Journal Menu
► Journal MenuJournal Browser
► Journal BrowserSpecial Issue "Deep Learning in Emerging Cloud Computing Architectures"
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: 31 December 2021.
Special Issue Editor
Interests: cloud computing; internet of things; fog/edge computing; cybersecurity; machine learning
Special Issues and Collections in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, the challenges faced by conventional cloud computing technology have provided motivation for the development of next-generation cloud computing architectures. The current trends and heuristic methods in cloud computing architectures generate large volumes of data. Processing this huge amount of data is beyond the capability of conventional intelligent algorithms. In this regard, deep learning algorithms are used for processing large-scale datasets. Deep learning algorithms have recently gained tremendous attention from researchers in problem-solving for state-of-the-art emerging cloud computing architectures.
Unfortunately, there is no comprehensive methodology nor literature available to review the applications of deep learning architectures for solving complex problems in emerging cloud computing architectures. This Special Issue aims to fill this gap by attracting and publishing high-quality manuscripts related to deep learning methods for emerging cloud computing architectures.
The purpose of this Special Issue is to provide a platform for researchers to share their research experiences, both theoretical and practical, in defining the issues, challenges, and proposed solutions to address deep learning and their role in emerging cloud computing architectures. Manuscripts particularly related to cloud infrastructure development, deployment, and deep learning analytics are encouraged for submission. We are looking for contributions in the best interests of the cloud, data centers, big data, and the deep learning industry and research community.
Potential topics for submissions include but are not limited to:
- Cloud computing architecture and datasets
- Layer-based nonlinear processing units for feature extraction and transformation in cloud storage
- NFV and cloud architectures for supervised and/or unsupervised feature extraction
- Representation techniques of different levels of abstraction
- Natural language processing methods for deep learning and analytics
- Open research issues on deep learning in emerging cloud computing
Prof. Dr. Jemal Hussein Abawajy
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 papers will be 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. 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 1800 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.
Keywords
- Cloud computing
- Convolutional neural network
- Deep learning
- Deep reinforcement learning
- Edge computing
- Fog computing
- Emerging cloud computing
- Serverless computing