- 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 | Editorial Board - MDPI
Interests: electrical machines; drives and power converters for e-mobility (silicon, GaN and SiC technologies) * Section: Electromechanical Energy Conversion Systems
- 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 | Instructions for Authors - MDPI
Machines requires that authors publish all experimental controls and make full datasets available where possible (see the guidelines on Supplementary Materials and references to unpublished data)
- Machines | Editorial Office - MDPI
Machines Editorial Office MDPI, Grosspeteranlage 5, 4052 Basel, Switzerland Tel : +41 61 683 77 34 (MDPI HQ Basel, Switzerland 9:00 - 17:00 CET) For queries regarding Special Issues, submission deadlines, editorial policies, discounts and vouchers, please contact the journal’s Editorial Office: machines@mdpi com Managing Editor Ms Missy Wu
- Machines | Aims Scope - MDPI
Aims Machines (ISSN 2075-1702) is an international, peer-reviewed journal on machinery and engineering It publishes research articles, reviews and communications Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible There is no restriction on the maximum length of the papers
- YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary . . . - MDPI
Hussain, M YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary Nature toward Digital Manufacturing and Industrial Defect Detection Machines 2023, 11, 677 https: doi org 10 3390 machines11070677
- The Challenges of Machine Learning: A Critical Review - MDPI
LLMs represent a significant milestone in machine learning, pushing the boundaries of what machines can understand and express in human language As they continue to evolve, their impact is expected to grow, reshaping the interaction between humans and technology
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