Jihyun Kim | Data-Driven Decision Making | Research Excellence Award

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Ms. Jihyun Kim | Data-Driven Decision Making | Research Excellence Award

Professor | University of Seoul | South Korea

Ms. Jihyun Kim is a researcher in Transportation Engineering with a focus on data-driven analysis of traffic systems and emerging mobility technologies. Her research explores traveler behavior, safety, and operational performance using advanced statistical modeling and simulation-based approaches. She has conducted studies on e-scooter operations on sidewalks using VR simulators to evaluate safety and applicability under realistic conditions. Her work also includes the development of intersection- and roundabout-specific gap acceptance models, incorporating environmental factors such as rainfall. Through her research, she contributes evidence-based insights to support safer, smarter, and more efficient urban transportation systems.

Research Metrics (Google Scholar)

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Featured Publications

Mahendra Gaikwad | Machine Learning | Best Researcher Award

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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.

Mr. Serhii Savin | Data Science | Data Science Excellence Award

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Mr. Serhii Savin | Data Science | Data Science Excellence Award 

Accomplished Data Scientist | Lyft | Poland

Mr. Serhii Savin is an accomplished data scientist specializing in artificial intelligence, machine learning, econometrics, and geospatial analytics, with extensive experience developing predictive and optimization models for real-world applications in transportation, finance, and technology. Mr. Savin holds a Master of Arts in Economics with a concentration in Business and Financial Economics from the Kyiv School of Economics in affiliation with the University of Houston, where he graduated with distinction and received a full merit scholarship for ranking in the top one percent of applicants. His academic foundation in data science, finance, and quantitative modeling serves as the cornerstone for his applied research and professional achievements. Mr. Savin’s professional experience spans global technology leaders, including Lyft (United States), Reface (Ukraine), Appflame (Genesis), and Civitta, where he has demonstrated excellence in data-driven decision-making, artificial intelligence deployment, and model optimization. At Lyft, he has developed advanced geospatial route optimization and time prediction models that significantly enhanced operational efficiency and reduced financial discrepancies, contributing to multi-million-dollar savings annually. His earlier tenure at Reface involved creating recommendation systems for intelligent user engagement, while his contributions at Appflame focused on optimizing revenue-generating analytics for streaming platforms and designing A/B testing frameworks to improve product performance. His consulting experience at Civitta strengthened his capabilities in market forecasting, financial modeling, and quantitative research, contributing to multiple innovation and grant projects funded by USAID. Mr. Savin’s research interests encompass predictive analytics, AI-driven forecasting, experimental design, and human-centered data science, integrating these disciplines to drive efficiency, fairness, and transparency in algorithmic systems. His technical expertise includes proficiency in Python, PySpark, SQL, R, Tableau, and Power BI, with strong grounding in supervised and unsupervised learning, A/B experimentation, and econometric analysis. He has completed advanced training programs such as the MIT MicroMasters in Statistics and Data Science and holds certifications in Machine Learning and Data Analysis from globally recognized platforms. Mr. Savin has received numerous honors, including a full merit academic scholarship from the Ampersand.Foundation, finalist recognition in McKinsey Business Diving (top one percent teams), and multiple national Olympiad awards in economics and mathematics.

Profile: Orcid

Featured Publications

  • Savin, S. (2023). Impact of Experts’ Forecast on UAH/USD Exchange Rate Volatility. KSE Working Paper Series, 12(3), 45–59. Citations: 18

 

Dr. Santosh Jagtap | AI and ML | Microsoft AI Award

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Dr. Santosh Jagtap | AI and ML | Microsoft AI Award

Assistant Professor, Prof. Ramkrishna More Arts, Commerce & Science College, India

Dr. Santosh Jagtap, Assistant Professor at Prof. Ramkrishna More College (Autonomous), is a highly accomplished researcher and academic in the fields of Artificial Intelligence (AI) and Cybersecurity, with extensive expertise in applying AI to smart agriculture, healthcare security, IoT-enabled educational systems, and AI-driven safety solutions. Dr. Jagtap holds advanced academic qualifications and has developed a distinguished research profile that emphasizes practical applications of emerging technologies to address societal challenges. His work integrates machine learning, blockchain, IoT, and real-time data processing, producing innovative solutions in areas such as intelligent irrigation systems, plant disease detection, AI-based emotion recognition for safety alerts, and secure healthcare frameworks. Over his career, Dr. Jagtap has contributed significantly to international research projects and collaborative studies, producing high-impact publications in reputed journals and conference proceedings, such as Materials Today: Proceedings, international conferences on electronics, computing, and applied AI. He has also been recognized for innovation through patent awards, notably for AI-based plant disease identification systems, reflecting his focus on technology transfer and real-world impact. Dr. Jagtap has played an active role in mentoring students, guiding research projects, and participating in professional networks that foster academic and technological growth. He has demonstrated a consistent record of research excellence, with a total of 78 citations across 4 Scopus-indexed publications and an h-index of 3, reflecting the growing impact of his work.

Profile: GOOGLE SCHOLAR | SCOPUS

Featured Publications

  • Jagtap, S. T., Phasinam, K., Kassanu. (2022). Towards application of various machine learning techniques in agriculture. Materials Today: Proceedings, 51, 793–797. 70 citations.

  • Jagtap, S. T., Thakar, (2021). A framework for secure healthcare system using blockchain and smart contracts. Second International Conference on Electronics and Sustainable Technologies. 22 citations.

  • Jagtap, S. T., Jagdale, K. C., & Thakar, C. M. (2023). Identification of plant disease device using artificial intelligence. IN Patent 391523-001. –

  • Pratiksha Bhise, D. S. J., & Jagtap, S. T. (2024). AI-driven emergency response system for women’s safety using real-time location and heart rate monitoring. IJRPR. –

  • Keskar,  A., Jagtap, S. T., et al. (2021). Big data preprocessing frameworks: Tools and techniques. Design Engineering, 1738–1746.

Mr. Sonjoy Ranjon Das | Computer Vision | AI & Machine Learning Award

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Mr. Sonjoy Ranjon Das | Computer Vision | AI & Machine Learning Award

Lecturer,  Global Banking School, United Kingdom

Mr. Sonjoy Ranjon Das (FHEA, MIEEE, MBCS) is a Lecturer in Computing at the Global Banking School, UK, PhD Candidate in Computer Science at London Metropolitan University, and an affiliated researcher with the AI & Data Science Research Group at London Metropolitan University. He is an emerging academic with expertise in artificial intelligence, soft biometrics, cybersecurity, and privacy-preserving surveillance frameworks aligned with ethical AI deployment and GDPR compliance. Mr. Sonjoy Ranjon Das earned his MSc in Cyber Security Technology with Distinction from Northumbria University, UK, following an MBA in Management Information Systems and a BSc (Hons) in Computer Science from Leading University, Bangladesh, which provided him with an integrated background in computing, management information systems, and advanced security practices. Professionally, he has served in diverse higher-education lecturing roles across the UK including Elizabeth School of London, New City College, Shipley College, and other institutions, as well as holding the position of Research Associate on the SoftMatrix and Surveillance (SMS) Project at Northumbria University, contributing to cross-disciplinary and international research. Mr. Sonjoy Ranjon Das’s research interests include privacy-preserving multimodal soft biometrics for identity verification, AI-driven covert surveillance, ethical and GDPR-compliant surveillance technologies, and the fusion of biometrics for crowd analytics in public safety and border security. His research skills encompass advanced machine learning and computer vision techniques, data analytics, Python and Java programming, cloud-IoT integration, and full-stack development, supported by proficiency in data visualization tools such as Power BI, Tableau, and MATLAB.

Profile :  GOOGLE SCHOLAR

Featured Publications

  • Das, S. R., Kruti, A., Devkota, R., & Sulaiman, R. B. (2023). Evaluation of machine learning models for credit card fraud detection: A comparative analysis of algorithmic performance and their efficacy. FMDB Transactions on Sustainable Technoprise Letters. 12 citations.

  • Thinesh, M. A., Varmann, S. S., Sharmila, S. L., & Das, S. R. (2023). Detection of credit card fraud using random forest classification model. FMDB Transactions on Sustainable Technologies Letters. 9 citations.

  • Pranav, R. P., Prawin, R. P., Subhashni, R., & Das, S. R. (2023). Enhancing remote sensing with advanced convolutional neural networks: A comprehensive study on advanced sensor design for image analysis and object detection. FMDB Transactions on Sustainable Computer Letters. 8 citations.

  • Das, S. R., Hassan, B., Patel, P., & Yasin, A. (2024). Global soft biometrics in surveillance: Benchmark analysis, open challenges, and recommendations. Multimedia Tools and Applications. 6 citations.

Assoc. Prof. Dr. Nana Yaw Asabere | Big Data | Best Researcher Award

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Assoc. Prof. Dr. Nana Yaw Asabere | Big Data | Best Researcher Award 

Assoc. Prof. Dr. Nana Yaw Asabere | Accra Technical University | Ghana

Assoc. Prof. Dr. Nana Yaw Asabere is a distinguished Associate Professor of Computer Science and currently serves as the Dean of the Faculty of Applied Sciences at Accra Technical University, Ghana. With a career spanning nearly two decades, he has established himself as a leading scholar, researcher, and academic leader in the fields of computer science, information and communication technology, and artificial intelligence. His expertise lies in teaching, supervising research, advancing innovative methodologies, and contributing impactful scholarship to the global academic community. Recognized both locally and internationally, Prof. Asabere has played a pivotal role in shaping academic excellence, research visibility, and technological advancement in Ghana and beyond.

Professional Profile

SCOPUS

GOOGLESCHOLAR

ORCID

Summary of Suitability

Assoc. Prof. Dr. Nana Yaw Asabere  is a highly accomplished researcher and academic leader in the field of Computer Science, ICT, and IT, with significant contributions to teaching, research, innovation, and academic leadership. His strong academic background (B.Sc., M.Sc., Ph.D.) is complemented by international training and recognition, including a Chinese Government Scholarship for his Ph.D., where he developed and evaluated novel algorithms to address complex challenges in socially-aware recommendation systems.

Education

Assoc. Prof. Dr. Nana Yaw Asabere educational journey demonstrates a solid foundation and progressive specialization in computer science and ICT. He completed a Bachelor of Science in Computer Science at the Kwame Nkrumah University of Science and Technology in Ghana, followed by a Master of Science in Information and Communication Technologies at Aalborg University, Denmark. He was later awarded a prestigious scholarship from the Chinese Government through the Chinese Scholarship Council to pursue his Doctor of Philosophy in Computer Science at Dalian University of Technology, China. His doctoral work significantly advanced socially-aware recommendation systems for smart conferences, where he designed and evaluated multiple algorithms addressing complex computational challenges. This robust academic training has underpinned his innovative contributions to teaching and research.

Experience

With more than eighteen years of teaching and research experience, Assoc. Prof. Dr. Nana Yaw Asabere has contributed substantially to both undergraduate and postgraduate education. He has held several leadership positions at Accra Technical University, including Head of the Department of Computer Science, Director of the Directorate of Research, Innovation, Publication and Technology Transfer, and Coordinator for Non-Tertiary and Professional Programmes. His academic leadership spans over six years, during which he has fostered innovation, research visibility, and institutional development. Beyond administration, he remains actively engaged in curriculum design, research mentorship, and the dissemination of knowledge through lectures, conferences, and international collaborations.

Research Interests

Assoc. Prof. Dr. Nana Yaw Asabere research focuses on cutting-edge areas in computer science, including software engineering, artificial intelligence, big data analytics, social recommender systems, data science, and ICT integration in education. His scholarly work has combined theoretical depth with practical applications, particularly in advancing recommendation systems for smart environments and applying AI in educational technologies such as e-learning and m-learning. He has authored and co-authored numerous high-impact journal articles and conference papers, many of which have been indexed in globally recognized databases such as Web of Science and Scopus. His contributions continue to shape emerging discussions in intelligent systems and their applications in education and society.

Awards

Assoc. Prof. Dr. Nana Yaw Asabere has received multiple recognitions for his innovative research and impactful contributions. His work on socially-aware recommendation algorithms earned him a Best Paper Award at a leading IEEE international conference on ubiquitous intelligence and computing. He has also received another Best Paper Award at a major IEEE international conference on adaptive science and technology. In addition to these honors, his research visibility, editorial contributions, and active involvement as a peer reviewer for top-tier journals and conferences reflect his standing as an influential researcher within the global academic community.

Publication Top Notes

An integrated multi-scale context-aware network for efficient desnowing

Improving Counseling Sessions Through an Interactive Web-Based Application in the Context of Higher Education

Acceptability and Feasibility of a Pilot Multifamily Group Intervention for Fostering Positive Racial Identity

Nighttime Object Detection with Denoising Diffusion-Probabilistic Models

Conclusion

Assoc. Prof. Dr. Nana Yaw Asabere embodies the qualities of an outstanding researcher, educator, and leader in computer science and ICT. His contributions extend beyond academic publications to institutional leadership, mentoring, and advancing technological innovation in education. With significant citations, impactful research, international recognition, and demonstrated excellence in teaching and leadership, he is a strong candidate for recognition through a Best Researcher Award. His work continues to inspire young scholars, advance computational sciences, and promote the integration of technology for societal benefit.

 

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

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.

Yonghong Song | Deep Learning | Best Researcher Award

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Prof. Yonghong Song | Deep Learning | Best Researcher Award

Professor at Xi’an Jiaotong University, China

Professor Song Yonghong is a distinguished academic and researcher at the School of Software Engineering, Xi’an Jiaotong University. As a recognized IEEE member and an active participant in several professional societies including the China Society of Image and Graphics (CSIG) and the China Computer Federation (CCF), she has significantly contributed to advancing the fields of computer vision and intelligent systems. She is also a certified Project Management Professional (PMP) by the American Project Management Institute, combining her academic insight with applied project management expertise. Her contributions to the field include a prolific output of over 100 high-quality publications and more than 20 authorized invention patents, which reflect her sustained impact in theoretical and applied research.

Profile

Scopus

Education

Professor Song’s educational background reflects a strong foundation in computer science and engineering. She pursued rigorous academic training in computer vision, pattern recognition, and artificial intelligence, which laid the groundwork for her subsequent contributions to academia and industry. Her academic preparation, combined with interdisciplinary training, equipped her to approach complex problems with a balance of theoretical depth and practical applicability. This educational trajectory enabled her to engage in and lead high-impact research projects both nationally and internationally, and to cultivate a strong research team within her institution.

Experience

Throughout her career, Professor Song has demonstrated consistent leadership in cutting-edge research and technological development. She has taken the lead on numerous international collaboration projects, national key R&D initiatives, and enterprise partnerships. Her work extends deeply into the real-world challenges associated with object detection and recognition in images and video, providing actionable insights and technological innovations for enterprises. In these roles, she has not only pushed forward the boundaries of academic research but has also ensured that the outcomes are translated into scalable, industry-grade solutions. Her experience spans applications such as intelligent copiers, automated steel surface inspection, and smart appliance systems, showcasing her commitment to cross-disciplinary impact and societal benefit.

Research Interests

Professor Song’s research interests primarily focus on computer vision, pattern recognition, and intelligent systems. She is particularly passionate about designing and refining methodologies for object detection and recognition, especially in real-time industrial environments. Her research addresses complex visual processing problems and develops intelligent solutions that are responsive to the demands of modern industrial applications. She has worked extensively on integrating deep learning algorithms into visual systems for improved performance and automation. Her work is characterized by a high degree of innovation, especially in translating theoretical frameworks into deployable systems.

Awards

Professor Song has been recognized for her excellence through several prestigious awards and honors. While many of her accolades are project-specific and rooted in collaborative successes, her standout achievement includes the development of the “Hot High-Speed Wire Surface Defect Online Detection System,” which was successfully implemented at Baoshan Iron and Steel Co., LTD. This system has proven to be stable, efficient, and internationally competitive in automating quality inspections. The industrial relevance and global recognition of this project exemplify the strength of her applied research. She has also received commendations for leadership in engineering practice and for promoting the industrialization of academic research outputs.

Publications

Professor Song has published over 100 articles in high-impact journals and conferences, with a focus on visual computing and intelligent systems. Selected publications include:

Song Y. et al., “Multi-Scale Feature Fusion for Surface Defect Detection,” IEEE Transactions on Industrial Informatics, 2021 – cited by 56 articles.

Song Y. et al., “Real-Time Target Detection in Complex Industrial Environments,” Pattern Recognition Letters, 2020 – cited by 47 articles.

Song Y. et al., “Deep Learning-based Anomaly Detection in Steel Production,” Journal of Visual Communication and Image Representation, 2019 – cited by 62 articles.

Song Y. et al., “Intelligent Vision System for Smart Appliances,” Sensors, 2022 – cited by 33 articles.

Song Y. et al., “CNN Architectures for Surface Quality Analysis,” Computer Vision and Image Understanding, 2020 – cited by 45 articles.

Song Y. et al., “Efficient Video Object Recognition using Hybrid Networks,” Neurocomputing, 2018 – cited by 50 articles.

Song Y. et al., “Robust Industrial Vision with Deep Supervision,” Machine Vision and Applications, 2021 – cited by 38 articles.

Conclusion

In summary, Professor Song Yonghong exemplifies the integration of academic excellence with industrial relevance. Her work in computer vision and intelligent systems is not only scientifically rigorous but also deeply practical, influencing both research and real-world systems. Her leadership in national and international collaborations, along with her commitment to solving critical industrial challenges, places her at the forefront of applied visual computing research. With an extensive portfolio of publications, patents, and successful enterprise collaborations, Professor Song continues to push the envelope in making intelligent technologies smarter, more robust, and more responsive to contemporary demands.

Farhat Nasim | Artificial Intelligence | Best Researcher Award

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Ms. Farhat Nasim | Artificial Intelligence | Best Researcher Award

ASSISTANT PROFESSOR GUEST at Jamia Millia Islamia, India

Ms. Farhat Nasim is a dedicated academician and researcher in the field of Control Systems and Instrumentation. With a keen interest in power system optimization and intelligent control methodologies, she has made significant contributions to the development of control strategies for wind power systems. Currently pursuing her Ph.D. at Jamia Millia Islamia, she focuses on designing and implementing intelligent controllers for wind power applications. Her research is driven by a commitment to advancing sustainable energy solutions through novel control techniques. Alongside her research, she serves as an Assistant Professor (Guest Basis) at Jamia Millia Islamia, where she teaches various electrical engineering subjects and undertakes additional academic responsibilities.

Profile

Scopus

Education

Ms. Farhat Nasim’s academic journey is marked by excellence in the field of electrical engineering and control systems. She is currently a Ph.D. candidate in Control Systems and Instrumentation at Jamia Millia Islamia, Central University, Delhi, with a dissertation titled “Design and Implementations of Intelligent Controllers for Wind Power System.” Prior to her doctoral studies, she earned her Master of Technology (M.Tech) in Control and Instrumentation from the same institution, further strengthening her expertise in control methodologies. She also holds a Bachelor of Technology (B.Tech) in Electrical Engineering from Jamia Millia Islamia, where she built a strong foundation in electrical power systems and control engineering.

Professional Experience

Ms. Nasim is currently an Assistant Professor (Guest Basis) at Jamia Millia Islamia, where she teaches a range of subjects, including Electrical Power Generation, Basics of Electrical Engineering, DC and Synchronous Machines, Control Systems, and Advanced Control Systems. Her commitment to academic excellence extends beyond teaching, as she actively engages in administrative and organizational responsibilities. She has served as the Coordinator for the 6th Semester B.Tech students’ Industrial Visit at Losung Automation Pvt. Ltd., Associate Editor for the Departmental Magazine, Co-convener for the Workshop on Syllabus Revision of the B.Tech (EE) program, and Attendance Compiling In-Charge for all B.Tech semesters. Additionally, she has contributed significantly to laboratory coordination, including managing the Control System Lab and Project Lab for NBA accreditation.

Research Interests

Ms. Nasim’s research interests lie at the intersection of power system optimization, intelligent control, and renewable energy integration. Her primary focus is on the design and implementation of advanced control strategies for wind energy systems, particularly Double-Fed Induction Generators (DFIG). She has worked extensively on hybrid ANFIS-PI-based optimization techniques to enhance power conversion efficiency in wind turbines. Her research also explores Ziegler-Nichols-based controller optimization and crowbar protection mechanisms for DFIG systems. Through her work, she aims to develop more efficient and robust control solutions that contribute to the reliability and sustainability of renewable energy sources.

Awards and Achievements

Ms. Nasim has received recognition for her contributions to research and academia. She has successfully published her work in high-impact journals and presented her findings at reputed international conferences. Her role in academic coordination and syllabus revision has been instrumental in improving the curriculum for electrical engineering students at Jamia Millia Islamia. Her dedication to mentoring students and enhancing laboratory infrastructure has further solidified her reputation as a committed educator and researcher.

Publications

Nasim, F., Khatoon, S., Ibraheem, Urooj, S., Shahid, M., Ali, A., & Nasser, N. (2025). Hybrid ANFIS-PI-Based Optimization for Improved Power Conversion in DFIG Wind Turbine. Sustainability, 17(6), 2454. https://doi.org/10.3390/su17062454 (SCI)

Nasim, F., Khatoon, S., Shahid, M., Baranwal, S., & Ahmad Wani, S. (2024). Ziegler-Nichols Based Controller Optimization for DFIG Wind Turbines. Tuijin Jishu/Journal of Propulsion Technology, 45(2). https://doi.org/10.52783/tjjpt.v45.i02.6966 (SCOPUS)

Nasim, F., et al. (2022). Effect of PI Controller on Power Generation in Double-Fed Induction Machine. 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), IEEE. doi: 10.1109/ICAC3N56670.2022.10074573.

Nasim, F., et al. (2024). Implementation of Crowbar Protection in DFIG. Advances in AI for Biomedical Instrumentation, Electronics and Computing, CRC Press. (Taylor and Francis Conference)

Nasim, F., et al. (2023). Field Control Grid Connected DFIG Turbine System. International Conference on Power, Instrumentation, Energy and Control (PIECON), IEEE. doi: 10.1109/PIECON56912.2023.10085726.

Conclusion

Ms. Farhat Nasim’s dedication to research and education reflects her commitment to advancing knowledge in control systems and renewable energy. Her work in optimizing wind power systems through intelligent control strategies has significant implications for sustainable energy solutions. As an educator, she continues to inspire and mentor students, ensuring that future engineers are equipped with the skills and knowledge necessary to address contemporary challenges in electrical engineering. With her strong academic background, research contributions, and teaching excellence, Ms. Nasim remains a key contributor to the field of control systems and instrumentation.

Tushar Kafare | Artificial Intelligence | Best Researcher Award

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Dr. Tushar Kafare | Artificial Intelligence | Best Researcher Award

Assistant Professor at Sinhgad College of Engineering, India

Dr. Tushar Vaman Kafare is an Assistant Professor in the Department of Electronics and Telecommunication (E&TC) at the Sinhgad Technical Education Society (STES). With over 14 years of experience in teaching, he has made a significant impact in the field of Electronics and Telecommunication. His research and expertise span across machine learning, deep learning, computer vision, embedded systems, and various programming languages like Python, MATLAB, C, and Embedded C. Dr. Kafare is known for his dedication to teaching and research, having guided numerous student projects and published research work, focusing particularly on machine learning applications in plant disease analysis.

Profile

Google Scholar

Education

Dr. Kafare holds an M.E. degree in Electronics and Telecommunication, as well as a B.E. in Electronics. His strong academic background has been further reinforced by his ranking 6th in his graduation. His academic qualifications, combined with extensive practical and theoretical knowledge, make him a highly skilled educator and researcher. His ongoing Ph.D. research focuses on plant disease analysis using machine learning models, showcasing his commitment to advancing technological applications in agriculture.

Experience

Having joined STES on September 7, 2022, Dr. Kafare brings with him a wealth of experience in academia and industry. His teaching career spans over 14 years, during which he has mentored undergraduate and postgraduate students. He has contributed significantly to course development and the enhancement of educational experiences for students, incorporating advanced techniques in machine learning and embedded systems. Additionally, Dr. Kafare has served as a resource person for numerous workshops and faculty development programs, further demonstrating his expertise and commitment to professional growth.

Research Interests

Dr. Kafare’s primary research interest lies in the application of machine learning and image processing for agricultural advancements. His Ph.D. research focuses on using machine learning models to analyze plant diseases, particularly in grape and apple plants, through advanced image processing techniques. He is also interested in deep learning, computer vision, and embedded systems, areas that allow for the development of innovative solutions for real-world problems. Through his research, he aims to contribute to the growing field of agri-tech by leveraging modern computational techniques to assist in plant disease diagnostics and management.

Awards

Dr. Kafare has been recognized for his outstanding contributions in teaching and research. He received the prestigious Digital Teacher Award from ICT Academy, highlighting his exceptional use of technology in education. Additionally, his academic excellence is reflected in his university ranking, securing 6th place in his graduation. In 2024, he was honored with the Best Paper Award at the International Conference on Machine Learning in Jaipur, India, acknowledging the high impact and relevance of his research in the machine learning community.

Publications

Dr. Kafare has made significant contributions to the field of machine learning and telecommunication through his publications. His work has been widely cited, demonstrating the importance of his research. Below is a list of selected publications:

Kafare, T.V. et al., “Analysis on Plant Disease Diagnosis Using Convolution Neural Networks,” International Journal of Machine Learning, 2023, Scopus/SCI.

Kafare, T.V. et al., “Segmentation Techniques for Plant Disease Detection,” Journal of Image Processing, 2022, Scopus.

Kafare, T.V., “Double Convolution in CNN for Improved Plant Disease Classification,” International Conference on Machine Learning, 2024, Conference paper.

Kafare, T.V., et al., “Fungal Disease Detection in Grapes Using Machine Learning,” Journal of Agricultural Technology, 2021, Scopus.

Conclusion

Dr. Tushar Vaman Kafare’s career is marked by his dedication to both teaching and research, with a clear focus on applying machine learning and image processing to solve practical problems in agriculture. With over 14 years of teaching experience, he has proven himself as a skilled educator and researcher. His ongoing Ph.D. research, along with his numerous publications and awards, highlights his expertise in his field. As an active participant in academic and professional activities, he continues to contribute to the development of students and the academic community at large, particularly in the domains of machine learning and embedded systems.