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Special Issue "Advanced Deep Learning Architecture and Related Technologies Based on Cloud Computing"
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 3738
Special Issue Editor
Interests: databases; big data analysis; music retrieval; multimedia systems; machine learning; knowledge management; computational intelligence
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Special Issue Information
Deep learning plays an essential role in solving various challenges, such as prediction, object recognition, and natural language processing. However, deep learning techniques require large amounts of data and hardware resources.
Various IT industries, including Amazon, provide virtual hardware called cloud computing. The provided virtual resources have various performances, and an environment requiring high performance can be constructed at a relatively low cost. So, a deep learning network can be built relatively free from restrictions on hardware resources.
Therefore, deep learning using cloud computing can train networks with a huge amount of parameters that are expected to show excellent performance, and furthermore, ensemble deep learning can be used to achieve one major goal. Alternatively, an optimized deep learning network can achieve reasonable results at a minimal cost.
Additionally, in order to use deep learning on a cloud computing system, a communication system capable of transmitting/receiving enormous amounts of data is required, and a security system is needed to protect datasets and the network under study.
This Special Issue aims to cover the state-of-the-art deep learning technologies and underlying technologies required to use deep learning in cloud computing. We pay attention to how to apply the latest deep learning technologies in cloud computing to get better results or how to optimize the network to avoid wasting resources and data communication and security methods, which are an important issue in using cloud computing systems. The main topics are related to :
- Ensemble deep learning networks for solving challenging problems;
- AI technology for cloud computing;
- Cost-effective deep learning architecture;
- Hyperparameter tuning on large-scale deep learning architecture;
- The application of state-of-the-art deep learning technology;
- Large data transmission/reception baseline suitable for deep learning using cloud computing;
- Advanced methods for security systems during data transmission and reception;
- Comprehensive deep learning technology based on edge computing;
- Deep learning network optimization methods for cost-effective cloud computing.
Prof. Dr. Seungmin Rho
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 100 words) can be sent to the Editorial Office for announcement on this website.
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences 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 2300 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.