Bin Yang – AI for Everything – Best Researcher Award

Bin Yang - AI for Everything - Best Researcher Award

Chongqing University of Posts and Telecommunications - China

AUTHOR PROFILE

SCOPUS

ORCID

🧑‍🏫 ACADEMIC BACKGROUND AND RESEARCH PASSION

Dr. Bin Yang, also known as Sean Bin Yang, is an Assistant Professor at Chongqing University of Posts and Telecommunications. With a deep passion for leveraging big data and artificial intelligence (AI) to address urban challenges, he has been making significant contributions to the field. He is also a member of the Chongqing Key Laboratory of Image Cognition, working closely with Prof. Xinbo Gao.

🎓 EDUCATION AND GLOBAL COLLABORATIONS

Dr. Yang obtained his Ph.D. in Computer Science from Aalborg University in 2022, under the guidance of Prof. Bin Yang and Associate Prof. Jilin Hu. During his doctoral studies, he collaborated with renowned researchers at the Center for Data-Intensive Systems (Daisy) and the Machine Learning Group. He also spent time at the Mila-Quebec AI Institute in Canada, working with Associate Prof. Jian Tang.

đź“š PROLIFIC PUBLICATION RECORD

Dr. Yang has authored more than 20 peer-reviewed publications in prestigious international journals and conferences, including KDD, ICML, and TKDE. His work, such as the development of lightweight path representation models, has gathered over 452 citations, with an h-index of 13. His innovative research in data mining, machine learning, and AI continues to push the boundaries of knowledge in these fields.

đź’ˇ INNOVATIVE PATENTS AND TECHNOLOGY APPLICATIONS

Dr. Yang's commitment to practical applications of his research is demonstrated by his filing of over 10 patents in China. These patents reflect his dedication to advancing technology through innovation, particularly in the fields of AI-driven solutions for urban and transportation challenges.

🎓 SUPERVISION AND MENTORSHIP

As a dedicated mentor, Dr. Yang has supervised numerous student research projects, including those on construction waste management through AI techniques. His guidance has led to the publication of impactful research articles, helping his students make meaningful contributions to the field of artificial intelligence and urban problem-solving.

🔬 RESEARCH IN AI AND URBAN CHALLENGES

Dr. Yang's research focuses on using AI to tackle complex urban issues, such as waste management, transportation optimization, and infrastructure development. His work in path representation learning, unsupervised learning, and predictive autoscaling has significantly contributed to the advancement of smart city technologies.

🏅 CONFERENCE AND JOURNAL INVOLVEMENT

Dr. Yang is an active member of the research community, serving as a Program Committee member for top conferences like ICML, KDD, and IJCAI. His expertise is frequently sought as a reviewer for leading journals such as IEEE Transactions on Knowledge and Data Engineering and IEEE Transactions on Intelligent Transportation Systems, highlighting his influence in the AI and big data research domains.

NOTABLE PUBLICATION

Title:Extended-state-observer-based double-loop integral sliding-mode control of electronic throttle valve
Authors: Y. Li, B. Yang, T. Zheng, Y. Li, M. Cui, S. Peeta
Journal: IEEE Transactions on Intelligent Transportation Systems
Year: 2015

Title: Unsupervised path representation learning with curriculum negative sampling
Authors: S.B. Yang, C. Guo, J. Hu, J. Tang, B. Yang
Journal: arXiv preprint arXiv:2106.09373

Title: Context-aware path ranking in road networks
Authors: S.B. Yang, C. Guo, B. Yang
Journal: IEEE Transactions on Knowledge and Data Engineering
Year: 2020

Title: Luenberger-sliding mode observer based fuzzy double loop integral sliding mode controller for electronic throttle valve
Authors: B. Yang, M. Liu, H. Kim, X. Cui
Journal: Journal of Process Control
Year: 2018

Title: An extended continuum model incorporating the electronic throttle dynamics for traffic flow
Authors: Y. Li, H. Yang, B. Yang, T. Zheng, C. Zhang
Journal: Nonlinear Dynamics
Year: 2018

Everton – Artificial Intelligence – Best Researcher Award

Everton - Artificial Intelligence - Best Researcher Award

Universidade Federal da Grande Dourados - Brazil

AUTHOR PROFILE

SCOPUS

ACADEMIC AFFILIATION

Everton is associated with Universidade CatĂłlica Dom Bosco, where he contributes to cutting-edge research in computer vision and its applications in agriculture and urban studies.

PRECISION AGRICULTURE RESEARCH

His research in precision agriculture includes the integration of UAV technology and machine learning to optimize farming practices. By improving weed and pest detection methods, his work supports sustainable agriculture and food security.

COMPUTER VISION IN AGRICULTURE

Everton's expertise in computer vision extends to various agricultural applications, from crop monitoring to automated harvesting systems. His innovative solutions help in increasing agricultural productivity and efficiency.

REAL-TIME WEED DETECTION IN SOYBEAN USING UAV IMAGES

Everton CastelĂŁo Tetila specializes in the real-time detection of weeds in soybean fields through the innovative use of UAV (Unmanned Aerial Vehicle) images. His work significantly contributes to precision agriculture, enabling farmers to identify and manage weeds more efficiently.

YOLO PERFORMANCE ANALYSIS FOR SOYBEAN PEST DETECTION

Everton has conducted extensive performance analysis of the YOLO (You Only Look Once) algorithm for the real-time detection of soybean pests. His research enhances pest management practices, ensuring timely interventions and reducing crop damage.

URBAN AREA CLASSIFICATION AND MONITORING USING COMPUTER VISION

He applies advanced computer vision techniques for the classification and monitoring of urbanized areas. This work aids in urban planning and development, providing detailed and accurate assessments of urban growth and infrastructure.

EDUCATIONAL CONTRIBUTIONS

He is dedicated to advancing education in his field, sharing his knowledge and findings through publications and presentations. His contributions help train the next generation of researchers and professionals in computer vision and its agricultural applications.

NOTABLE PUBLICATION

YOLO performance analysis for real-time detection of soybean pests
Authors: E.C. Tetila, F.A.G. da Silveira, A.B. da Costa, H. Pistori, J.G.A. Barbedo
Year: 2024
Journal: Smart Agricultural Technology, 7, 100405

Title: Classification and monitoring of urbanized areas using computer vision techniques | Classificação e monitoramento de áreas urbanizadas usando técnicas de visão computacional
Authors: E.C. Tetila, P.M. de Moraes, M. Constantino, M.M.D.M. Greco, H. Pistori
Year: 2023
Journal: Desenvolvimento e Meio Ambiente, 61, pp. 32–42

Title: An approach for applying natural language processing to image classification problems
Authors: G. Astolfi, D.A. Sant'Ana, J.V.D.A. Porto, E.T. Matsubara, H. Pistori
Year: 2022
Journal: Neurocomputing, 513, pp. 372–382

Title: Performance Analysis of YOLOv3 for Real-Time Detection of Pests in Soybeans
Authors: F.A.G. Silveira, E.C. Tetila, G. Astolfi, A.B. Costa, W.P. Amorim
Year: 2021
Conference: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13074 LNAI, pp. 265–279

Title: Associative classification model for forecasting stock market trends
Authors: E.C. Tetila, B.B. MacHado, J.F. Rorigues, M. Constantino, H. Pistori
Year: 2021
Journal: International Journal of Business Intelligence and Data Mining, 19(1), pp. 97–112