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
Summary
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.
Keywords
deep learning; multimedia analysis; big data analysis; deep learning architectures; machine learning
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