NU SEDS Guest talk “Smart City Transportation: Impact of Machine Learning Methods on Real-Time Vehicle Data Study”
Professor Ikechi Ukaegbu, Assistant Professor of the Department of Electrical and Computer Engineering at NU SEDS invites you to attend the Guest talk “Smart City Transportation: Impact of Machine Learning Methods on Real-Time Vehicle Data Study” by Dhananjay Singh, Director of ReSENSE Lab Chair/Head in the Global Division of Information Technology [2013-2018] Full Professor in the Department of Electronics Engineering Hankuk University of Foreign Studies (HUFS), Seoul, South Korea
Date: November 21, 2022, Monday
Time: 12 pm
Location: Block C3, Room 1010 + ZOOM
Dhananjay Singh is a Full Professor/Director of ReSENSE Lab – Members in the Dept. of Electronics Engineering and also served as the Head of the Global Division of Information Technology (2013 – 2018) at Hankuk University of Foreign Studies (HUFS), Seoul, South Korea. Previously, he worked as a PostDoc and senior researcher on Future Internet Architecture at National Institute for Mathematical Sciences (NIMS) and Electronics and Telecommunication Research Institute (ETRI), Daejeon, South Korea (2010 – 2012). He is a co-author/editor of 7 books, 15 chapters, 15 patents, and 150+ research articles. He has delivered 100+ invited and keynote talks at Universities, conferences, and workshops. He is a Senior Member of ACM and IEEE societies. He serves as an editor and reviewer for several major conferences and journals. Dr. Singh is a chief technical advisor of successful startups of vestellalab.com and MTOV, and Coikosity. He is the recipient of the State Government of India for his notable and distinguished contribution in the field of Technology at the 15th Non-Residence of Indian (NRI) Diwas Convention, Varanasi, India, on January 21, 2019.
ABSTRACT: Last few years, autonomous vehicle-related projects have been evolving rapidly because automotive industries are desperately in need of solutions that can improve the safety of driving, and the security of vehicles, need to reduce the cost of ownership of the automobile industry. This lecture helps to understand and identify a necessity to evaluate the metadata features of vehicles which could help improve the vehicle driver’s skill to prevent accidents and evaluate the change in the quality of cars over time. And also, we would like to discuss the vehicle data study using a supervised learning-based linear regression model used as an estimator for Driver’s Safety Metrics and Economic Driving Metrics. We have considered machine learning, NLP, and Deep learning methods to study real-time OBD-II sensorial vehicle data to show the importance of vehicle data such as text, audio, and video forms of study to control and prevention/monitoring services. In addition, I would also like to discuss future research plans and challenges related to Smart city transportation for Autonomous vehicle markets.
ZOOM link: https://nu-edu-kz.zoom.us/j/96447969780?pwd=UVpuVXMxSk1UcDAyckZyS3FxS01Edz09
