Changda Lei | Artificial intelligence | Best Academic Researcher Award

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Dr. Changda Lei | Artificial intelligence | Best Academic Researcher Award

Resident physician at First Affiliated Hospital of Soochow University, China

Professional Profile

Scopus
Orcid

Summary

Dr. Changda Lei is a medical doctor and early-career researcher specializing in gastrointestinal tumors, digestive endoscopy, and artificial intelligence (AI) in clinical diagnostics. He is currently affiliated with the Department of Gastroenterology at the First Affiliated Hospital of Soochow University, China. His interdisciplinary expertise bridges medical imaging, clinical gastroenterology, and AI-driven diagnostic systems, with a particular focus on enhancing early cancer detection.

Educational Details

Dr. Lei earned his Doctor of Medicine degree with a residency in Gastroenterology from the First Affiliated Hospital of Soochow University, China. His doctoral thesis, titled “Artificial intelligence for early gastric cancer boundary recognition in NBI and NF-NBI endoscopic images,” was supervised by Prof. Rui Li and represents a significant contribution to AI applications in endoscopic image analysis for early cancer detection.

Professional Experience

During his residency and doctoral training at Soochow University, Dr. Lei gained clinical and research experience in gastroenterological procedures and endoscopic imaging. He has collaborated closely with multidisciplinary teams, including radiologists, computer scientists, and oncologists, to develop and validate AI-assisted diagnostic tools. His co-authored works involve both methodological development and clinical validation, marking him as a key contributor to translational medicine in the field of digestive oncology.

Research Interests

Dr. Lei’s research interests lie at the intersection of gastrointestinal tumor diagnostics, digestive endoscopy, and artificial intelligence. He focuses on improving early detection of gastric cancer, particularly through boundary recognition in NBI and NF-NBI endoscopic images. His broader research also explores multi-task learning, semantic segmentation, and clinical integration of AI tools for real-time diagnostic support in endoscopy suites.

Author Metrics

Although early in his academic career, Dr. Lei has co-authored multiple peer-reviewed articles in reputable international journals. His publications in Annals of Medicine, Scandinavian Journal of Gastroenterology, and Expert Systems with Applications demonstrate a growing influence in both clinical and AI research communities. His ORCID ID is 0000-0001-7908-7011, and his citation metrics are expected to rise with his growing publication footprint in multidisciplinary fields.

Awards and Honors

While formal awards are not explicitly listed, Dr. Lei’s contributions to high-impact publications and participation in cutting-edge research—such as the application of task-specific prompting in AI models for endoscopy—indicate peer recognition and significant academic promise. His collaborative work with senior scientists like Prof. Rui Li and publication in top-tier journals positions him as a rising expert in the medical AI and gastroenterology research community.

Publication Top Notes

1. Artificial Intelligence-Assisted Diagnosis of Early Gastric Cancer: Present Practice and Future Prospects

  • Authors: Changda Lei, Wenqiang Sun, Kun Wang, Ruirong Weng, Xiuji Kan, Rui Li

  • Journal: Annals of Medicine

  • Volume/Issue: Volume 57, Issue 1

  • Article ID: 2461679

  • Publication Date: 2025

  • DOI: 10.1080/07853890.2025.2461679

  • Summary:
    This article reviews current applications and future directions for artificial intelligence (AI) in the diagnosis of early gastric cancer (EGC). It highlights advances in endoscopic imaging, especially NBI (Narrow Band Imaging) and AI-based pattern recognition, and discusses clinical integration, challenges, and prospects for real-time implementation.

2. Neonatal Lupus Erythematosus: An Acquired Autoimmune Disease to Be Taken Seriously

  • Authors: Wenqiang Sun, Changchang Fu, Xinyun Jin, Changda Lei, Xueping Zhu

  • Journal: Annals of Medicine

  • Publication Date: December 31, 2025

  • DOI: 10.1080/07853890.2025.2476049

  • Summary:
    This clinical review focuses on neonatal lupus erythematosus (NLE), a rare but significant autoimmune condition affecting newborns. The article emphasizes early diagnosis, maternal screening, and therapeutic strategies, highlighting the need for interdisciplinary vigilance and patient-specific care.

Conclusion

Dr. Changda Lei is an exceptionally promising early-career academic who has already made meaningful contributions to the convergence of AI and gastrointestinal oncology. His research is not only innovative and clinically relevant but also indicative of leadership in next-generation diagnostic solutions.

Fadi Sibai | Network | Best Researcher Award

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Assoc. Prof. Dr. Fadi Sibai | Network | Best Researcher Award

Associate Professor at Gulf University for Science and Technology, Kuwait

Dr. Fadi Sibai is an accomplished Associate Professor at Gulf University for Science and Technology (GUST) in Kuwait, with over 35 years of experience in electrical and computer engineering, AI, cybersecurity, and academic leadership. He holds a Ph.D. in Electrical Engineering from Texas A&M University and has held senior roles in prestigious institutions like Intel, Saudi Aramco, and UAE University. Dr. Sibai’s interdisciplinary research focuses on AI, machine learning, cybersecurity, and digital design, with numerous publications, including over 78 journal articles and 10 books. He has received multiple prestigious awards, such as the IBM Faculty Award and nVIDIA Research Grant, and is recognized as a leading innovator in smart cities, healthcare, and AI ethics. His work has significantly impacted education, industry, and society, making him an ideal candidate for the Best Researcher Award.

Professional Profile
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Orcid
Google Scholar

Educational Details

Dr. Sibai earned his Ph.D. and M.S. degrees in Electrical Engineering from Texas A&M University, specializing in computer engineering with minors in computer science and communications. His doctoral research focused on AI-based coprocessor architectures. He also received his B.S. in Electrical Engineering from the University of Texas at Austin, concentrating on computer science and engineering. His academic qualifications are bolstered by certifications such as PMP, CISSP, CQRM, and advanced studies at Stanford University.

Professional Experience

Dr. Sibai’s career spans academia, industry, and research leadership. He served as Dean of Engineering at Prince Mohammad Bin Fahd University, Dean at American International University Kuwait, and Associate Dean at GUST, where he played a key role in curriculum development, program accreditation (ABET, NCAAA), and launching AI and cybersecurity programs. In the industry, he held senior positions at Intel Corporation and Saudi Aramco, overseeing impactful projects in HPC, network security, robotics, and IT/OT integration. He has also been a consultant, educator, and researcher at various global institutions, including Walden University, KFUPM, and UAE University.

Research Interests

Dr. Sibai's research is interdisciplinary, focusing on AI, machine learning, computer and embedded systems, high-performance computing, digital design, data science, robotics, and cybersecurity. His recent work includes AI for smart cities , AI ethics and regulation , ensemble learning models in healthcare , and network-on-chip architectures. He is also an expert in neural networks , fault-tolerant computing, and energy-aware systems, blending theoretical modeling with real-world applications.

Author Metrics

Dr. Sibai has authored or co-authored over 250 scholarly publications, including 78+ journal articles, 10 books/book chapters, and numerous conference papers . Many of these are indexed in Scopus and IEEE Xplore, with his work being cited nearly 1,000 times. He is a long-time member of editorial boards and technical committees for top journals and conferences.

Awards and Honors

Dr. Sibai’s career has been recognized with numerous prestigious awards, including the IBM Shared University Research Award , IBM Faculty Award, nVIDIA Research Grant, and multiple Intel awards for innovation and performance excellence. He was named among IBC’s Top 100 Engineers and Educators and featured in Who’s Who in the World. He is also a distinguished speaker at international conferences and won the Best Research Project Award at UAE University. As a Senior IEEE Member and a member of organizations like PMI and ISC2, he is widely respected in both academia and industry.

Publication Top Notes
1. ParaEyes: The Smart Eye Bot System for Paralyzed Patients

  • Authors: M. Al Jaziri, G. Al Ali, H. Al Swailmeen, A.A. El-Moursy, F.N. Sibai

  • Journal: Journal of Circuits, Systems and Computers, 2025

  • Summary:
    This paper presents ParaEyes, a smart assistive bot system that enables paralyzed patients to interact with their environment using eye movements and blinking patterns. Leveraging computer vision, machine learning, and sensor fusion, the system facilitates tasks such as communication and device control. The research highlights its real-world applicability in neurorehabilitation and patient autonomy, especially for individuals with ALS or spinal cord injuries.

2. Using The Cancer Genome Atlas from cBioPortal to Develop Genomic Datasets for Machine Learning Assisted Cancer Treatment

  • Authors: A. Asaduzzaman, C.C. Thompson, F.N. Sibai, M.J. Uddin

  • Platform: bioRxiv preprint, 2025.02.17.638660

  • Summary:
    This study extracts and preprocesses genomic data from The Cancer Genome Atlas (TCGA) using cBioPortal, creating machine-learning-ready datasets for the development of predictive models in personalized cancer treatment. It lays the foundation for using ML classifiers to assist oncologists in tailoring treatment plans based on genomic signatures, supporting precision oncology research.

3. The Robustness of AI-Classifiers in the Face of AI-Assisted Plagiarism: The Case of Turnitin AI Content Detector

  • Authors: K. Ibrahim, D. Otaibi, F.N. Sibai

  • Journal: International Journal of Computer-Assisted Language Learning and Teaching (IJCALLT), 2025

  • Summary:
    This paper examines the reliability and vulnerability of AI-based plagiarism detection tools, particularly Turnitin’s AI Content Detector, in detecting content generated or modified by large language models (LLMs). The findings expose potential weaknesses in current detection algorithms and propose methods for enhancing AI classifier robustness in academic integrity systems.

4. Application of Ensemble Learning Models in Computer-Aided Diagnosis of Skin Diseases

  • Authors: A. Asaduzzaman, C.C. Thompson, F.N. Sibai, M.J. Uddin

  • Journal: Neural Computing and Applications, 2025

  • Summary:
    This research evaluates the performance of ensemble learning models (such as Random Forest, XGBoost, and AdaBoost) for diagnosing skin diseases using clinical and dermoscopic image data. The models showed high accuracy, sensitivity, and specificity, demonstrating that ensemble-based approaches can significantly enhance AI-aided dermatological diagnostics.

5. A Collaborative Adaptive Cybersecurity Algorithm for Cognitive Cities

  • Authors: A. Abonamah, F.N. Sibai

  • Journal: Journal of Computer Information Systems, 2025, pp. 1–16

  • Summary:
    This paper proposes a collaborative and adaptive cybersecurity framework designed for cognitive cities. It integrates machine learning algorithms and shared threat intelligence to enable smart urban infrastructure to defend against evolving cyber threats. The model improves resilience, detection, and adaptability in real-time, supporting safe digital environments in smart city ecosystems.

Conclusion

Assoc. Prof. Dr. Fadi Sibai is eminently qualified for the Best Researcher Award, standing out for his long-term research excellence, technological innovation, and strategic leadership in academic and applied AI domains. His work addresses societal challenges through AI, pushes forward smart infrastructure and cybersecurity, and contributes meaningfully to medical, ethical, and educational AI ecosystems.

His record of scholarship, innovation, mentorship, and global engagement makes him a deserving and ideal candidate for this prestigious honor. The award would not only recognize his decades of impact but also support his ongoing contributions to shaping the future of AI, education, and engineering.

Ahmad Hassanat | Machine Learning | Best Researcher Award

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Prof. Ahmad Hassanat | Machine Learning | Best Researcher Award

Professor at Mutah University, Jordan

Professional Profile

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Summary

Prof. Ahmad B. A. Hassanat is a Full Professor of Computer Science at Mutah University, Jordan, and a senior IEEE member. He is globally recognized for his extensive contributions to artificial intelligence, machine learning, biometrics, and image processing. With over two decades of academic and research experience, he has authored numerous impactful papers and books and is widely known for pioneering innovative techniques like the "Hassanat Distance" metric and deep learning-based biometric systems. He is also active in international collaborations, editorial work, and AI-driven healthcare research.

Educational Details

Prof. Hassanat earned his Ph.D. in Computer Science from the University of Buckingham, UK,, with a focus on automatic lip-reading. He holds an M.Sc. in Computer Science from Al al-Bayt University, Jordan, where he specialized in fast string matching algorithms. He completed his B.Sc. in Computer Science at Mutah University, Jordan. His academic foundation reflects a strong blend of theoretical depth and applied research skills in computing and AI.

Professional Experience

Prof. Hassanat has served in multiple academic roles across Jordan and Saudi Arabia, including as a Full Professor at Mutah University and the University of Tabuk. He was Head of the IT Department at Mutah University and a visiting researcher at the Sarajevo School of Science and Technology. Earlier in his career, he worked for the Jordanian Armed Forces as a programmer and systems analyst, where he developed over a dozen mission-critical ICT systems. He is also a founder or co-founder of academic programs, conferences, and novel biometric solutions.

Research Interests

His research spans machine learning, artificial intelligence, image processing, biometrics, pattern recognition, and evolutionary algorithms. He is known for practical innovations such as deep learning for veiled-face recognition, genetic algorithm optimization, voice-based Parkinson’s detection, and machine learning models for epidemiology, security, and finance. He also created the widely referenced Hassanat Distance, improving classifier performance in imbalanced data scenarios.

Author Metrics

Prof. Hassanat has published over 100 journal articles and conference papers, with an H-index of 33, i10-index of 56, and more than 4,000 citations. His work is featured in top journals such as IEEE Access, PLOS ONE, Sustainability, Applied Sciences, and Computers. His algorithmic contributions and models are highly cited in the fields of AI, healthcare informatics, and big data analytics.

Awards and Honors

Prof. Hassanat has been named among the world’s top 2% scientists by Stanford–Elsevier in 2021, 2022, and 2023. He has received the Best Scientist award at Mutah University for 2023 and 2024, and multiple competitive research grants from Jordan and Saudi Arabia. He was the recipient of Mutah University’s Distinguished Researcher Award (2018, 2019), and granted IEEE Senior Membership for his research excellence. His innovations, including terrorist identification from hand gestures and COVID-19 forecasting tools, have received global media attention.

Publication Top Notes

1. Deep learning computer vision system for estimating sheep age using teeth images

  • Authors: AB Hassanat, MA Al-Sarayreh, AS Tarawneh, MA Abbadi, et al.

  • Journal: Connection Science

  • Volume/Issue: 37 (1)

  • Article ID: 2506456

  • Year: 2025

  • Summary:
    This study presents a deep learning-based computer vision system designed to estimate the age of sheep by analyzing images of their teeth. The model likely leverages convolutional neural networks (CNNs) or similar architectures to accurately assess age-related dental features, offering a non-invasive and automated method for livestock age estimation that can assist farmers and veterinarians.

  • Citations: Not provided

  • Access: Details not provided

2. ICT: Iterative Clustering with Training: Preliminary Results

  • Authors: AB Hassanat, AS Tarawneh, AS Alhasanat, M Alghamdi, K Almohammadi, et al.

  • Conference: 2025 International Conference on New Trends in Computing Sciences (ICTCS)

  • Year: 2025

  • Summary:
    This paper introduces a novel method named Iterative Clustering with Training (ICT), presumably a machine learning or data clustering approach. Preliminary results demonstrate its effectiveness in improving clustering accuracy or training efficiency for datasets common in computing science. The approach likely combines clustering with supervised training iterations for better performance.

3. Decision tree-based learning and laboratory data mining: an efficient approach to amebiasis testing

  • Authors: E Al-Khlifeh, AS Tarawneh, K Almohammadi, M Alrashidi, R Hassanat, et al.

  • Journal: Parasites & Vectors

  • Volume/Issue: 18 (1)

  • Article Number: 33

  • Year: 2025

  • Summary:
    This research applies decision tree-based machine learning techniques to mine laboratory data for efficient and accurate diagnosis of amebiasis. The study demonstrates how data mining on clinical data combined with decision trees can improve testing accuracy and streamline diagnostic procedures in parasitology.

4. Non-Invasive Cancer Detection Using Blood Test and Predictive Modeling Approach

  • Authors: AS Tarawneh, AK Al Omari, EM Al-Khlifeh, FS Tarawneh, M Alghamdi, et al.

  • Book/Series: Advances and Applications in Bioinformatics and Chemistry

  • Pages: 159-178

  • Year: 2024

  • Summary:
    This paper proposes a non-invasive method for cancer detection by combining blood test results with predictive modeling approaches, likely using machine learning algorithms. The approach aims to provide an early, cost-effective screening tool for cancer by analyzing biomarkers and patterns in blood test data.

5. Extended spectrum beta-lactamase bacteria and multidrug resistance in Jordan are predicted using a new machine-learning system

  • Authors: EM Al-Khlifeh, IS Alkhazi, MA Alrowaily, M Alghamdi, M Alrashidi, et al.

  • Journal: Infection and Drug Resistance

  • Pages: 3225-3240

  • Year: 2024

  • Summary:
    This study develops and applies a machine learning system to predict the occurrence of extended spectrum beta-lactamase (ESBL) producing bacteria and multidrug resistance patterns in Jordan. The predictive model aids in understanding and managing antibiotic resistance, supporting healthcare decision-making and antimicrobial stewardship.

Conclusion

Prof. Ahmad Hassanat embodies the qualities of a world-class researcher—his work is innovative, deeply applied, and globally relevant. From introducing original metrics and models in AI to developing life-saving diagnostic systems and biometric security applications, his impact is both academic and practical.

His dedication to research excellence, mentorship, and cross-disciplinary innovation makes him highly deserving of the Best Researcher Award in Machine Learning.

Gan Xu – Artificial Intelligence – Best Researcher Award

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Gan Xu – Artificial Intelligence – Best Researcher Award

Mr. Gan Xu distinguished academic and researcher in the field Artificial Intelligence.

🌐 Professional Profile

Educations📚📚📚

He is currently pursuing a Ph.D. in Finance at the Capital University of Economics and Business in Beijing, China, since September 2021. Prior to this, he completed his Master’s in Finance from Beijing Union University, Beijing, China, graduating in June 2021. His academic journey began with a Bachelor’s degree in Biotechnology, which he obtained from Guilin Medical University, Guilin, Guangxi, China, in June 2010.

Research Experience

He participated in the Project of the National Social Science Foundation of China, focusing on the “Research on Level Measurement, Spatial and Temporal Divergence, and Improvement Path of Rural Financial Services for Rural Revitalization” (19BJY158), where he was mainly responsible for the research design of some sub-topics and participated in enterprise research. Additionally, he contributed to the Key Topic of the China Mobile Communication Federation on the “Research on the Application of Blockchain Technology in Finance” (CMCA2018ZD01), taking charge of the research design of certain sub-topics and writing research reports. Furthermore, he was involved in the research project on “Financial Support for Deepening Financial Services for Private and Micro and Small Enterprises” as part of the Comprehensive Reform Pilot City Project in Jincheng City, Shanxi Province, where he was responsible for independently participating in application writing.

Social Experience

He has co-authored several significant publications, including “Financial Density of Village Banks and Income Growth of Rural Residents” with Yang, G.Z., Xu, G., Zhang, Y., and others, published in Economic Issues in 2021. Additionally, he contributed to “Knowledge Mapping Analysis of Seven Decades of Rural Finance Research in China” with Zhang, F., Xu, G., Zhang, X.Y., and Cheng, X., which appeared in Rural Finance Research in 2020. He also co-authored “A Review of Blockchain Applications in the Financial Sector” with Zhang, F. and Cheng, X., published in Technology for Development in 2019.

Honors

  • Received Beijing Outstanding Graduates in 2020
  • Outstanding graduate of Beijing Union University in 2020
  • First Prize of Excellent Paper in the First Annual Meeting of the Financial Technology Professional Committee of the China Society for Technology Economics, 2019
  • Second Prize of Excellent Paper in the 13th China Rural Finance Development Forum, 2019
  • Second Prize of Excellent Paper of the 9th Annual Conference of China Regional Finance and Xiongnu Financial Technology Forum, 2019

📝🔬Publications📝🔬

Erfan Shojaei Barjuei – Automated planning and scheduling Award – Top Researcher Award

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Erfan Shojaei Barjuei – Automated planning and scheduling Award – Top Researcher Award

Dr. Erfan Shojaei Barjuei distinguished academic and researcher in the field  Automated Planning and Scheduling. Throughout my career journey, I’ve traversed diverse roles and geographical locations, accumulating a wealth of experience in research and engineering. From delving into the intricacies of mechatronics engineering at institutions like the Italian Institute of Technology and the Sant’Anna School of Advanced Studies in Italy, to contributing to cutting-edge research in mechanical and automation engineering at the National Institute of Applied Sciences in France, I’ve always been driven by a passion for innovation and problem-solving.

My academic pursuits have taken me across borders, from pursuing a research fellowship in robotics and artificial intelligence at Sapienza University of Rome to my current role as a postdoctoral researcher in mechanical and manufacturing engineering at the University of Calgary in Canada. These experiences have not only deepened my understanding of the field but also honed my skills in tackling complex challenges and pushing the boundaries of technological advancement.

In addition to my academic endeavors, I’ve also had the opportunity to apply my expertise in industry settings. As a part-time mechatronics engineering consultant at Halo Beauty in the USA, I provided valuable insights and solutions, leveraging my expertise to contribute to the company’s projects. Currently, as a product software engineer at Cure Data in the USA, I am engaged in developing software products with a focus on data analysis, leveraging my diverse background to drive innovation and deliver impactful solutions.

My journey has been characterized by a relentless pursuit of knowledge and a commitment to excellence, and I look forward to continuing to contribute to the forefront of research and innovation in the years to come.

🌐 Professional Profiles

Educations📚📚📚

He pursued his academic journey with zeal and dedication, starting with a Bachelor of Science in Electrical Engineering from Azad University in Iran, where he laid the foundation for his future endeavors. Building upon this strong base, he pursued a Master of Science in Mechatronics Engineering at Sharif University of Technology in Iran from 2006 to 2008, delving into the interdisciplinary realm of engineering. Eager to deepen his knowledge further, he embarked on a PhD in Industrial and Information Engineering with a focus on Mechatronics Curricula at the University of Udine in Italy from 2013 to 2016, where he honed his research skills and expertise. Continuing his pursuit of academic excellence, he completed a Master of Science in Computer Engineering with a specialization in Human-Computer Interaction & Artificial Intelligence Curricula at the University of Genoa in Italy from 2018 to 2022, further broadening his horizons and enhancing his proficiency in cutting-edge technologies. Through his academic endeavors, he has demonstrated a relentless commitment to learning and growth, equipping himself with the knowledge and skills to thrive in the ever-evolving field of engineering and technology.

RESEARCH INTERN

During his time as a research intern at Karlstad University in Sweden from August 2015 to December 2015, he immersed himself in the field of assistive robotics. Focused on the design of a human-friendly walking assist robot vehicle, he dedicated his efforts to refining control systems and variable stiffness mechanisms. Through his internship, he gained valuable hands-on experience in the development of innovative solutions aimed at enhancing mobility and accessibility. His contributions during this period underscored his commitment to leveraging technology for the betterment of society, particularly in improving the quality of life for individuals with mobility impairments.

 

He has a keen interest in a wide array of research areas within the realm of engineering and technology. His primary focus lies in robotics and mechatronic systems, where he explores the intricate dynamics and control mechanisms governing these complex systems. With a passion for advancing the field, he dedicates his efforts to refining models and control algorithms to optimize the performance of mechatronic systems. Additionally, he delves into the realm of artificial intelligence, leveraging cutting-edge techniques to enhance the capabilities of autonomous systems and intelligent agents. His research also extends to advanced manufacturing, where he explores innovative approaches to streamline production processes and improve efficiency. Furthermore, he is fascinated by the potential of vision systems in industrial automation, investigating novel techniques to enhance perception and decision-making capabilities in automated systems. Through his research endeavors, he continually seeks to push the boundaries of engineering innovation and contribute to the development of transformative technologies.

INDUSTRIAL PROJECTS

In his current role at Cure Data in the USA from February 2024 to the present, he collaborates closely with a multidisciplinary R&D team comprising engineers, scientists, and physicians. Together, they are dedicated to the development of an extensive digital twin, a digital replica used for modeling and simulating human organs. His contributions to this project involve leveraging his expertise in various fields to enhance the accuracy and functionality of the digital twin, aiming to revolutionize medical research and treatment approaches.

During his tenure at Halo Beauty in the USA from September 2023 to December 2023, he played a pivotal role in developing core aspects of control and electrical engineering, as well as a vision system for an automated hair braider. Working in tandem with a mechanical engineering team, he focused on refining the end-effector, sensor-based controls, and retraction systems. His collaborative efforts ensured the seamless integration of electrical and control components, optimizing the performance and efficiency of the automated hair braider.

AWARDS AND HONORS

Throughout his career, he has garnered recognition for his outstanding achievements and contributions to the field of engineering. Notably, in September 2019, he received the Best Conference Paper Award at the IEEE International Conference on Cyborg and Bionic Systems (CBS 2019) held in Munich, Germany, showcasing his excellence in academic research and innovation. His dedication to excellence was further acknowledged with an Internship Fellowship at Karlstad University in Sweden from August 2015 to December 2015, providing him with valuable practical experience in the field.

In March 2015, he was honored with the NI (National Instruments) Engineering Impact Awards 2015 for Best Application in Advanced Research, recognizing his significant contributions to the advancement of engineering technologies. This accolade was bestowed upon him during NIDays 2015 in Milan, Italy, highlighting his innovative approaches and impactful research endeavors.

Additionally, he was awarded a Winter School Scholarship in February 2015 to attend the SAPHARI NMMI Winter School in Rome, Italy, demonstrating his commitment to continuous learning and professional development. His academic prowess was evident during his master’s studies, as he successfully completed the Master’s Degree Honors Program at Sharif University of Technology in Iran in July 2008, attesting to his exceptional academic performance and dedication to scholarly pursuits.

Moreover, his leadership abilities and accomplishments were recognized early in his career when he received a Travel Grant in November 2003 for his successful leadership of the executive team in the National Robotic Race of Intelligent Devices at Azad University in Iran, underscoring his capability to excel in both academic and practical domains, as well as his adeptness in leading collaborative endeavors.

📝🔬Publications📝🔬
  • Meghdari.A, Mirfakhree.F, Akrami.S.M, Shojaei Barjuei.E
    Introduction to Robotics: Mechanics and Control (By: Craig.J.J)
    Third Edition, Sharif University Press, Iran, 2009 (ISBN: 978‐964‐208-019‐9)
  • Akrami.S.M, Shojaei Barjuei.E
    Mechatronics: Dynamics of Electromechanical and Piezoelectric Systems (By:Preumont.A)
    Fan Afzar press, Tabriz, Iran, 2009 (ISBN: 978-964-8150-24-9).
  • Shojaei Barjuei.E, Shin. J, Kim. K, Lee. J
    Precision improvement of robotic bioprinting via vision-based tool path compensation
    Accepted in Scientific Reports.
  • Shojaei Barjuei.E, Capitanelli.A, Bertolucci.R, Courteille.E, Mastrogiovanni.F, Maratea.M
    Digital Workflow for Printability and Prefabrication Checking in Robotic Construction 3D Printing
    Based on Artificial Intelligence Planning
    Engineering Applications of Artificial Intelligence, vol. 133, p. 108254, 2024.
  • Shojaei Barjuei.E, Courteille.E, Rangeard.D, Marie.F, Perrot.A
    Real-time vision-based control of industrial manipulators for layer-width setting in concrete 3D
    printing applications
    Advances in Industrial and Manufacturing Engineering (2022): 100094.
  • Bianchi.F, Masaracchia.A, Damone.A, Falotico.F, Shojaei Barjuei.E, Oddo.C, Dario.P, Ciuti.G
    Hybrid 6-DoFs magnetic localization for robotic capsule endoscopes compatible with high-grade
    magnetic field navigation
    IEEE Access 10 (2021): 4414-4430.
  • Shojaei Barjuei.E, Darwin.G.C, Ortiz.J
    Bond Graph Modeling and Kalman Filter Observer Design for a Back-Support Exoskeleton
    Designs 4.4 (2020): 53.
  • Shojaei Barjuei.E, Ortiz.J
    A Comprehensive performance comparison of Linear Quadratic Regulator (LQR) controller, Model
    Predictive Controller (MPC), H∞ loop shaping and μ-synthesis on spatial compliant linkmanipulators
    International Journal of Dynamics and Control 9 (2021): 121-140.
  • Bianchi.F, Masaracchia.A, Shojaei Barjuei.E, Menciassi.A, Arezzo.A, Koulaouzidis.A, Stoyanov.D,
    Oddo.C, Dario.P, Ciuti.G
    Localization strategies for robotic endoscopic capsules: a review
    Expert review of medical devices 16.5 (2019): 381-403.
  • Li.J, Shojaei Barjuei.E, Ciuti.G, Hao.Y, Zhang.P, Shi.Q, Menciassi.A, Huang.Q, Dario.P
    Magnetically-driven medical robots: an analytical magnetic model for endoscopic capsules design
    Journal of Magnetism and Magnetic Materials 452 (2018): 278-287.
  • Shojaei Barjuei.E
    Hybrid position/force control of a spatial compliant mechanism
    International Journal of Automotive and Mechanical Engineering, ISSN: 2229-8649 (Print); ISSN:
    2180-1606 (Online); Volume 14, Issue 3 pp. 4531-4541 September 2017
  • Shojaei Barjuei.E, Boscariol.P, Gasparetto.A, Vidoni.R
    Robust control of Three-Dimensional Compliant Mechanisms
    Journal of Dynamic Systems, Measurement & Control, 2016, 138, 101009-1-14.
  • Abdolshah.S, Shojaei Barjuei.E
    Linear Quadratic Optimal Control of Cable-Driven Parallel Robots
    Journal of Frontiers of Mechanical Engineering, FME-15038-AS, vol.10, no. 4, pp. 344–351, 2015

Guangli Wu – Video Summarization – Best Researcher Award

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Guangli Wu – Video Summarization

prof Dr. Guangli Wu  distinguished academic and researcher in the field  Video summarization.  The existence of software vulnerabilities will cause serious network attacks and information leakage problems. Timely and accurate detection of vulnerabilities in software has become a research focus on the security field. Most existing work only considers instruction-level features, which to some extent overlooks certain syntax and semantic information in the assembly code segments, affecting the accuracy of the detection model. In this paper, we propose a binary code vulnerability detection model based on multi-level feature fusion. The model considers both word-level features and instruction-level features. In order to solve the problem that traditional text embedding methods cannot handle polysemy, this paper uses the Embeddings from Language Models (ELMo) model to obtain dynamic word vectors containing word semantics and other information. Considering the grammatical structure in the assembly code segment, the model randomly embeds the normalized assembly code segment to represent it. Then the model uses bidirectional Gated Recurrent Unit (GRU) to extract word-level sequence features and instruction-level sequence features respectively.

Eduvation

He pursued his academic journey with a solid foundation in computer science and technology, earning a Bachelor’s degree from Shandong Technology and Business University in 2003. Building upon this, he delved into the realm of Computer Application Technology, completing his Master’s degree at Northwest Minzu University in 2007. Driven by a passion for cultural diversity and linguistic exploration, he further expanded his expertise by attaining a doctoral degree in Chinese Minority Ethnic Languages and Literature from Northwest Minzu University in 2011. This educational trajectory reflects his commitment to a multidisciplinary approach, seamlessly blending computer technology with a profound understanding of language and culture.
Professional Profiles:

RESEARCH INTEREST

⚫ Video Summarization
⚫ Temporal Language Localization in videos
⚫ Botnet Detection
⚫ Binary Code Vulnerability Detection
⚫ Video Abnormal Event Detection
FUND PROJECTS
1. Natural Science Foundation of Gansu Province (17JR5RA161, 21JR7RA570)
2. Gansu University of Political Science and Law Major Scientific Research and Innovation Projects
(GZF2020XZDA03)
3. Young Doctoral Fund Project of Higher Education Institutions in Gansu Province (2022QB-123)
4. Gansu Province Higher Education Innovation Fund Project (2017A-068)
5. University-level Innovative Research Team of Gansu University of Political Science and Law
6. Longyuan Youth Innovation and Entrepreneurship Talent Project (2022QB-123)

MAIN SCIENTIFIC PUBLICATIONS

1. Guangli Wu,ShengTao Wang,Shipeng Xu. “Feature fusion over hyperbolic graph convolution networks for
video summarization.” IET Computer Vision,2023.
2. Guangli Wu,Tongjie Xu. “Video Moment Localization Network Based on Text Multi-semantic Clues
Guidance.” Advances in Electrical and Computer Engineering,2023,23(3):85-92.
3. Guangli Wu,Huili Tang. “Binary Code Vulnerability Detection Based on Multi-Level Feature Fusion.” IEEE
Access,2023,11: 63904-63915.
4. Guangli Wu,Shanshan Song,Leiting Li. “Video Summarization Generation Model Based on Transformer
and Deep Reinforcement Learning.” in 2023 8th International Conference on Computer and
Communication Systems (ICCCS). IEEE, 2023: 916-921.
5. Guangli Wu,Shengtao Wang,Liping Liu. “Fast Video Summary Generation Based On Low Rank Tensor
Decomposition.” IEEE Access,2021,9:127917-127926.
6. Guangli Wu,Zhenzhou Guo,Mianzhao Wang,Leiting Li and Chengxiang Wang. “Video Abnormal Event
Detection Based on CNN and Multiple Instance Learning.” in twelfth international conference on signal
processing systems. SPIE,2021:134-139.
7. Guangli Wu,Zhenzhou Guo,Leiting Li and Chengxiang Wang. “Video Abnormal Event Detection Based on
CNN and LSTM.” in 2020 IEEE 5th International Conference on Signal and Image Processing (ICSIP).
IEEE,2020: 334-338.
8. Guangli WU,Leiting LI,Zhenzhou GUO,Chengxiang WANG and Yanpeng, YAO. “Video summarization
Based on ListNet Scoring Mechanism.” in 2020 5th International Conference on Computer and
Communication Systems (ICCCS). IEEE,2020: 281-285.
9. Guangli WU,Liping LIU,Chen Zhang and Dengtai TAN. “Video Abnormal Event Detection Based on ELM.”
in 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP).IEEE,2019: 367-371