Over the past decade, we have witnessed an enormous amount of research effort dedicated
to the design of efficient outlier detection techniques while taking into consideration …

Anomaly detection is a critical task in ensuring the security and safety of infrastructure and
individuals in smart environments. This paper provides a comprehensive analysis of recent …

Detecting outliers is a significant problem that has been studied in various research and
application areas. Researchers continue to design robust schemes to provide solutions to …

Efforts devoted to mitigate the effects of road traffic congestion have been conducted since
1970s. Nowadays, there is a need for prominent solutions capable of mining information …

This paper introduces a new model to identify collective abnormal human behaviors from
large pedestrian data in smart cities. To accurately solve the problem, several algorithms …

Traffic congestion is becoming the issues of the entire globe. This study aims to explore and
review the data mining and machine learning technologies adopted in research and industry …

Urban anomalies may result in loss of life or property if not handled properly. Automatically
alerting anomalies in their early stage or even predicting anomalies before happening is of …

In this study, we combine graph optimization and prediction in a single pipeline to
investigate an innovative convolutional graph-based neural network for urban traffic flow …

Traffic accident detection and anticipation is an obstinate road safety problem and
painstaking efforts have been devoted. With the rapid growth of video data, Vision-based …

The growing quantities of data allow for advanced analysis. A prime example of it are smart
city applications with forecasting urban traffic flow as a key application. However, data …