Fang Yang – Transportation Engineering – Best Researcher Award

Fang Yang - Transportation Engineering - Best Researcher Award

Kunming University of Science and Technology - China

AUTHOR PROFILE

SCOPUS

EXPERT IN ELECTRIC VEHICLE CHARGING SAFETY

Fang Yang is a leading researcher in the field of electric vehicle technology, with a focus on enhancing the safety and efficiency of electric bike charging systems. His work explores innovative methods for detecting charging anomalies and promoting safe charging practices through advanced data analysis and machine learning techniques.

PROLIFIC AUTHOR IN ENGINEERING AND TRANSPORTATION

Fang has contributed significantly to academic literature with several high-impact publications. Notably, his paper on electric bike charging anomaly detection was published in Engineering Applications of Artificial Intelligence, highlighting his expertise in big data applications for transportation systems.

MAJOR PROJECT CONTRIBUTOR

Fang has played a pivotal role in various major projects, including evaluating traffic impacts and organizing traffic during the construction of Guiyang Rail Transit Line S2. His contributions extend to optimizing safety operations for new energy vehicle charging piles and researching big data public services for Kunming mobile signaling.

ADVANCING MACHINE LEARNING IN TRANSPORTATION

His research also includes leveraging machine learning to enhance the safety of electric bicycle charging systems. His work in this area has been featured in iScience, reflecting his commitment to applying cutting-edge technology to real-world transportation challenges.

RESEARCH IN URBAN RAIL TRANSIT DEMANDS

Fang's research extends to the predictability of passenger demands in urban rail transit. His study, published in Transportation, delves into short-term predictions for passenger origins and destinations, showcasing his expertise in optimizing urban transit systems.

FOCUS ON DATA-DRIVEN FORECASTING

His paper on battery swapping demands for electric bicycles, published in the Journal of Transportation Systems Engineering and Information Technology, underscores his proficiency in data-driven forecasting and its applications in improving transportation infrastructure.

DIVERSE RESEARCH EXPERIENCE

With extensive experience across multiple research projects, Fang Yang's work spans from safety analysis of new energy vehicle infrastructure to public service optimization using big data. His diverse expertise reflects a broad commitment to advancing transportation systems through innovative research.

NOTABLE PUBLICATION

Predictability of Short-Term Passengers’ Origin and Destination Demands in Urban Rail Transit.
Authors: F. Yang, C. Shuai, Q. Qian, M. He, J. Lee
Year: 2023
Journal: Transportation, 50(6), pp. 2375–2401

Online Car-Hailing Origin-Destination Forecast Based on a Temporal Graph Convolutional Network.
Authors: C. Shuai, X. Zhang, Y. Wang, F. Yang, G. Xu
Year: 2023
Journal: IEEE Intelligent Transportation Systems Magazine, 15(4), pp. 121–136

Intelligent Diagnosis of Abnormal Charging for Electric Bicycles Based on Improved Dynamic Time Warping.
Authors: C. Shuai, Y. Sun, X. Zhang, X. Ouyang, Z. Chen
Year: 2023
Journal: IEEE Transactions on Industrial Electronics, 70(7), pp. 7280–7289

Promoting Charging Safety of Electric Bicycles via Machine Learning.
Authors: C. Shuai, F. Yang, W. Wang, Z. Chen, X. Ouyang
Year: 2023
Journal: iScience, 26(1), 105786

Battery Swapping Demands Forecast for Electric Bicycles Based on Data-Driven.
Authors: C.-Y. Shuai, F. Yang, X. Ouyang, G. Xu
Year: 2021
Journal: Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology, 21(2), pp. 173–179

Rasha Al-Huthaifi – Transportation – Best Researcher Award

Rasha Al-Huthaifi - Transportation - Best Researcher Award

Southwest Jiaotong University - China

AUTHOR PROFILE

GOOGLE SCHOLAR

RASHA AL-HUTHAIFI: INNOVATIVE RESEARCHER IN MACHINE LEARNING AND CYBERSECURITY 🌟

EDUCATION AND EARLY CAREER JOURNEY 🎓

Rasha Al-Huthaifi is a dedicated researcher and educator with a robust background in computer science and engineering. She earned her Bachelor's degree from Sanaa University, where she excelled in teaching Object Oriented Programming using C#. Her academic journey continued with a Master's degree, focusing on enhancing antivirus security systems through innovative memory on-access scanner technologies. This groundbreaking work led to publications in prestigious journals and conferences, including the International Conference on Computer Science, Computer Engineering and Education Technologies.

EXPERTISE IN MACHINE LEARNING AND CYBERSECURITY 🔍

Rasha's expertise spans across a wide range of technologies and methodologies essential for modern cybersecurity and machine learning applications. Her proficiency includes Python programming, federated learning, and the development of secure traffic flow prediction systems using graph-based models. She has also contributed significantly to projects in data mining, bioinformatics, and information retrieval, demonstrating a versatile skill set in tackling complex technological challenges.

SIGNIFICANT PROJECT CONTRIBUTIONS 🚀

Throughout her career, Rasha has spearheaded numerous impactful projects. These include the development of FedAGAT and FedGODE systems for real-time traffic flow prediction in smart cities, leveraging federated learning and graph-based models to ensure privacy and efficiency. Her contributions extend to cybersecurity innovations such as antivirus enhancements and anti-copying software, addressing critical security vulnerabilities in software applications.

ACADEMIC AND PROFESSIONAL ENGAGEMENTS 💼

Rasha's professional journey includes roles as a PhD student at Southwest Jiaotong University, where she continues to contribute to cutting-edge research initiatives. Previously, she served as a Teacher Assistant at Jordan University of Science and Technology, imparting her knowledge in programming labs and mentoring students in computer science fundamentals.

RESEARCH IMPACT AND PUBLICATIONS 📚

Rasha's research has been published in reputable journals and conferences, highlighting her dedication to advancing the field of cybersecurity and machine learning. Her work on automatic Arabic multi-document summarizers and glaucoma disease detection algorithms reflects her commitment to leveraging technology for societal benefit and healthcare advancements.

AWARDS AND RECOGNITIONS 🏆

Rasha's academic achievements include the distinction of having the best graduation project in her Bachelor's program, showcasing her innovative spirit and academic excellence. Her contributions to cybersecurity and machine learning have earned her recognition within the academic community, underscoring her leadership and impact in the field.

FUTURE DIRECTIONS AND INNOVATIONS 🌐

Looking ahead, Rasha remains committed to pushing the boundaries of cybersecurity and machine learning research. Her future endeavors aim to integrate emerging technologies and methodologies to address global cybersecurity challenges and enhance the efficiency of machine learning applications in various domains.

NOTABLE PUBLICATION

Authors: Rasha Al-Huthaifi, Tianrui Li, Zaid Al-Huda, Chongshou Li
Publication date: 2024/5/1
Journal: Information Sciences
Publisher: Elsevier
Authors: Rasha Al-Huthaifi, Tianrui Li, Zaid Al-Huda, Wei Huang, Zhipeng Luo, Peng Xie
Publication date: 2024/6/10
Journal: Knowledge-Based Systems
Publisher: Elsevier
Authors: Rasha Al-Huthaifi, Tianrui Li, Wei Huang, Jin Gu, Chongshou Li
Publication date: 2023/6/1
Source: Information Sciences
Publisher: Elsevier.
Authors: Muneer Bani Yassein, Shadi Aljawarneh, Rasha K Al-huthaifi
Publication date: 2017/8/21
Conference: 2017 International Conference on Engineering and Technology (ICET)
Publisher: IEEE