Symmetry/Asymmetry in Artificial Intelligence
Deadline for manuscript submissions: 15 May 2024 | Viewed by 57
Interests: Artificial Intelligence; image and audio/video intelligent information processing; educational data mining; machine learning; human-computer interaction; virtual reality; game technology; data mining; deep learning
With the development of Artificial Intelligence technologies, especially in the fields of machine learning and deep learning, research on data symmetry and asymmetry has begun in various domains, such as image recognition, object detection, speech recognition, natural language understanding, and more. Significant progress has been made in utilizing the symmetry and asymmetry properties of data in machine learning and deep learning, including techniques for increasing training data by exploiting these properties, quantifying symmetry and asymmetry attributes through domain transformations, and adding symmetry or asymmetry constraints in model optimization.
Furthermore, researchers have started using machine learning and deep learning to study data symmetry and asymmetry in multiple fields to aid in solving various problems. When building predictive models using machine learning and deep learning, data may exhibit symmetry or asymmetry constraints. Effectively utilizing these symmetric and asymmetric properties can lead to the development of better machine learning and deep learning models.
This Special Issue aims to explore the issues of symmetry and asymmetry in Artificial Intelligence, utilizing methods from information theory, topology, deep learning, machine learning, and more to effectively address these problems. Relevant research may include but is not limited to symmetry/asymmetry in deep learning, machine learning, transfer learning, computer vision, natural language processing, speech processing, and pattern recognition. The goal of this Special Issue, titled "Symmetry/Asymmetry in Artificial Intelligence", is to present the latest advancements in these related topics. We cordially invite all researchers working in this field to contribute to this Special Issue.
We eagerly anticipate receiving your contributions.
Dr. Zhifeng Wang
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.
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.
- symmetry/asymmetry in deep learning
- machine learning
- transfer learning
- computer vision
- natural language processing
- speech processing
- pattern recognition