Ali Mehrizi | Machine Learning | Best Paper Award

Dr. Ali Mehrizi | Machine Learning | Best Paper Award

Lecturer at Ferdowsi University of Mashhad, Iran.

Ali Mehrizi is a distinguished researcher and lecturer in Artificial Intelligence (AI) and Machine Learning at Ferdowsi University of Mashhad (FUM), Iran. With a wealth of experience exceeding a decade, his expertise spans adaptive probabilistic models, distributed learning, multi-target tracking, time series forecasting, and Gaussian Mixture Probability Hypothesis Density (GMPHD) methods. Dr. Mehrizi has published multiple impactful articles in renowned journals such as Expert Systems with Applications and Fuzzy Sets and Systems. He is deeply committed to advancing the understanding and application of AI techniques and has successfully mentored numerous students in areas ranging from Data Mining to Advanced Operating Systems.

Profile

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Education

Dr. Mehrizi educational background is rooted in Artificial Intelligence. He is currently pursuing a Ph.D. in AI at Ferdowsi University of Mashhad (2017–2024), under the supervision of Professor H. Sadoghi Yazdi. His dissertation focuses on financial time series forecasting using experience-based adaptive learning, a project that has already produced several publications in top-tier journals. Previously, he earned an M.Sc. in AI from Azad University of Mashhad (2011–2013), where he worked on adaptive semi-supervised learning, optimizing self-organizing map models. His early academic journey began with a B.Sc. in Computer Engineering from the University of Birjand, later transferring to Azad University of Mashhad.

Experience

Dr. Mehrizi professional career spans various roles, beginning in 2001 when he became the IT & Network Manager at the Faculty of Engineering. In this capacity, he significantly improved the system performance and network management. Since 2011, he has been involved in research in AI and Machine Learning, contributing to the development of machine learning models and publishing his findings in high-impact journals. He has also served as a lecturer since 2013, teaching a variety of undergraduate and graduate courses, including Data Mining, Operating Systems, and Advanced Operating Systems. As a researcher, he has mentored students in their theses, particularly in machine learning and pattern recognition, fostering the next generation of AI experts.

Research Interests

Dr. Mehrizi  research interests are broad, focusing on several key areas within the domain of AI. His work on distributed adaptive learning, particularly through Diffusion LMS and Diffusion RLS, aims to optimize decentralized data processing for dynamic systems. In addition, he has contributed to probabilistic and hypothesis-based learning, exploring the use of Gaussian Mixture Probability Hypothesis Density (GMPHD) models for uncertainty-based learning and tracking. His research also delves into time series analysis and forecasting, with a particular focus on financial markets. Dr. Mehrizi’s interest in multi-target tracking extends to real-time tracking algorithms, emphasizing performance in noisy and incomplete data environments. He is also committed to semi-supervised learning, exploring hybrid methods that bridge supervised and unsupervised learning approaches in scenarios with limited labeled data.

Awards

Dr. Mehrizi contributions to the fields of AI and machine learning have earned him recognition in various academic and professional circles. He has been nominated for multiple awards for his research, particularly in adaptive learning and time series forecasting. His work is highly regarded in the academic community, and he continues to push the boundaries of AI research, especially in the areas of distributed learning and multi-target tracking.

Publications

Dr. Mehrizi has authored several articles in well-respected journals in AI and machine learning. His key publications include:

Mehrizi, A., & Yazdi, H. S. (2019). “Adaptive probabilistic methods for long-term financial time series forecasting.” Expert Systems with Applications.

Mehrizi, A., & Yazdi, H. S. (2020). “Semi-supervised learning using GSOM for adaptive classification.” Fuzzy Sets and Systems.

Mehrizi, A. (2022). “Distributed adaptive learning for dynamic systems using Diffusion LMS and RLS.” Emerging Markets Finance and Trade.

Mehrizi, A., & Yazdi, H. S. (2021). “Gaussian Mixture Probability Hypothesis Density for multi-target tracking.” Journal of Machine Learning Research.

These publications have been cited extensively by various researchers in the fields of machine learning, AI, and financial forecasting, underscoring Dr. Mehrizi’s significant impact on the academic community.

Conclusion

Dr. Ali Mehrizi is a leading researcher and educator in the field of Artificial Intelligence and Machine Learning, with a deep commitment to advancing these fields through his innovative research. His extensive academic background and his practical experience in both teaching and real-world applications have made him an invaluable asset to Ferdowsi University of Mashhad. With a strong focus on adaptive learning, probabilistic models, and time series forecasting, Dr. Mehrizi continues to contribute to the evolution of AI. His work not only shapes academic research but also provides vital insights into practical AI solutions for industries like finance and engineering. As a mentor and educator, he remains dedicated to shaping the future of AI professionals and researchers.

Mamoona Humayun | Artificial intelligence | Best Researcher Award

Dr. Mamoona Humayun | Artificial intelligence | Best Researcher Award

Senior Lecturer at University of Roehampton, United Kingdom

Dr. Mamoona Humayun is a distinguished academician and researcher with over 15 years of experience in teaching and administrative roles across international institutions. She holds a Ph.D. in Computer Sciences from Harbin Institute of Technology, China. Her expertise encompasses artificial intelligence, cybersecurity, predictive analytics, and IoT integration in healthcare. She has authored over 200 publications and secured more than 20 funded research grants, reflecting her commitment to advancing innovation and technology-driven solutions in various domains.

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Education

Dr. Humayun has an impressive educational background. She earned her Ph.D. in Computer Science from Harbin Institute of Technology, China, in 2014. She holds two master’s degrees: one in Software Engineering from International Islamic University, Islamabad (2011), and another in Computer Science from the same institution (2005). Her academic journey began with a Bachelor of Science in Mathematics from F.G. College for Women, Islamabad, where she graduated with honors in 2002.

Experience

Dr. Humayun has held significant positions throughout her career. She currently serves as a Senior Lecturer at the University of Roehampton, London, UK. Previously, she was an Assistant Professor at Jouf University, Saudi Arabia, where she also coordinated research and accreditation programs. She has served in various roles at PMAS-Arid Agriculture University, Pakistan, and other institutions, contributing extensively to curriculum development, research supervision, and administrative operations.

Research Interests

Dr. Humayun’s research interests lie in artificial intelligence, cybersecurity, healthcare informatics, and IoT systems. She focuses on AI-driven chronic disease management, secure software development, and IoT integration for remote patient monitoring. Her innovative work extends to disability advocacy through AI and predictive analytics for improving healthcare outcomes.

Awards

Dr. Humayun’s accolades include being named a distinguished researcher at Jouf University for 2021-2022. She received the second-best researcher award at the College of Computer and Information Sciences. Additionally, her innovative projects and contributions have garnered recognition across academic and professional platforms.

Publications

“Cyber security threats and vulnerabilities: a systematic mapping study”

  • Year: 2020
  • Citations: 395

“Emerging smart logistics and transportation using IoT and blockchain”

  • Year: 2020
  • Citations: 278

“Internet of things and ransomware: Evolution, mitigation and prevention”

  • Year: 2021
  • Citations: 254

“Detection of skin cancer based on skin lesion images using deep learning”

  • Year: 2022
  • Citations: 208

“Secure healthcare data aggregation and transmission in IoT—A survey”

  • Year: 2021
  • Citations: 204

“Analysis of software development methodologies”

  • Year: 2019
  • Citations: 150

“Blockchain for Internet of Things (IoT) research issues challenges & future directions: A review”

  • Year: 2019
  • Citations: 132

“Energy optimization for smart cities using IoT”

  • Year: 2022
  • Citations: 121

“Cyber security issues and challenges for smart cities: A survey”

  • Year: 2019
  • Citations: 119

“Hybrid smart grid with sustainable energy efficient resources for smart cities”

  • Year: 2021
  • Citations: 117

“Privacy protection and energy optimization for 5G-aided industrial Internet of Things”

  • Year: 2020
  • Citations: 116

Conclusion

Dr. Mamoona Humayun’s exceptional achievements in research, innovation, and academic leadership make her an outstanding candidate for the “Research for Best Researcher Award.” Her contributions have not only advanced her field but also inspired students, peers, and the global research community.

Abdultaofeek Abayomi | Machine Learning | Best Researcher Award

Dr. Abdultaofeek Abayomi | Machine Learning | Best Researcher Award

Researcher at Walter Sisulu University, South Africa

ABDULTAOFEEK ABAYOMI, Ph.D., is a distinguished academic and researcher with a rich career in Information Technology and Computer Science. He holds a Ph.D. from Durban University of Technology, South Africa, and has been an influential figure in various educational institutions, including Mangosuthu University of Technology, where he served as a Postdoctoral Research Fellow and Lecturer. His extensive experience spans roles in teaching, research, and industry, with a specific focus on ICT, machine learning, and telecommunications. Dr. Abayomi’s contributions extend beyond academia, having held positions in major banks and IT firms, where he influenced projects in system analysis, IT infrastructure, and banking operations.

Profile

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Education

Dr. Abayomi’s academic journey began with a B.Sc. in Computer Science from the University of Ilorin, Nigeria, where he graduated with a Second Class Upper Division. This was followed by a Master’s in Technology (Computer Science) and an MBA from the Federal University of Technology, Akure, Nigeria. He then pursued a Ph.D. in Information Technology at Durban University of Technology, South Africa, where his doctoral research explored real-time tracking of individuals in distress situations using physiological signals, a significant contribution to the field of IT and human-centered computing.

Experience

Dr. Abayomi’s professional career spans teaching, research, and leadership roles in the technology sector. He has lectured and conducted research at various universities, including Durban University of Technology and Mangosuthu University of Technology in South Africa. Additionally, he has worked as a system analyst and instructor for IT certifications such as MCSE and MCSA at JIT Solutions in Akure, Nigeria. His career in the banking sector includes roles as a Profit Centre Manager and ICT System Administrator at United Bank for Africa Plc., where he contributed to improving operational efficiency and implementing IT solutions. Dr. Abayomi has also been involved in research projects aimed at addressing pressing issues in ICT and society, particularly focusing on the intersection of technology and human needs.

Research Interests

Dr. Abayomi’s research interests lie at the convergence of Information Technology, machine learning, and network systems. His work has explored deep learning, cognitive radio networks, spectrum sensing, and software-defined networks. He is particularly interested in the application of artificial intelligence to solve real-world problems, such as dynamic spectrum access and health insurance prediction. Dr. Abayomi’s research aims to improve the way technology interacts with human and environmental factors, making significant contributions to both academic and applied research.

Awards

Dr. Abayomi has received numerous accolades in recognition of his academic and research excellence. He was honored with the Research Award for Most Productive Postdoctoral Research Fellow in 2022 at Mangosuthu University of Technology, South Africa. He has also been an active participant in international conferences, serving as a session chair for various events such as the 22nd International Conference on Hybrid Intelligent Systems in 2022 and the 13th International Conference on Soft Computing and Pattern Recognition in 2021. His contributions to research are further exemplified by his involvement in winning the South African National Research Foundation’s Infrastructure Bridging Funding in 2016.

Publications

Dr. Abayomi’s scholarly work is well-regarded in academic circles, with several impactful publications in peer-reviewed journals. His notable publications include:

Ukpong, U.C., Idowu-Bismark, O., Adetiba, E., Kala, J.R., Owolabi, E., Oshin, O., Abayomi, A., Dare, O.E. (2025). “Deep reinforcement learning agents for dynamic spectrum access in television whitespace cognitive radio networks.” Scientific African, 27, e02523.

Dare, O.E., Okokpujie, K., Adetiba, E., Idowu-Bismark, O., Abayomi, A., Kala, R.J., Owolabi, E., Ukpong, U.C. (2024). “Development of a Conditional Generative Adversarial Network Model for Television Spectrum Radio Environment Mapping.” IEEE Access, 12, 197632-197644.

Mavundla, K., Thakur, S., Adetiba, E., Abayomi, A. (2024). “Predicting Cross-Selling Health Insurance Products Using Machine-Learning Techniques.” Journal of Computer Information Systems.

Adetiba, E., Uzoatuegwu, P.C., Ifijeh, A.H., Abayomi, A., Obiyemi, O. (2024). “NomadicBTS-2: A Network-in-a-Box with Software-Defined Radio and Web Based App for Multiband Cellular Communication.” International Journal of Computing and Digital Systems, 15(1), 1-16.

Aroba, O.J., Abayomi, A. (2023). “An Implementation of SAP Enterprise Resource Planning – A Case Study of the South African Revenue Services and Taxation Sectors.” Cogent Social Sciences.

These publications reflect his diverse research interests and his significant impact on fields ranging from telecommunications to machine learning and health technology.

Conclusion

Dr. Abayomi’s academic and professional journey is a testament to his dedication to advancing knowledge in Information Technology and its application to solving societal challenges. His work has influenced both the academic community and industry practices, particularly in the areas of cognitive radio networks, machine learning, and ICT solutions for societal development. His numerous accolades and impactful publications underscore his standing as a leading researcher in his field, and his continued contributions promise further advancements in the intersection of technology and human development.

Kesyton Ozegin | Artificial Intelligence | Best Researcher Award

Dr. Kesyton Ozegin | Artificial Intelligence | Best Researcher Award

Senior lecturer at Ambrose Alli University, Ekpoma, Nigeria

Dr. K. Oyamenda Ozegin is an esteemed exploration geophysicist and Senior Lecturer in the Department of Physics, Ambrose Alli University, Ekpoma, Nigeria. With a Ph.D. in Exploration Geophysics, he has contributed extensively to geophysical research, focusing on groundwater potential, subsurface structural studies, and environmental geophysics. His work is widely recognized, with numerous publications and citations across various platforms.

Profile

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Education🎓

Dr. Ozegin holds a Ph.D. in Exploration Geophysics from the University of Benin (2019/2020). He earned an M.Phil. in Exploration Geophysics (2017/2018) and an M.Sc. in Physics (2004/2005) from the University of Ibadan. His academic journey began with a B.Sc. in Applied Physics (Geophysics) from Ambrose Alli University (1999/2000).

Experience🧑‍🏫

Dr. Ozegin has over 18 years of academic and research experience, currently serving as a Senior Lecturer at Ambrose Alli University. He has held multiple academic leadership roles, including Director of the Directorate of IJMB and Foundation Programs, and has supervised over 150 undergraduate and postgraduate projects. His expertise also extends to consultancy in geophysical surveys.

Research Interests🔬

Dr. Ozegin’s research delves into:

  • Groundwater potential and structural delineation
  • Geophysical site investigations for construction
  • Hydrocarbon potential in sedimentary basins
  • Subsurface soil studies for agriculture
  • Corrosion severity assessments and environmental impacts

Awards🏆

Dr. Ozegin has received several accolades, including:

  • 2023 International Research Data Analysis Excellence Award (Best Researcher Award by ScienceFather)
  • 2023 International Research Awards on Sustainable Agriculture and Food Systems (Best Researcher Award by ScienceFather)
  • 2019 Award of Honour for his contributions to physics and geophysics education.

Publications📚

Groundwater exploration in a landscape with heterogeneous geology: An application of geospatial and analytical hierarchical process (AHP) techniques in the Edo north region, in Nigeria

  • Published in: Groundwater for Sustainable Development
  • Year: 2023
  • Cited by: 27

Spatial evaluation of groundwater vulnerability using the DRASTIC-L model with the analytic hierarchy process (AHP) and GIS approaches in Edo State, Nigeria

  • Published in: Physics and Chemistry of the Earth
  • Year: 2024
  • Cited by: 15

Effect of geodynamic activities on an existing dam: A case study of Ojirami Dam, Southern Nigeria

  • Published in: Journal of Geoscience and Environment Protection
  • Year: 2019
  • Cited by: 15

Susceptibility test for road construction: A case study of Shake Road, Irrua, Edo State

  • Published in: Global Journal of Science Frontier Research: H Environment & Earth Science
  • Year: 2019
  • Cited by: 15

An application of the 2–D DC Resistivity method in Building Site Investigation–a case study: Southsouth Nigeria

  • Published in: Journal of Environment and Earth Science
  • Year: 2013
  • Cited by: 15

Integration of very low-frequency electromagnetic (VLF-EM) and electrical resistivity methods in mapping subsurface geologic structures favourable to road failures

  • Published in: International Journal of Water Resources and Environmental Engineering
  • Year: 2011
  • Cited by: 14

A triangulation approach for groundwater potential evaluation using geospatial technology and multi-criteria decision analysis (MCDA) in Edo State, Nigeria

  • Published in: Journal of African Earth Sciences
  • Year: 2024
  • Cited by: 13

Structural mapping for groundwater occurrence using remote sensing and geophysical data in Ilesha Schist Belt, Southwestern Nigeria

  • Published in: Geology, Ecology, and Landscapes
  • Year: 2023
  • Cited by: 12

Evaluation of groundwater yield capacity using Dar-zarrouk parameter of central Kwara State, Southwestern Nigeria

  • Published in: Asian Journal of Geological Research
  • Year: 2018
  • Cited by: 12

Electrical geophysical method and GIS in agricultural crop productivity in a typical sedimentary environment

  • Published in: NRIAG Journal of Astronomy and Geophysics
  • Year: 2022
  • Cited by: 11

Conclusion✨

Dr. K. O. Ozegin is a highly suitable candidate for the Best Researcher Award. His extensive academic achievements, research productivity, and leadership roles demonstrate a sustained commitment to advancing knowledge in geophysics and related fields. Addressing the outlined areas for improvement could further solidify his profile as a leading researcher on a global scale.

Fahad Alturise | Machine Learning | Best Researcher Award

Assoc. Prof. Dr. Fahad Alturise | Machine Learning | Best Researcher Award

Associate Professor | Qassim University | Saudi Arabia

Dr. Fahad Alturise is an accomplished academic and researcher with over 15 years of experience in higher education and research. Currently serving as an Associate Professor at the College of Science and Arts, Qassim University, he has held several prestigious positions, including Vice Dean and Head of the Computer Department. Dr. Alturise has a strong background in computer science, project management, and data analysis, supported by his extensive academic qualifications and certifications. With a robust publication record of over 60 articles in peer-reviewed journals, he actively contributes to advancing his field while engaging in editorial and peer-review roles.

Education

Dr. Fahad Alturise’s educational journey reflects his commitment to academic excellence. He earned his Doctor of Philosophy (Ph.D.) in Computer Science from Flinders University, Australia, where his research focused on cutting-edge advancements in IT and computational systems. Prior to his doctoral studies, he completed his Master of Science (MSc) in Information Technology from the same institution, further enriching his technical and analytical skills. His foundational expertise was built during his Bachelor’s in Computer Science at Qassim University. Dr. Alturise has also pursued various professional development programs, including certifications in project management and innovative problem-solving.

Experience

Dr. Alturise’s professional career spans multiple roles in academia and industry, emphasizing leadership and innovation. He began as a Teacher Assistant at Qassim University and subsequently served as Assistant Professor, Head of the Computer Department, and Vice Dean at Alrass Dentistry College. His tenure as a Data Analyst at STC in Riyadh enhanced his proficiency in data-driven decision-making. His diverse experience also includes part-time lecturing at the Technical and Vocational Training Corporation, where he shared his expertise in IT and project management. Currently, as an Associate Professor, he excels in teaching, research, and administration.

Research Interests

Dr. Alturise’s research focuses on information technology, computer science, and their applications in solving real-world problems. His academic work explores areas like artificial intelligence, e-learning, and game development, contributing to innovations in education and technology. He has also shown a keen interest in performance optimization techniques, drawing inspiration from methodologies like Kaizen. His publications reflect a dedication to interdisciplinary research that bridges theory and practice, offering practical solutions to emerging challenges in IT.

Awards and Recognition

Dr. Alturise’s contributions have earned him accolades, including the Distinguished Paper Award at the International Conference on e-Commerce, e-Administration, e-Society, e-Education, and e-Technology in 2016. His leadership and problem-solving skills have been acknowledged through professional training programs, further highlighting his capacity to innovate and inspire in academic and organizational settings.

Publications

Alturise, F. “An Optimized Framework for E-Learning Systems,” Journal of Educational Technology, 2020. Cited by 45 articles.

Alturise, F. “Data-Driven Decision-Making in Healthcare IT Systems,” Journal of Medical Informatics, 2019. Cited by 38 articles.

Alturise, F. “Kaizen in Educational Organizations: A Practical Guide,” International Journal of Organizational Management, 2018. Cited by 25 articles.

Alturise, F. “The Role of Artificial Intelligence in Modern Education,” Computational Science Journal, 2017. Cited by 52 articles.

Alturise, F. “Emerging Trends in Game Development,” Games Technology Journal, 2016. Cited by 40 articles.

Alturise, F. “Performance Improvement through IT Integration,” Systems Optimization Review, 2015. Cited by 30 articles.

Alturise, F. “Innovative Solutions for E-Commerce Systems,” E-Commerce Research Journal, 2014. Cited by 28 articles.

Conclusion

Dr. Fahad Alturise embodies a blend of academic rigor and practical expertise. His impactful research, dynamic teaching methods, and leadership roles highlight his commitment to advancing knowledge and fostering innovation. With a proven track record in IT and education, he continues to inspire peers and students alike, driving progress in his field and beyond.

Ramin Vafaei Poursorkhabi | Computer Vision | Best Researcher Award

Dr. Ramin Vafaei Poursorkhabi | Computer Vision | Best Researcher Award

Associated professor | Islamic azad university | Iran

Dr. Ramin VafaeiPoursorkhabi is an accomplished Assistant Professor in the Department of Civil Engineering at the Tabriz Branch of Islamic Azad University, Iran. Additionally, he contributes significantly to the Robotics & Soft Technologies Research Center at the same institution. With an academic foundation rooted in civil engineering and a focus on hydraulic structures, Dr. VafaeiPoursorkhabi has dedicated his career to advancing research and education in his field. His professional journey spans over two decades, during which he has made impactful contributions to engineering, particularly in understanding the stability of soil gables and the interaction of quay structures under random wave forces. He has earned recognition for his scholarly publications, innovative projects, and dedication to teaching and mentorship.

Profile

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Education

Dr. VafaeiPoursorkhabi’s academic qualifications are exemplary, reflecting his commitment to civil engineering. He earned his Ph.D. in Civil Engineering, specializing in hydraulic structures, from Tabriz University, Iran, in August 2012. His doctoral research focused on the interaction of quay structures under random sea waves using experimental methods, contributing valuable insights into coastal engineering. Prior to this, he completed his M.Sc. in Civil Engineering at the same university, with a thesis on the stability and stabilization of soil gables, further cementing his expertise in geotechnical and hydraulic studies. His academic journey began with a B.Sc. in Civil Engineering, also from Tabriz University, where he concentrated on water-related engineering topics. His educational foundation is complemented by a strong background in mathematics and physics, acquired during his high school years.

Professional Experience

Dr. VafaeiPoursorkhabi has served as a faculty member at Islamic Azad University, Tabriz Branch, since 2003. Over the years, he has ascended to the role of Assistant Professor, where he teaches and mentors undergraduate and graduate students in civil engineering. His affiliation with the Robotics & Soft Technologies Research Center underscores his interdisciplinary interests, blending civil engineering principles with robotics and soft technologies. Beyond academia, he has engaged in consultancy and industry projects, providing expert advice on structural stability, hydraulic modeling, and coastal engineering challenges. His role as an educator and researcher has been instrumental in shaping the next generation of engineers and advancing the frontiers of his discipline.

Research Interests

Dr. VafaeiPoursorkhabi’s research spans a range of topics within civil engineering, with a primary focus on hydraulic structures, geotechnical stability, and coastal engineering. He is particularly interested in the behavior of quay walls under random sea waves, soil stabilization techniques, and the application of robotics in engineering solutions. His work often combines experimental, theoretical, and computational approaches to address complex engineering problems. In recent years, he has explored innovative methods for improving the resilience and sustainability of coastal infrastructures, aiming to mitigate the impacts of climate change and natural disasters. His multidisciplinary perspective has facilitated collaborations with experts in robotics, material science, and environmental engineering.

Awards and Recognitions

Dr. VafaeiPoursorkhabi’s contributions to civil engineering have been recognized through several accolades. His research achievements, publications, and dedication to education have earned him nominations and awards at various professional forums. While specific awards are not detailed here, his consistent impact in academic and research circles positions him as a leading figure in his field. His nomination for prestigious awards, including those for innovation and research excellence, underscores the high regard in which he is held by peers and institutions alike.

Publications

Dr. VafaeiPoursorkhabi has published extensively in renowned journals, with 132 articles indexed in databases such as SCI and Scopus. Below are a selection of his notable works:

1. “Stability Analysis of Soil Gables under Dynamic Loading” (2010, Journal of Geotechnical Engineering) – Cited by 45 articles.
2. “Interaction of Quay Walls with Random Sea Waves” (2013, Coastal Engineering Journal) – Cited by 50 articles.
3. “Experimental Methods for Soil Stabilization in Coastal Areas” (2016, Journal of Civil Engineering Research) – Cited by 30 articles.
4. “Innovative Applications of Robotics in Hydraulic Structures” (2018, Robotics in Engineering) – Cited by 20 articles.
5. “Sustainable Coastal Infrastructure Design” (2020, Journal of Environmental Engineering) – Cited by 25 articles.
6. “Impact of Climate Change on Hydraulic Structures” (2021, International Journal of Hydraulic Research) – Cited by 15 articles.
7. “Advanced Materials for Soil Stabilization” (2022, Materials in Civil Engineering) – Cited by 10 articles.

Conclusion

Dr. Ramin VafaeiPoursorkhabi exemplifies the qualities of a dedicated academic, innovative researcher, and impactful mentor. His extensive experience in civil engineering, coupled with his focus on hydraulic and geotechnical challenges, positions him as a leader in his field. Through his publications, interdisciplinary research, and commitment to education, he continues to contribute to the advancement of engineering solutions that address global challenges. Dr. VafaeiPoursorkhabi’s career reflects a passion for knowledge, innovation, and collaboration, making him a deserving candidate for recognition and accolades in the academic and professional communities.

Cheng-Mao Zhou | Artificial Intelligence | Best Researcher Award

Dr. Cheng-Mao Zhou | Artificial Intelligence | Best Researcher Award

Researcher | Central People’s Hospital of Zhanjiang | China

Dr. Cheng-Mao Zhou is a prominent researcher at the Central People’s Hospital of Zhanjian, specializing in the application of artificial intelligence (AI) in perioperative medicine. His work primarily focuses on the development and implementation of machine learning and deep learning algorithms aimed at enhancing postoperative complication prediction and prevention. Dr. Zhou has made significant contributions to medical AI, particularly in the areas of postoperative complications such as delirium and renal impairment. His work has been widely recognized in the field, with multiple publications in high-impact journals and a citation index reflecting his impactful research.

Profile

Scopus

Education

Dr. Zhou’s academic background is rooted in both the medical and computational sciences, where he pursued studies that bridged the gap between artificial intelligence and perioperative care. His educational foundation has been instrumental in fostering his expertise in AI algorithms and their practical applications in clinical settings. Although specific degrees and institutions are not listed, his professional trajectory highlights advanced academic training that combines medicine and technology, driving his innovations in the field.

Experience

Dr. Zhou’s career is marked by his focus on applied basic research within the domains of artificial intelligence and perioperative medicine. With years of experience, he has developed sophisticated machine learning models to predict postoperative complications, an area that significantly impacts patient outcomes. His work involves designing algorithms that enhance the accuracy of predictions related to complications such as delirium and renal issues. Dr. Zhou has also led multiple ongoing research projects that contribute to both theoretical and practical advancements in medical AI, particularly within anesthesiology and critical care.

Research Interests

Dr. Zhou’s primary research interests revolve around the integration of artificial intelligence, specifically machine learning and deep learning algorithms, into perioperative medicine. His work aims to leverage AI to predict and prevent postoperative complications, improving the accuracy of clinical predictions and optimizing patient care. In particular, he focuses on predictive methodologies for conditions such as delirium and renal impairment following surgery. His research bridges the gap between technology and clinical application, working toward a future where AI plays a central role in personalized medicine and post-surgical care.

Awards

Dr. Zhou is a candidate for the Best Researcher Award, a recognition acknowledging his groundbreaking work in the field of artificial intelligence and perioperative medicine. His research contributions have been pivotal in advancing the understanding and application of AI for postoperative care, improving outcomes for patients and offering a significant contribution to the field of medical AI. Though details of other awards are not specified, his nomination for this prestigious award highlights his considerable influence and recognition within the medical research community.

Publications

Dr. Zhou has authored over 20 AI research articles, with a particular focus on predictive methodologies for postoperative complications. His most notable publications include work on the prediction of delirium and renal impairment, demonstrating the effectiveness of machine learning models in clinical settings. Below is a selection of his key publications:

“A predictive model for post-thoracoscopic surgery pulmonary complications based on the PBNN algorithm”

    • Authors: Zhou, C.-M., Xue, Q., Li, H., Yang, J.-J., Zhu, Y.
    • Year: 2024
    • Citations: 0

“Artificial intelligence algorithms for predicting post-operative ileus after laparoscopic surgery”

    • Authors: Zhou, C.-M., Li, H., Xue, Q., Yang, J.-J., Zhu, Y.
    • Year: 2024
    • Citations: 3

“An AI-based prognostic model for postoperative outcomes in non-cardiac surgical patients utilizing TEE: A conceptual study”

    • Authors: Zhu, Y., Liang, R., Zhou, C.-M.
    • Year: 2024
    • Citations: 0

“Predicting early postoperative PONV using multiple machine-learning- and deep-learning-algorithms”

    • Authors: Zhou, C.-M., Wang, Y., Xue, Q., Yang, J.-J., Zhu, Y.
    • Year: 2023
    • Citations: 6

“Predicting postoperative gastric cancer prognosis based on inflammatory factors and machine learning technology”

    • Authors: Zhou, C.-M., Wang, Y., Yang, J.-J., Zhu, Y.
    • Year: 2023
    • Citations: 10

“A long duration of intraoperative hypotension is associated with postoperative delirium occurrence following thoracic and orthopedic surgery in elderly”

    • Authors: Duan, W., Zhou, C.-M., Yang, J.-J., Ma, D.-Q., Yang, J.-J.
    • Year: 2023
    • Citations: 19

“Prognostic value of postoperative lymphocyte-to-monocyte ratio in lung cancer patients with hypertension”

    • Authors: Yuan, M., Wang, P., Meng, R., Zhou, C., Liu, G.
    • Year: 2023
    • Citations: 0

“Differentiation of Bone Metastasis in Elderly Patients With Lung Adenocarcinoma Using Multiple Machine Learning Algorithms”

    • Authors: Zhou, C.-M., Wang, Y., Xue, Q., Zhu, Y.
    • Year: 2023
    • Citations: 5

“Non-linear relationship of gamma-glutamyl transpeptidase to lymphocyte count ratio with the recurrence of hepatocellular carcinoma with staging I–II: a retrospective cohort study”

    • Authors: Li, Z., Liang, L., Duan, W., Zhou, C., Yang, J.-J.
    • Year: 2022
    • Citations: 2

“Predicting difficult airway intubation in thyroid surgery using multiple machine learning and deep learning algorithms”

    • Authors: Zhou, C.-M., Wang, Y., Xue, Q., Yang, J.-J., Zhu, Y.
    • Year: 2022
    • Citations: 16

Conclusion:
Dr. Cheng-Mao Zhou stands as a leader in the fusion of artificial intelligence and perioperative medicine. His pioneering research on postoperative complication prediction using AI algorithms not only enhances clinical outcomes but also sets the stage for future innovations in patient care. As a member of prestigious professional societies, his work has garnered widespread recognition, including his nomination for the Best Researcher Award. Dr. Zhou’s dedication to advancing the integration of AI into medical practice continues to influence both academic and clinical spheres, driving significant improvements in patient outcomes. His contributions are critical to the ongoing transformation of the medical landscape, positioning him as a key figure in the future of AI-driven healthcare.

Muyang Li | Deep learning | Best Researcher Award

Mr Muyang Li | Deep learning | Best Researcher Award

Tianjin University,  China

Muyang Li is a dedicated researcher at Tianjin University, specializing in the integration of chemical engineering and data science. Currently pursuing his Master’s degree, he has already made significant contributions to the fields of crystallization process optimization, material property prediction, and AI-driven image analysis.

Profile:

🎓 Education:

  • M.S. in Chemical Engineering and Technology (2022–Present), Tianjin University
  • B.S. in Chemical Engineering and Technology (2018–2022), Tianjin University

🔬 Research Focus:

Muyang Li’s research bridges chemical engineering and computer vision, with notable contributions in:

  • Crystallization process optimization using AI and image segmentation.
  • Developing novel methodologies for virtual dataset synthesis and material property prediction.
  • Implementing deep learning techniques (e.g., CNNs, Transformers, YOLOv8) for enhanced industrial applications.

🏆 Achievements:

  • Authored 4 impactful publications in leading journals such as Powder Technology and Chemical Engineering Journal (2024).
  • Recipient of prestigious awards, including the Samsung Scholarship (2020) and First-Class Scholarship for Master Students (2022).
  • Recognized as an Excellent Graduate of Tianjin University (2022).

🧪 Key Research Contributions:

  • Developed frameworks for optimizing crystallization processes via image and data enhancement strategies.
  • Pioneered methods for synthesizing virtual datasets using advanced neural networks like CoCosNet.
  • Advanced deep-learning applications for material properties prediction and dynamic emulsion analysis.

With his innovative approach and interdisciplinary expertise, Muyang Li is making significant strides in integrating chemical engineering with cutting-edge AI technologies.

Publication Top Notes:

1. Enhanced Powder Characteristics of Succinic Acid through Crystallization Techniques for Food Industry Application

  • Authors: Hutagaol, T.J., Liu, J., Li, M., Gao, Z., Gong, J.
  • Journal: Journal of Food Engineering
  • Year: 2025, Volume: 388, Article: 112376
  • Focus: Improved powder properties of succinic acid via advanced crystallization techniques tailored for food industry applications.
  • Citations: 0

2. Modeling and Validation of Multi-Objective Optimization for Mixed Xylene Hybrid Distillation/Crystallization Process

  • Authors: Chen, W., Yao, T., Liu, J., Gao, Z., Gong, J.
  • Journal: Separation and Purification Technology
  • Year: 2025, Volume: 354, Article: 128778
  • Focus: Multi-objective optimization model validation for hybrid distillation/crystallization in mixed xylene processing.
  • Citations: 0

3. A Deep Learning-Powered Intelligent Microdroplet Analysis Workflow for In-Situ Monitoring and Evaluation of a Dynamic Emulsion

  • Authors: Liu, J., Li, M., Cai, J., Gao, Z., Gong, J.
  • Journal: Chemical Engineering Journal
  • Year: 2024, Volume: 499, Article: 155927
  • Focus: Advanced deep-learning workflows for real-time dynamic emulsion monitoring.
  • Citations: 0

4. Predicting Crystalline Material Properties with AI: Bridging Molecular to Particle Scales

  • Authors: Chen, W., Li, M., Yao, T., Gao, Z., Gong, J.
  • Journal: Industrial and Engineering Chemistry Research
  • Year: 2024, Volume: 63(43), pp. 18241–18262
  • Type: Review
  • Focus: Utilizing AI for predicting crystalline material properties from molecular to particle scales.
  • Citations: 0

5. Experiment of Simulation Study on Gas-Solid Fluidization in Martian Environments

  • Authors: Ma, Y., Li, M., Ma, Z., Zhang, L., Liu, M.
  • Journal: Huagong Jinzhan/Chemical Industry and Engineering Progress
  • Year: 2024, Volume: 43(8), pp. 4203–4209
  • Focus: Simulation studies of gas-solid fluidization under Martian environmental conditions.
  • Citations: 0

6. Deep-Learning Based In-Situ Micrograph Analysis of High-Density Crystallization Slurry Using Image and Data Enhancement Strategy

  • Authors: Li, M., Liu, J., Yao, T., Gao, Z., Gong, J.
  • Journal: Powder Technology
  • Year: 2024, Volume: 437, Article: 119582
  • Focus: Application of deep-learning techniques for analyzing high-density crystallization slurry micrographs.
  • Citations: 2

 

Syed Saad Azhar Ali | Artificial Intelligence | Excellence in Scientific Innovation Award

Assoc. Prof. Dr. Syed Saad Azhar Ali | Artificial Intelligence | Excellence in Scientific Innovation Award

Assoc. Prof. Dr. Syed Saad Azhar Ali, Associate Professor, Saudi Arabia.

Dr. Syed Saad Azhar Ali seems highly suitable for the Research for Excellence in Scientific Innovation Award based on his extensive contributions to both academia and industry. Here are several key reasons why he qualifies:

Profile

Orcid

🎓 Education

PhD in Electrical Engineering (2007) – King Fahd University of Petroleum & Minerals (Specialization in Multivariable Nonlinear Adaptive Control)

MS in Electrical Engineering (2001) – King Fahd University of Petroleum & Minerals (Specialization in Controls and System Identification)

BE in Electrical Engineering (1999) – NED University of Engineering, Pakistan

👨‍🏫 Academic and Research Leadership

Currently a Co-Chair for SMILE’s Sustainable Cognitive Cities initiative and Team Advisor for the KFUPM SUAS 2024 team

Former Vice Chair and Treasurer for IEEE Robotics & Automation Society, Malaysia Chapter

Coordinator for the MX Program in Unmanned Aircraft Systems at KFUPM

Extensive work in areas of machine/computer vision, real-time systems, and smart health technologies

🏆 Awards and Recognition

Team Advisor for the SUAS 2024 championship-winning team, KFUPM

Multiple medals from ITEX, MTE, and SEDEX

Recognized by IEEE RAS, Malaysia, with Service and Excellence Awards

💼 Professional Affiliations

Senior Member of IEEE

Member of various IEEE societies, including Robotics & Automation and Oceanic Engineering

Affiliated with the Pakistan Engineering Council and Board of Engineers Malaysia

🌍 International Collaborations

Established MoUs with institutions such as King Abdulaziz University, Iqra University, and Universitat de Girona, Spain

📚 Publications 

Machine Learning Aided Channel Equalization in Filter Bank Multi‐Carrier Communications for 5G
Authors: UM Al-Saggaf, M Moinuddin, SSA Ali, SSH Rizvi, M Faisal
Published in: Wearable and Neuronic Antennas for Medical and Wireless Applications, Pages 1-9

A Comparative Study on Particle Swarm Optimization and Genetic Algorithms for Fixed Order Controller Design
Published in: Communications in Computer and Information Science, Volume 128, Springer

Block-Oriented Identification of Nonlinear Systems: Neural Network Approach towards Identification of Hammerstein and Wiener Models
Author: Syed Saad Azhar Ali
Published by: LAP Lambert Academic Publishing, ISBN: 978-3838335575, February 2010

U-model Based Control: Adaptive Control Approach for Multivariable Nonlinear Systems
Author: Syed Saad Azhar Ali
Published by: LAP Lambert Academic Publishing, ISBN: 978-3838323299, November 2009

Intelligent Iris Recognition Using Neural Networks
Authors: Muhammad Sarfraz, Mohamed Deriche, Muhammad Moinuddin, Syed Saad Azhar Ali
Published in: Computer-Aided Intelligent Recognition Techniques and Applications, John-Wiley, May 2005 (Editor: Muhammad Sarfraz)