer science through investigations in causal inference, explainable artificial intelligence, reinforcement learning, large language models, autonomous systems, and rare event detection. His publication record, citation performance, and participation in internationally recognized conferences indicate sustained engagement with contemporary research challenges and emerging computational methodologies.[1]

Abstract

Dehui Du’s research focuses on the intersection of machine learning, causal reasoning, explainable artificial intelligence, and intelligent systems. His scholarly output addresses practical and theoretical problems associated with reinforcement learning, counterfactual analysis, autonomous driving, and large language models. Through conference publications and collaborative research efforts, he has contributed to the development of computational frameworks designed to improve transparency, reliability, and performance in artificial intelligence systems.[2]

Keywords

Artificial Intelligence, Computer Science, Reinforcement Learning, Causal Inference, Explainable AI, Large Language Models, Counterfactual Analysis, Autonomous Driving.

Introduction

Recent advances in artificial intelligence increasingly require interpretable, reliable, and data-efficient learning systems. Researchers working at the intersection of machine learning and causal reasoning play an important role in addressing these challenges. Dehui Du’s work reflects this direction by integrating explainability, counterfactual reasoning, and advanced learning architectures into practical computational frameworks that support decision-making and predictive performance.[3]

Research Profile

With 68 indexed publications, 504 citations, and an h-index of 11, Dehui Du has developed a scholarly profile characterized by interdisciplinary research across machine learning and intelligent computing. His collaborations span topics including causal inference, experience replay methods, language model reasoning, autonomous systems, and counterfactual identifiability. These areas are increasingly relevant to both academic research and industrial applications.[1]

Research Contributions

  • Development of explainable reinforcement learning approaches supported by causal inference.
  • Advancement of counterfactual generation techniques for rare event detection.
  • Research on preference-guided reverse reasoning for large language models.
  • Theoretical investigations into exogenous isomorphism and counterfactual identifiability.
  • Contributions to imitation learning frameworks for autonomous driving systems.

Publications

  1. Enhancing Rare Event Detection via Counterfactual Generation with Exogenous Variables.
  2. ERCI: An Explainable Experience Replay Approach with Causal Inference for Deep Reinforcement Learning.
  3. Reversal of Thought: Enhancing Large Language Models with Preference-Guided Reverse Reasoning Warm-up.
  4. Exogenous Isomorphism for Counterfactual Identifiability.
  5. Multi-Task Invariant Representation Imitation Learning for Autonomous Driving.

Research Impact

The research output of Dehui Du demonstrates influence across multiple areas of artificial intelligence. His publications appear in recognized venues such as WWW, AAAI, ACL, ICML, and ICRA, reflecting engagement with leading scholarly communities. The combination of theoretical and applied research contributes to improved interpretability, reliability, and effectiveness of machine learning systems in real-world environments.[4]

Award Suitability

Dehui Du’s academic accomplishments align with the objectives of the Innovative Research Award. His work addresses contemporary challenges in artificial intelligence through innovative methodologies and interdisciplinary perspectives. The quality of publication venues, measurable citation indicators, and contributions to explainable and trustworthy AI collectively support consideration for recognition within the Top Teachers Awards framework.[5]

Conclusion

The scholarly record of Dehui Du reflects sustained contributions to computer science research, particularly in machine learning, causal inference, and intelligent systems. Through publications, collaborations, and methodological innovations, he has contributed to the advancement of explainable and reliable artificial intelligence technologies. These achievements provide a strong foundation for recognition through the Innovative Research Award.

References

  1. Elsevier. (n.d.). Scopus author details: Dehui Du, Author ID 14044898400. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=14044898400
  2. Du, D., Tian, L., Chen, Y., Li, Y., & Li, Y. (2025). ERCI: An Explainable Experience Replay Approach with Causal Inference for Deep Reinforcement Learning.
  3. Yuan, J., Du, D., Zhang, H., Di, Z., & Naseem, U. (2025). Reversal of Thought: Enhancing Large Language Models with Preference-Guided Reverse Reasoning Warm-up.
  4. Chen, Y., & Du, D. (2025). Exogenous Isomorphism for Counterfactual Identifiability.
  5. Peng, J., Yu, X., Wang, J., Tian, L., & Du, D. (2025). Multi-Task Invariant Representation Imitation Learning for Autonomous Driving.
  6. Tian, L., Du, D., & Chen, Y. (2026). Enhancing Rare Event Detection via Counterfactual Generation with Exogenous Variables.

Reagan Mandiya | Information Technology | Young Researcher Award

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Mr. Reagan Mandiya | Information Technology | Young Researcher Award

University of Lorraine | France

Mr. Reagan Mandiya is an emerging researcher in Artificial Intelligence and data science, with a growing academic impact reflected in 17 Scopus citations, 3 documents, and an h-index of 2. His research focuses on advanced machine learning, deep learning, and AI-driven healthcare applications, particularly in cardiac arrhythmia diagnosis using large language models. He has contributed to innovative solutions in COVID-19 detection, fraud analytics, and language translation using transformer-based models. His interdisciplinary approach integrates bioinformatics, cybersecurity, and big data analytics. Through collaborative research and mentorship, he actively advances applied AI solutions addressing real-world challenges.

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Sema Servi | Computer Science | Best Research Article Award

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Assist. Prof. Dr. Sema Servi | Computer Science | Best Research Article Award

Selçuk University | Turkey

Asst. Prof. Dr. Sema Servi is a researcher in computer engineering with a strong foundation in applied mathematics, specializing in machine learning, artificial intelligence, and numerical methods for complex problem solving. Her work focuses on data-driven approaches, including clustering algorithms, optimization techniques, and computer vision applications in healthcare and engineering. She has contributed to interdisciplinary research spanning digital competence analysis, bioinformatics, and intelligent systems. Asst. Prof. Dr. Sema Servi actively supervises postgraduate research and advises innovative, technology-driven projects supported by national programs. She has a solid research impact with 62 Scopus citations, 15 indexed documents, and an h-index of 5.

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Xulei Cao | Computer Science | Research Excellence Award

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Mr. Xulei Cao | Computer Science | Research Excellence Award

University of Science and Technology of China | China

Mr. Xulei Cao research centers on advancing intelligent communication systems, large-scale machine learning, and adaptive networked environments, with a primary emphasis on vehicular ad hoc networks (VANETs), device–edge–cloud collaboration, and large language models. His work explores street-centric and microtopology-based routing strategies to address the challenges of dynamic mobility, frequent topology changes, and complex urban communication environments, proposing opportunistic routing protocols that leverage link correlation to enhance reliability, reduce packet loss, and optimize end-to-end performance. He has contributed to routing solutions grounded in urban road structure awareness, improving scalability and robustness in dense vehicular networks and supporting next-generation intelligent transportation systems. In parallel, his research extends into intelligent computing frameworks that integrate device, edge, and cloud layers to enable efficient distributed learning, resource-aware decision-making, and latency-sensitive AI applications. He also investigates algorithmic innovation within large language models, emphasizing scalability, deployment efficiency, and real-world applicability. Additionally, his work on biometric recognition, including palmprint feature extraction and direction coding, demonstrates expertise in pattern recognition and vision-based authentication systems. Supported by growing scholarly recognition, his work has been cited 212 times overall, including 101 citations since 2020, with an h-index of 3 and an i10-index of 2, underscoring the increasing impact and relevance of his contributions to networking, artificial intelligence, and intelligent mobility research.

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