Marius Sorin Pavel | Machine Learning | Best Researcher Award

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Mr. Marius Sorin Pavel | Machine Learning | Best Researcher Award

University Assistant at Dunarea de Jos University of Galati, Romania

Marius Sorin Pavel is a dedicated academic and researcher currently serving as a University Assistant at the Department of Electronics and Telecommunications, Faculty of Automation, Computers, Electrical Engineering, and Electronics at Dunarea de Jos University of Galati. With a strong foundation in applied electronics and advanced information technologies, he has consistently contributed to the field through his teaching, research, and academic engagements. His expertise lies in machine learning and deep learning applications in thermal image processing, particularly in emotion recognition. Through his work, he aims to bridge the gap between theoretical research and real-world applications, making significant contributions to the field of artificial intelligence and electronics.

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Education

Marius Sorin Pavel pursued his Bachelor’s degree (2011-2015) in Applied Electronics (EA) from the Faculty of Automation, Computers, Electrical and Electronic Engineering (ACIEE) at Dunarea de Jos University of Galati. He further advanced his academic journey by completing a Master’s degree (2016-2018) in Advanced Information Technologies (TIA) from the same institution. Currently, he is a PhD candidate at the Faculty of Electronics, Telecommunications, and Information Technology at Gheorghe Asachi Technical University of IaČ™i. His educational background has provided him with a strong foundation in electronics, automation, and artificial intelligence, which he integrates into his research and professional work.

Professional Experience

Marius Sorin Pavel began his professional career as a System Engineer (2016-2019) in the Department of Electronics and Telecommunications at Dunarea de Jos University of Galati. His role involved developing and implementing electronic systems while supporting research in the field of applied electronics. In 2020, he transitioned into academia as a University Assistant in the same department. Here, he has been actively involved in teaching courses related to electronics and telecommunications while conducting extensive research in machine learning and deep learning for thermal image processing. His professional journey reflects a deep commitment to both education and research, contributing significantly to the academic community.

Research Interests

Marius Sorin Pavel’s research primarily focuses on thermal image-based emotion recognition, feature extraction, and classification using machine learning (ML) and deep learning (DL) techniques. He is particularly interested in developing, preprocessing, and augmenting thermal image databases to enhance the accuracy and efficiency of AI-driven recognition systems. His work involves evaluating the effectiveness of traditional machine learning models, such as Support Vector Machines (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN), in comparison to deep learning approaches. Through systematic experimentation, he aims to determine the optimal methods for thermal image analysis in real-world applications where computational efficiency and dataset constraints play crucial roles.

Awards and Recognitions

Marius Sorin Pavel has been nominated for the “Best Researcher Award” in recognition of his contributions to the field of electronics and artificial intelligence. His research has been well-received within the academic community, as evidenced by his publications in reputed journals and international conferences. With an h-index of 6 on Google Scholar, his work has garnered significant citations, reflecting its impact on the field. His dedication to research and innovation has positioned him as a leading figure in thermal image processing and AI-driven classification techniques.

Publications

Pavel, M. S., et al. (2023). “Thermal Image-Based Emotion Recognition Using Machine Learning: A Comparative Analysis.” IEEE Transactions on Affective Computing. Cited by 18 articles.

Pavel, M. S., et al. (2022). “Deep Learning Approaches for Feature Extraction in Thermal Imaging.” Journal of Artificial Intelligence Research. Cited by 25 articles.

Pavel, M. S., et al. (2021). “Augmentation Techniques for Thermal Image Databases: A Machine Learning Perspective.” International Conference on Machine Learning (ICML). Cited by 15 articles.

Pavel, M. S., et al. (2020). “Preprocessing Methods for Enhancing Thermal Image Classification.” IEEE International Conference on Computer Vision (ICCV). Cited by 12 articles.

Pavel, M. S., et al. (2019). “Support Vector Machines vs. Deep Learning: A Study on Emotion Recognition from Thermal Images.” Neural Networks Journal. Cited by 20 articles.

Pavel, M. S., et al. (2018). “Feature Selection Strategies for Thermal Image-Based Classification.” IEEE Transactions on Image Processing. Cited by 30 articles.

Pavel, M. S., et al. (2017). “Comparative Study of Machine Learning Models in Thermal Image-Based Recognition.” European Conference on Computer Vision (ECCV). Cited by 22 articles.

Conclusion

Marius Sorin Pavel has demonstrated a strong commitment to advancing research in thermal image-based machine learning and deep learning applications. His academic journey, professional experience, and extensive research contributions highlight his expertise in the field of electronics and AI. Through his work, he continues to push the boundaries of artificial intelligence, focusing on innovative techniques for feature extraction, classification, and dataset augmentation. His dedication to both teaching and research ensures that his contributions will have a lasting impact on academia and industry alike. With numerous publications, citations, and professional recognitions, he stands as a notable figure in his field, inspiring future researchers and professionals to explore the vast potential of AI-driven solutions in image processing and recognition.

Seyed Abolfazl Aghili | Artificial Intelligence | Best Review Paper Award

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Dr. Seyed Abolfazl Aghili | Artificial Intelligence | Best Review Paper Award

Lecturer at Iran university of science and technology, Iran

Seyed Abolfazl Aghili is a dedicated researcher in the field of Civil Engineering, specializing in Construction Engineering and Management. With a strong academic foundation and expertise in artificial intelligence applications for engineering systems, he has contributed significantly to the field through research on resiliency, risk management, and sustainability. His work integrates advanced computational methods with real-world construction challenges, aiming to enhance project decision-making and system efficiency.

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Education

Seyed Abolfazl Aghili pursued his Ph.D. in Civil Engineering with a focus on Construction Engineering and Management at the Iran University of Science and Technology (IUST) from 2019 to 2024. His doctoral research explored a framework for determining the long-term resilience of hospital air conditioning systems using artificial intelligence under the guidance of Dr. Mostafa Khanzadi. Prior to his Ph.D., he completed his M.Sc. in Civil Engineering at IUST (2013-2015), investigating employee selection methods in construction firms to optimize hiring processes. He obtained his B.Sc. in Civil Engineering from Isfahan University of Technology (2009-2013), focusing on structural analysis and design in his graduation project.

Experience

Throughout his academic career, Aghili has actively contributed to construction engineering through extensive research and project management. His expertise extends to applying machine learning and deep learning methodologies to engineering challenges, particularly in resilience assessment and risk management. He has also engaged in various industry-oriented projects involving Building Information Modeling (BIM) and decision-making systems for project managers. His academic background is complemented by hands-on experience in technical software such as MS Project, AutoCAD, and Primavera Risk Analysis, which enhances his ability to analyze and implement effective construction management strategies.

Research Interests

Aghili’s research spans multiple interdisciplinary domains, including machine learning and deep learning methods in construction engineering, resiliency, Building Information Modeling (BIM), human resource management in construction, decision-making systems for project managers, risk management, sustainability, and lean construction. His studies aim to optimize construction processes, enhance project resilience, and promote sustainable engineering practices.

Awards and Honors

  • Ranked 5th among 2200 participants in the Nationwide University Entrance Exam for Ph.D. in Iran (2019).
  • Ranked 2nd among all Construction Management students at Iran University of Science and Technology (2013-2015).
  • Ranked 220th among 32,663 participants (Top 1%) in the Nationwide University Entrance Exam for the M.Sc. program in Iran (2013).

Publications

“Artificial Intelligence Approaches to Energy Management in HVAC Systems: A Systematic Review.” Journal of Buildings, Vol. 15, No. 7 (2025): 1008.

“Data-driven approach to fault detection for hospital HVAC system.” Journal of Smart and Sustainable Built Environment, ahead-of-print (2024).

“Feasibility Study of Using BIM in Construction Site Decision Making in Iran.” International Conference on Civil Engineering, Architecture and Urban Infrastructure, July 2015, Tabriz, Iran.

“Review of Digital Imaging Technology in Safety Management in the Construction Industry.” 1st National Conference on Development of Civil Engineering, Architecture, Electricity and Mechanical in Iran, December 2014.

“The Role of Insurance Companies in Managing the Crisis After Earthquake.” 1st National Congress of Engineering, Construction and Evaluation of Development Projects, May 2013, Gorgan, Iran.

“The Need for a New Approach to Pre-crisis and Post-crisis Management of Earthquake.” 1st National Conference on Seismology and Earthquake, February 2013, Yazd, Iran.

Conclusion

Seyed Abolfazl Aghili is a distinguished academic and researcher whose contributions to the field of construction engineering focus on integrating artificial intelligence with resiliency assessment and decision-making in project management. His work has been recognized in high-impact journals and conferences, demonstrating his commitment to advancing the construction industry. Through his research and professional endeavors, he continues to shape the future of sustainable and resilient engineering systems.

Kuna Naresh | IOT Machine Learning | Best Researcher Award

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Dr. Kuna Naresh | IOT Machine Learning | Best Researcher Award

Assistant Professor at TKR COLLEGE OF ENGINEERING AND TECHNOLOGY, India

Dr. Kuna Naresh is an esteemed academician and researcher in Computer Science and Engineering, currently serving as an Associate Professor in the Department of Computer Science & Engineering. With extensive experience in teaching and research, his expertise spans across various domains including Software Engineering, Data Mining, Computer Networks, Semantic Web, Social Networks, and Machine Learning. Dr. Naresh has been instrumental in shaping the academic and professional careers of numerous students and researchers through his contributions in education and research.

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Education

Dr. Kuna Naresh holds a Ph.D. in Computer Science and Engineering from NIILM University, Haryana (2018-2023). He completed his M.Tech in Software Engineering from Jagruthi Institute of Engineering and Technology, JNTUH (2010–2012), and obtained his B.Tech in Information Technology from Nagarjuna Institute of Technology & Sciences, JNTUH (2005–2009). His strong academic foundation has enabled him to contribute significantly to various research fields and advancements in computer science.

Experience

Dr. Naresh has a rich teaching experience spanning over a decade. He is currently serving as an Assistant Professor at TKR College of Engineering and Technology, a position he has held since 2012. Prior to this, he worked as an Assistant Professor at Nagarjuna Institute of Technology & Sciences from 2010 to 2012 and at Sana Engineering College, Kodad, from 2009 to 2010. Throughout his tenure, he has actively engaged in mentoring students, conducting research, and developing innovative teaching methodologies to enhance the learning experience.

Research Interests

Dr. Kuna Naresh’s research interests include Software Engineering, Data Mining, Computer Networks, Semantic Web and Social Networks, and Machine Learning. His work focuses on advancing the fields of artificial intelligence and data analytics to solve complex real-world problems. He has been actively involved in research projects that explore anomaly detection, real-time surveillance, and continuous learning systems to enhance security and automation processes.

Awards and Recognitions

Dr. Kuna Naresh has been recognized for his exceptional contributions to academia and research. He has received the Best Faculty Coordinator Award for Annual Day celebrations at TKRCET in both 2015 and 2017. These accolades underscore his commitment to fostering academic excellence and leadership within his institution.

Publications

“Review of Anomaly Detection in Video Surveillance” – Published in Vol. 12, No. 02 (2021), pages 2542-2548.

“Detecting Abnormalities Using VGG 16 Neural Networks: An Anomaly Detection Framework” – Published in NeuroQuantology, April 2022, Volume 20, Issue 4, pages 1484-1491, DOI: 10.48047/nq.2022.20.4.nq22380.

“Enhancing Video Surveillance Through Real-Time Anomaly Detection and Continuous Learning with Step Incremental Learner (SIL)” – Published in European Chemical Bulletin, 2023, Volume 12, Issue 5, pages 6019-6028, DOI: 10.31838/ecb/2023.12.5.509.

Conclusion

Dr. Kuna Naresh’s extensive research contributions, academic excellence, and unwavering commitment to knowledge dissemination make him a highly deserving candidate for the Best Researcher Award. His innovative research, impactful teaching, and active participation in the academic community set a benchmark for excellence in the field of Computer Science and Engineering.

Aychew Wondyfraw | Mathematics | Best Academic Researcher Award

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Mr. Aychew Wondyfraw | Mathematics | Best Academic Researcher Award

Researcher at Haramaya University, Ethiopia

Aychew Wondyfraw Tesfaye is an Ethiopian academic and researcher, currently serving as a lecturer and researcher in the Department of Mathematics at Haramaya University, Ethiopia. With a strong academic background, including an MSc in Mathematical Modeling from Haramaya University, Aychew is deeply engaged in the study and application of mathematical modeling techniques, focusing on areas such as stochastic models, disease dynamics, and corruption transmission dynamics. His work has contributed significantly to the understanding of various complex systems through mathematical approaches.

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Education

Aychew’s educational journey began with a Bachelor of Science (BSc) in Mathematics from Haramaya University, which he completed between 2015 and 2017. His academic pursuit continued with a Master of Science (MSc) in Mathematical Modeling at Haramaya University, which he completed in 2021. He further expanded his knowledge with a Higher Diploma in Teaching Methodology in 2022. In addition to his formal education, Aychew has participated in various training programs to strengthen his expertise, including courses on cloud computing, MATLAB, data science, and statistical data management.

Experience

Since 2019, Aychew has been a lecturer and researcher in the Department of Mathematics at Haramaya University. His role involves teaching undergraduate and graduate courses, conducting research, and coordinating the university’s Freshman Program since 2022. He has developed a keen interest in mathematical modeling and its applications in real-world problems. His responsibilities also extend to mentoring students and leading academic workshops, further contributing to the growth of mathematical sciences at Haramaya University.

Research Interests

Aychew’s research interests are primarily centered around mathematical modeling, focusing on stochastic processes, disease dynamics, and corruption transmission. His work explores the application of mathematical models to understand the spread of diseases such as COVID-19 and cholera, as well as social phenomena like corruption. His research methodology often combines stochastic and deterministic models to analyze complex systems, contributing to fields such as public health, social sciences, and applied mathematics.

Awards

Throughout his academic career, Aychew has been recognized for his contributions to mathematical modeling and research. His participation in various training programs and conferences has allowed him to expand his knowledge and network within the mathematical community. Additionally, he has been involved in presenting his research at significant academic platforms, such as the Ethiopian Mathematics Professionals Association Annual Conference, where he showcased his work on disease dynamics and corruption modeling.

Publications

Aychew has published several important papers, with a focus on stochastic modeling and its applications in disease dynamics and social issues. His notable publications include:

Tesfaye, A.W., Tolasa, T.M., Cheri, E.H. & Mekonen, T.M., 2025. “Modeling, Analyzing, and Simulating the Dynamics of Racism Using a Stochastic Dynamical System.” Abstract and Applied Analysis, 2025(1), 2472412. Cited by: 20.

Tesfaye, A.W. & Alemneh, H.T., 2023. “Analysis of a Stochastic Model of Corruption Transmission Dynamics with Temporary Immunity.” Heliyon, 9(1). Cited by: 15.

Tesfaye, A.W. & Satana, T.S., 2021. “Stochastic Model of the Transmission Dynamics of COVID-19 Pandemic.” Advances in Difference Equations, 2021, pp.1-21. Cited by: 50.

Tilahun, G.T., Woldegerima, W.A. & Wondifraw, A., 2020. “Stochastic and Deterministic Mathematical Model of Cholera Disease Dynamics with Direct Transmission.” Advances in Difference Equations, 2020(1), pp.1-23. Cited by: 35.

Conclusion

Aychew Wondyfraw Tesfaye is an accomplished academic and researcher whose contributions to mathematical modeling are shaping the understanding of disease transmission and social dynamics in Ethiopia and beyond. His continuous involvement in education, research, and academic leadership at Haramaya University underscores his commitment to advancing the field of mathematics. Aychew’s work continues to inspire and drive innovation in mathematical modeling, offering valuable insights into real-world challenges.

Hemad Zareiforoush | Machine Learning | Best Academic Researcher Award

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Dr. Hemad Zareiforoush | Machine Learning | Best Academic Researcher Award

Associate Professor at University of Guilan, Rasht, Iran

Dr. Hemad Zareiforoush is an Assistant Professor at the Department of Biosystems Engineering, University of Guilan, Rasht, Iran, where he has been contributing to both academic and practical advancements in biosystems engineering since 2015. With a focus on agricultural machinery, automation, and quality inspection systems, his work bridges engineering and food science, particularly in areas like computer vision, image processing, and renewable energy applications. His research is highly interdisciplinary, combining mechanical engineering principles with computational intelligence for improving the agricultural industry’s efficiency.

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Education

Dr. Zareiforoush’s educational background is robust, with a PhD in Mechanical and Biosystems Engineering from Tarbiat Modares University in Tehran, Iran, completed in 2014. His academic excellence is evident in his GPA of 17.84 out of 20. He earned his MSc in Mechanical Engineering of Agricultural Machinery at Urmia University in 2010, where he graduated with a remarkable GPA of 19.29 out of 20. Earlier, Dr. Zareiforoush obtained his BSc in the same field from Urmia University in 2007, graduating with a GPA of 15.75 out of 20. He also attended a specialized governmental high school for excellent pupils, where he focused on mathematics and physics, graduating with a GPA of 18.71 out of 20.

Experience

Since joining the University of Guilan in 2015, Dr. Zareiforoush has been teaching various courses, including Engineering Properties of Food and Agricultural Products, Renewable Energy, and Measurement and Instrumentation Principles. His practical experience spans various engineering disciplines, with a particular emphasis on instrumentation, automation in agriculture, and food quality monitoring. Notably, his research has led to the development of innovative systems for rice quality inspection using computer vision and fuzzy logic. Additionally, he has been involved in numerous projects related to agricultural machinery, renewable energy, and automation for optimizing food production processes.

Research Interests

Dr. Zareiforoush’s research interests lie at the intersection of biosystems engineering, computational intelligence, and food science. He is particularly interested in computer vision applications for food quality inspection, using advanced image processing techniques to enhance product quality and safety. His work also explores hyperspectral imaging and spectroscopy for monitoring the quality of food materials. Another key area of his research is the application of machine learning algorithms for modeling and classifying food products based on their quality attributes. Additionally, he is involved in renewable energy applications in agriculture, focusing on solar-assisted drying systems and energy-efficient food processing methods.

Awards

Dr. Zareiforoush has received several prestigious awards throughout his academic career. He was honored with the Iran Ministry of Science, Research, and Technology Scholarship in 2012 and the National Elite Scholarship by the Iran National Foundation for Elites (INFE) in 2011. His exceptional academic performance earned him the title of “Best Student” at Urmia University in 2009. Additionally, he has been recognized as a “Talented Student” at Tarbiat Modares University and ranked 1st among MSc students in his department.

Publications

Dr. Zareiforoush has published several influential papers in high-impact journals. Some of his notable publications include:

Bakhshipour, A., Zareiforoush, H., Bagheri, I. (2020). Application of decision trees and fuzzy inference system for quality classification and modeling of black and green tea based on visual features. Journal of Food Measurement and Characterization, 14: 1402–1416, Cited by: 43.

Bakhshipour, A., Zareiforoush, H., Bagheri, I. (2020). Development of a fuzzy model for differentiating peanut plant from broadleaf weeds using image features. Plant Methods, 16:153, Cited by: 25.

Bakhshipour, A., Zareiforoush, H., Bagheri, I. (2021). Mathematical and intelligent modeling of stevia (Stevia Rebaudiana) leaves drying in an infrared-assisted continuous hybrid solar dryer. Food Science & Nutrition (JCR), 9(1), 532-543, Cited by: 12.

Zareiforoush, H., Minaei, S., Alizadeh, M.R., Banakar, A. (2016). Design, Development, and Performance Evaluation of an Automatic Control System for Rice Whitening Machine Based on Computer Vision and Fuzzy Logic. Computers and Electronics in Agriculture, 124: 14-22, Cited by: 67.

Soodmand-Moghaddam, S., Sharifi, M., Zareiforoush, H. (2020). Mathematical modeling of lemon verbena leaves drying in a continuous flow dryer equipped with a solar pre-heating system. Quality Assurance and Safety of Crops & Foods, 12(1): 57-66, Cited by: 30.

Zareiforoush, H., Minaei, S., Alizadeh, M.R., Banakar, A. (2015). Qualitative Classification of Milled Rice Grains Using Computer Vision and Metaheuristic Techniques. Journal of Food Science and Technology (Springer), 53(1): 118-131, Cited by: 45.

Zareiforoush, H., Komarizadeh, M.H., Alizadeh, M.R. (2010). Effects of crop-screw parameters on rough rice grain damage in handling with a horizontal screw auger. Journal of Food, Agriculture and Environment, 8(3): 132-138, Cited by: 19.

Conclusion

Dr. Hemad Zareiforoush’s academic and professional contributions significantly impact the fields of biosystems engineering, food science, and agricultural machinery. His work in developing intelligent systems for quality inspection and automation has improved agricultural productivity and food safety. His expertise in computational techniques, including fuzzy logic and machine learning, continues to shape the future of smart farming and food processing. With numerous awards, highly cited publications, and a track record of excellence, Dr. Zareiforoush is a leading figure in his field.

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.

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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.

Gulcay Ercan Oguzturk | Artificial Intelligence | Best Researcher Award

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Assist. Prof. Dr. Gulcay Ercan Oguzturk | Artificial Intelligence | Best Researcher Award

Assistant Professor at Recep Tayyip ErdoÄźan University, Turkey

Dr. GĂĽlcay Ercan OÄźuztĂĽrk is an esteemed Assistant Professor at Recep Tayyip ErdoÄźan University, specializing in landscape architecture. With a deep passion for ecological planning and campus design, Dr. OÄźuztĂĽrk focuses on sustainable urban development and green infrastructure. Her research incorporates climate-responsive strategies, nature-based solutions, and spatial transformations to enhance environmental sustainability. She has contributed significantly to integrating ecological principles into urban and rural landscapes, emphasizing resilient planning approaches. Dr. OÄźuztĂĽrk has been actively involved in interdisciplinary research, advancing smart irrigation technologies and autonomous systems in plant adaptation. Her contributions have greatly influenced the development of sustainable campus environments and urban green spaces.

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Education

Dr. GĂĽlcay Ercan OÄźuztĂĽrk has a strong academic background in landscape architecture, having pursued her education with a focus on ecological planning and spatial change. Her studies have provided her with expertise in sustainable urban planning, natural plant production, and visual quality assessment. With a commitment to integrating research-driven solutions into her field, she has continuously explored new methodologies in environmental sustainability and green infrastructure. Her academic journey has shaped her holistic approach to urban and landscape planning, emphasizing resilience and adaptability in contemporary environmental challenges.

Experience

As an Assistant Professor at Recep Tayyip ErdoÄźan University, Dr. OÄźuztĂĽrk has been actively engaged in research and teaching in the Department of Landscape Architecture. She has led various research projects on ecological planning and campus sustainability, focusing on nature-based solutions to urban environmental issues. Dr. OÄźuztĂĽrk has collaborated with academic and industry professionals, contributing to interdisciplinary studies on smart irrigation systems, green infrastructure, and climate-responsive design. Her academic career includes mentoring students in landscape architecture and ecological planning, guiding them toward innovative research approaches. Additionally, she has been involved in projects funded by organizations such as TĂśBÄ°TAK, further enhancing her contributions to sustainable environmental design.

Research Interests

Dr. Oğuztürk’s research interests encompass ecological planning, sustainable campus development, and spatial transformation. Her work emphasizes the integration of green infrastructure in urban planning, with a focus on mitigating climate change effects through landscape architecture. She has explored the role of autonomous systems in plant adaptation, as well as the impact of green spaces on urban microclimates. Her interdisciplinary approach combines ecological aesthetics, environmental planning, and smart technologies to develop innovative solutions for landscape sustainability. Dr. Oğuztürk is particularly interested in the use of sensor-based autonomous systems for plant monitoring and adaptation, contributing to the advancement of smart agricultural practices and sustainable landscaping.

Awards and Recognitions

Dr. GĂĽlcay Ercan OÄźuztĂĽrk has been recognized for her contributions to landscape architecture and ecological planning. Her research has received support from prestigious funding bodies, including TĂśBÄ°TAK, for projects on green infrastructure and urban sustainability. She has also been nominated for academic awards for her outstanding work in campus planning and climate-responsive landscape design. Her publications and collaborative efforts have garnered attention within the academic community, further solidifying her position as a leading researcher in sustainable urban planning.

Selected Publications

Çimen, N., Pulatkan, M., & Ercan-O, G. (2022). GA(3) treatments on seed germination in Rhodothamnus sessilifolius, an endangered species in Turkey. CALDASIA, 44(2), 241-247. [Cited by 10 articles]

Ercan OÄźuztĂĽrk, G., Murat, C., & Yurtseven, M. (2025). The Effects of AI-Supported Autonomous Irrigation Systems on Water Efficiency and Plant Quality: A Case Study of Geranium psilostemon Ledeb. Plants, 14(770). https://doi.org/10.5390/plants14050770 [Cited by 5 articles]

Ercan OÄźuztĂĽrk, G., & YĂĽksek, T. (2024). Rainwater Management Model in Fener Campus in Recep Tayyip ErdoÄźan University. International Studies and Evaluations in the Field of Landscape Architecture, 45-60. [Cited by 8 articles]

Ercan OÄźuztĂĽrk, G., & Pulatkan, M. (2024). An Assessment of Recreational Opportunities in the KTU Kanuni Campus. Architectural Sciences and Sustainable Approaches, 528-546. [Cited by 7 articles]

Ercan OÄźuztĂĽrk, G., & Pulatkan, M. (2023). Interaction of Urban and University Campuses: KTU Kanuni Campus Example. Architectural Sciences and Urban/Environmental Studies, 22-43. [Cited by 6 articles]

Ercan OÄźuztĂĽrk, G., & Pulatkan, M. (2023). Evaluation of Urban University Campuses Within the Scope of Sustainability: Some Urban Campus Examples. Landscape Research, 111-134. [Cited by 4 articles]

Ercan Oğuztürk, G., & Pulatkan, M. (2020). The Effect of the Historical Hevsel Gardens on the Urban Identity of Diyarbakır. Academic Studies in Architecture, Planning and Design, 119-191. [Cited by 9 articles]

Conclusion

Dr. Gülcay Ercan Oğuztürk’s work in landscape architecture and ecological planning has significantly contributed to sustainable urban and campus development. Her research integrates smart technologies, nature-based solutions, and spatial planning to enhance green infrastructure and environmental sustainability. Through her interdisciplinary approach, she has addressed key challenges in urban resilience, climate adaptation, and ecological aesthetics. Dr. Oğuztürk’s contributions continue to shape the field of landscape architecture, inspiring future researchers and practitioners to adopt innovative, sustainable, and climate-responsive planning strategies.

Sara Masiero | Artificial Intelligence | Outstanding Contributions in Academia Award

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Mrs. Sara Masiero | Artificial Intelligence | Outstanding Contributions in Academia Award

Collaboratrice at Scuola Universitaria Professionale della Svizzera Italiana, Switzerland

Sara Masiero is a dedicated and forward-thinking management engineer with a strong passion for innovation and digital transformation. She thrives on discovering new concepts and implementing solutions that enhance industrial efficiency, sustainability, and resilience. A firm believer in the power of serenity, she fosters an environment conducive to creativity and proactive engagement. Beyond her professional endeavors, Sara embraces adventure and cultural exploration, always seeking experiences that resonate with her positive energy.

Profile

Scopus

Education

Sara Masiero pursued her higher education at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI), where she obtained a Master of Science in Engineering (2018-2021). During her academic journey, she actively engaged in research projects focusing on optimizing industrial systems and integrating digital tools for process enhancement. Prior to her master’s degree, she earned a Bachelor of Science in Ingegneria Gestionale (2015-2018) from the same institution. She further honed her expertise through specialized programs, including the English Summer School at Horner School of English, AIGreen Business Lab by EIT Digital, and professional training in learning assessment methodologies.

Experience

Sara Masiero has amassed substantial experience in both academia and industry, contributing to projects that merge theoretical research with practical applications. Since November 2018, she has been serving as a scientific collaborator at SUPSI, where she plays a pivotal role in research and scientific development within the realm of Industry 4.0 and 5.0. Her work emphasizes human-centered industrial paradigms, sustainability, and resilience, while she also manages digital processes for EU H2020 projects and provides training in Industrial Engineering courses.

Between January 2023 and February 2024, Sara worked as a Business Process Manager at Masiero G. Srl and Z. Account Service Srl, overseeing financial and commercial processes related to sales, customer service, and supplier relations. She also ensured regulatory compliance and operational efficiency through effective bureaucratic and administrative process management. Earlier, she collaborated with STISA SA and LINNEA (September 2020 – February 2021) to develop her master’s thesis on optimizing material flows and warehouse layouts in logistics systems. Additionally, during her bachelor’s studies, she worked with RIRI SA (June 2018 – September 2018) on a thesis analyzing raw material purchasing processes with a focus on sustainability.

Research Interests

Sara Masiero’s research interests are deeply rooted in industrial innovation, digital transformation, and sustainability. She focuses on the integration of advanced digital tools in production systems, addressing the challenges and opportunities presented by Industry 4.0 and 5.0. Her work revolves around Quality Management advancements, human-centric industrial paradigms, and AI-driven digital platforms that enhance manufacturing processes. Furthermore, she explores methodologies for optimizing supply chain operations and ensuring regulatory compliance within rapidly evolving technological landscapes.

Awards and Recognition

Throughout her academic and professional journey, Sara has been recognized for her contributions to research and process optimization in industrial settings. Her innovative approach to digital transformation and industrial efficiency has earned her accolades in academic conferences and industry collaborations. She has actively participated in prestigious projects and workshops, further cementing her reputation as a knowledgeable and influential figure in the field of industrial engineering and management.

Publications

Corti, D., Masiero, S., & Gladysz, B. (2021). “Impact of Industry 4.0 on Quality Management: Identification of main challenges towards a Quality 4.0 approach.” IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), pp. 1-8.

Masiero, S., Qosaj, J., & Cutrona, V. (2024). “Digital Datasheet model: enhancing value of AI digital platforms.” Procedia Computer Science, 232, 149-158.

Masiero, S., Qosaj, J., Bettoni, A., & Gladysz, B. (2024). “Technology-Driven Measures for Human Centricity in the Manufacturing Sector.” International Association for the Management of Technology Conference, pp. 81-88, Cham: Springer Nature Switzerland.

Conclusion

Sara Masiero exemplifies the essence of a modern engineer—one who seamlessly integrates research, industry expertise, and a passion for innovation. Her extensive experience in digital transformation, quality management, and process optimization makes her a valuable contributor to the fields of industrial engineering and management. With a strong academic background, diverse professional experience, and a commitment to sustainability and human-centric methodologies, Sara continues to drive meaningful advancements in Industry 4.0 and 5.0. Her contributions to research and industry projects underscore her ability to bridge theoretical knowledge with practical applications, paving the way for smarter, more resilient production systems in the future.

Mathew Habyarimana | Artificial Intelligence | Best Academic Researcher Award

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Dr. Mathew Habyarimana | Artificial Intelligence | Best Academic Researcher Award

Research Scholar at Durban University of Technology, South Africa

Mathew Habyarimana, Ph.D., is an accomplished electrical engineer with expertise in electrical machines, power electronics, and renewable energy. He is a self-motivated researcher and educator committed to advancing knowledge and mentoring students in the field of electrical engineering. With a strong background in academia and industry, he has contributed significantly to the development of energy systems, power electronics applications, and machine optimization techniques. His career spans several years in research, lecturing, and engineering roles, with a focus on intelligent power systems and electrical energy optimization.

Profile

Scopus

Education

Dr. Habyarimana obtained his Ph.D. in Electrical Engineering from the University of KwaZulu-Natal, Durban, South Africa, in September 2022. His doctoral research, funded by the Eskom Power Plant Engineering Institute (EPPEI), focused on electrical machines and power system optimization. Prior to this, he completed his MSc. in Electrical Engineering at the same institution in 2016, specializing in power electronics. His undergraduate studies were conducted at the University of Rwanda, College of Science and Technology, where he earned a BSc. in Electrical Engineering with a focus on renewable energy. His strong educational foundation has shaped his expertise in energy conversion, machine performance improvement, and sustainable energy solutions.

Experience

Dr. Habyarimana has held various academic and research positions throughout his career. Currently, he is a Postdoctoral Research Fellow at Durban University of Technology, where he is engaged in high-impact research on electrical power systems. Previously, he served as a Postdoctoral Research Fellow at the University of Johannesburg from 2023 to 2024, authoring scientific papers and presenting his findings at international conferences.

His academic contributions also include lecturing positions at Durban University of Technology, where he taught courses such as Illumination and Digital Signal Processing in the Electrical and Electronic Engineering Department. As a Senior Lecturer, he developed curricula, designed assessment tools, and guided students through complex electrical engineering concepts.

Before transitioning into academia, Dr. Habyarimana worked as a Project Engineer at Rwanda Energy Group, contributing to rural electrification projects. Additionally, he served as a mathematics tutor and lab demonstrator at the University of KwaZulu-Natal, mentoring students in power electronics and electrical machines. His extensive experience bridges theoretical research and practical engineering applications.

Research Interests

Dr. Habyarimana’s research interests lie in electrical machines, power electronics, renewable energy, and intelligent power management systems. He is particularly focused on optimizing induction motors, mitigating in-rush currents, and integrating artificial intelligence into power systems for enhanced energy efficiency. His work aims to address challenges in energy sustainability, improve motor efficiency, and develop hybrid energy systems that balance renewable and conventional energy sources.

Awards

Dr. Habyarimana has received multiple accolades for his contributions to research and innovation. He was awarded the Best Commercialization Project by the UKZN Inqubate Intellectual Property initiative in 2014. In addition, he received a Certificate of Appreciation for judging at the Eskom Expo for Young Scientists in 2015. His academic excellence is further recognized through his University Teaching Assistant certification, highlighting his dedication to education and student mentorship.

Publications

M. Habyarimana, G. Sharma, P. N. Bokoro, and K. A. Ogudo, “Intelligent power source selection for solar energy optimization,” International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, 2024.

M. Habyarimana, G. Sharma, and P. N. Bokoro, “The Effect of Tuned Compensation Capacitors in the Induction Motors,” WSEAS Transactions on Power Systems, 2024.

Habyarimana, M., Dorrell, D. G., & Musumpuka, R., “Reduction of Starting Current in Large Induction Motors,” Energies, 2022.

Habyarimana, M., Musumpuka, R., & Dorrell, D. G., “Mitigating In-rush Currents for Induction Motor Loads,” IEEE Southern Power Electronics Conference, 2021.

Habyarimana, M., & Dorrell, D. G., “Methods to reduce the starting current of an induction motor,” IEEE International Conference on Power, Control, Signals and Instrumentation Engineering, 2017.

Venugopal, C., Subramaniam, P. R., & Habyarimana, M., “A Fuzzy Based Power Switching Selection for Residential Application to Beat Peak Time Power Demand,” Intelligent Decision Support Systems for Sustainable Computing, 2017.

Habyarimana, M., & Venugopal, C., “Automated hybrid solar and mains system for peak time power demand,” International Conference on the Domestic Use of Energy, 2015.

Conclusion

Dr. Mathew Habyarimana is a distinguished electrical engineer and researcher whose work significantly impacts electrical power systems and renewable energy integration. His extensive experience in academia and industry, coupled with his research contributions, underscores his commitment to innovation in energy optimization and power electronics. Through his lecturing, mentoring, and research initiatives, he continues to shape the next generation of electrical engineers while advancing knowledge in intelligent power management and sustainable energy solutions.

Haonan Xu | Business Intelligence | Best Researcher Award

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Assoc. Prof. Dr. Haonan Xu | Business Intelligence | Best Researcher Award

Dr. Haonan Xu is a distinguished scholar in the field of management science and engineering, specializing in multimodal transport, shipping economics, and supply chain management. His academic and research contributions have significantly advanced knowledge in these areas, particularly regarding AI integration in port operations, emission reduction in shipping supply chains, and multimodal transportation strategies. With a robust publication record in high-impact journals and recognition through prestigious awards, Dr. Xu continues to influence both academic and industrial landscapes. His work is characterized by a strong analytical approach, incorporating mathematical modeling, empirical analysis, and policy evaluation to address real-world transportation and logistics challenges.

Profile

Scopus

Education

Dr. Xu obtained his Ph.D. in Management Science and Engineering from Dalian Maritime University, where he was recognized as an excellent postgraduate. Prior to this, he completed his Master of Engineering in Logistics from Chongqing Jiaotong University, earning accolades for his volunteer work and research excellence. His undergraduate studies in Economics at the same university provided him with a solid foundation in economic theories and quantitative analysis. Throughout his academic journey, Dr. Xu has demonstrated exceptional leadership, contributing to teaching missions and earning multiple recognitions for his outstanding academic and extracurricular achievements.

Experience

Dr. Xu has a rich background in both academia and practical research. His teaching experience includes serving at Chongqing Jiaotong University, where he currently leads research initiatives. Earlier in his career, he contributed to educational development at Garden Primary School, successfully securing national funding for information technology projects. His work has extended to collaborative research on port operations and supply chain management, engaging with international scholars and policymakers to address critical global challenges in transportation and logistics.

Research Interests

Dr. Xu’s research primarily focuses on multimodal transport, shipping economics, and supply chain management. His studies explore the integration of AI in port decision-making, the impact of carbon reduction policies on shipping logistics, and strategies for optimizing rail-water multimodal transportation. His research employs advanced analytical methods, including game theory, econometric modeling, and network analysis, to provide insights into improving efficiency, sustainability, and competitiveness in global transportation systems.

Awards

Dr. Xu has received multiple prestigious awards for his academic and research excellence. These include:

  • First Prize for Thesis Award from the Chongqing Municipal Education Commission.
  • First Prize for Creative Education from the Chongqing Education Information Technology and Equipment Center.
  • Recognition as an Excellent Student Leader and recipient of the Outstanding Graduation Thesis award.
  • Chair of the Social Science Planning Program of Chongqing, leading a significant funded research project. These accolades highlight his contributions to both theoretical advancements and practical implementations in logistics and transportation research.

Selected Publications

Dr. Xu has published extensively in top-tier journals, with several of his works being highly cited. Below are some of his key publications:

“The impact of AI technology adoption on operational decision-making in competitive heterogeneous ports,” published in Transportation Research Part E (2024). This study examines the role of AI in enhancing port service quality and its implications for social welfare. (Cited by: Highly Cited in ESI)

“Emission reduction technologies for shipping supply chains under carbon tax with knowledge sharing,” published in Ocean & Coastal Management (2024). This paper explores the influence of carbon tax policies on green shipping technologies. (Cited by: High-impact environmental studies)

“Incentive policy for rail-water multimodal transport: Subsidizing price or constructing dry port,” published in Transport Policy (2024). It analyzes policy incentives for optimizing multimodal transportation efficiency. (Cited by: Notable transportation policy papers)

“The effects and conflicts of co-opetition in a rail-water multimodal transportation system,” published in Annals of Operations Research (2023). This study develops a multi-party game model for conflict resolution in multimodal transport. (Cited by: High-impact logistics research)

“International container intermodal competitiveness: an empirical study from Chinese hub ports,” published in Ocean & Coastal Management (2024). This empirical study evaluates the competitiveness of China’s multimodal transport hubs. (Cited by: Global trade logistics research)

“Economic-environmental coordination and influencing factors under dual-carbon goals: A spatial empirical analysis,” published in Environment, Development, and Sustainability (2024). The study uses network DEA to analyze sustainable transportation policies. (Cited by: ESI Highly Cited)

“The study of OEM/ODM supply chain decision-making considering supply risk,” published in China Management Science (2024). It develops a multi-party game model to address supply chain risk management. (Cited by: Supply chain strategy scholars)

Conclusion

Dr. Haonan Xu’s academic and research journey exemplifies his commitment to advancing the fields of transportation, logistics, and supply chain management. His work integrates innovative methodologies with real-world applications, influencing both policy and industry practices. With a strong publication record, notable awards, and active participation in funded research projects, he continues to contribute significantly to the academic and professional community. His research not only enhances theoretical knowledge but also provides actionable insights for improving efficiency and sustainability in global transport systems.