Special Issues

Deep Learning for Multimedia Processing

Submission Deadline: 30 November 2023 (closed) View: 9

Guest Editors

Asso. Prof. Uzair Aslam Bhatti, Hainan University, Hainan, China
Prof. Konstantinos E. Psannis, University of Macedonia, School of Information Sciences, Greece
Asso. Prof. Muhammad Aamir, Huanggang Normal University, China
Dr. Sibghatullah Bazai, Balochistan university, BUITMS, Quetta


Deep learning (DL) methods have emerged as a key component of artificial intelligence for multimedia data processing. DL has been effectively investigated in a variety of multimedia applications in recent years, including natural language processing, visual data analytics, speech recognition, and so on. DL draws inspiration from the neuroscience area, constructing neural networks (NN) built to imitate the human brain. Given that multimedia data is huge, unstructured, and heterogeneous, DL offers the ability to solve these problems by allowing computers to simply and automatically extract characteristics from unstructured data without requiring human participation. The convergence of big annotated data and affordable CPU/GPU hardware has allowed the training of neural networks for multimedia analysis. However, there are a lot of critical aspects in multimedia DL: (1) multimedia big data efficient management;(2) utilization of different data modalities exploiting DL; and (3) explainability, insight view and understanding of the DL decision-making mechanisms.


The main aim of this Special Issue is to seek high-quality submissions that highlight latest research findings, suggesting theories and practical solutions for various applications on multimedia analysis utilizing deep learning technologies.


deep learning; multimedia analysis; big data analysis; deep learning architectures; machine learning

Published Papers

  • Open Access


    Real-Time Spammers Detection Based on Metadata Features with Machine Learning

    Adnan Ali, Jinlong Li, Huanhuan Chen, Uzair Aslam Bhatti, Asad Khan
    Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 241-258, 2023, DOI:10.32604/iasc.2023.041645
    (This article belongs to the Special Issue: Deep Learning for Multimedia Processing)
    Abstract Spammer detection is to identify and block malicious activities performing users. Such users should be identified and terminated from social media to keep the social media process organic and to maintain the integrity of online social spaces. Previous research aimed to find spammers based on hybrid approaches of graph mining, posted content, and metadata, using small and manually labeled datasets. However, such hybrid approaches are unscalable, not robust, particular dataset dependent, and require numerous parameters, complex graphs, and natural language processing (NLP) resources to make decisions, which makes spammer detection impractical for real-time detection. For… More >

  • Open Access


    Recognition System for Diagnosing Pneumonia and Bronchitis Using Children’s Breathing Sounds Based on Transfer Learning

    Jianying Shi, Shengchao Chen, Benguo Yu, Yi Ren, Guanjun Wang, Chenyang Xue
    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3235-3258, 2023, DOI:10.32604/iasc.2023.041392
    (This article belongs to the Special Issue: Deep Learning for Multimedia Processing)
    Abstract Respiratory infections in children increase the risk of fatal lung disease, making effective identification and analysis of breath sounds essential. However, most studies have focused on adults ignoring pediatric patients whose lungs are more vulnerable due to an imperfect immune system, and the scarcity of medical data has limited the development of deep learning methods toward reliability and high classification accuracy. In this work, we collected three types of breath sounds from children with normal (120 recordings), bronchitis (120 recordings), and pneumonia (120 recordings) at the posterior chest position using an off-the-shelf 3M electronic stethoscope.… More >

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