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  • Open Access


    Short Term Traffic Flow Prediction Using Hybrid Deep Learning

    Mohandu Anjaneyulu, Mohan Kubendiran*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1641-1656, 2023, DOI:10.32604/cmc.2023.035056

    Abstract Traffic flow prediction in urban areas is essential in the Intelligent Transportation System (ITS). Short Term Traffic Flow (STTF) prediction impacts traffic flow series, where an estimation of the number of vehicles will appear during the next instance of time per hour. Precise STTF is critical in Intelligent Transportation System. Various extinct systems aim for short-term traffic forecasts, ensuring a good precision outcome which was a significant task over the past few years. The main objective of this paper is to propose a new model to predict STTF for every hour of a day. In… More >

  • Open Access


    Identification of Anomaly Scenes in Videos Using Graph Neural Networks

    Khalid Masood1, Mahmoud M. Al-Sakhnini2,3, Waqas Nawaz4,*, Tauqeer Faiz5,6, Abdul Salam Mohammad7, Hamza Kashif8

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5417-5430, 2023, DOI:10.32604/cmc.2023.033590

    Abstract Generally, conventional methods for anomaly detection rely on clustering, proximity, or classification. With the massive growth in surveillance videos, outliers or anomalies find ingenious ways to obscure themselves in the network and make conventional techniques inefficient. This research explores the structure of Graph neural networks (GNNs) that generalize deep learning frameworks to graph-structured data. Every node in the graph structure is labeled and anomalies, represented by unlabeled nodes, are predicted by performing random walks on the node-based graph structures. Due to their strong learning abilities, GNNs gained popularity in various domains such as natural language… More >

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