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Advance in Machine Learning
This special issue belongs to the section “AI-Enabled Process Engineering“.
Special Issue Information
Dear Colleagues,
Machine learning is filling the gaps between theory and practice and helps to change virtually every aspect of modern lives. Today, advance in machine learning algorithms accomplishes tasks to solving real-world problems that until recently only expert humans could perform.
In this Special Issue, we seek research and case studies that demonstrate the application of machine learning to support applied scientific research, in any area of science and technology. Example topics include (but are not limited to) the following topics applied to machine learning:
- New machine learning algorithms
- New optimization techniques
- Distributed machine learning systems and architectures
- New applications on real-time/big data analytics
- Intelligent applications
- Quantum machine learning
- Data and code integration
- Visualization of modern systems and networks
- High-throughput data analysis
- Comparison and alignment methods
Dr. Konstantinos Demertzis
Prof. Dr. Lazaros Iliadis
Dr. Nikos Tziritas
Dr. Panayotis Kikiras
Guest Editors
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. Processes is an international peer-reviewed open access monthly 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.
Keywords
- Deep Learning
- Spiking Neural Computation
- Big Data Architectures
- Data Lakes
- Quantum Machine Learning
- Stream Learning
- Meta-Learning
- Ambient Intelligence
- Real-Time Analytics
- Distributed Systems
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Published Papers
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