Wang Hongyuan | Artificial Intelligence | Best Researcher Award

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Mr. Wang Hongyuan | Artificial Intelligence | Best Researcher Award

Guangdong University of Science and Technology | China

Mr. Wang Hongyuan is an accomplished researcher at the Guangdong University of Science and Technology, Dongguan, China, whose work bridges the fields of artificial intelligence, Internet of Things (IoT), and intelligent computing systems. His research primarily emphasizes explainable AI, machine learning applications, edge computing, and data-driven fault detection systems for industrial and healthcare environments. Mr. Wang Hongyuan has authored 11 research publications indexed in Scopus with 10 citations and an h-index of 2, reflecting his growing influence in computational intelligence research. His recent contributions include studies on reinforcement learning-based computation offloading for wireless IoT, AI-based analysis of neurodegenerative disorders, and machine learning approaches for network security and IoT healthcare systems. He also led the Dongguan Songshan Lake Sci-Tech Commissioner Project, focusing on big data-based monitoring and early fault warning systems for electrical equipment. Through his innovative and interdisciplinary approach, Mr. Wang Hongyuan demonstrates a strong commitment to advancing AI-driven automation, smart industrial systems, and intelligent healthcare technologies—establishing himself as a promising and forward-thinking researcher in modern computational sciences.

Profile: Scopus

Featured Publications

  • Wang, H. (2025). An adaptive computation offloading strategy for wireless IoT: A reinforcement learning-based solution. Journal of the Institution of Engineers (India) Series

  • Wang, H. (2024). Dongguan Songshan Lake Sci-Tech Commissioner Project: Research and industrialization of a monitoring and fault early-warning system for high and low voltage electrical equipment based on big data mining (Project ID: 20234415-01KCJ-G). [Completed Project].

  • Wang, H. (2025). Neurodegenerative disorders: A holistic study of the explainable artificial intelligence applications. Engineering Applications of Artificial Intelligence.

  • Wang, H. (2023). Machine learning-based centralized link coding attack detection in software-defined network. Wireless Networks.

  • Wang, H. (2023). MEC-IoT-Healthcare: Analysis and prospects. Computers, Materials & Continua, 75(1), 193–206.

Mehdi Moayed Mohseni | Machine Learning | Best Researcher Award

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Assist. Prof. Dr. Mehdi Moayed Mohseni | Machine Learning | Best Researcher Award

Islamic Azad University Science and Research Branch | Iran

Assist. Prof. Dr. Mehdi Moayed Mohseni is a distinguished chemical engineer at Islamic Azad University, Tehran, Iran, with expertise in non-Newtonian fluid mechanics, convective heat transfer, viscoelastic fluids, rheology, and exergy analysis. He earned his Ph.D. in Chemical Engineering from Amirkabir University of Technology, Iran, focusing on hydrodynamic and heat transfer modeling and entropy analysis of viscoelastic fluids in centric and eccentric annuli, followed by an M.Sc. on heat transfer of Giesekus viscoelastic fluids and a B.Sc. on biological natural gas sweetening processes. His research integrates analytical and semi-analytical methods (HPM, perturbation, homotopy) with computational fluid dynamics (CFD) and mass/heat transfer studies. Assist. Prof. Dr. Mehdi Moayed Mohseni has authored 17 publications, including studies on thermal and rheological performance of nanofluids, contributing to 229 citations and an h-index of 8. He actively participates in conferences, such as the National Iranian Chemical Engineering Congress, and his work demonstrates a strong commitment to advancing understanding of complex fluid behavior and transport phenomena.

Profile: Scopus | Google Scholar

Featured Publications

Mohseni, M. M., Jouyandeh, M., Sajadi, S. M., Hejna, A., Habibzadeh, S., … (2022). Metal-organic frameworks (MOF) based heat transfer: A comprehensive review. Chemical Engineering Journal, 449, 137700.

Montazeri, N., Salahshoori, I., Feyzishendi, P., Miri, F. S., Mohseni, M. M., … (2023). pH-sensitive adsorption of gastrointestinal drugs (famotidine and pantoprazole) as pharmaceutical pollutants by using the Au-doped@ ZIF-90-glycerol adsorbent: Insights from … Journal of Materials Chemistry A, 11(47), 26127–26151.

Salahshoori, I., Vaziri, A., Jahanmardi, R., Mohseni, M. M., Khonakdar, H. A. (2024). Molecular simulation studies of pharmaceutical pollutant removal (rosuvastatin and simvastatin) using novel modified-MOF nanostructures (UIO-66, UIO-66/chitosan, and UIO-66 …). ACS Applied Materials & Interfaces, 16(20), 26685–26712.

Mohseni, M. M., & Rashidi, F. (2010). Viscoelastic fluid behavior in annulus using Giesekus model. Journal of Non-Newtonian Fluid Mechanics, 165(21-22), 1550–1553.

Bateni, A., Salahshoori, I., Jorabchi, M. N., Mohseni, M. M., Asadabadi, M. R., … (2025). Molecular simulation-based assessing of a novel metal-organic framework modified with alginate and chitosan biopolymers for anionic reactive black 5 and cationic crystal violet … Separation and Purification Technology, 354, 128986.

Kachi Anvesh | Machine Learning | Best Researcher Award

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Mr. Kachi Anvesh | Machine Learning | Best Researcher Award

Vardhaman College of Engineering | India

Mr. Kachi Anvesh is an Assistant Professor in the Department of Information Technology at Vardhaman College of Engineering, Hyderabad, with over 12 years of teaching and research experience. He is currently pursuing a Ph.D. in Computer Science at Visvesvaraya Technological University, Belagavi, and holds an M.Tech in Software Engineering with distinction and a B.Tech in Information Technology. His research focuses on medical image processing, deep learning, machine learning, and intelligent systems, with notable contributions including the detection of tessellated retinal disease, hypertensive retinopathy, glaucoma, cataract, and wheat head detection using advanced AI models. He has published in reputed journals and conferences such as JIKM, TSP-CMES, and Journal of Autonomous Intelligence, accumulating 13 citations and an h-index of 2. Mr. Anvesh has led innovative projects including bone age detection from X-ray images, facial expression recognition, emotion detection, foreign object debris detection, and predictive analytics systems, and holds certifications in AI and deep learning from IIT Ropar and other platforms, reflecting his strong contribution to engineering and AI research.

Profile: Scopus | Orcid | Google Scholar

Featured Publications

Anvesh, K., Prasad, S., Laxman, V. V. S. R., & Narayana, B. S. (2019). Automatic student analysis and placement prediction using advanced machine learning algorithms. International Journal of Innovative Technology and Exploring Engineering, 8, 9.

Suma, K., Sunitha, G., Karnati, R., Aruna, E. R., Anvesh, K., Kale, N., & Kishore, P. K. (2024). CETR: CenterNet-Vision transformer model for wheat head detection. Journal of Autonomous Intelligence, 7(3), 6.

Venkatesh, M., Dhanalakshmi, C., Adapa, A., Manzoor, M., & Anvesh, K. (2023). Criminal face detection system.

Anvesh, K., Srilatha, M., Raghunadha Reddy, T., Gopi Chand, M., & Jyothi, G. D. (2018). Improving student academic performance using an attribute selection algorithm. Proceedings of the First International Conference on Artificial Intelligence and Cognitive…, 3.

Rajendar, B., Bhavana, K., Divya, C., Swarna, M., & Anvesh, K. (2017). Evaluation of cardiac tonic activity of methanolic leaf extract of Moringa oleifera. International Journal of Pharma Sciences and Research, 8(6), 152–156.

Alper Mitincik | Artificial Intelligence | Best Researcher Award

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Mr. Alper Mitincik | Artificial Intelligence | Best Researcher Award

Galatasaray University | Turkey

Mr. Alper Mitincik is an accomplished software engineer and researcher with extensive expertise in Java, Python, SQL, and scalable data-driven applications. He has led significant projects, including a national cloud storage system and one of the largest Turkish-language crawling-based search engines, demonstrating exceptional skills in search engine architecture, Elasticsearch optimization, ranking algorithms, and large-scale data pipelines. Alper has published research on information retrieval and deep learning, notably “Text-Based Image Retrieval System Using Semantic Visual Content for Re-Ranking” in Engineering Applications of Artificial Intelligence (2025), and his M.Sc. thesis focused on semantic search frameworks. Currently pursuing a Ph.D. in Computer Engineering, his research emphasizes advanced recommendation systems, transformers, and graph attention networks. With experience mentoring engineers, implementing best practices, and designing robust software architectures, Alper combines industrial impact with academic innovation. Recognized with awards such as Turkcell’s CXO Award and holding certifications in machine learning,

Profile: Google Scholar

Featured Publications

Parlak, İ. B., & Mıtıncık, A. (2022). Designing an information framework for semantic search. Avrupa Bilim ve Teknoloji Dergisi, 682–689.

Topcu, B., Mıtıncık, A., Erdem, M. G., & Yanikoglu, B. (2025). Text-based image retrieval system using semantic visual content for re-ranking. Engineering Applications of Artificial Intelligence, 160, 111770.

Xiongjun Zhao | Artificial Intelligence | Best Researcher Award

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Assist. Prof. Dr. Xiongjun Zhao | Artificial Intelligence | Best Researcher Award

Assist. Prof. Dr. Xiongjun Zhao, Hunan University, China

Assist. Prof. Dr. Xiongjun Zhao is a biomedical AI researcher currently serving as an Assistant Researcher at the School of Information Science and Engineering, Hunan University. He holds a Ph.D. in Computer Science and Technology and a bachelor’s degree in Software Engineering, both from Hunan University, where he was recognized as an Outstanding Graduate and Student Leader. His research focuses on biomedical big data analysis, multi-modal learning, and intelligent medical systems. Dr. Zhao has authored five significant papers, including a CCF-A ACM MM conference paper and three SCI-indexed journal articles. He has also secured four patents in medical AI technologies and leads multiple funded research projects backed by prominent Chinese institutions. An awardee of national honors in mathematics and programming competitions, he brings strong technical expertise in Python, C++, and deep learning frameworks. Dr. Zhao actively mentors students and contributes to international conferences such as IEEE BIBM and ACM MM.

Publication Profile

Orcid

Work Experience

Assist. Prof. Dr. Xiongjun Zhao currently holds the position of Assistant Researcher at the School of Information Science and Engineering, Hunan University, a role he began in July 2025. In this capacity, he is deeply involved in cutting-edge research and development focused on biomedical big data analysis, multi-modal learning, and intelligent medical systems. His responsibilities include leading and contributing to a range of scientific research projects, many of which involve national and provincial collaborations. Dr. Zhao plays a pivotal role in advancing academic innovation through his contributions to interdisciplinary projects and by leveraging artificial intelligence in healthcare-related research. In addition to his research activities, he actively mentors students, fostering a dynamic and innovative research environment in the laboratory. His work exemplifies a commitment to technological advancement and academic leadership, positioning him as a key contributor to the development of intelligent medical solutions through data-driven methodologies.

Educational Background

Assist. Prof. Dr. Xiongjun Zhao received his comprehensive academic training from Hunan University, a prestigious institution recognized under China’s Double First-Class Initiative. He pursued a Direct Ph.D. Program in Computer Science and Technology from September 2020 to June 2025, under the mentorship of Prof. Ying Jianguo, a distinguished Changjiang Scholar. His doctoral studies focused on advanced topics in biomedical data science and artificial intelligence. Prior to that, Dr. Zhao completed his Bachelor’s degree in Software Engineering at the same university between September 2016 and June 2020. During his undergraduate years, he was honored as an Outstanding Graduate and recognized for his leadership and community involvement with accolades such as Outstanding Student Leader and Outstanding Volunteer. His solid educational foundation, shaped by rigorous training and guided mentorship, has laid the groundwork for his successful academic and research career in the fields of machine learning and intelligent medical systems.

Research Experience

Assist. Prof. Dr. Xiongjun Zhao has a strong and focused research background in biomedical big data analysis, multi-modal learning, and intelligent medical models. His work integrates advanced machine learning techniques with complex medical datasets to develop deep learning frameworks—particularly Transformer-based models—for clinical diagnostics and decision support systems. His notable academic contributions include the publication of one top-tier conference paper and three SCI-indexed journal articles, along with the successful registration of four patents in the medical AI domain. Dr. Zhao has led and participated in several high-impact research projects, notably those funded by the Hunan Provincial Department of Health and the China Association for Science and Technology. His role as a core researcher in these projects highlights his leadership in interdisciplinary and applied medical research. He has also presented at major international conferences such as ACM MM and IEEE BIBM, reflecting his active engagement in the global scientific community.

Awards and Recognitions

Assist. Prof. Dr. Xiongjun Zhao has received several prestigious awards that reflect his academic excellence and problem-solving capabilities in both theoretical and applied domains. He was awarded the National Second Prize in the National University Student Mathematics Competition (ASC), showcasing his strong analytical and mathematical skills. In competitive programming, he earned the Regional Second Prize in the ACM-ICPC International Collegiate Programming Contest, a globally recognized event for algorithmic proficiency. His team received an Honorable Mention (Top 15%) in the Mathematical Contest in Modeling (MCM/ICPC), further emphasizing his strength in interdisciplinary problem-solving. Additionally, he secured the National Third Prize in the China Graduate Mathematical Modeling Competition and the Provincial Third Prize in the Hunan “Internet+” Innovation and Entrepreneurship Competition. These accolades collectively highlight Dr. Zhao’s commitment to academic rigor, computational excellence, and innovation, and they underscore his readiness to tackle real-world challenges through collaborative and research-driven approaches.

Research Focus

Assist. Prof. Dr. Xiongjun Zhao’s research is primarily centered on biomedical big data analysis, intelligent medical systems, and multi-modal machine learning for clinical decision-making. His work integrates advanced deep learning techniques—such as Transformers, Graph Neural Networks (GNNs), and multimodal prompt learning—into healthcare applications, particularly in medical image analysis, electronic health record (EHR) modeling, and medication guidance systems. His studies include developing continual learning models for EHRs (TransEHR), large language models for drug recommendations (ShennongMGS), and novel diagnostic approaches using multimodal X-ray interpretation and ECG analysis (ECGNN). He also explores multitask and multi-view learning frameworks to enhance predictive accuracy in various clinical settings. His research lies at the intersection of artificial intelligence, medical informatics, and computational biology, aiming to improve diagnostic accuracy, clinical workflow, and personalized medicine. Through high-impact publications and patents, Dr. Zhao contributes significantly to the advancement of AI-driven healthcare technologies and smart medical diagnostics.

Publication Top Notes

  • TransEHR: Alignment-Free Electronic Health Records Continual Learning Across Feature Spaces, Expert Systems with Applications, 2025, DOI: 10.1016/j.eswa.2025.129020

  • ShennongMGS: An LLM-based Chinese Medication Guidance System, ACM Transactions on Management Information Systems, 2025, DOI: 10.1145/3658451

  • Report-Concept Textual-Prompt Learning for Enhancing X-ray Diagnosis, ACM MM (Conference), 2024, DOI: 10.1145/3664647.3681568

  • ECGNN: Enhancing Abnormal Recognition in 12-Lead ECG with Graph Neural Network, IEEE BIBM, 2022, DOI: 10.1109/BIBM55620.2022.9995419

  • UniMed: Multimodal Multitask Learning for Medical Predictions, IEEE BIBM, 2022, DOI: 10.1109/BIBM55620.2022.9995044

  • A Knowledge-aware Machine Reading Comprehension Framework for Dialogue Symptom Diagnosis, IEEE BIBM, 2021, DOI: 10.1109/bibm52615.2021.9669717

  • Multi-View Weighted Feature Fusion Using CNN for Pneumonia Detection on Chest X-Rays, IEEE HEALTHCOM, 2021, DOI: 10.1109/healthcom49281.2021.9399029

Eugene Levner | Artificial Intelligence | Best Researcher Award

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Prof. Eugene Levner | Artificial Intelligence | Best Researcher Award

Professor at Holon Institute of Technology, Israel

Prof. Eugene Levner is a renowned expert in computational mathematics, operations research, and artificial intelligence, with a career spanning over five decades. He earned his Ph.D. from the Central Economic-Mathematical Institute of the USSR Academy of Sciences, focusing on graph models and scheduling problems. He has held prominent academic positions in Russia and Israel, including Holon Institute of Technology, Bar Ilan University, and The Hebrew University of Jerusalem. Prof. Levner has authored numerous influential publications in top-tier journals and received multiple Best Paper and Excellence in Teaching awards. His research spans scheduling theory, robotics, fuzzy logic, and digital medicine, with over 1,500 citations highlighting his global impact. He has been a guest lecturer at institutions across Europe, North America, and Asia and has served on editorial boards of leading journals. His work continues to influence the fields of algorithm design, risk management, and smart manufacturing systems.

Professional Profile

Google Scholar

Academic Background

Prof. Eugene Levner holds an exceptional academic background in computational mathematics and systems science. He earned his B.S. and M.S. degrees in Computational Mathematics from Moscow State Lomonosov University between 1963 and 1968, where he developed a strong foundation in algorithmic thinking and mathematical modeling. He went on to complete his Ph.D. in Computer and Systems Science at the Central Economic-Mathematical Institute of the USSR Academy of Sciences from 1969 to 1973. His doctoral research focused on the design of graph models and methods for solving scheduling problems, laying the groundwork for a lifelong career in optimization and operations research. Prof. Levner was mentored by distinguished scholars, including Prof. Boris T. Polyak and Prof. David B. Yudin, both influential figures in applied mathematics. His education equipped him with advanced skills in mathematical programming, which he later applied across multiple disciplines such as artificial intelligence, robotics, and digital medicine.

Professional Background

Prof. Eugene Levner has had a distinguished professional career marked by academic leadership and groundbreaking research in computer science, operations research, and artificial intelligence. Beginning as a researcher at the Institute of Automation and Remote Control in Moscow, he went on to serve at the Central Economic-Mathematical Institute of the USSR Academy of Sciences for over two decades. He later held academic positions at Moscow State University and The Hebrew University of Jerusalem. From 1994 to 2010, he was a professor at the Holon Institute of Technology in Israel, where he also received multiple excellence awards. He further contributed as a lecturer at Bar Ilan University and served as a full-time professor at Ashkelon Academic College. Prof. Levner has been a visiting lecturer at leading institutions across Europe, Asia, and North America. Currently, he serves as Emeritus Professor at the Holon Institute of Technology, continuing to mentor students and contribute to international research.

Awards and Honors

Prof. Eugene Levner has received numerous prestigious awards and honors in recognition of his outstanding contributions to research, teaching, and academic leadership. Early in his career, he was awarded the Silver Diploma by the USSR Institute of Control Problems in 1972 and received the Best Paper Award from the Moscow Government in 1981. His international recognition includes listings in Marquis’ Who’s Who in Science and Engineering and 2000 Outstanding Scientists of the 20th Century. He has earned multiple Best Paper Awards at international conferences in Russia, Mexico, and Israel, including INCOM-IFAC and MICAI. In addition to research excellence, he was honored with Excellence in Teaching and Research Awards at the Holon Institute of Technology between 2009 and 2021. He also received a special award from Shanghai Jiao Tong University in 2010 for his exceptional instruction in operations research. These accolades reflect his lasting global impact in applied mathematics and computer science.

Research Focus

Prof. Eugene Levner’s research spans several core areas in computational mathematics and applied computer science, with a primary focus on algorithm design, scheduling theory, and operations research. He has made significant contributions to the development of graph-based models and approximation algorithms for complex scheduling and optimization problems, particularly in manufacturing systems and robotics. His work integrates artificial intelligence techniques with digital medicine, risk management, and decision-making under uncertainty. Prof. Levner has also advanced research in fuzzy logic and its applications in intelligent systems and supply chain resilience. His recent studies explore adaptive scheduling, energy-efficient computing, and the ripple effects of environmental risks using entropy-based models. He has published extensively in high-impact journals, contributing to both theoretical foundations and real-world applications. Through multidisciplinary research and international collaborations, Prof. Levner continues to influence areas such as smart manufacturing, autonomous systems, and computational logistics, maintaining relevance in both academic and industrial research communities.

Publication Top Notes

Integer Programming and Flows in Networks
Year: 1974 | Cited by: 472

Fast Approximation Algorithm for Job Sequencing with Deadlines
Year: 1981 | Cited by: 121

Computational Complexity of Approximation Algorithms for Combinatorial Problems
Year: 1979 | Cited by: 124

An Improved Algorithm for Cyclic Flowshop Scheduling in a Robotic Cell
Year: 1997 | Cited by: 139

Cyclic Scheduling in Robotic Flowshops
Year: 2000 | Cited by: 280

Multiple-Part Cyclic Hoist Scheduling Using a Sieve Method
Year: 2002 | Cited by: 111

Adaptive Scheduling Server for Power-Aware Real-Time Tasks
Year: 2004 | Cited by: 130

Perishable Inventory Management with Dynamic Pricing Using Time–Temperature Indicators Linked to Automatic Detecting Devices
Year: 2014 | Cited by: 145

Complexity of Cyclic Scheduling Problems: A State-of-the-Art Survey
Year: 2010 | Cited by: 231

Entropy-Based Model for the Ripple Effect: Managing Environmental Risks in Supply Chains
Year: 2018 | Cited by: 110

Conclusion

Prof. Eugene Levner is a distinguished scholar with a lifelong dedication to advancing computational mathematics, operations research, and artificial intelligence. With a Ph.D. from the Central Economic-Mathematical Institute of the USSR Academy of Sciences and mentorship under world-renowned experts, his foundational work in graph models, scheduling, and optimization has had lasting global impact. He has published extensively in high-impact journals, with several highly cited papers influencing both theoretical and applied research. Prof. Levner has held senior academic positions in leading institutions across Russia and Israel and delivered invited lectures worldwide. His pioneering research in scheduling theory, robotics, fuzzy logic, and digital medicine, combined with multiple international awards and recognition for both teaching and research excellence, solidifies his reputation as a leader in his field. Through mentoring, interdisciplinary innovation, and global collaboration, Prof. Levner’s work continues to shape contemporary science and technology, making him an exceptional and highly deserving recipient of the “Best Researcher Award.”

 

 

Rania Hamdani | Computer science | Best Researcher Award

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Mrs. Rania Hamdani | Computer science | Best Researcher Award

Mrs. Rania Hamdani, University of Luxembourg, Luxembourg

Rania Hamdani is a research scientist specializing in operational research, data management, and cloud architecture for Industry 5.0. Based in Luxembourg, she is currently affiliated with the University of Luxembourg, where she explores advanced methodologies for integrating and managing heterogeneous data sources. She holds an engineering degree in Software Engineering and has extensive experience in software development, AI, and DevOps. Rania has worked on multiple industry and academic projects, publishing three research papers in Ontology-Driven Knowledge Management and Cloud-Edge AI. With a strong background in programming, cloud computing, and AI-driven solutions, she has contributed to platforms ranging from job recommendation systems to adaptive human-computer interaction systems. Her expertise includes Python, SpringBoot, Kubernetes, and Azure DevOps. She is also an active member of IEEE and other technical organizations, promoting innovation and knowledge-sharing in AI and cloud technologies. 🌍💻🔬

Publication Profile

Orcid

🎓 Education

Rania Hamdani holds an Engineering Degree in Software Engineering from the National Higher School of Engineers of Tunis (2021–2024), where she specialized in advanced design, service-oriented architecture, object-oriented programming, database management, and operational research. Prior to this, she completed a two-year preparatory cycle at the Preparatory Institute for Engineering Studies of Tunis (2019–2021), undertaking intensive coursework in mathematics, physics, and technology to prepare for engineering studies. She also earned a Mathematics-specialized Baccalaureate from Pioneer High School Bourguiba Tunis (2015–2019), graduating with honors. Throughout her academic journey, she gained expertise in artificial intelligence, machine learning, cloud computing, and DevOps methodologies. Her education provided a solid foundation in programming languages, data processing techniques, and full-stack development. Additionally, she holds multiple Microsoft certifications in Azure fundamentals, AI, data security, and compliance, reinforcing her expertise in cloud-based solutions and AI-driven applications. 📚🎓💡

💼 Experience

Rania Hamdani is a research scientist at the University of Luxembourg, where she focuses on integrating and managing heterogeneous data sources for cloud-based decision-making. Previously, she was a research intern at the same institution, contributing to Ontology-Driven Knowledge Management and Cloud-Edge AI, with three published papers. She also worked as a part-time software engineer at CareerBoosts in Quebec (2021–2025), specializing in Python, Azure DevOps, Docker, and test automation. She gained industry experience through internships at Qodexia (Paris), Sagemcom (Tunisia), and Tunisie Telecom, working on smart recruitment platforms, employee management systems, and server monitoring solutions using SpringBoot, Angular, and PostgreSQL. Her technical expertise spans full-stack development, DevOps, AI-driven applications, and cloud computing. She has contributed to major projects, including an adaptive human-computer interaction system, a job recommendation system, and a problem-solving platform, demonstrating her versatility in research and software engineering. 🚀🖥️🔍

🏆 Awards & Honors

Rania Hamdani has been recognized for her outstanding contributions to AI-driven cloud computing and operational research. She received excellence awards during her engineering studies at the National Higher School of Engineers of Tunis and was among the top-performing students in her Mathematics-specialized Baccalaureate. Her research papers in Ontology-Driven Knowledge Management and Cloud-Edge AI have been acknowledged in academic circles, contributing to the advancement of Industry 5.0 technologies. She has also earned multiple Microsoft certifications in cloud and AI fundamentals, reinforcing her technical expertise. As an active member of IEEE and the Youth and Science Association, she has been involved in technology outreach and innovation-driven initiatives. Her leadership in ENSIT Junior Enterprise as a project manager further showcases her ability to lead and contribute to tech communities. These recognitions highlight her dedication to research, software development, and cloud-based AI applications. 🏅📜🌟

🔬 Research Focus

Rania Hamdani’s research focuses on operational research, data management, cloud-edge AI, and Industry 5.0 applications. She specializes in ontology-driven knowledge management, exploring methodologies for integrating heterogeneous data sources to optimize cloud-based decision-making processes. Her work includes artificial intelligence, machine learning, reinforcement learning, and human-computer interaction systems. She has contributed to projects involving job recommendation systems, adaptive human-computer interaction platforms, and cloud-based problem-solving platforms. Rania is particularly interested in scalable cloud architectures, leveraging technologies like FastAPI, Kubernetes, Docker, and Azure DevOps to build efficient AI-powered solutions. Her research also integrates graph databases, Apache Airflow, and big data analytics for enhanced data processing. By combining AI and cloud computing, she aims to develop innovative, data-driven solutions for automation, decision support, and optimization in various industrial applications. Her expertise bridges the gap between theoretical research and real-world software engineering. ☁️🤖📊

 

Publication Top Notes

Adaptive human-computer interaction for industry 5.0: A novel concept, with comprehensive review and empirical validation

 

Zuheng Ming | Artificial intelligence | Best Researcher Award

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Dr. Zuheng Ming | Artificial intelligence | Best Researcher Award

Associate professor at Sorbonne Paris North University, France

🧑‍🏫 Dr. Zuheng Ming is an Assistant Professor at L2TI, Sorbonne Paris North University, France. He earned his PhD in 2013 from Grenoble Alpes University 🇫🇷, specializing in speech parameter mapping. His expertise spans multimodal learning, computer vision, and deep learning 🤖. Dr. Ming has 30+ publications 📝 in top-tier journals (JCR Q1/Q2) and conferences (ICIP, ICPR, ICDAR). He has supervised doctoral and master’s theses and collaborated internationally with CVC, RIKEN AIP, and Oulu University 🌍. He has led funded research projects on face anti-spoofing and document analysis 📄. Additionally, he serves as a guest editor and reviewer for prestigious journals. ✨

Publication Profile

Google Scholar

🏅 Professional Experience

Dr. Zuheng Ming is an accomplished researcher and educator in computer vision and deep learning 🤖. Since September 2022, he has been serving as an Assistant Professor at L2TI, Sorbonne Paris North University, France 🇫🇷. Prior to this, he was a Lecture-Researcher at L3i, La Rochelle University (2021-2022) 📚. From 2016 to 2021, he worked as a Postdoctoral Fellow and Assistant Lecturer at L3i, La Rochelle University. Earlier, from 2014 to 2015, he pursued a postdoctoral fellowship at Bordeaux University 🏛️, contributing significantly to cutting-edge research in multimodal learning and artificial intelligence. ✨

🎓 Educational Background

Dr. Zuheng Ming holds a PhD in Computer Science from Grenoble Alpes University, France (2013) 🇫🇷, where he specialized in spectral parameters mapping for cued speech using multi-linear and GMM approaches 🔬. He earned his Master’s degree in Pattern Recognition and Artificial Intelligence from Beijing Institute of Technology (2008) 🎭🤖. His academic journey began with a Bachelor’s degree in Electronic and Automatic Systems Engineering from Hunan University, China (2003) ⚡. His strong educational foundation has driven his research contributions in computer vision, deep learning, and multimodal learning 📚✨.

🔬 Research Activities

Dr. Zuheng Ming has been actively involved in research supervision, mentoring 1 PhD thesis, 2 Master’s theses, and 6 internships 🎓📖. He has established six international collaborations with prestigious institutions, including CVC (Spain) 🇪🇸, RIKEN AIP (Japan) 🇯🇵, Oulu University (Finland) 🇫🇮, Northwestern Polytechnical University (China) 🇨🇳, and Xidian University (China) 🇨🇳. His global academic engagement also includes an academic visit to Kyoto University, Japan, in 2015 🌍🏫. Through his extensive research network, Dr. Ming continues to make significant contributions to computer vision, deep learning, and multimodal learning 📊🤖.

🎓 Teaching Experience

Dr. Zuheng Ming has extensive teaching experience in cutting-edge technologies related to artificial intelligence and computer vision 🧠📸. He has taught courses on Deep Learning, Advanced Image Processing, and Intelligent Systems in Computer Vision 🤖🖼️, equipping students with the latest advancements in AI. Additionally, he has imparted knowledge in Database Management and Object-Oriented Programming 💾💻, fostering strong software development skills. His expertise in both theoretical foundations and practical applications makes him a valuable mentor in the field of AI and computer vision, guiding students toward innovative research and industry-ready solutions 🚀📚.

🔍 Research Focus

Dr. Zuheng Ming’s research primarily focuses on computer vision, deep learning, and document security 🧠📸🔏. His contributions span facial recognition, anti-spoofing techniques, and face liveness detection 🤖😃, enhancing biometric security. He has also worked extensively on document image classification and authentication 📄🔍, improving identity verification systems. His expertise in multi-modal learning, pattern recognition, and deep feature fusion enables advancements in AI-driven document forensics and secure authentication 🚀🔐. Collaborating internationally, he applies machine learning and self-attention networks to solve real-world challenges in face recognition, fraud detection, and intelligent systems 🌍🔬.

Publication Top Notes

📸 A survey on anti-spoofing methods for facial recognition with RGB cameras of generic consumer devices – Z Ming, M Visani, MM Luqman, JC Burie | Journal of Imaging | 88 citations | 2020

📄 Visual and textual deep feature fusion for document image classification – S Bakkali, Z Ming, M Coustaty, M Rusiñol | IEEE/CVF Conference on Computer Vision | 63 citations | 2020

🔍 Simple triplet loss based on intra/inter-class metric learning for face verification – Z Ming, J Chazalon, MM Luqman, M Visani, JC Burie | IEEE/CVF International Conference on Computer Vision | 57 citations | 2017

😊 Facial action units intensity estimation by fusion of features with multi-kernel SVM – Z Ming, A Bugeau, JL Rouas, T Shochi | IEEE International Conference on Automatic Face and Gesture Recognition | 54 citations | 2015

🆔 MIDV-2020: A comprehensive benchmark dataset for identity document analysis – BK Bulatovich, EE Vladimirovna, TD Vyacheslavovich, SN Sergeevna, … | Computer Optics | 51 citations | 2022

🙂 Dynamic Multi-Task Learning for Face Recognition with Facial Expression – Z Ming, J Xia, MM Luqman, JC Burie, K Zhao | IEEE/CVF International Conference on Computer Vision Workshop | 40 citations | 2019

📜 VLCDoC: Vision-language contrastive pre-training model for cross-modal document classification – S Bakkali, Z Ming, M Coustaty, M Rusiñol, OR Terrades | Pattern Recognition | 33 citations | 2023

🔐 FaceLiveNet: End-to-end networks combining face verification with interactive facial expression-based liveness detection – Z Ming, J Chazalon, MM Luqman, M Visani, JC Burie | International Conference on Pattern Recognition | 30 citations | 2018

📑 Cross-modal deep networks for document image classification – S Bakkali, Z Ming, M Coustaty, M Rusiñol | IEEE International Conference on Image Processing | 23 citations | 2020

📃 Document liveness challenge dataset (DLC-2021) – DV Polevoy, IV Sigareva, DM Ershova, VV Arlazarov, DP Nikolaev, Z Ming, … | Journal of Imaging | 21 citations | 2022

📹 ViTransPAD: Video Transformer using convolution and self-attention for Face Presentation Attack Detection – Z Ming, Z Yu, M Al-Ghadi, M Visani, M Muzzamil Luqman, JC Burie | IEEE International Conference on Image Processing | 21 citations | 2022

🌲 Multiple sources data fusion via deep forest – J Xia, Z Ming, A Iwasaki | IGARSS IEEE International Geoscience and Remote Sensing Symposium | 15 citations | 2018

🆔 Face detection in camera captured images of identity documents under challenging conditions – S Bakkali, MM Luqman, Z Ming, JC Burie | International Conference on Document Analysis and Recognition Workshops | 11 citations | 2019

📑 EAML: Ensemble self-attention-based mutual learning network for document image classification – S Bakkali, Z Ming, M Coustaty, M Rusiñol | International Journal on Document Analysis and Recognition | 10 citations | 2021

🧠 Synthetic evidential study as augmented collective thought process – Preliminary report – T Nishida, M Abe, T Ookaki, D Lala, S Thovuttikul, H Song, Y Mohammad, … | ACIIDS Asian Conference | 10 citations | 2015

🆔 Identity documents authentication based on forgery detection of guilloche pattern – M Al-Ghadi, Z Ming, P Gomez-Krämer, JC Burie | arXiv preprint | 8 citations | 2022

 

ShengHsun Hsu | AI | Best Researcher Award

Published on by Academic Awards

Prof. ShengHsun Hsu | AI | Best Researcher Award

Prof. ShengHsun, Chung Hua University, Taiwan

📚 Prof. Sheng-Hsun Hsu is a full-time professor in the Department of Management at Chu Hua University, Taiwan. He holds a Ph.D. in Business Administration from Taiwan University (2004), a Master’s in Computer Science, and a Bachelor’s in Mathematics from Hsing Hua University.💼 With extensive experience, Prof. Hsu has served as an Assistant Professor, Associate Professor, and Chairman at Chu Hua University. His research focuses on organizational behavior, customer satisfaction indices, brand equity, and psychological capital. His work has been published in top journals like Total Quality Management & Business Excellence and Service Industries Journal (SSCI).🌟 In addition to his academic contributions, Prof. Hsu actively supports curriculum planning, faculty evaluations, and student recruitment efforts. His expertise bridges business management and research, with a commitment to fostering excellence.

Publication Profile

Scopus

📘 Academic Journey

Prof. Sheng-Hsun Hsu boasts an impressive academic background spanning business administration, computer science, and mathematics. He earned his Ph.D. in Business Administration from Taiwan University (1999–2004) 🎓. Before that, he completed his Master’s degree in Computer Science at Hsing Hua University (1993–1995) 💻. His academic journey began with a Bachelor’s degree in Mathematics from the same institution (1990–1993) ➗. This diverse educational foundation reflects his interdisciplinary expertise and commitment to excellence in both theoretical and practical domains of knowledge. 🌟

 

💼 Professional Experience

Prof. Sheng-Hsun Hsu has had an illustrious career at Chu Hua University, contributing as a scholar and leader. He began as an Assistant Professor (1993–1996) 🧑‍🏫, advancing to Associate Professor (1996–1999) 📚. His dedication and expertise led to his promotion as a Professor, a position he has held since May 2014 🌟. Beyond teaching and research, he served as Chairman of the university from August 2013 to August 2014 🏢. Prof. Hsu’s professional journey reflects his commitment to academia and leadership in higher education. 🎓

 

📊 Research Focus

Prof. Sheng-Hsun Hsu’s research primarily revolves around Total Quality Management (TQM) and Business Excellence, particularly focusing on improving organizational performance and strategic alignment in various industries. His studies explore the integration of information technology (IT) and business strategies, emphasizing IT competence and the roles of CIOs in business success. Additionally, Prof. Hsu has contributed to the development of models for customer satisfaction, alumni satisfaction, and psychological capital in organizational contexts. His work bridges behavioral economics, higher education, and business management, aiming to enhance both quality management and consumer experience. 🔍📈

 

Publication Top Notes  

  • A GPT-Aided literature review process for total quality management and business excellence (2024) – Cited by 1
  • The effects of IT chargeback on strategic alignment and performance: the contingent roles of business executives’ IT competence and CIOs’ business competence (2023) – Cited by 3
  • Topic analysis of studies on total quality management and business excellence: an update on research from 2010 to 2019 (2022) – Cited by 11
  • Constructing a consumption model of fine dining from the perspective of behavioral economics (2018) – Cited by 8
  • Developing a decomposed alumni satisfaction model for higher education institutions (2016) – Cited by 23
  • Building business excellence through psychological capital (2014) – Cited by 15
  • Developing a decomposed customer satisfaction index: An example of the boutique motel industry (2013) – Cited by 4
  • Constructing an index for brand equity: A hospital example (2011) – Cited by 31
  • A dyadic perspective on knowledge exchange (2010) – Cited by 6
  • A two-stage architecture for stock price forecasting by integrating self-organizing map and support vector regression (2009) – Cited by 108

Yunge Zou | Computer Science | Best Scholar Award

Published on by Academic Awards

Dr. Yunge Zou | Computer Science | Best Scholar Award

Dr. Yunge Zou, Chongqing University, China

Dr. Yunge Zou is a Ph.D. scholar at Chongqing University, specializing in hybrid powertrain design and battery degradation in the Department of Automotive Engineering. He is a talent under the Chongqing Excellence Program and a Shapingba Elite Talent (2023–2025). Dr. Zou has led key projects, including the National Key R&D Program, focusing on high-efficiency powertrain technologies. His contributions include innovative methods like Hyper-Rapid Dynamic Programming, which optimizes multi-mode hybrid powertrains. With multiple patents and high-impact publications, he collaborates with leading automotive firms like Chang’an New Energy, advancing sustainable transportation. 🚗🔋📚

 

Publication Profile

Orcid

Google Scholar

Academic and Professional Background 🔋

Dr. Yunge Zou earned his B.E. degree in Automotive Engineering from Chongqing University, China, in 2018. Currently, he is pursuing his Ph.D. in hybrid powertrain design and optimization at the Vehicle Power System Lab, Department of Automotive Engineering, Chongqing University. Recognized for his exceptional talent, Dr. Zou is part of the prestigious Chongqing Excellence Program and was honored as a Shapingba Elite Talent for 2023–2025. His research focuses on hybrid powertrain topology design, battery degradation, energy management systems (EMS), and enhancing battery life, contributing to sustainable transportation innovation. 📚🔧🌱

 

Research and Innovations 🚗

Dr. Yunge Zou is leading several groundbreaking research projects in the field of hybrid powertrain design and optimization. His work includes the National Key Research and Development Program of China on high-efficiency range extender assembly and electric vehicle integration (2022-2024), with a funding of 2.5 million yuan. He is also working on optimizing hybrid electric vehicle design through the National Science Fund for Excellent Young Scholars (2023-2025). Additionally, he contributes to various projects focusing on hybrid vehicle dynamics, energy efficiency, and low-emission technologies, backed by substantial funding from multiple prestigious organizations. 🛠️⚡

 

🛠️ Research Focus

Dr. Yunge Zou’s research primarily focuses on hybrid powertrain design and optimization for electric and range-extended vehicles. His work includes the development of control strategies and topology design for hybrid systems, aiming to improve fuel economy, efficiency, and reduce emissions. Dr. Zou has made significant advancements in aging-aware optimization and mode-switching mechanisms for multi-mode hybrid vehicles. His contributions also extend to battery degradation, energy management, and the computational efficiency of fuel economy assessment using innovative algorithms like Hyper Rapid Dynamic Programming (HR-DP). His work is instrumental in the evolution of transportation electrification. 🚗⚡

 

Publication Top Notes

  • “Design of all-wheel-drive power-split hybrid configuration schemes based on hierarchical topology graph theory” – Energy 242, 122944 (Cited by 14, 2022) 🔋
  • “Aging-aware co-optimization of topology, parameter and control for multi-mode input-and output-split hybrid electric powertrains” – Journal of Power Sources 624, 235564 (Cited by 1, 2024) ⚙️
  • “Design of optimal control strategy for range extended electric vehicles considering additional noise, vibration and harshness constraints” – Energy 310, 133287 (Cited by 1, 2024) 🚗
  • “Computationally efficient assessment of fuel economy of multi-modes and multi-gears hybrid electric vehicles: A Hyper Rapid Dynamic Programming Approach” – Energy, 133811 (Cited by 0, 2024) 🔧