Yuxuan Zhao – Deep Learning – Best Researcher Award

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Yuxuan Zhao - Deep Learning - Best Researcher Award

Xi’an Jiaotong-Liverpool University - China

AUTHOR PROFILE

SCOPUS

ACADEMIC BACKGROUND

Dr. Yuxuan Zhao is an accomplished Assistant Professor at the School of AI and Advanced Computing (AIAC) at Xi’an Jiaotong-Liverpool University (XJTLU), Taicang Campus. He completed his PhD in Computer Science from the University of Liverpool in 2022 and holds an MSc in Computer Graphics, Vision and Imaging from University College London, earned in 2017. His academic journey reflects a robust foundation in advanced computing and artificial intelligence.

RESEARCH INTERESTS

Dr. Zhao’s research encompasses a broad spectrum of advanced technological fields. His primary interests include deep learning, image and video processing, computer vision, and the application of machine learning techniques. His work aims to push the boundaries of how these technologies can be utilized to solve complex real-world problems.

RESEARCH PROJECTS

Dr. Zhao is engaged in several cutting-edge research projects. His work on "Deep Learning in Video Anomaly Detection" and "Radar Signal Processing Based on Neural Network Sequence Mode" highlights his focus on innovative applications of machine learning. He is also involved in "Computer Vision-Based Traffic Accident Detection" and the governmental project "Intelligent Multimodal Data Analysis for Digital Twin Cities," reflecting his commitment to advancing technology in practical and impactful ways.

PROCEEDINGS AND PUBLICATIONS

Dr. Zhao has contributed to numerous high-profile conferences and journals. His recent papers include "Appearance-Motion United Memory Autoencoder for Video Anomaly Detection," presented at the 2023 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, and "YOLOv5s-Transformer: Improved YOLOv5 Network for Real-Time Detection of Cigarette Smoking," showcased at the 2023 4th International Seminar on Artificial Intelligence, Networking, and Information Technology. His research demonstrates a deep engagement with current trends and challenges in computer vision and AI.

CONFERENCE PARTICIPATION

Dr. Zhao has actively participated in the academic community by presenting at leading conferences and contributing to the advancement of knowledge in his field. His presentations at various international seminars and conferences underscore his role in shaping the future of AI and computer vision.

INNOVATIVE RESEARCH CONTRIBUTIONS

Dr. Zhao’s innovative contributions include the development of intelligent algorithms for multi-mobile robot systems and autonomous alert generation for disaster management. His early work, such as the design of algorithms for Wi-Fi access point roaming, highlights his long-standing dedication to addressing diverse technological challenges.

GOVERNMENTAL RESEARCH PROJECTS

Currently, Dr. Zhao is involved in several governmental research initiatives, including the "Suzhou Multimodal Big Data Innovation Application Lab," which aims to enhance the application of big data and AI technologies. These projects reflect his ongoing commitment to leveraging advanced technologies for societal benefit.

NOTABLE PUBLICATION

A Novel Two-Stream Structure for Video Anomaly Detection in Smart City Management

Authors: Zhao, Y., Man, K.L., Smith, J., Guan, S.-U.
Year: 2022
Journal: Journal of Supercomputing
Pages: 3940–3954

Improved Two-Stream Model for Human Action Recognition

Authors: Zhao, Y., Man, K.L., Smith, J., Siddique, K., Guan, S.-U.
Year: 2020
Journal: Eurasip Journal on Image and Video Processing
Article: 24

Erratum: RingText: Dwell-free and Hands-free Text Entry for Mobile Head-Mounted Displays Using Head Motions

Authors: Xu, W., Liang, H.-N., Zhao, Y., Yu, D., Monteiro, D.
Year: 2019
Journal: IEEE Transactions on Visualization and Computer Graphics
Volume: 25
DOI: 10.1109/TVCG.2019.2898736

DMove: Directional Motion-based Interaction for Augmented Reality Head-Mounted Displays

Authors: Xu, W., Liang, H.-N., Zhao, Y., Yu, D., Monteiro, D.
Year: 2019
Conference: Conference on Human Factors in Computing Systems - Proceedings

RingText: Dwell-free and Hands-free Text Entry for Mobile Head-Mounted Displays Using Head Motions

Authors: Xu, W., Liang, H.-N., Zhao, Y., Yu, D., Monteiro, D.
Year: 2019
Journal: IEEE Transactions on Visualization and Computer Graphics

BASEM A. ALKHALEEL – Machine learning – Best Paper Award

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BASEM A. ALKHALEEL - Machine learning - Best Paper Award

King Saud University - Saudi Arabia

PROFESSIONAL SUMMARY

Basem A. Alkhaleel is a dedicated Assistant Professor in Industrial Engineering with a distinguished record of academic excellence and practical expertise in process reengineering and business continuity. Combining his extensive background in teaching, research, project management, and consulting, he brings a unique perspective to both academia and industry. With skills in leading cross-functional teams, analyzing complex systems, and implementing innovative solutions, he excels in driving process optimization, operational efficiency, and organizational resilience.

EDUCATION

Basem A. Alkhaleel holds a Ph.D. in Industrial Engineering from the University of Arkansas (2021), an MSc in Industrial and Systems Engineering from Texas A&M University (2015), and a BSc in Industrial Engineering from King Saud University (2012).

ACADEMIC AND RESEARCH EXPERIENCE

As an Assistant Professor at King Saud University since September 2021, Basem A. Alkhaleel has been involved in directing the counseling unit for undergraduate engineering students, participating in various departmental committees, and supervising numerous undergraduate student graduation projects. His research focuses on machine learning applications in critical infrastructure resilience, reliability engineering, and business continuity.

PROFESSIONAL EXPERIENCE

In addition to his academic roles, Basem A. Alkhaleel is a Management Consultant at ES Consulting in Riyadh, Saudi Arabia, where he develops and implements process improvement strategies and business continuity plans. He has also served as a Project Manager at the Arkansas Department of Transportation, leading the development and implementation of a decision support system for multi-modal transportation operations.

PROJECT MANAGEMENT AND CONSULTING

Basem A. Alkhaleel has managed several high-profile projects, including process documentation and modeling for the Ministry of Municipal and Rural Affairs and Housing and the Advanced Electronics Company. His work involves process modeling, documentation, digitalization, and KPI alignment to improve operational and strategic efficiencies.

RESEARCH AND INNOVATION

During his Ph.D. research at the University of Arkansas, Basem A. Alkhaleel developed resilience-based restoration models for disrupted critical infrastructures and combined risk mitigation with resilience restoration and simulation modeling. His MSc research at Texas A&M University focused on data-driven approaches to improve decision-making processes in engineering projects.

TEACHING AND MENTORING

With a strong commitment to education, Basem A. Alkhaleel has lectured on various industrial engineering subjects, including manufacturing processes and reliability engineering, at King Saud University. His teaching philosophy emphasizes innovative strategies to enhance student performance and academic success.

STRATEGIC PLANNING AND DEVELOPMENT

Earlier in his career, Basem A. Alkhaleel worked as a Strategic Planner at Ma’aden Company, developing long-term strategic plans and applying business development tools to identify areas of improvement. His strategic insights and communication skills have been instrumental in achieving organizational objectives and driving continuous improvement initiatives.

NOTABLE PUBLICATIONS

Risk and resilience-based optimal post-disruption restoration for critical infrastructures under uncertainty 2022 (44)

Hybrid simulation to support interdependence modeling of a multimodal transportation network 2021 (19)

Machine learning applications in the resilience of interdependent critical infrastructure systems—A systematic literature review 2023 (8)

Model and solution method for mean-risk cost-based post-disruption restoration of interdependent critical infrastructure networks 2022 (10)

Dajian Zhong – Scene Text Recognition – Best Researcher Award

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Dajian Zhong - Scene Text Recognition - Best Researcher Award

Shanghai Maritime University - China

AUTHOR PROFILE

Scopus

EARLY ACADEMIC PURSUITS

Dr. Dajian Zhong's academic journey commenced with a strong foundation in Computer Science and Technology, beginning with a Bachelor's degree from Suzhou University of Science and Technology. He furthered his studies with a Master's degree from East China University of Science and Technology, specializing in Computer Science and Technology. Dr. Zhong's academic pursuits culminated in a Ph.D. in Computer Application Technology from East China Normal University. Throughout his educational journey, he exhibited a keen interest in advancing the field of computer vision, particularly in the domain of scene text recognition.

PROFESSIONAL ENDEAVORS

Dr. Zhong currently serves as a Lecturer in the College of Information Engineering at Shanghai Maritime University, where he imparts knowledge and expertise to aspiring students. His professional career is characterized by a commitment to excellence in research and education, with a focus on computer vision, text detection, and recognition. Through his role as a lecturer, Dr. Zhong continues to inspire and mentor the next generation of computer scientists and engineers.

CONTRIBUTIONS AND RESEARCH FOCUS

Dr. Zhong's research is centered around the advancement of scene text recognition, a critical area within computer vision. His work explores novel algorithms and techniques to improve the accuracy and efficiency of text detection and recognition in complex scenes. By leveraging approaches such as semantic GANs, attention networks, and transformer networks, Dr. Zhong aims to address the challenges associated with arbitrarily oriented and shaped text in real-world environments. His contributions have been published in reputable journals and presented at international conferences, demonstrating his expertise and impact in the field.

IMPACT AND INFLUENCE

Dr. Zhong's research has made a significant impact on the field of scene text recognition, garnering recognition from peers and researchers worldwide. His innovative algorithms and methodologies have advanced the state-of-the-art in text detection and recognition, facilitating applications in various domains, including document analysis, image understanding, and augmented reality. Through his collaborative efforts and interdisciplinary approach, Dr. Zhong continues to shape the future of computer vision and inspire advancements in intelligent systems and technologies.

ACADEMIC CITES

Dr. Zhong's publications have received significant citations from researchers and practitioners in the field of computer vision, attesting to the relevance and impact of his work. His research findings have been instrumental in advancing the understanding and capabilities of scene text recognition systems, contributing to the development of more accurate and robust algorithms for real-world applications. Dr. Zhong's influence extends beyond academia, as his work continues to shape the landscape of computer vision research and technology.

LEGACY AND FUTURE CONTRIBUTIONS

As Dr. Zhong continues to pursue his research endeavors, his focus remains on pushing the boundaries of scene text recognition and computer vision. Through ongoing collaborations, mentorship, and knowledge dissemination, he seeks to further advance the field and foster innovations that benefit society at large. Dr. Zhong's legacy lies in his dedication to excellence, his passion for advancing knowledge, and his commitment to addressing real-world challenges through cutting-edge research in scene text recognition.

NOTABLE PUBLICATION

LRATNet: Local-Relationship-Aware Transformer Network for Table Structure Recognition 2024

NDOrder: Exploring a novel decoding order for scene text recognition 2024