Nelly
Elsayed
Assistant Professor at University of Cincinnati
"Green Machine Perception in Smart City Transportation Safety "
"Green Machine Perception in Smart City Transportation Safety "
24 - 10:50 AM EDT/New York (See in local time)
Add to Calendar
05/24/2025 10:50 AM
05/24/2025 11:30 AM
America/New_York
#WTGC2025
"Green Machine Perception in Smart City Transportation Safety "
#WTGC2025
"Green Machine Perception in Smart City Transportation Safety "
https://www.womentech.net/ringcentral
https://www.womentech.net/ringcentral
"Green Machine Perception in Smart City Transportation Safety "
"Green Machine Perception in Smart City Transportation Safety "
Don’t miss out and join visionaries, innovators, and thought leaders from all over the world at the Women in Tech Global Conference.
Vote by Sharing
Unite 100 000 Women in Tech to Drive Change with Purpose and Impact.
Do you want to see this session? Help increase the sharing count and the session visibility. Sessions with +10 votes will be available to career ticket holders.
Please note that it might take some time until your share & vote is reflected.
Session: Green Machine Perception in Smart City Transportation Safety
Traffic accidents are a pressing global concern and a significant public health issue, resulting in numerous fatalities and injuries annually. These accidents have far-reaching consequences on individuals and communities, warranting urgent attention and effective measures to mitigate their impact. Effective traffic accident recognition systems hold immense potential for society, providing invaluable data for analyzing accident patterns, identifying high-risk areas, and understanding the root causes of accidents. Machine Perception algorithms can be employed to recognize traffic accidents via integrating and utilizing the smart devices and sensors in smart cities, such as surveillance cameras and motion sensors. Such systems can be helpful if they are connected with the first responders' systems to provide early alerts of accident occurrences and provide sufficient help at the accident scene. However, such systems also require additional energy resources. This presentation provides insights into how such a system can be built, how to consider energy resources, and how to establish low-cost solutions that can help achieve green AI solutions for smart city transportation safety systems.Key Takeaways
- Machine perception algorithms can help to achieve higher safety standards in smart city transportation safety
- Building smart systems requires additional considerations regarding the cost and energy requirements to help save the environment
- There are several research and development open questions that can be addresses to enhance the current solutions of smart city transportation safety
- The current sensors and cameras can provide a sufficient data to build reliable machine perception systems
Bio
Dr. Nelly Elsayed is an Assistant Professor at the School of Information Technology. She is the Leader and Founder of the Applied Machine Learning and Intelligence Lab. She received a BS. and MS. degree in Computer Science from Alexandria University, and she received her MS. Eng. and Ph.D. degrees from the University of Louisiana at Lafayette. She is an IEEE Computational Intelligence Society active member. She has served as a principal investigator and co-principle investigator in different federal, educational, and industrial level-funded research projects. She received the Faculty Incentive Award for Research and Scholarship from the CECH, UC, recognizing her research contributions, journal and conference peer-reviewed publications, and professional presentations. She received the Love of Learning Award from the Honor Society Phi Kappa Phi in 2019, 2021, and 2023. She received the Golden Apple Award for Excellence in Teaching (Graduate Level), CECH. She received the UCAADA Sarah Grant Barber Outstanding Advising Faculty Award from University of Cincinnati. She has been an Ambassador for Goodwill of Lafayette, Louisiana, since 2017.