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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.