Dinh-Thai Kim | Computer Science and Artificial Intelligence | Best Researcher Award

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Best Researcher Award

Dinh-Thai Kim
Vietnam National University, Vietnam

Dinh-Thai Kim
Affiliation Vietnam National University
Country Vietnam
Scopus ID 57265279400
Documents 49
Citations 194
h-index 8
Subject Area Computer Science and Artificial Intelligence
Event International Forensic Scientist Awards
ORCID 0000-0002-9060-4769

Dinh-Thai Kim is a researcher affiliated with Vietnam National University whose documented research profile is situated within computer science and artificial intelligence. The available bibliometric record lists 49 documents, 194 citations, and an h-index of 8. [1] Recent publications demonstrate work spanning computer vision, deep learning, federated learning, speech emotion recognition, agricultural imaging, and Vietnamese sign-language classification.

Abstract

Dinh-Thai Kim’s research profile reflects applied artificial intelligence research addressing visual recognition, natural-language and speech-related intelligence, and machine-learning applications. Recent work includes lightweight object detection for cashew kernel assessment, growth-stage detection in lychee orchards, privacy-preserving speech emotion recognition, and graph-based Vietnamese sign-language classification. [2]–[5]

Keywords

Artificial intelligence; computer vision; deep learning; object detection; federated learning; speech emotion recognition; sign-language classification; agricultural AI.

Introduction

The research record associated with Kim illustrates the application of contemporary machine-learning methods to practical recognition problems. The publications supplied for this profile indicate an emphasis on deployable computer-vision systems and specialized intelligent models, including applications involving agricultural products, orchard monitoring, human speech, and gesture-based language recognition.

Research Profile

Kim’s documented research spans several complementary areas of artificial intelligence. The work on YOLOv8s-based lychee detection applies object-detection techniques under natural orchard conditions, while CSTAR-Det addresses lightweight vision-based assessment of cashew kernels. [2] [3] Other research extends machine learning toward Vietnamese speech emotion recognition and sign-language classification. [4] [5]

Research Contributions

  • Development of lightweight vision-based approaches for agricultural quality assessment.
  • Application of object detection to crop growth-stage recognition under natural conditions.
  • Investigation of prototype-based federated learning for privacy-preserving speech emotion recognition.
  • Use of graph convolutional networks and skeletal points for Vietnamese sign-language classification.

Publications

CSTAR-Det: A lightweight vision-based framework for conveyor-based cashew kernel quality assessment. Array, listed publication date December 2026. [2]

YOLO-Lychee: A YOLOv8s-Based Detector for Lychee Growth-Stage Detection under Natural Orchard Conditions. EAI Endorsed Transactions on AI and Robotics, 27 July 2026. [3]

EmoFedProto: Privacy-Preserving Vietnamese Speech Emotion Recognition via Prototype-Based Federated Learning. EAI Endorsed Transactions on AI and Robotics, 23 April 2026. [4]

Deep Learning Based Graph Convolutional Network Using Hand Skeletal Points For Vietnamese Sign Language Classification. Iranian Journal of Electrical and Electronic Engineering, March 2026. [5]

Research Impact

The supplied Scopus metrics record 194 citations across 49 documents, with an h-index of 8. [1] These indicators provide a quantitative view of scholarly visibility, while the publication record shows application-oriented research across multiple AI domains. Citation indicators should be interpreted in relation to publication age, field practices, and database coverage.

Award Suitability

The documented record provides evidence relevant to recognition in a research category focused on computer science and artificial intelligence. The combination of bibliometric activity and recent research involving computer vision, federated learning, and deep-learning classification offers a factual basis for consideration under the Best Researcher Award category. [1]–[5]

Conclusion

Dinh-Thai Kim’s documented research profile combines measurable scholarly activity with recent applications of artificial intelligence to agricultural vision, speech processing, privacy-preserving learning, and sign-language recognition. The available record supports a multidisciplinary description within computer science and artificial intelligence and provides the principal bibliographic and impact indicators presented on this page.

References

  1. Elsevier. (n.d.). Scopus author details: Dinh-Thai Kim, Author ID 57265279400. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57265279400
  2. Kim, Dinh-Thai. (2026). CSTAR-Det: A lightweight vision-based framework for conveyor-based cashew kernel quality assessment. Array.
    https://doi.org/10.1016/j.array.2026.101282
  3. Kim, Dinh-Thai. (2026). YOLO-Lychee: A YOLOv8s-Based Detector for Lychee Growth-Stage Detection under Natural Orchard Conditions. EAI Endorsed Transactions on AI and Robotics.
    https://doi.org/10.4108/airo.12140
  4. Kim, Dinh-Thai. (2026). EmoFedProto: Privacy-Preserving Vietnamese Speech Emotion Recognition via Prototype-Based Federated Learning. EAI Endorsed Transactions on AI and Robotics.
    https://doi.org/10.4108/airo.11595
  5. Kim, Dinh-Thai. (2026). Deep Learning Based Graph Convolutional Network Using Hand Skeletal Points For Vietnamese Sign Language Classification. Iranian Journal of Electrical and Electronic Engineering.
    https://doi.org/10.22068/IJEEE.22.1.3717
  6. ORCID. (n.d.). Dinh-Thai Kim, ORCID record.
    https://orcid.org/0000-0002-9060-4769
  7. International Forensic Scientist Awards. (n.d.). Official award website.
    forensicscientist.org

Amrita Ganguly | Computer Science and Artificial Intelligence | Research Excellence Award

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Mrs. Amrita Ganguly | Computer Science and Artificial Intelligence | Research Excellence Award

George Mason University | United States

Mrs. Amrita Ganguly is a researcher working at the intersection of Responsible and Ethical Artificial Intelligence, Human–AI Interaction, and AI applications in education. Her scholarly contributions focus on ethical governance of generative AI, human-centered AI design, and collaborative intelligence frameworks. According to Scopus, she has 7 peer-reviewed publications, 19 citations, and an h-index of 2. Her work appears in high-impact international journals and premier conferences in AI ethics, human–computer interaction, and computing education. Her research emphasizes evidence-based guidelines, design frameworks, and ethical considerations that shape responsible AI deployment in academic and socio-technical contexts.

                            Citation Metrics (Scopus)

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Citations
19
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Citations

Documents

h-index

View Scopus Profile  View Google Scholar Profile  View ORCID Profile

Featured Publications


Collaborative job seeking for people with autism: Challenges and design opportunities

– CHI ’24: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems · Cited by 18


ShadowMagic: Designing Human-AI Collaborative Support for Comic Professionals’ Shadowing

– UIST ’24: Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology · Cited by 5

Bingqin Han | Computer Science and Artificial Intelligence | Research Excellence Award

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Mr. Bingqin Han | Computer Science and Artificial Intelligence | Research Excellence Award

Tianjin University | China

Mr. Bingqin Han, is a leading researcher at the intersection of artificial intelligence, consumer psychology, and business ethics, focusing on the factors that influence AI adoption in both consumer and managerial contexts. His work integrates behavioral science, marketing, and technology studies to examine how trust, risk perception, ethical considerations, and cognitive biases shape the acceptance and utilization of AI technologies. He has pioneered innovative frameworks for modeling human–AI interaction, most notably the “onion model” of AI humanoid robot adoption in domestic settings, which conceptualizes acceptance across technical, psychological, and social layers, offering both theoretical and practical insights. Mr. Bingqin Han’s scholarly contributions include three peer-reviewed publications, which have garnered three citations and an h-index of 1 according to Scopus, reflecting his emerging influence in the field. Beyond publications, he has contributed to interdisciplinary research projects, participated in editorial and peer-review activities, and engaged in collaborative initiatives that bridge academia and industry, highlighting his commitment to advancing both theory and practice. His work has significantly enriched understanding of human–AI integration, providing actionable guidance for developers, policymakers, and researchers aiming to optimize AI deployment in real-world environments. By combining rigorous empirical research, innovative conceptual frameworks, and interdisciplinary collaboration, Han has made substantial contributions to AI adoption research, behavioral decision-making, and ethical technology development, establishing a foundation for future innovations and informing responsible, human-centered implementation of AI technologies across diverse contexts.

Profile: Scopus

Featured Publications

  • Han, B., Song, S., Liu, D., & Mo, J. (2024). Mechanism of online public opinion formation in major risk events in China: A qualitative comparative analysis.

  • Han, B., & Liu, D. (2025). Peeling back acceptance: An onion model of AI humanoid robot adoption in homes.

 

Zhiqiang Wang | Computer Science and Artificial Intelligence | Best Academic Researcher Award

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Dr. Zhiqiang Wang | Computer Science and Artificial Intelligence | Best Academic Researcher Award

North China University of Science and Technology | China

Dr. Zhiqiang Wang is a dynamic researcher specializing in intelligent control, industrial process optimization, and data-driven modeling, with a particular emphasis on complex mineral processing systems such as copper flotation. His work integrates advanced control theory, artificial intelligence, subspace identification, transfer learning, and machine vision to enhance stability, efficiency, and real-time decision-making within large-scale industrial environments. He has contributed notable innovations in intelligent optimization strategies, expert-knowledge-assisted control frameworks, flotation-foam image analysis for process evaluation, and adaptive control algorithms designed for systems with nonlinear, highly variable operating conditions. His research extends across intelligent manufacturing, big-data-driven industrial analytics, and cross-disciplinary automation technologies, advancing next-generation solutions for process monitoring, fault diagnosis, and operational optimization. He has been actively involved in major national research initiatives focusing on full-process industrial evaluation, intelligent control system development, and 5G-enabled optimization technologies for metallurgy and beneficiation. According to Scopus, Wang has authored 35 indexed publications, accumulated 403 citations from 348 citing documents, and achieved an h-index of 9, reflecting his growing academic influence in industrial automation and intelligent systems engineering. His publications in leading journals including ISA Transactions, IEEE Transactions on Instrumentation and Measurement, Canadian Journal of Chemical Engineering, and the International Journal of Control, Automation and Systems demonstrate his contributions to advancing data-driven modeling, feature engineering, and real-time operational control. Through sustained collaboration, innovative methodologies, and impactful research, Wang continues to advance the field of intelligent industrial process control and smart manufacturing technologies.

Profile: Scopus

Featured Publications

  • Wang, Z., He, D., Wang, Z., & Li, Q. (2025). Subspace identification method-based setpoints tracking control and its applications to the column cleaning process. ISA Transactions.

  • Wang, Z., He, D., Wang, Z., & Li, Q. (2023). Timeliness and stability-based operation optimization for copper flotation industrial process. IEEE Transactions on Instrumentation and Measurement.

  • Wang, Z., Peng, F., Li, Q., & He, D. (2023). State evaluation of copper flotation process based on transfer learning and a layered and blocked framework. Canadian Journal of Chemical Engineering.

  • Wang, Z., Zhang, X., & He, D. (2023). Dynamic global feature extraction and importance-correlation selection for the prediction of concentrate copper grade and recovery rate. Canadian Journal of Chemical Engineering.

  • Wang, Z., He, D., Zhang, Q., & Shi, J. (2020). Observer-based finite-time model reference adaptive state tracking control with actuator saturation. International Journal of Control, Automation and Systems.

Ana Josselinne Alegre Mondragón | Data Science and Analytics | Editorial Board Member

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Prof. Ana Josselinne Alegre Mondragón | Data Science and Analytics | Editorial Board Member

Geospatial Information Sciences Research Center | Mexico

Prof. Ana Josselinne Alegre Mondragón is a distinguished geospatial information sciences researcher whose scholarly contributions advance the understanding of violence dynamics, clandestine graves detection, and geospatial applications for public security and forensic investigations. Her research integrates geographic profiling, spatial statistics, remote sensing, UAV-based photogrammetry, and geointelligence modeling to address critical national challenges, particularly the search for missing persons and the analysis of organized crime patterns. She has authored influential book chapters, peer-reviewed journal articles, and scientific reports published by Springer, Forensic Sciences International, and leading academic institutions in Mexico. Her work includes the development of spectral indices for detecting clandestine graves, multivariate regional analyses of burial sites, geospatial modeling of crime, assessment of soil alterations linked to criminal activities, and advanced methodologies for drone-based terrain morphology assessment. She has actively contributed to high-impact research projects such as Espacio Clandestino, geospatial infrastructures for the Ayotzinapa investigation, and scientific applications supporting national systems for the search of missing persons. Her publications and research tools are widely referenced, shaping new standards for evidence-based forensic search protocols. She has collaborated on cross-disciplinary initiatives involving forensic science, anthropology, criminology, and remote sensing, generating innovative methodologies and academic outputs recognized both nationally and internationally. Her scholarly trajectory demonstrates a sustained commitment to advancing geospatial science for human rights, security analysis, and public policy, positioning her as a leading figure in Mexico’s emerging field of forensic geospatial analytics.

Profile: Scopus | ORCID

Featured Publications

  • Alegre-Mondragón, A. J. (2024). Cambios organizacionales en la policía de la Ciudad de México. Del modelo CompStat a la evaluación con transparencia y apertura hacia la ciudadanía. Revista Digital de Estudios Organizacionales, Universidad de Xalapa, Veracruz, México, 53–92. DOI: https://doi.org/10.69509/wxj14d34

  • Silván-Cárdenas, J. L., Alegre-Mondragón, A. J., Ramírez Aceves E. D., Campos Cornejo, D. R. & Bautista Nadalón, M. (2024). Diseño y aplicación de índices espectrales para la detección de fosas clandestinas. En Interpretar la naturaleza para encontrar a quienes nos faltan. COBUPEJ & CentroGeo, 355–389.

  • Alegre-Mondragón, A. J. & Silván-Cárdenas, J. L. (2024). Morfología del terreno mediante fotogrametría con drones: oportunidades y limitaciones para la detección de fosas clandestinas. En Interpretar la naturaleza para encontrar a quienes nos faltan. COBUPEJ & CentroGeo, 323–350.

  • Silván-Cárdenas, J. L. & Alegre-Mondragón, A. J. (2024). Espacio Clandestino: A Nationwide Platform to Support Clandestine Graves Search in Mexico. In Recent Developments in Geospatial Information Sciences. Springer, 175–186.

  • Alegre-Mondragón, A. J., Vilalta-Perdomo, C. J., Silván-Cárdenas, J. L. & Silva-Arias, C. (2024). Characteristics of Clandestine Burial Sites in Mexico: A Regional Overview with Multivariate Analysis. In Recent Developments in Geospatial Information Sciences. Springer, 53–64.