From Edge Devices to Cloud Computing and Datacenters: Emerging Machine Learning Applications, Algorithms, and Optimizations
MDPI mathematics 2022
- URL: https://www.mdpi.com/journal/mathematics/special_issues/Mach_Learn_Algorithm_Optim
- Event Date: ~
- Submission Date: 2022-10-31
Dear Colleagues,
In the last decade, machine learning has emerged as an important tool for a tremendous number of applications, such as computer vision, medicine, fintech, autonomous systems, speech recognition, and many others. Machine learning models offer state-of-the-art accuracy and robustness in many applications. The increasing deployment of machine learning algorithms from edge and IoT devices to high-end computational infrastructures, such as supercomputers, the cloud, and datacenters, introduces major computational challenges due to the growing amount of data and also the major growth in their model size and complexity. This Special Issue looks for novel developments of emerging machine learning applications, algorithms, and optimization in diverse computational platforms such as:
Novel IoT and edge devices’ machine learning applications;
High-performance computing machine learning algorithms;
Machine learning applications in cloud and fog computing;
Fusion of machine learning models between edge and cloud;
Machine learning optimization methods such as pruning and deep compression.
Prof. Dr. Freddy Gabbay
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. Mathematics 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 1600 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
machine learning
deep neural network
deep compression
machine learning optimizations
machine learning under constrained resources
This special issue is now open for submission.
In the last decade, machine learning has emerged as an important tool for a tremendous number of applications, such as computer vision, medicine, fintech, autonomous systems, speech recognition, and many others. Machine learning models offer state-of-the-art accuracy and robustness in many applications. The increasing deployment of machine learning algorithms from edge and IoT devices to high-end computational infrastructures, such as supercomputers, the cloud, and datacenters, introduces major computational challenges due to the growing amount of data and also the major growth in their model size and complexity. This Special Issue looks for novel developments of emerging machine learning applications, algorithms, and optimization in diverse computational platforms such as:
Novel IoT and edge devices’ machine learning applications;
High-performance computing machine learning algorithms;
Machine learning applications in cloud and fog computing;
Fusion of machine learning models between edge and cloud;
Machine learning optimization methods such as pruning and deep compression.
Prof. Dr. Freddy Gabbay
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. Mathematics 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 1600 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
machine learning
deep neural network
deep compression
machine learning optimizations
machine learning under constrained resources
This special issue is now open for submission.
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