activity
20132020
most citedDeep Reinforcement Learning for Intelligent Transportation Systems: A Survey

3 citations · 6 across the 4 of their papers we have counts for

collaborators

14 papers

cs.NI2020

Deep Reinforcement Learning for Adaptive Network Slicing in 5G for Intelligent Vehicular Systems and Smart Cities

Almuthanna Nassar, Yasin Yilmaz

Intelligent vehicular systems and smart city applications are the fastest growing Internet of things (IoT) implementations at a compound annual growth rate of 30%. In view of the r…

cs.CV2020

Online Anomaly Detection in Surveillance Videos with Asymptotic Bounds on False Alarm Rate

Keval Doshi, Yasin Yilmaz

Anomaly detection in surveillance videos is attracting an increasing amount of attention. Despite the competitive performance of recent methods, they lack theoretical performance a…

cs.CR20201 cited

Timely Detection and Mitigation of Stealthy DDoS Attacks via IoT Networks

Keval Doshi, Yasin Yilmaz, Suleyman Uludag

Internet of Things (IoT) networks consist of sensors, actuators, mobile and wearable devices that can connect to the Internet. With billions of such devices already in the market w…

cs.LG20203 cited

Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey

Ammar Haydari, Yasin Yilmaz

Latest technological improvements increased the quality of transportation. New data-driven approaches bring out a new research direction for all control-based systems, e.g., in tra…

cs.CV2020

Continual Learning for Anomaly Detection in Surveillance Videos

Keval Doshi, Yasin Yilmaz

Anomaly detection in surveillance videos has been recently gaining attention. A challenging aspect of high-dimensional applications such as video surveillance is continual learning…

cs.CV2020

Any-Shot Sequential Anomaly Detection in Surveillance Videos

Keval Doshi, Yasin Yilmaz

Anomaly detection in surveillance videos has been recently gaining attention. Even though the performance of state-of-the-art methods on publicly available data sets has been compe…