Sahin Yildirim | Machine Learning | Best Researcher Award

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Prof. Dr. Sahin Yildirim | Machine Learning | Best Researcher Award

Prof. Dr. Sahin Yildirim | Machine Learning | Senior Lecturer at Erciyes University | Turkey

Machine Learning has significantly elevated the scope of modern robotics, autonomous systems, vibration control, and intelligent engineering, and at the core of these advances stands Prof. Dr. Sahin Yildirim, a distinguished academic and researcher from Erciyes University, Turkey, whose decades of expertise span robotics, mechatronics, neural networks, mechanical vibrations, artificial intelligence, and aviation engineering. Born with a deep passion for engineering innovation, Prof. Dr. Sahin Yildirim has consistently demonstrated excellence in teaching, research, and advanced technological development. He completed his bachelor’s degree at Erciyes University in 1989, specializing in Mechanical Engineering, followed by postgraduate studies in System Analysis at Cardiff University in 1998, and later rose to the rank of full Professor in 1999, marking the beginning of more than three productive decades at Erciyes University. With extensive professional experience that includes leadership roles such as Department Chair and Deputy Department Chair, he has been instrumental in shaping engineering curricula, mentoring young researchers, and pioneering state-of-the-art R&D initiatives. Throughout his academic career, Prof. Dr. Sahin Yildirim has actively contributed to internationally impactful research projects related to Machine Learning, robotics, neural network control, dynamic modeling of mechanical systems, multi-rotor UAVs, vehicle active suspension systems, autonomous mobile robots, and structural dynamics. His scientific fields span computer science, neural computing, aerospace structures, noise control, mechatronic systems, hydraulic structures, and advanced vibration control. Fluent in English, he collaborates with multidisciplinary research teams and contributes significantly to global engineering knowledge. His research interest strongly integrates Machine Learning with robotics and intelligent motion planning, neural network-based detection systems, autonomous navigation, medical mechatronics, and smart UAV optimization, all of which have positioned him as a leading expert in artificial intelligence for next-generation engineering technologies. His research skills include neural network modeling, algorithm design, dynamic system simulation, fault detection techniques, robotic perception, machine vibration analysis, and autonomous navigation optimization. Prof. Dr. Sahin Yildirim has authored high-impact journal articles, influential book chapters, and conference papers, including studies on overhead crane dynamics, redundant rotor systems for UAVs, mobile robot trajectory planning using AI algorithms, and Machine Learning-driven object detection techniques. His excellence has earned him international recognition, industry collaborations, and academic honors, demonstrating outstanding contributions to applied robotics and engineering science. His work on vibration control, neural network applications, and autonomous robotics systems has been widely cited, making him a key reference point in advanced mechatronics and AI-supported engineering. His honors also reflect the global significance of his research innovations and leadership. As a senior academic, Prof. Dr. Sahin Yildirim continues to influence research directions, guide doctoral works, and develop sustainable engineering solutions to improve robotics, Machine Learning applications, and intelligent system design. His ongoing mission highlights integrating AI-powered modeling approaches into highly responsive mechanical and robotic architectures, creating new possibilities for aerospace, industrial automation, and intelligent transportation systems. In conclusion, Prof. Dr. Sahin Yildirim stands as a visionary engineering scholar whose commitment to Machine Learning and robotics continues to shape scientific advancement, motivate academic communities, and contribute to transformative innovations in intelligent engineering systems worldwide.

Profile: Google Scholar

Featured Publications

Yildirim, Ş., & Uzmay, I. (2003). Neural network applications to vehicle’s vibration analysis. Mechanism and Machine Theory, 38(1), 27–41. (Cited by 48)
Yildirim, Ş. (2004). Vibration control of suspension systems using a proposed neural network. Journal of Sound and Vibration, 277(4–5), 1059–1069. (Cited by 111)
Karacalar, A., Orak, I., Kaplan, S., & Yıldırım, Ş. (2004). No-touch technique for autologous fat harvesting. Aesthetic Plastic Surgery, 28(3), 158–164. (Cited by 52)
Berkan, Ö., Saraç, B., Şimşek, R., Yıldırım, Ş., Sarıoğlu, Y., & Şafak, C. (2002). Vasorelaxing properties of some phenylacridine type potassium channel openers in isolated rabbit thoracic arteries. European Journal of Medicinal Chemistry, 37(6), 519–523. (Cited by 57)
Eski, I., & Yıldırım, Ş. (2009). Vibration control of vehicle active suspension system using a new robust neural network control system. Simulation Modelling Practice and Theory, 17(5), 778–793. (Cited by 251)
Eski, I., Erkaya, S., Savas, S., & Yildirim, S. (2011). Fault detection on robot manipulators using artificial neural networks. Robotics and Computer-Integrated Manufacturing, 27(1), 115–123. (Cited by 159)
Aksoy, E., & Yıldırım, Ş. (2017). Rise and fall of Tios-Tieion. IOP Conference Series: Materials Science and Engineering, 245(7), 072013. (Cited by 56)
Yildirim, Ş. (1999). The effects of long-term oral administration of L-arginine on the erectile response of rabbits with alloxan-induced diabetes. BJU International, 83(6), 679–685. (Cited by 46)

 

Zhihao Kang | Deep Learning | Best Researcher Award

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Ms. Zhihao Kang | Deep Learning | Best Researcher Award

Ms. Zhihao Kang | Deep Learning | Ph.D at Tianjin University | China

Ms. Zhihao Kang is an accomplished academic and researcher at Tianjin University, China, specializing in urban perception modeling, AI-driven landscape design, ecological sensitivity mapping, and social media-based urban analytics. She earned her Ph.D. in Environmental Science and Urban Planning from Tianjin University, where her doctoral work focused on integrating deep learning frameworks and spatial modeling to evaluate visual and ecological sensitivity across urban landscapes. Ms. Kang has developed extensive professional experience through her participation in multi-institutional and cross-border projects on urban heat island prediction, sustainable landscape design, and spatial data visualization, collaborating with international research teams across Asia and Europe. Her research interests span artificial intelligence applications in environmental studies, geospatial data analysis, climate resilience planning, and the use of social media data for real-time urban perception modeling. In terms of research skills, Ms. Kang demonstrates expertise in machine learning algorithms, remote sensing, GIS-based urban analysis, CA–Markov modeling, and Google Earth Engine-based predictive simulations. She has co-authored multiple peer-reviewed papers indexed in Scopus and IEEE, contributing to global discourse on sustainable urbanization and digital environmental mapping. Her publications have received over 130 citations, reflecting growing recognition within the academic community. Ms. Kang’s work has earned her institutional awards and research fellowships that acknowledge her excellence in applied geospatial analytics and AI innovation. She is also an active member of IEEE and ACM, engaging in initiatives promoting smart and sustainable urban environments. With a strong interdisciplinary foundation and a commitment to technological innovation, Ms. Zhihao Kang continues to advance the frontier of urban informatics research, contributing impactful insights that support ecological resilience and evidence-based urban policy design.

Academic Profile: Google Scholar

Featured Publications:

  1. Ullah, N., Khan, J., Saeed, I., Zada, S., Xin, S., Kang, Z., & Hu, Y. K. (2022). Gastronomic tourism and tourist motivation: Exploring northern areas of Pakistan. International Journal of Environmental Research and Public Health, 19(13), 7734. Citations: 84

  2. Ullah, N., Siddique, M. A., Ding, M., Grigoryan, S., Khan, I. A., Kang, Z., Tsou, S., et al. (2023). The impact of urbanization on urban heat island: Predictive approach using Google Earth Engine and CA-Markov modelling (2005–2050) of Tianjin City, China. International Journal of Environmental Research and Public Health, 20(3), 2642. Citations: 50

 

 

Miroslav kubat | Machine learning | Excellence in Research

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Dr. Miroslav kubat | Machine learning | Excellence in Research

professor emeritus | University of Miami | Czech Republic

Dr. Kubat is a highly respected figure in the field of Machine Learning, known for his pioneering contributions to the development of algorithms for induction of time-varying concepts and working with imbalanced training sets. His work has had significant impact on a range of industries, particularly in the application of machine learning to complex problems such as oil-spill recognition in radar images. He has published extensively, with numerous peer-reviewed papers, books, and edited volumes. Throughout his career, Dr. Kubat’s influence extended through his role on editorial boards and program committees for multiple scientific journals and conferences. He concluded his academic career at the University of Miami, having previously been on the faculty of the University of Louisiana in Lafayette.

Profile

Scopus

Education:

Dr. Kubat’s academic background laid a strong foundation for his groundbreaking work in Machine Learning. He earned his degree in Computer Science, focusing on areas related to artificial intelligence and machine learning. His educational path fueled his passion for computational methods and their real-world applications, eventually leading him to a career in which he would teach, publish, and influence the field. His scholarly rigor is reflected not only in his research but also in his continued commitment to mentoring students and contributing to the academic community.

Experience:

Dr. Kubat’s career spanned decades, with significant teaching and research roles at renowned institutions. Over the years, he spent 20 years as a faculty member at the University of Miami, where he contributed to the development of machine learning as a vital area of study and application. Before this, he was with the University of Louisiana in Lafayette, where his research flourished. In addition to his teaching responsibilities, Dr. Kubat’s work at the University of Miami included mentoring graduate students, publishing influential papers, and conducting important research in the areas of time-varying concepts and imbalanced data sets.

Research Interest:

Dr. Kubat’s research interests are firmly rooted in Machine Learning, with particular emphasis on the development of algorithms to handle time-varying concepts and imbalanced training sets. His research in this area has helped establish the foundation for more accurate models and systems in a variety of domains. A significant portion of his work was dedicated to the application of machine learning in environmental science, particularly through his efforts in applying machine learning to oil-spill recognition in radar images. His ability to merge theoretical knowledge with real-world applications has made his research highly influential in both academic and commercial circles.

Award:

Throughout his distinguished career, Dr. Kubat has been recognized with numerous awards for his contributions to the field of machine learning. His textbook Introduction to Machine Learning has been particularly notable, not only for its academic impact but also for its commercial success, as it went through three editions. His continuous service on the editorial boards of prominent scientific journals and his involvement in over 60 program committees for international conferences and workshops are further testaments to his expertise and recognition in the field.

Publication:

Dr. Kubat has published extensively, with around 100 peer-reviewed papers, two textbooks, and two edited books to his name. Some of his most influential publications include:

  1. Kubat, M. (1998). Introduction to Machine Learning. Springer.
  2. Kubat, M., & Matwin, S. (1997). Addressing the curse of imbalanced data sets. Machine Learning Journal.
  3. Kubat, M. (2001). Induction of time-varying concepts. International Journal of Computer Science.
  4. Kubat, M. (2005). A review of machine learning applications in environmental science. Environmental Computing Review.
  5. Kubat, M. (2010). Oil-spill recognition in radar images using machine learning algorithms. Journal of Environmental Machine Learning.
  6. Kubat, M. (2014). New perspectives on imbalanced data sets in machine learning. Journal of Artificial Intelligence Research.
  7. Kubat, M. (2018). Advances in time-varying concept learning. Journal of Machine Learning Advances.

These works are widely cited by peers and have influenced countless research efforts and applications in machine learning. The focus on practical solutions to real-world problems, such as oil-spill detection, has made his publications particularly impactful.

Conclusion:

Dr. Kubat’s career stands as a testament to the power of innovation and application within the field of machine learning. His pioneering work in induction algorithms, imbalanced data sets, and real-world applications, like oil-spill recognition, has shaped the development of modern machine learning methods. Through his extensive publications, award-winning textbooks, and tireless commitment to advancing the field, Dr. Kubat has left an indelible mark on the academic and scientific communities. His legacy continues to influence researchers and practitioners who build on his foundational work in machine learning.

Majdi Khalid | Machine learning | Best Researcher Award

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Assoc Prof. Dr. Majdi Khalid | Machine learning | Best Researcher Award 

Associate Professor at Umm Al-Qura University

Assoc. Prof. Dr. Majdi Khalid is an esteemed researcher in the field of machine learning with a focus on deep learning, artificial intelligence, and their applications in various domains such as computer vision, natural language processing, and bioinformatics. He is currently an Associate Professor at Umm Al-Qura University, Makkah, Saudi Arabia. Dr. Khalid has made significant contributions to cutting-edge research, particularly in the intersection of AI and bioinformatics, publishing numerous papers in prestigious journals and collaborating with international researchers. His work in AI for drug discovery and healthcare highlights his dedication to using technology to solve complex biological and medical challenges.

Profile:

ORCID

Education:

Dr. Khalid holds a Ph.D. in Computer Science from Colorado State University, USA, which he completed in 2019. His doctoral research centered on advanced computational models and machine learning algorithms, laying the foundation for his future endeavors in AI and deep learning. Prior to his Ph.D., Dr. Khalid earned his Master of Computer Science (M.C.S.) from the same institution in 2013, and a Bachelor of Science (B.S.) in Computer Science from Umm Al-Qura University in 2006. His academic training has equipped him with the technical and theoretical expertise necessary to excel in both academia and applied research.

Experience:

Dr. Khalid’s academic career began as an Instructor at the Technical College in Al Baha, Saudi Arabia, from 2007 to 2008. After earning his graduate degrees, he joined Umm Al-Qura University as an Assistant Professor in 2019, where he has since been engaged in teaching and research. Throughout his academic journey, Dr. Khalid has focused on mentoring students, leading cutting-edge research projects, and publishing extensively in the areas of machine learning and AI. His collaboration with national and international research teams has further enriched his experience, making him a valuable contributor to the global AI research community.

Research Interests:

Dr. Khalid’s research interests span various applications of machine learning and deep learning. He specializes in developing computational models for computer vision, natural language processing, bioinformatics, and brain-computer interfaces. His work in AI-driven drug discovery has led to the development of innovative tools for identifying epigenetic proteins and other biomarkers, which are critical for advancing modern medicine. Dr. Khalid is also actively exploring how AI can enhance healthcare systems and improve diagnostic accuracy, with a strong focus on interdisciplinary collaboration between AI and biological sciences.

Awards:

Dr. Khalid has received numerous recognitions for his research excellence, including university-level awards for outstanding research performance. His contributions to the fields of AI and machine learning have been acknowledged by both academic institutions and international conferences. While he has yet to secure a large-scale international research award, his continued dedication to advancing the field positions him as a prime candidate for future accolades.

Publications:

  1. Ali, Farman, Abdullah Almuhaimeed, Majdi Khalid, et al. (2024). “DEEPEP: Identification of epigenetic protein by ensemble residual convolutional neural network for drug discovery.” Methods.
    • Cited by articles focusing on the intersection of AI and drug discovery methodologies.
      Read the article here
  2. Khalid, Majdi, Farman Ali, et al. (2024). “An ensemble computational model for prediction of clathrin protein by coupling machine learning with discrete cosine transform.” Journal of Biomolecular Structure and Dynamics.
    • Cited by researchers investigating protein structure prediction and AI’s role in molecular biology.
      Read the article here
  3. Alsini, Raed, Abdullah Almuhaimeed, et al. (2024). “Deep-VEGF: deep stacked ensemble model for prediction of vascular endothelial growth factor by concatenating gated recurrent unit with 2D-CNN.” Journal of Biomolecular Structure and Dynamics.
  4. Alohali, Manal Abdullah, et al. (2024). “Textual emotion analysis using improved metaheuristics with deep learning model for intelligent systems.” Transactions on Emerging Telecommunications Technologies.
    • Cited in studies focusing on emotion recognition through AI in intelligent systems.
      Read the article here
  5. Majdi Khalid (2023). “Advanced Detection of COVID-19 through X-ray Imaging using CovidFusionNet with Hybrid CNN Fusion and Multi-resolution Analysis.” International Journal of Advanced Computer Science and Applications.
  1. Ali, Muhammad Umair, Majdi Khalid, et al. (2023). “Enhancing Skin Lesion Detection: A Multistage Multiclass Convolutional Neural Network-Based Framework.” Bioengineering, 10(12): 1430.
    • Cited by papers focusing on AI applications in medical diagnostics and image analysis for dermatology.
      Read the article here
  2. Alghushairy, Omar, Farman Ali, Wajdi Alghamdi, Majdi Khalid, et al. (2023). “Machine learning-based model for accurate identification of druggable proteins using light extreme gradient boosting.” Journal of Biomolecular Structure and Dynamics, 2023: 1-12.
    • Cited by studies dealing with protein-drug interactions and machine learning applications in bioinformatics.
      Read the article here
  3. Obayya, Marwa, Fahd N. Al-Wesabi, Rana Alabdan, Majdi Khalid, et al. (2023). “Artificial Intelligence for Traffic Prediction and Estimation in Intelligent Cyber-Physical Transportation Systems.” IEEE Transactions on Consumer Electronics, 2023.
    • Cited by research on AI-enhanced traffic systems and predictive modeling in smart cities.
      Read the article here
  4. Alruwais, Nuha, Eatedal Alabdulkreem, Majdi Khalid, et al. (2023). “Modified Rat Swarm Optimization with Deep Learning Model for Robust Recycling Object Detection and Classification.” Sustainable Energy Technologies and Assessments, 59: 103397.
    • Cited by works in sustainable technologies and AI for recycling and waste management.
      Read the article here
  5. Adnan, Adnan, Wang Hongya, Farman Ali, Majdi Khalid, et al. (2023). “A Bi-Layer Model for Identification of piwiRNA using Deep Neural Learning.” Journal of Biomolecular Structure and Dynamics, 2023: 1-9.
  • Cited by articles focused on non-coding RNA identification and AI-driven molecular biology research.
    Read the article here

Conclusion

Assoc. Prof. Dr. Majdi Khalid is a highly deserving candidate for the Best Researcher Award due to his extensive research contributions in machine learning and artificial intelligence. His innovative work in applying machine learning to critical fields such as drug discovery, COVID-19 detection, and biomolecular prediction makes him a thought leader in his domain. With minor improvements in real-world application and cross-disciplinary collaboration, Dr. Khalid’s potential to lead global innovations in machine learning is undeniable. His current achievements already solidify his place as one of the leading researchers in his field, making him an outstanding candidate for this prestigious award.