• Cluster-based information retrieval using pattern mining 

      Djenouri, Youcef; Belhadi, Asma; Djenouri, Djamel; Lin, Jerry Chun-Wei (Peer reviewed; Journal article, 2020)
      This paper addresses the problem of responding to user queries by fetching the most relevant object from a clustered set of objects. It addresses the common drawbacks of cluster-based approaches and targets fast, high-quality ...
    • Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection 

      Belhadi, Asma; Djenouri, Youcef; Srivastava, Gautam; Djenouri, Djamel; Lin, Jerry Chun-Wei; Fortino, Giancarlo (Peer reviewed; Journal article, 2021)
      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 have been proposed in this paper. These can ...
    • Intelligent Deep Fusion Network for Anomaly Identification in Maritime Transportation Systems 

      Djenouri, Youcef; Belhadi, Asma; Djenouri, Djamel; Srivastava, Gautam; Lin, Jerry Chun-Wei (Peer reviewed; Journal article, 2022)
      Abstract: This paper introduces a novel deep learning architecture for identifying outliers in the context of intelligent transportation systems. The use of a convolutional neural network with decomposition is explored ...
    • Machine Learning for Identifying Group Trajectory Outliers 

      Belhadi, Asma; Djenouri, Youcef; Djenouri, Djamel; Michalak, Tomasz; Lin, Jerry Chun-Wei (Peer reviewed; Journal article, 2021)
      Prior works on the trajectory outlier detection problem solely consider individual outliers. However, in real-world scenarios, trajectory outliers can often appear in groups, e.g., a group of bikes that deviates to the ...
    • Vehicle detection using improved region convolution neural network for accident prevention in smart roads 

      Djenouri, Youcef; Belhadi, Asma; Srivastava, Gautam; Djenouri, Djamel; Lin, Jerry Chun-Wei (Peer reviewed; Journal article, 2022)
      This paper explores the vehicle detection problem and introduces an improved regional convolution neural network. The vehicle data (set of images) is first collected, from which the noise (set of outlier images) is removed ...