Deep Learning Techniques for Big Data Analysis

Dear Colleagues, 

Deep learning (DL) is a subset of artificial intelligence (AI) which is applied to automatically dig through large volumes of data to identify patterns and extract features from complex unsupervised data without the involvement of humans. Classic neural networks, convolutional neural networks (CNN), recurrent neural networks (RNNs), generative adversarial networks, deep reinforcement learning, etc. are some of the important DL algorithms which could be used for image data analysis, pattern recognition, speech recognition, and to perform several computer vision tasks. Applications of DL for image, audio, video, and text data in unstructured form can help us to make smart decisions and build effective strategies by focusing on perspectives and needs of real-world buyers and users of technology. The exponential growth of big data would be meaningless unless technologies such as AI and DL are used for the anomaly detection, pattern recognition, and industrial fault detection. It is pivotal to design efficient algorithms, models, and methodologies of DL to analyze the big data generated from the industry, healthcare, smart cities, and the medical field.

The topics of interest in this SI include but are not limited to:

• Deep learning algorithms
• Deep learning for image data analysis
• Deep learning in the Internet of Things (IoT)
• Deep learning for industrial data analysis
• Deep learning in natural language processing
• Speech recognition with deep learning
• Medical imaging data analysis with deep learning
• Pattern recognition and applications of deep learning
• Computer vision tasks and deep learning

Prof. Prasan Kumar Sahoo
Guest Editor

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