Sahin Yildirim | Machine Learning | Best Researcher Award

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)

 

Angeliki Antoniou | AI | Best Researcher Award

Assoc. Prof. Dr. Angeliki Antoniou | AI | Best Researcher Award

Assoc. Prof. Dr. Angeliki Antoniou | AI | Associate Professor at University of West Attica | Greece

Assoc. Prof. Dr. Angeliki Antoniou is a distinguished scholar in the field of Human-Computer Interaction (HCI), Educational Technologies, and Digital Cultural Heritage, currently serving at the University of West Attica, Department of Archival, Library and Information Studies, Greece. She earned her Doctor of Informatics (Ph.D.) from the University of Peloponnese, focusing on adaptive educational technologies for museums, and holds an MSc in Human-Computer Interaction with Ergonomics from University College London (UCL). Additionally, she possesses undergraduate degrees in Psychology from the University of Kent and Early Childhood Education from the National and Kapodistrian University of Athens, illustrating her interdisciplinary foundation that bridges education, psychology, and informatics. Professionally, Assoc. Prof. Dr. Angeliki Antoniou has accumulated extensive teaching and research experience across institutions such as the University of Peloponnese and the University of West Attica, where she has led courses in cognitive psychology, human-computer interaction, and digital learning environments. Her research interests include user-centered design, cognitive modeling, serious games, digital storytelling, and technology-enhanced museum learning. She has successfully contributed to and coordinated several international and national projects on cultural heritage technologies, and her work is well-cited in high-impact academic journals indexed in Scopus and IEEE. Assoc. Prof. Dr. Angeliki Antoniou’s research skills encompass experimental design, usability evaluation, qualitative and quantitative analysis, and the development of adaptive systems for education and culture. She has received academic recognition for her leadership in interdisciplinary research, along with honors for her contributions to digital culture and innovation in educational informatics. In conclusion, Assoc. Prof. Dr. Angeliki Antoniou exemplifies academic excellence, innovative vision, and global impact through her scholarly research, educational leadership, and enduring contributions to the advancement of digital cultural heritage and human-computer interaction.

Profile: Google Scholar

Featured Publications 

  1. Lykourentzou, I., Antoniou, A., Naudet, Y., & Dow, S. P. (2016). Personality matters: Balancing for personality types leads to better outcomes for crowd teams. Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing. Citations: 158

  2. Theodoropoulos, A., & Antoniou, A. (2022). VR games in cultural heritage: A systematic review of the emerging fields of virtual reality and culture games. Applied Sciences, 12(17), 8476. Citations: 108

  3. Antoniou, A., & Lepouras, G. (2010). Modeling visitors’ profiles: A study to investigate adaptation aspects for museum learning technologies. Journal on Computing and Cultural Heritage (JOCCH), 3(2), 1–19. Citations: 84

  4. Lykourentzou, I., Claude, X., Naudet, Y., Tobias, E., Antoniou, A., & Lepouras, G. (2013). Improving museum visitors’ quality of experience through intelligent recommendations: A visiting style-based approach. Workshop Proceedings of the 9th International Conference on Intelligent Environments. Citations: 76

  5. Antoniou, A., Lepouras, G., Bampatzia, S., & Almpanoudi, H. (2013). An approach for serious game development for cultural heritage: Case study for an archaeological site and museum. Journal on Computing and Cultural Heritage (JOCCH), 6(4), 1–19. Citations: 69

  6. Katifori, A., Perry, S., Vayanou, M., Antoniou, A., Ioannidis, I. P., & McKinney, S. (2020). “Let them talk!” Exploring guided group interaction in digital storytelling experiences. Journal on Computing and Cultural Heritage (JOCCH), 13(3), 1–30. Citations: 67

  7. Antoniou, A., Katifori, A., Roussou, M., Vayanou, M., Karvounis, M., & Kyriakidi, M. (2016). Capturing the visitor profile for a personalized mobile museum experience: An indirect approach. Proceedings of the Digital Heritage International Congress. Citations: 60

 

Nikolai Simonov | Artificial Intelligence | Best Researcher Award

Dr. Nikolai Simonov | Artificial Intelligence | Best Researcher Award

Dr. Nikolai Simonov | Artificial Intelligence – Senior researcher at Valiev Institute of Physics and Technology, Russia

Dr. Nikolai Anatolievich Simonov is a distinguished senior scientist whose career reflects profound expertise in physics, mathematics, applied electromagnetics, and artificial intelligence. He has been instrumental in bridging fundamental science with advanced technologies, contributing to several internationally recognized institutions across Russia and South Korea. His interdisciplinary work ranges from microwave tomography and electromagnetic theory to cognitive modeling and neuromorphic system development. With over 80 publications and a widely cited body of work, he stands as a leading authority in modern radio-electronics and intelligent systems.

🎓Academic Profile

Orcid | Scopus | Google Scholar

🎓 Education

Dr. Simonov completed his Master of Science degree in 1978 from Moscow State University, Faculty of Physics, with a specialization in radio-physics. He subsequently earned a Ph.D. in Physics and Mathematics in 1986 from the same university, focusing on radio-physics and radio-electronics. His academic training laid a robust theoretical foundation that continues to guide his scientific innovations today.

💼 Experience

His career trajectory began in 1978 as an Engineer-Physicist at the Scientific and Research Institute of Radio Engineering in Moscow. He advanced to senior scientific roles at Scientific and Industrial Company Vzlet, Research and Development Company Modus, and the Institute of Theoretical and Applied Electromagnetism (ITAE), part of the Russian Academy of Sciences. Internationally, he held leadership roles in South Korea, including positions at Credipass Co., Ceyon Technology Co., the Electronics and Telecommunications Research Institute (ETRI), and Yonsei University. Currently, he serves as a Senior Scientist at NRC “Kurchatov Institute” – Valiev IPT, continuing his pioneering research in AI-driven semantic modeling and electromagnetic theory.

🔬 Research Interest

Dr. Simonov’s early research revolved around applied electromagnetics, radio-frequency imaging, and microwave scattering. Over time, he expanded into millimeter-wave measurements and developed high-resolution microwave tomography systems with biomedical applications. His recent focus includes the conceptualization of a novel “Model of Spots” for mental imagery representation—an approach that blends cognitive psychology, mathematical modeling, and artificial intelligence to support neuromorphic system development. He actively explores mathematical foundations for AI semantic structures and inverse problem-solving in sensor systems.

🏅 Award

In recognition of his scientific excellence, Dr. Simonov was honored with the IEEE Antennas and Propagation Society’s Piergiorgio L. E. Uslenghi Letters Prize Paper Award in 2020. This prestigious international award was granted for his innovative research on human-body electromagnetic scattering models, underscoring his impact in both theoretical and applied electromagnetic science.

📚 Selected Publications

📡 “Method for Scattering of Electromagnetic Waves from the Human Body…” IEEE Antennas and Wireless Propagation Letters, 2019 – Cited in clinical and wearable device research.
🧠 “Spots Concept for Problems of Artificial Intelligence…” Russian Microelectronics, 2020 – Influential in neuromorphic system modeling.
🧬 “Advanced Fast 3D Electromagnetic Solver for Microwave Tomography Imaging” IEEE Transactions on Medical Imaging, 2017 – Widely cited for algorithmic advancement in medical diagnostics.
🧲 “Overcoming Insufficient Microwave Scattering Data in Microwave Tomographic Imaging” IEEE Access, 2021 – Applied in imaging systems where data resolution is limited.
💡 “Application of the Model of Spots for Inverse Problems” Sensors, 2023 – Bridging cognitive modeling with AI-driven sensor development.
🧘 “Mental Imagery Representation by Model of Spots in Psychology” Natural Systems of Mind, 2023 – Cited in cognitive neuroscience and AI literature.
🤖 “Development of an Apparatus of Imaginative Information Representation for Neuromorphic Devices” Russian Microelectronics, 2024 – Gaining attention in the neuromorphic computing community.

🧾 Conclusion

Dr. Nikolai Simonov’s scientific journey is marked by persistent curiosity, interdisciplinary mastery, and a passion for innovation. From foundational electromagnetic theory to AI-inspired cognitive modeling, his research contributions continue to shape modern science and technology. His award-winning publications, global research engagements, and visionary approach to complex problems make him a compelling and deserving nominee for the Best Researcher Award. His ongoing work not only pushes the boundaries of scientific understanding but also opens transformative pathways for the future of intelligent systems.

Arturo Benayas Ayuso | Generative Artificial Intelligence | Best Researcher Award

Prof. Arturo Benayas Ayuso | Generative Artificial Intelligence | Best Researcher Award

PhD Candidate at Polytechnic University of Madrid, Spain

Arturo Benayas Ayuso is a highly skilled naval architect with over two decades of experience in naval shipbuilding, digitization, and PLM (Product Lifecycle Management) systems integration. Known for his contributions to advancing digital solutions in the naval sector, he currently leads the integration efforts for NAVANTIA’s “El Cano” platform, which leverages cutting-edge technologies under the Industry 4.0 paradigm. This platform integrates complex processes in ship design, construction, and maintenance, marking a significant stride in naval digitization. Arturo is recognized for his leadership, technical expertise, and commitment to continuous improvement, which have consistently contributed to both national defense and international maritime innovation. His career reflects a dynamic blend of hands-on expertise, theoretical knowledge, and thought leadership within his field.

Profile

ORCID

Education

Arturo’s educational background is grounded in naval architecture, with a Master’s degree from the prestigious Universidad Politécnica de Madrid. His specialized training in marine motors provided him with a strong foundation for understanding the technical demands of naval engineering. Currently, Arturo is pursuing a PhD focused on IoT applications in ship design, construction, and management, further expanding his research in digitalization and its transformative impacts on the naval industry. His academic pursuits are complemented by numerous advanced courses in PLM platforms, machine learning, and materials science, reflecting his commitment to staying at the forefront of technological advancements relevant to his field.

Professional Experience

Arturo’s professional career spans pivotal roles in renowned engineering firms and projects within the naval and aerospace industries. His experience includes serving as a Technical Account Manager, Solution Architect, and Associate Manager, where he has spearheaded complex PLM integrations, notably in projects such as the Spanish Navy’s S80P submarine and the collaborative development of the Royal Navy’s CVF program. His role as Integration Lead for the “El Cano” platform exemplifies his capability to manage large teams, oversee end-to-end PLM implementations, and introduce digital solutions that optimize naval operations on an international scale. Throughout his career, Arturo has contributed to innovative projects, ensuring seamless transitions across software platforms and providing critical support for project management in challenging environments.

Research Interests

Arturo’s research interests lie at the intersection of naval architecture, digital transformation, and the Internet of Things (IoT). His doctoral research focuses on applying IoT to streamline and enhance various stages of ship design, manufacturing, and management. By leveraging data analytics, he explores ways to optimize shipbuilding efficiency and reduce costs. Arturo is also passionate about cybersecurity in IoT networks, recognizing the importance of robust security measures in protecting sensitive maritime operations. Additionally, he has an interest in machine learning and its potential applications in automating design processes, which could significantly advance naval engineering and shipyard productivity.

Awards and Recognitions

While Arturo has not received specific awards to date, his role as a thought leader and influential practitioner in naval PLM integration has earned him considerable recognition in his field. His significant contributions to NAVANTIA’s “El Cano” platform have been widely regarded as a benchmark for digital transformation within the naval industry. Furthermore, his insights on naval digitization and IoT applications in shipbuilding have been published in respected journals and presented at international conferences. These accomplishments underscore his impact on the industry and his commitment to innovation.

Publications

Benayas Ayuso, A. & Cebollero, A. (2011). “Integrated Development Environment in Shipbuilding Computer Systems.” ICAS Conference Paper. Cited by 17.
Benayas-Ayuso, A., & Pérez Fernández, R. (2018). “Automated/Controlled Storage for an Efficient MBOM Process in the Shipbuilding Managing the IoT Technology.” RINA Smart Ship Technology. Cited by 22.
Pérez Fernández, R., & Benayas-Ayuso, A. (2018). “Data Management for Smart Ship or How to Reduce Machine Learning Cost in IoS Applications.” RINA Smart Ship Technology. Cited by 18.
Benayas-Ayuso, A., & Pérez Fernández, R. (2019). “What does the Shipbuilding Industry Expect from the CAD/CAM/CAE Systems in the Next Years?” Naval Architect Magazine. Cited by 13.
Benayas Ayuso, A. (2021). “Internet of Things Cybersecurity – Blockchain as First Securitisation Layer of an IoT Network.” In Introduction to IoT in Management Science and Operations Research. Cited by 25.

Conclusion

Arturo Benayas Ayuso’s career exemplifies a blend of practical expertise and research-driven innovation. His contributions to naval digitalization, particularly through his work on the “El Cano” platform, highlight his commitment to integrating advanced technologies in shipbuilding. Arturo’s focus on IoT and cybersecurity, coupled with his passion for teaching, positions him as a forward-thinking leader in his field. As he continues to contribute to the academic and professional spheres, his research has the potential to reshape naval engineering, making him a strong candidate for the Best Researcher Award. His work reflects a dedication to innovation, resilience in navigating complex projects, and a vision for the future of naval architecture and digital integration.

Majdi Khalid | Machine learning | Best Researcher Award

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.