Mahendra Gaikwad | Machine Learning | Best Researcher Award

Dr. Mahendra Gaikwad | Machine Learning | Best Researcher Award

Assistant Professor at Veermata Jijabai Technological Institute (VJTI) | Mumbai | India

Dr. Mahendra Uttam Gaikwad is a forward-thinking mechanical and manufacturing engineering professional whose work reflects a deep commitment to advancing modern machining, smart materials research, sustainable manufacturing, and AI-driven optimization in industrial systems. Renowned for his ability to bridge theoretical innovation with practical engineering applications, he has built a strong scholarly footprint through impactful publications in SCI and Scopus-indexed journals, contributions to influential book chapters, and editorial leadership in notable international volumes focused on advanced materials and digital-age manufacturing. His research explores critical themes such as electrical discharge machining, surface integrity analysis, optimization algorithms, additive manufacturing, fatigue modelling, and machine learning applications in production environments, consistently demonstrating an aptitude for tackling complex engineering challenges through empirical investigation and computational modelling. In addition to his academic contributions, he has shown commendable innovation through multiple national and international patents addressing smart systems, sustainable material utilization, and intelligent manufacturing solutions. He has also been an active collaborator with academic institutions, research groups, and industry partners, contributing to advancements in machining automation, performance benchmarking, and data-driven design methodologies. A dedicated mentor, he has guided numerous undergraduate and postgraduate research projects, fostering a research-oriented learning environment and supporting the next generation of engineers. His work as a reviewer, conference contributor, and knowledge disseminator further underscores his commitment to strengthening global engineering discourse. Known for his leadership qualities, professional integrity, and continuous pursuit of technological excellence, Dr. Gaikwad has earned recognition for his contributions to teaching and research, positioning himself as a noteworthy contributor to the evolving landscape of smart and sustainable manufacturing.

Profiles: ORCID | Google Scholar

Featured Publications

Gaikwad, M. U., Somatkar, A. A., Ghadge, M., Majumder, H., Shinde, A. M., & Lohakare, A. V. (2025). Effect of dry and wet machining environments on surface quality of Al6061 using particle swarm optimization (PSO).

Sargar, T., Gautam, N. K., Jadhav, A., & Gaikwad, M. U. (2025). A comparative investigation of kerf width during CO₂ and fiber laser machining of SS 316L material.

Khan, M. A. J., Pohekar, S. D., Bagade, P. M., Gaikwad, M. U., & Singh, M. (2025). CFD analysis of NACA 4415 marine propeller ducts for managing flow separation.

Nishandar, S. V., Pise, A. T., Bagade, P. M., Gaikwad, M. U., & Singh, A. (2025). Computational modelling and analysis of heat transfer enhancement in straight circular pipe with pulsating flow.

Gaikwad, M. U., Gaikwad, P. U., Ambhore, N., Sharma, A., & Bhosale, S. S. (2025). Powder bed additive manufacturing using machine learning algorithms for multidisciplinary applications: A review and outlook.

Gabriel Osei Forkuo | Machine Learning | Best Researcher Award

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

Orcid

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.