Asymmetry in Machine Learning
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".
Deadline for manuscript submissions: 30 July 2024 | Viewed by 159
Special Issue Editors
Interests: machine vision; intelligent manufacturing; mechanical equipment quality inspection and software robot
Interests: IoT security; blockchain; mobile security; NFC/RFID security; authentication; data privacy; cryptology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Machine learning enables machines to learn automatically without explicit programming. The main process is to use advanced algorithms and statistical techniques to access the data and predict accuracy, instead of using a rule-based system. There are many well-established algorithms for prediction and analysis, such as supervised learning. Machine learning algorithms include support vector machine (SVM), KNN, YOLO, etc. Scipy, Scikit, OpenCV, Matplotlib, and Keras are popular libraries used for image segmentation. The dataset is a primary component of machine learning accuracy prediction. As a result, the data are more relevant, and the prediction is more accurate. Machine learning has been used in different fields, such as finance, retail, and the healthcare industry. Especially, the increasing use of machine learning in healthcare provides more opportunities for disease diagnosis and treatment. Machine learning continually improves, enalbing more accurate data prediction and classification for analysis. The prediction model will learn to make a better decisions for accurate prediction, as more data are gathered. Asymmetry in machine learning has recently demonstrated outstanding results in the fields of engineering, health, agriculture, astronomy, sports, cyber security, and education. This Special Issue mainly focuses on novel machine learning models motivated by symmetry/asymmetry.
Dr. Hui Zhang
Prof. Dr. Kuo-Hui Yeh
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. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.
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Keywords
- machine learning
- prediction algorithms
- accuracy
- SVM
- data set
