Symmetry and Asymmetry Study in Data Analysis
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".
Deadline for manuscript submissions: closed (30 April 2025)
Special Issue Editors
Interests: steel
Interests: operational feedback/optimization control for intelligent manufacturing; data-driven modeling, control and optimization
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
The investigation of symmetry and asymmetry holds significant theoretical and practical importance in data science. Symmetry, as an idealized assumption in statistical modeling, facilitates model construction and statistical inference in data analysis while also providing a robust foundation for model stability and interpretability. Nonetheless, real-world phenomena often exhibit pronounced asymmetry, particularly in domains such as informatics, industrial production, biomedicine, and social sciences. Asymmetry not only reflects the inherent complexity of data but also elucidates the underlying mechanisms of structural generation. Addressing these asymmetries through effective data transformations and model adjustments can substantially enhance the predictive accuracy of data analyses, thereby offering a more reliable foundation for practical decision-making.
Exploring and comprehending the symmetry and asymmetry in data analysis processes not only advances the frontiers of statistical theory but also profoundly enhances the generalization capacity of data science in tackling complex real-world problems. This Special Issue aims to spotlight methodological innovations and applications of symmetry and asymmetry within the realm of big data analysis. Submissions are encouraged to focus on continuous discoveries and improvements from both theoretical and practical dimensions.
Thematic areas
Specifically, the submissions we seek (research and review articles) are dedicated to the expansive themes pertaining to the intersection of data analysis/symmetry relationships. These encompass a spectrum of domains, including (but not restricted to) the following:
- Symmetry and asymmetry in foundational statistical models;
- The roles of symmetry and asymmetry in large-scale data analytics;
- Asymmetry within data preprocessing techniques;
- Symmetry and asymmetry in pattern recognition and feature engineering;
- Assumptions of symmetry in machine learning models;
- Symmetry and asymmetry in time-series data;
- Applications of asymmetry in data mining;
- Explanation and application of statistical skewness and kurtosis;
- Symmetry and asymmetry in biomedical data analysis;
- Symmetry and asymmetry in industrial production processes;
- Symmetry and asymmetry in socio-economic data analysis;
- Symmetry and asymmetry in signal processing.
Dr. Zhi-min Lv
Prof. Dr. Ping Zhou
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.
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
- • Hypothesis testing and distribution fitting
- • Data normalization and statistical inference
- • Feature and model selection strategies
- • Applications of machine learning/deep learning technologies
- • Data preprocessing/exploratory analysis/visualization techniques
- • Data risk management and quality assessment
- • Model interpretability
- • Time series analysis
- • Big data reliability and real-time analytics applications
- • Algorithm optimization.
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