Browsing SINTEF Open by Author "Djenouri, Djamel"

Now showing items 1-10 of 10

    • 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 ...

    • Emergent Deep Learning for Anomaly Detection in Internet of Everything 

      Djenouri, Youcef; Djenouri, Djamel; Belhadi, Asma; Srivastava, Gautam; Lin, Jerry Chun-Wei (Peer reviewed; Journal article, 2021)
      This research presents a new generic deep learning framework for anomaly detection in the Internet of Everything (IoE). It combines decomposition methods, deep neural networks, and evolutionary computation to better detect ...

    • Hybrid RESNET and Regional Convolution Neural Network Framework for Accident Estimation in Smart Roads 

      Djenouri, Youcef; Srivastava, Gautam; Djenouri, Djamel; Belhadi, Asma; Lin, Jerry Chun-Wei (Peer reviewed; Journal article, 2022)
      Road safety is tackled and an intelligent deep learning framework is proposed in this work, which includes outlier detection, vehicle detection, and accident estimation. The road state is first collected, while an intelligent ...

    • 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)
      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 to find ...

    • 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 ...

    • A recurrent neural network for urban long-term traffic flow forecasting 

      Belhadi, Asma; Djenouri, Youcef; Djenouri, Djamel; Lin, Jerry Chun-Wei (Peer reviewed; Journal article, 2020)
      This paper investigates the use of recurrent neural network to predict urban long-term traffic flows. A representation of the long-term flows with related weather and contextual information is first introduced. A recurrent ...

    • Trajectory outlier detection: New problems and solutions for smart cities 

      Djenouri, Youcef; Djenouri, Djamel; Lin, Jerry Chun-Wei (Peer reviewed; Journal article, 2021)
      This article introduces two new problems related to trajectory outlier detection: (1) group trajectory outlier (GTO) detection and (2) deviation point detection for both individual and group of trajectory outliers. Five ...

    • 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 ...

    • When the Decomposition Meets the Constraint Satisfaction Problem 

      Djenouri, Youcef; Djenouri, Djamel; Habbas, Zineb; Lin, Jerry Chun-Wei; Michalak, Tomasz P.; Cano, Alberto (Peer reviewed; Journal article, 2020)
      This paper explores the joint use of decomposition methods and parallel computing for solving constraint satisfaction problems and introduces a framework called Parallel Decomposition for Constraint Satisfaction Problems ...