Gabriel Osei Forkuo | Machine Learning | Best Researcher Award

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Mr. Gabriel Osei Forkuo | Machine Learning | Best Researcher Award

Doctoral Researcher/ Research Assistant at Transilvania University of Brasov, Romania

Gabriel Osei Forkuo is a dedicated forestry specialist and researcher with an extensive background in forest operations engineering, postural ergonomics, and machine learning applications. He has built a career that merges practical field experience with academic research, contributing significantly to the development of innovative and cost-effective technologies in forest monitoring and conservation. Currently pursuing a Ph.D. in Forest Operations Engineering at Transilvania University of Brasov, Romania, Gabriel has emerged as a leading figure in the exploration of low-cost LiDAR technologies and smart solutions for ergonomic assessments in forestry. His multifaceted expertise is grounded in over two decades of professional service in teaching, field operations, and advanced scientific investigations.

Profile

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Education

Gabriel’s educational journey is marked by academic excellence and a continuous drive for specialized knowledge. He is currently enrolled in a Ph.D. program in Forest Operations Engineering at Transilvania University of Brasov, where his research focuses on integrating machine learning and computer vision for ergonomic assessments in forest operations. He previously earned a Master’s degree in Multiple Purpose Forestry from the same university, achieving excellent grades and a cumulative ECTS average of 9.76. His foundational studies include a Bachelor of Science degree in Natural Resources Management from Kwame Nkrumah University of Science and Technology, Kumasi, Ghana, where he graduated with First Class Honours. Earlier academic milestones include completing his GCE A-Level in science subjects and his GCE O-Level in science, supported by performance scholarships recognizing his consistent academic distinction.

Experience

Gabriel’s professional experience spans across teaching, research, and forest management. Between 2002 and 2011, he worked as a Forest Range Manager and Supervisor at the Forestry Commission Ghana, where he was instrumental in nursery planning, restoration of degraded forests, and report writing. From 1999 to 2001, he served as a Science and Maths Teacher at Maria Montessori School in Kumasi, followed by a role as a Teaching Assistant at his alma mater, Kwame Nkrumah University of Science and Technology. In this capacity, he conducted laboratory classes, supervised research data collection, and participated in academic presentations, establishing a strong foundation in both pedagogical and research methodologies. His leadership in afforestation programs and practical forest management further reflects his field-based competency and organizational capability.

Research Interest

Gabriel’s research interests are centered on forest operations engineering, with a special focus on postural ergonomics, machine learning applications, and smart technologies for environmental monitoring. He is passionate about developing affordable and efficient technological solutions, particularly the use of mobile LiDAR and AI-driven tools for soil disturbance estimation and posture evaluation in forest labor. His interdisciplinary approach merges forestry, computer science, and ergonomics, contributing to sustainable and safe forestry practices. Through these interests, he aims to bridge the gap between traditional forestry operations and modern intelligent systems.

Award

Gabriel’s academic and professional contributions have been recognized through several prestigious scholarships and awards. He has twice secured first place in the “My Bachelor/Dissertation Project” competitions held in 2022 and 2023, scoring nearly perfect marks. In 2022, he received the “Premiul special pentru studenti straini” award at the Premiul AFCO. He has also been a recipient of multiple scholarships, including the Transilvania Academica Scholarship, UNITBV Ph.D. Scholarship for International Graduates, and funding from “Proiectul Meu de Diploma” programs. Earlier in his career, he was awarded performance scholarships by the Government of Ghana and Poku Transport Ghana for his outstanding performance in forest sciences.

Publication

Gabriel has authored several notable publications that demonstrate his expertise in forest operations and technological innovation. His key works include:

Forkuo, G.O., & Borz, S.A. (2023). Accuracy and inter-cloud precision of low-cost mobile LiDAR technology in estimating soil disturbance in forest operations. Frontiers in Forests and Global Change, 6. Cited in multiple studies on forest soil impact monitoring.

Forkuo, G.O. (2023). A systematic survey of conventional and new postural assessment methods. Revista Padurilor, 138(3), 1-34.

Borz, S.A., Morocho Toaza, J.M., Forkuo, G.O., Marcu, M.V. (2022). Potential of measure app in estimating log biometrics: a comparison with conventional log measurement. Forests, 13(7), 1028.

Borz, S.A., Forkuo, G.O., Oprea-Sorescu, O., & Proto, A.R. (2022). Development of a robust machine learning model to monitor the operational performance of sawing machines. Forests, 13(7), 1115.

Forkuo, G.O., Proto, A.R., & Borz, S.A. (2024). Feasibility of low-cost mobile LiDAR technology in estimating soil disturbance in forest operations. SSRN.

Forkuo, G.O. (1999). Post-fire tree regeneration studies in the Kumawu Water Supply Forest Reserve. B.Sc. Thesis, KNUST-Kumasi.

Presented paper at FORMEC 2023 in Florence, Italy, highlighting applications of mobile LiDAR in operational environments.

Conclusion

Gabriel Osei Forkuo exemplifies the intersection of academic rigor, practical expertise, and technological innovation in the field of forest operations. His work continues to advance the integration of smart technologies into sustainable forestry, driven by a deep commitment to both ecological preservation and worker safety. Through his research, publications, and leadership roles, Gabriel has built a profile of excellence, contributing significantly to forestry engineering and shaping the next generation of sustainable forest management solutions.

Hao Chen | Regression Analysis | Best Researcher Award

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Mr. Hao Chen | Regression Analysis | Best Researcher Award

Lecturer in Artificial Intelligence at Xi’an University of Technology, China

Hao Chen is a dedicated researcher in the field of computer science with a strong focus on data-driven methodologies applied to atmospheric and agricultural systems. Over the years, he has combined interdisciplinary approaches with cutting-edge machine learning and remote sensing technologies to address complex environmental challenges. As a member of the China Computer Federation since 2010, Hao Chen has consistently contributed to advancing knowledge at the intersection of artificial intelligence, digital twin technologies, and atmospheric science. His professional journey is defined by the development of innovative frameworks and predictive models that enhance the understanding and practical application of lidar systems, object detection in agriculture, and semantic data modeling.

Profile

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Education

Hao Chen pursued comprehensive academic training in computer science, with an emphasis on artificial intelligence, environmental informatics, and machine learning. His formal education laid a robust foundation for interdisciplinary research, fostering his ability to integrate computational intelligence into atmospheric studies. Through his academic trajectory, he engaged deeply with subjects such as lidar signal processing, pattern recognition, and digital modeling, positioning him to contribute to forward-looking research initiatives. His education also included exposure to simulation environments and optimization techniques, which would later become essential tools in his scholarly work.

Experience

Throughout his research career, Hao Chen has taken part in several pioneering projects, most notably as a principal investigator in a grant-funded initiative supported by the National Natural Science Foundation of China. This project, spanning from 2019 to 2021, focused on exploring refined inversion methods for aerosol optical parameters based on single-wavelength Mie-scattering lidar systems. His contributions extend beyond project leadership into the development of machine learning pipelines and ontological frameworks that improve data interpretation and decision-making processes in atmospheric research and agriculture. Additionally, his involvement in system design and algorithm refinement reflects a deep commitment to practical application and technological advancement.

Research Interest

Hao Chen’s research interests lie at the confluence of machine learning, environmental modeling, and intelligent system design. He is particularly fascinated by the application of artificial intelligence techniques to lidar data processing, digital twin development, and agricultural automation. His work often addresses the challenges of interpreting complex signal patterns, semantic data representation, and improving real-time system responsiveness. With a strong emphasis on both theoretical underpinnings and field-level implementation, his investigations are geared toward enhancing the accuracy and robustness of prediction models across diverse domains such as atmospheric science and precision agriculture.

Award

Hao Chen has been recognized through competitive national funding for his innovative research directions. One of his notable achievements includes receiving support from the National Natural Science Foundation of China for his project on aerosol optical parameters using lidar signal interpretation. This award underscores his credibility and capacity to lead high-impact research with both scientific and societal relevance. His role as a trusted contributor in computational modeling has earned him esteem in academic circles and among professional associations, such as the China Computer Federation.

Publication

Hao Chen has published several notable articles that reflect his interdisciplinary expertise and innovative approach. In 2025, his article titled “BORF: A Bayesian Optimized Random Forest for Prediction of Aerosol Extinction Coefficient from Mie Lidar Signal” was published in Applied Soft Computing, offering a novel integration of Bayesian optimization with ensemble learning (cited by 12 articles). Also in 2025, he co-authored “A Robust YOLOv5 Model with SE Attention and BIFPN for Jishan Jujube Detection in Complex Agricultural Environments”, published in Agriculture, enhancing object detection capabilities in real-time agricultural scenarios (cited by 9 articles). His 2023 work “Design and Application of Logical Range Framework Based on Digital Twin”, published in Applied Sciences, presented a scalable architecture for virtual-to-physical data interaction (cited by 6 articles). Earlier, in 2017, his article “Atmospheric Lidar Data Storage Model Based on Ontology” appeared in Scientific Programming, establishing a semantic foundation for lidar data storage and retrieval (cited by 14 articles). These publications collectively demonstrate his consistent contributions to advancing applied AI and data modeling.

Conclusion

Hao Chen’s academic and professional journey reveals a researcher driven by a clear vision: to bridge artificial intelligence with real-world environmental and agricultural challenges. His work is characterized by methodological rigor, innovation, and a deep appreciation for interdisciplinary applications. Whether enhancing prediction accuracy for atmospheric particles or deploying intelligent detection in agriculture, he consistently demonstrates the potential of computational methods to solve pressing global issues. With a strong track record of impactful publications and funded research, Hao Chen continues to be a valuable contributor to the global scientific community, shaping the future of intelligent systems and environmental analytics.