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- Machines | An Open Access Journal from MDPI
Machines is an international, peer-reviewed, open access journal on machinery and engineering published monthly online by MDPI The IFToMM is affiliated with Machines and its members receive a discount on the article processing charges
- Machines | Aims Scope - MDPI
About Machines Aims Machines (ISSN 2075-1702) is an international, peer-reviewed journal on machinery and engineering It publishes research articles, reviews and communications
- Machines | Special Issues - MDPI
Machines publishes Special Issues to create collections of papers on specific topics, with the aim of building a community of authors and readers to discuss the latest research and develop new ideas and research directions
- Machines | Editorial Board - MDPI
Interests: design of high precision machines; air-bearings design; micro cutting; ultraprecision machining; smart tooling * Section: Advanced Manufacturing Special Issues, Collections and Topics in MDPI journals
- Machines | Instructions for Authors - MDPI
Manuscripts for Machines should be submitted online at susy mdpi com The submitting author, who is generally the corresponding author, is responsible for the manuscript during the submission and peer review process
- State-of-the-Art in Service and Rehabilitation Machines - MDPI
Machines 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)
- Machines | Article Processing Charges - MDPI
All articles published in Machines (ISSN 2075-1702) are published in full open access An article processing charge (APC) of CHF 2400 (Swiss francs) applies to papers accepted after peer review This article processing charge is to cover the costs of peer review, copyediting, typesetting, long-term archiving, and journal management
- A Review of Machine Learning and Deep Learning Methods for . . . - MDPI
The problem with a person disappearing temporarily due to occlusions or scene changes proposes a large area for research Soon, it laid the groundwork for many popular algorithms like Ensemble RankSVM, which enhanced robustness by combining multiple Support Vector Machines (SVMs) to improve pairwise ranking Unlike traditional classification
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