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

 

 

Angeliki Antoniou | AI | Best Researcher Award

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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

 

Amena Darwish | Machine learning | Best Researcher Award

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Ms. Amena Darwish | Machine learning | Best Researcher Award

Ms. Amena Darwish | Machine learning | PhD Student at University of Skovde | Sweden

Ms. Amena Darwish is a data scientist whose expertise lies in the integration of artificial intelligence and data-driven approaches into industrial and scientific applications. With a strong foundation in software engineering and advanced data science, she has established herself as a researcher focused on applying deep learning models to solve complex real-world challenges. Her work emphasizes predictive analytics, intelligent manufacturing, and process optimization, where she leverages the power of machine learning and information fusion to uncover insights often overlooked by traditional models. She has demonstrated her capacity to translate academic knowledge into applied innovations, bridging the gap between research and industry.

Academic Profile

Scopus |  ORCID

Education

Ms. Amena Darwish has pursued a solid academic path in information technology and data science, beginning with formal studies in software engineering that laid the groundwork for her understanding of computational systems and programming. She advanced her qualifications with a master’s degree in data science, where she deepened her expertise in advanced statistical modeling, neural networks, and machine learning techniques. Building upon this foundation, she is currently engaged in doctoral research in data science at the University of Skövde, focusing on industrial applications of deep learning for process modeling and optimization. Her educational journey reflects a consistent commitment to advancing her knowledge and contributing to the rapidly evolving field of artificial intelligence.

Experience

Ms. Amena Darwish has accumulated diverse experience in both academic and industrial research environments. She has served as a research assistant, contributing to projects that combined machine learning techniques with practical applications such as driver behavior modeling and industrial defect detection. Her experience also includes collaborative work with global industrial partners, where she applied predictive simulation and data-driven models to optimize processes in manufacturing. Beyond research, she has worked as a programmer and educator, developing software solutions and teaching programming fundamentals to students. These experiences demonstrate her versatility, as she has effectively balanced theoretical research with applied problem-solving and knowledge dissemination.

Research Interest

Ms. Amena Darwish’s research interests center on deep learning, artificial intelligence, and data-driven modeling with a focus on industrial systems. She is particularly engaged in developing predictive models for welding process optimization, defect detection, and quality improvement in advanced manufacturing. Her work often involves combining neural networks with multispectral sensor analysis, data mining, and simulation techniques to achieve greater accuracy and efficiency. She is also interested in information fusion and business intelligence, exploring how data can be integrated from multiple sources to inform decision-making and enhance system performance. Her broader interest lies in shaping intelligent, adaptive systems that can improve safety, efficiency, and reliability across different industrial domains.

Award

Ms. Amena Darwish has been recognized for her academic excellence and research contributions in artificial intelligence and data science. Her achievements in bridging theoretical AI concepts with industrial applications have earned her acknowledgment within academic and professional circles. By contributing to high-quality publications indexed in leading databases and participating in collaborative projects with industry leaders, she has established herself as a promising researcher whose work contributes both to academic advancement and societal impact. Her ability to combine innovation, collaboration, and technical expertise positions her as a candidate for prestigious international recognition.

Selected Publication

  • Investigating the ability of deep learning to predict welding depth and pore volume in hairpin welding (Published 2025, Citations: 16)

  • Weld Defect Detection in Laser Beam Welding Using Multispectral Emission Sensor Features and Machine Learning (Published 2024, Citations: 22)

  • Learning Individual Driver’s Mental Models Using POMDPs and BToM (Published 2020, Citations: 31)

Conclusion

Ms. Amena Darwish is a data scientist of exceptional promise whose academic background, research expertise, and practical experience reflect her commitment to advancing artificial intelligence and its applications. Her work addresses critical industrial challenges through data-driven methods that improve efficiency, safety, and quality in manufacturing and beyond. With strong contributions to international research, active collaborations with industry, and impactful publications in reputable venues, she has demonstrated both scholarly excellence and practical relevance. Ms. Darwish embodies the qualities of an innovative researcher and future leader, making her highly deserving of recognition through this award. Her trajectory suggests continued impactful contributions to data science and artificial intelligence, both in academia and in broader society.

Sathiyabhama Balasubramaniam | Artificial Intelligence | Best Researcher Award

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Dr. Sathiyabhama Balasubramaniam | Artificial Intelligence | Best Researcher Award

Professor at Sona College of Technology, India

Dr. B. Sathiyabhama is a highly accomplished academician and researcher, currently serving as the Professor and Head of the Department of Computer Science and Engineering at Sona College of Technology, Salem, Tamil Nadu, India. She holds the distinguished position of Dean Admissions and Chief Coordinator of International Relations at the same institution. With an extensive career spanning over three decades, Dr. Sathiyabhama has contributed significantly to the fields of data mining, big data analytics, computational intelligence, and health informatics. Her leadership and commitment to higher education have earned her widespread recognition, both nationally and internationally.

Profile:

Google Scholar

Education:

Dr. Sathiyabhama’s educational journey began with a Bachelor of Engineering (B.E.) degree, followed by a Master of Technology (M.Tech.) from a prestigious institution. She completed her M.Tech project internship at the renowned Bioinformatics Centre, Indian Institute of Science (IISC), Bangalore, where she also secured a university rank. Her academic pursuits culminated with a Doctor of Philosophy (Ph.D.) from the National Institute of Technology, Tiruchirappalli, one of India’s leading engineering institutes. Dr. Sathiyabhama’s academic excellence and commitment to her research have provided her with a solid foundation for her career in both teaching and research.

Experience:

Dr. Sathiyabhama brings nearly 31 years of teaching experience to her profession, imparting knowledge in diverse areas of computer science and engineering. She has a wealth of expertise in areas such as data mining, big data analytics, bioinformatics, algorithm analysis, compiler design, and optimization. Throughout her career, she has not only focused on delivering high-quality education but also on fostering a research-driven environment that encourages students to engage in innovative projects. Her dedication to her students is reflected in her consistent ability to produce excellent results. Additionally, Dr. Sathiyabhama has held key administrative positions, including as the Head of the Centre for Data Mining and Database System Design, further enhancing her role as a leader in academic innovation.

Research Interests:

Dr. Sathiyabhama’s research interests lie primarily in the fields of data mining, computational intelligence, health informatics, bioinformatics, and big data analytics. Her work focuses on developing advanced algorithms for the analysis of large datasets and applying these techniques in various domains such as healthcare and bioinformatics. She is deeply committed to exploring how technology can be used to solve real-world problems, especially in healthcare, through innovations like wearable devices and data-driven healthcare monitoring systems. Dr. Sathiyabhama has also contributed to research on optimization techniques and machine learning, with a focus on improving the impact of healthcare systems through the application of AI and data analytics.

Awards and Recognitions:

Throughout her career, Dr. Sathiyabhama has received numerous accolades recognizing her contributions to education, research, and the professional community. She has been honored with awards such as the Best Outgoing PG Student Award during her M.Tech course and the Best Women Engineer award by the Institution of Engineers (India). Dr. Sathiyabhama is a recipient of the Excellence in Teaching award and has been recognized for producing outstanding academic results. She has also been selected as a candidate for the “Who’s Who in the World” and “Cambridge Who’s Who” editions, a prestigious recognition for her work in science and engineering. Dr. Sathiyabhama has received multiple nominations and awards for her work in research and development, including a patent granted in her name and recognition for her leadership in AICTE-UKIERI leadership development programs.

Publications:

Dr. Sathiyabhama has made significant contributions to the academic community, with 144 publications across international and national journals, conferences, and books. Her notable works include a book chapter on IoT-based non-invasive wearable healthcare monitoring systems published by Wiley and co-authored books on Professional Ethics and Fundamentals of Computing. Dr. Sathiyabhama’s research has also been widely cited by other academic articles and continues to influence the fields of computational intelligence, bioinformatics, and big data analytics. Below are a few of her significant publications:

  1. Sathiyabhama, B., & Rajeswari, K. C. (Year). “IoT based Noninvasive Wearable and Remote Intelligent Pervasive Healthcare Monitoring Systems for Elderly.” Wiley Publications.

  2. Sathiyabhama, B., & others (Year). “Fundamentals of Computing.” Sonaversity Publications.

  3. Sathiyabhama, B., & others (Year). “Professional Ethics.” Sonaversity Publications.

Conclusion:

In conclusion, Dr. B. Sathiyabhama stands as a distinguished academician and researcher whose work in data mining, big data analytics, and health informatics has had a profound impact on both her students and the academic community. With decades of teaching experience and numerous accolades to her name, she continues to inspire and lead in the fields of education and technology. Dr. Sathiyabhama’s ongoing research and her commitment to advancing knowledge and innovation ensure that her contributions will have a lasting impact on the future of technology and education. As she continues to make strides in her professional career, her work remains at the forefront of integrating technology with real-world solutions, particularly in the healthcare sector.

Ahmed Ghazi BLAIECH | Artificial intelligence | Best Researcher Award

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Mr. Ahmed Ghazi BLAIECH | Artificial intelligence | Best Researcher Award

Mr. Ahmed Ghazi BLAIECH | Artificial intelligence-Associate professor at Higher Institute of Applied Sciences and Technology of Sousse, Tunisia

Ahmed Ghazi Blaiech is a distinguished academic and researcher in the field of computer science, currently serving as an Assistant Professor at the High Institute of Applied Sciences and Technology of Sousse (ISSATSo), University of Sousse. With extensive experience in artificial intelligence, machine learning, and real-time computing, he has made significant contributions to the development of innovative deep learning models and neural networks. His research focuses on medical imaging, embedded systems, and FPGA-based accelerators. Over the years, he has been instrumental in fostering cutting-edge technological advancements through both research and academic mentoring.

Profile:

Orcid | Scopus | Google Scholar

Education:

Ahmed Ghazi Blaiech has an extensive academic background in computer science and informatics systems. He obtained his Habilitation thesis in Engineering of Informatics Systems from the National Engineering School of Sfax (ENIS) in 2022. Prior to that, he earned his PhD in Engineering of Informatics Systems in 2015 from the same institution, graduating with first-class honors. He also holds a Master’s degree in Safety and Security of Industrial Systems with a specialization in Real-Time Computer Science from the High Institute of Applied Sciences and Technology of Sousse. His foundational academic journey began with a Licence degree in Computer Science from the same institute in 2006.

Experience:

Dr. Blaiech has accumulated over a decade of teaching and research experience in academia. Since 2017, he has been an Assistant Professor at ISSATSo, contributing to various undergraduate and postgraduate courses. Before this, he served as an Assistant in Computer Science at ISSATSo (2016-2017) and at the High Institute of Computer Science and Multimedia of Gabes, University of Gabes (2011-2015). He also worked as a contractual assistant at the Faculty of Sciences of Monastir, University of Monastir (2008-2011). In addition to his teaching roles, he has actively led numerous research initiatives and coordinated academic programs.

Research Interests:

Dr. Blaiech’s research interests span multiple domains within artificial intelligence, machine learning, and real-time computing. His work is particularly focused on deep learning applications in medical imaging, embedded systems, and hardware-accelerated computing using FPGA-based architectures. He has also contributed to the advancement of intelligent pervasive systems and neural networks for real-time applications. His research outputs have been widely recognized in high-impact journals, showcasing innovative methodologies in biomedical signal processing, image synthesis, and classification techniques.

Awards and Recognitions:

Throughout his career, Dr. Blaiech has received several accolades for his contributions to the field of computer science. He holds multiple prestigious certifications, including the Huawei Certified ICT Associate (HCIA) in Artificial Intelligence and the Microsoft Technology Associate (MTA) for Python programming. He has also been recognized for his mentorship and coaching in AI-related competitions, playing a crucial role in fostering innovation among students and researchers.

Publications:

Dr. Blaiech has authored numerous research papers in high-impact journals, contributing to advancements in artificial intelligence and medical imaging. Some of his notable publications include:

📌 “CNN-based classification of epileptic states for seizure prediction using combined temporal and spectral features” – Biomedical Signal Processing and Control, 2022. DOI 📖
📌 “An innovative medical image synthesis based on dual GAN deep neural networks for improved segmentation quality” – Applied Intelligence, 2022. DOI 📖
📌 “Comparison by multivariate auto-regressive method of epileptic seizures prediction for real patients and virtual patients” – Biomedical Signal Processing and Control, 2021. DOI 📖
📌 “Innovative deep learning models for EEG-based vigilance detection” – Neural Computing and Applications, 2020. DOI 📖
📌 “A Novel Hardware Systolic Architecture of a Self-Organizing Map Neural Network” – Computational Intelligence and Neuroscience, 2019. DOI 📖
📌 “A New Hardware Architecture for Self-Organizing Map Used for Colour Vector Quantization” – Journal of Circuits, Systems, and Computers, 2019. DOI 📖
📌 “A Survey and Taxonomy of FPGA-based Deep Learning Accelerators” – Journal of Systems Architecture, 2019. DOI 📖

Conclusion:

Dr. Ahmed Ghazi Blaiech’s contributions to the field of artificial intelligence and medical computing have been impactful in both research and academia. His dedication to technological innovation, particularly in neural networks and real-time computing, has positioned him as a leader in the domain. His extensive research output, coupled with his teaching and mentoring experience, underscores his significant role in advancing knowledge and fostering the next generation of AI researchers. Through his work, he continues to drive progress in medical imaging, deep learning applications, and FPGA-based architectures, making a lasting impact in his field.

Oswald Chong | Artificial Intelligence | Best Researcher Award

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Dr. Oswald Chong | Artificial Intelligence | Best Researcher Award

Dr. Oswald Chong | Artificial Intelligence-Associate Professor at Arizona State University, United States

Dr. Wai Oswald Chong is an esteemed Associate Professor at Arizona State University, specializing in sustainable engineering and the built environment. His pioneering work integrates artificial intelligence, data science, and engineering principles to optimize infrastructure design, construction, and sustainability. With a focus on carbon-neutral solutions and resource optimization, his research has significantly influenced the fields of green building, lifecycle assessment, and energy efficiency. Over the years, Dr. Chong has led numerous groundbreaking projects, contributing to the advancement of engineering practices and sustainability in the built environment.

Profile:

Scopus | Orcid

Education:

Dr. Chong pursued his higher education in engineering, earning advanced degrees that laid the foundation for his expertise in sustainable engineering. His academic journey was marked by a strong commitment to integrating data science and engineering, equipping him with the skills to develop innovative solutions for complex infrastructure challenges. Throughout his academic training, he focused on optimizing construction processes, reducing environmental impact, and enhancing resource efficiency.

Experience:

With an extensive background in academia and industry, Dr. Chong has held key roles in research, teaching, and consultancy. As an Associate Professor at Arizona State University, he has mentored students, conducted cutting-edge research, and collaborated with global institutions. His work spans multiple disciplines, including civil, fire, electrical, mechanical, and green engineering. His involvement in international projects and consultancy roles has strengthened his reputation as a leading expert in sustainable engineering, contributing valuable insights to the industry’s evolution.

Research Interests:

Dr. Chong’s research focuses on the intersection of engineering, artificial intelligence, and sustainability. His key areas of interest include:

  • Knowledge Systems and Models: Integrating codes, standards, regulations, and best practices across multiple engineering domains.
  • Data-Driven Engineering Optimization: Utilizing AI and big data to enhance project design, safety, cost efficiency, and lifecycle management.
  • Resource Optimization: Enhancing the sustainable use of energy, water, raw materials, and carbon in construction projects.
  • Carbon-Neutral Solutions: Developing predictive analytics and lifecycle assessments to minimize environmental footprints.
  • Circular Economy in Semiconductor Industry: Establishing frameworks to improve sustainability in high-tech industries.

Awards & Recognitions:

Dr. Chong’s contributions have been widely recognized through prestigious awards and accolades. His innovative research in sustainable engineering has earned him funding from leading institutions, including the National Science Foundation and various governmental agencies. His projects on carbon emissions modeling and lifecycle performance have been instrumental in shaping policies and best practices in energy-efficient engineering.

Selected Publications 📚:

  1. Event-Induced Anomalies in Energy Consumption – ASCE Journal of Architectural Engineering (2025) 📅 🔗 https://ascelibrary.org/article/10.1061/(ASCE)AE.1943-5568.0000231
    🔍 Cited by 15 articles
  2. Optimizing HVAC Systems for Semiconductor Fabrication – Journal of Building Engineering (2024) 📅 🔗 https://doi.org/10.1016/j.jobe.2024.109397
    🔍 Cited by 30 articles
  3. Semiconductor Fab Energy Optimization – Engineering Technology (2024) 📅 🔗 https://juniperpublishers.com/etoaj/pdf/ETOAJ.MS.ID.555674.pdf
    🔍 Cited by 22 articles
  4. Determining Critical Success Factors for Urban Residential Reconstruction – Sustainable Cities and Society (2023) 📅 🔗 https://doi.org/10.1016/j.scs.2023.104977
    🔍 Cited by 18 articles
  5. Empowering Owners of Small and Medium Commercial Buildings – Energies (2023) 📅 🔗 https://doi.org/10.3390/en16176191
    🔍 Cited by 12 articles
  6. Quality Management Platform During COVID-19 – Journal of Civil Engineering and Management (2023) 📅 🔗 https://doi.org/10.3846/jcem.2023.18687
    🔍 Cited by 10 articles
  7. Big Data and Cloud Computing for Sustainable Building Energy Efficiency – Elsevier Science and Technology (2016) 📅 🔗 https://doi.org/10.1016/j.jobe.2024.109397
    🔍 Cited by 50 articles

Conclusion:

Dr. Wai Oswald Chong is a distinguished researcher whose work has significantly advanced the field of sustainable engineering. His dedication to integrating AI and data science into engineering has led to the development of more efficient, environmentally friendly, and cost-effective construction practices. With a strong record of publications, ongoing research, and impactful industry collaborations, he stands as a deserving candidate for the Best Researcher Award. His expertise and contributions continue to shape the future of engineering, promoting sustainable development and innovation in the built environment.

 

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.

Mohammad Javad Mahmoodabadi | AI Engineering | Best Paper Award

Published on by Academic Achievements

Assoc. Prof. Dr. Mohammad Javad Mahmoodabadi | AI Engineering | Best Paper Award

Assoc. Prof. Dr. Mohammad Javad Mahmoodabadi | AI Engineering – Associate Professor at Sirjan University of Technology, Iran

Dr. Mohammad Javad Mahmoodabadi is an accomplished academic and researcher, currently serving as an Associate Professor in the Department of Mechanical Engineering at Sirjan University of Technology, Iran. With an impressive track record in mechanical engineering and control theory, Dr. Mahmoodabadi has made significant contributions to the fields of optimization algorithms, machine learning, and mechanical design. He is highly regarded for his innovative approaches in robotics, control engineering, and computational methods. His research has been widely published and cited, establishing him as a leader in his area. Dr. Mahmoodabadi has also played an instrumental role in mentoring graduate students, guiding them through cutting-edge research in nonlinear systems and robotics.

Professional Profile

ORCID | Scopus

Education

Dr. Mahmoodabadi’s educational background reflects a solid foundation in mechanical engineering. He earned his Ph.D. in Mechanical Engineering from the University of Guilan, Iran, in 2012. His dissertation focused on the multi-objective optimization of linear and nonlinear controllers, combining powerful optimization techniques such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). During his Ph.D., Dr. Mahmoodabadi achieved excellent academic performance, earning a GPA of 18.80 out of 20 and a dissertation grade of 19 out of 20. Prior to this, he completed his Master’s degree in Mechanical Engineering at Shahid Bahonar University of Kerman, Iran, where his thesis dealt with elasto-static problems using meshless methods. His academic achievements have provided him with a deep understanding of both theoretical and applied mechanics, which have been pivotal in his research career.

Experience

Dr. Mahmoodabadi’s academic career spans over a decade, during which he has held several important positions. After earning his Ph.D., he served as an Assistant Professor at Sirjan University of Technology from 2012 to 2019, before advancing to the role of Associate Professor. Throughout his career, he has taught various undergraduate and graduate courses, including robotics, control of robots, linear control, fuzzy logic, and optimization. His extensive teaching experience in mechanical engineering and related disciplines has earned him recognition for his ability to convey complex concepts with clarity. In addition to his teaching roles, Dr. Mahmoodabadi has served as the head of the Department of Mechanical Engineering and the Graduate Student Office at his university. His leadership has contributed to the development of academic programs and research initiatives within the department.

Research Interests

Dr. Mahmoodabadi’s research interests are diverse, with a primary focus on control theory, machine learning, computational methods, and optimization algorithms. He has worked on various topics such as adaptive robust control, fuzzy logic systems, and multi-objective optimization in the context of nonlinear dynamic systems. His research also extends to robotics, where he has developed novel control strategies for autonomous systems. Additionally, Dr. Mahmoodabadi’s work on mechanical design and analysis of complex systems has led to innovative solutions in both theoretical and applied engineering. His approach integrates computational techniques with practical applications, particularly in optimization and control engineering.

Awards

Throughout his career, Dr. Mahmoodabadi has received numerous accolades for his contributions to research and teaching. His excellence in academic leadership and groundbreaking research has earned him recognition within his institution and the broader academic community. Notably, his work in the development of control algorithms and optimization methods has received significant attention from his peers, reflected in his high citation count and his role as a mentor to graduate students. Although Dr. Mahmoodabadi has not explicitly listed awards in the traditional sense, his impact on the academic and research community through his publications, patents, and leadership roles can be considered as a testament to his achievements.

Publications

M.J. Mahmoodabadi, N.R. Babak, Pareto optimum design of an adaptive robust backstepping controller for an unmanned aerial vehicle, Asian Journal of Control (2022). 📚
R. Abedzadeh Maafi, S. Etemadi Haghighi, M.J. Mahmoodabadi, A novel multi-objective optimization algorithm for Pareto design of a fuzzy full state feedback linearization controller applied on a ball and wheel system, Transactions of the Institute of Measurement and Control 44 (7) (2022), 1388–1409. 🛠
M.J. Mahmoodabadi, S. Hadipour Lakmesari, Optimal design of an adaptive robust controller using a multi-objective artificial bee colony algorithm for an inverted pendulum system, Transactions of the Canadian Society for Mechanical Engineering 46 (1) (2022), 89–102. 📈
S.H. Lakmesari, M.J. Mahmoodabadi, Adaptive sliding mode control of HIV-1 infection model, Informatics in Medicine Unlocked 25 (2021), 100703. 💡
M.J. Mahmoodabadi, Moving least squares approximation-based online control optimized by the team game algorithm for Duffing-Holmes chaotic problems, Cyber-Physical Systems 7 (2) (2021), 1-21. ⚙️
M.J. Mahmoodabadi, A.R. Nemati, A new optimum numerical method for analysis of nonlinear conductive heat transfer problems, Journal of the Brazilian Society of Mechanical Sciences and Engineering 43 (5) (2021), 1-8. 🔥
R. Abedzadeh Maafi, S. Etemadi Haghighi, M.J. Mahmoodabadi, Pareto optimal design of a fuzzy adaptive hierarchical sliding-mode controller for an XZ inverted pendulum system, IETE Journal of Research (2021). 🔄

Conclusion

Dr. Mohammad Javad Mahmoodabadi’s academic and research career exemplifies excellence in mechanical engineering and control systems. His innovative work in optimization algorithms, machine learning, and mechanical design has earned him recognition as a leader in his field. With a strong publication record and significant contributions to the academic community, he is a well-deserving candidate for the “Best Researcher Award.” His ability to blend theoretical advancements with practical applications, along with his mentorship of future researchers, positions him as a key figure in the development of engineering solutions for complex systems. Dr. Mahmoodabadi’s dedication to advancing knowledge, combined with his academic leadership and impactful research, makes him an outstanding nominee for this prestigious award.

Zhiqiang He | Artificial Intelligence | Best Researcher Award

Published on by Academic Achievements

Dr. Zhiqiang He | Artificial Intelligence | Best Researcher Award 

Ph.D. at The university of Electro-Communications, China

Zhiqiang He is an emerging researcher specializing in reinforcement learning and artificial intelligence (AI), with a focus on developing and optimizing control algorithms for complex systems. He has made significant contributions to both academic research and industrial applications, demonstrating expertise in designing innovative AI solutions for real-world problems. His educational background in control science and engineering, combined with practical experiences at leading tech companies, has shaped his career and led to several impactful publications in renowned journals. Zhiqiang’s accomplishments, recognized through various academic awards and industry achievements, make him a strong candidate for the “Best Researcher Award.”

Profile

ORCID

Education

Zhiqiang pursued his Master of Science in Control Science and Engineering at Northeastern University (NEU), Shenyang, China, from September 2019 to June 2022, where he maintained a commendable GPA of 3.29/4. During his master’s program, he specialized in the development of reinforcement learning algorithms, which formed the cornerstone of his research. Prior to this, he earned his Bachelor of Science in Automation at East China Jiaotong University (ECJTU), Nanchang, China, from September 2015 to June 2019, with a GPA of 3.42/4. His undergraduate studies laid a strong foundation in automation and control systems, providing the technical skills and knowledge that fueled his passion for AI and intelligent decision-making.

Experience

Throughout his academic journey, Zhiqiang actively engaged in research and industry roles that enriched his experience in the field of AI. He served as a team leader at the Institute of Deep Learning and Advanced Intelligent Decision-Making at NEU, where he worked on the development of reinforcement learning algorithms. Leading projects from September 2020 to June 2021, he conducted research on model-based reinforcement learning, optimized algorithm performance, and supervised students in their projects. Additionally, his early experience as a team leader at the Jiangxi Province Advanced Control and Key Optimization Laboratory involved applying reinforcement learning to control problems from 2016 to 2019, where he gained hands-on skills in analyzing system behaviors and establishing Markov Decision Process (MDP) models.

In the industry, Zhiqiang took on roles that deepened his technical expertise. He was an intern at Baidu, Beijing, China, where he pioneered the development of the Expert Data-Assisted Multi-Agent Proximal Policy Optimization (EDA-MAPPO) algorithm, an innovative approach to multi-agent cooperative adversarial AI. Later, as a reinforcement learning algorithms engineer at InspirAI in Hangzhou, he led the development of AI strategies for popular card games, showcasing his ability to apply AI solutions to commercial projects and enhance algorithmic performance.

Research Interest

Zhiqiang’s research interests are centered on reinforcement learning, AI, and control systems. He focuses on designing algorithms that improve the efficiency and accuracy of AI models in decision-making tasks. His work involves exploring new methods for multi-agent reinforcement learning, optimizing algorithms for real-time applications, and addressing challenges in intelligent control. By bridging theoretical research with practical applications, he aims to push the boundaries of AI, making it more adaptable and applicable to various industries. His dedication to advancing reinforcement learning techniques aligns with the future trajectory of AI research, where automation and intelligent decision-making are key drivers of innovation.

Awards

Zhiqiang has received recognition for his academic excellence and research contributions throughout his career. He was honored as an “Outstanding Graduate” by East China Jiaotong University in 2019, acknowledging his academic achievements and leadership potential. In addition, he secured the Third Prize in the 15th “Challenge Cup” Jiangxi Division in 2017 and the Second Prize in the International Mathematical Modeling Competition for American College Students in 2018, demonstrating his problem-solving skills and competitive spirit. His active engagement in professional development is further highlighted by his certifications in network technology and programming languages, which add to his multidisciplinary skill set.

Publications

He Z, Qiu W, Zhao W, et al. Understanding World Models through Multi-Step Pruning Policy via Reinforcement Learning. Information Sciences, 2024: 121361. – Cited by 32 articles.

Chen P, He Z, Chen C, et al. Control strategy of speed servo systems based on deep reinforcement learning. Algorithms, 2018, 11(5): 65. – Cited by 15 articles.

Wang J, Zhang L, He Z, et al. Erlang planning network: An iterative model-based reinforcement learning with multi-perspective. Pattern Recognition, 2022, 128: 108668. – Cited by 27 articles.

Zhang L, He Z, Zhao Y, et al. Reinforcement Learning-based Control of Robotic Manipulators. Journal of Robotics, 2023, 12(3): 112-121. – Cited by 19 articles.

He Z, Zhao W, Zhang L, et al. Multi-Agent Deep Reinforcement Learning in Dynamic Environments. Artificial Intelligence Review, 2022, 55(2): 456-472. – Cited by 24 articles.

Chen C, He Z, Qiu W, et al. Optimal Control for Nonlinear Systems Using Reinforcement Learning. Control Theory and Applications, 2021, 59(4): 553-566. – Cited by 18 articles.

Conclusion

Zhiqiang He’s contributions to AI and reinforcement learning, coupled with his practical experience and research output, position him as a promising researcher in the field. His work not only advances the academic understanding of intelligent control but also finds applications in industry, where AI solutions are critical to technological development. By consistently pushing for excellence in his projects, he demonstrates qualities that make him a deserving candidate for the “Best Researcher Award.” His trajectory reflects a commitment to innovation, making him an asset to the research community and a potential leader in future AI advancements.