Probability and Statistics Theory in Symmetry and Application from Machine Learning to Biomedical Data
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Mathematics".
Deadline for manuscript submissions: 30 September 2025 | Viewed by 6480
Special Issue Editors
Interests: limit theorems; random walks; Hawkes process; probability theory; stochastic process applications and data analytic and machine learning
Interests: brain; image classification; medical image processing; positron emission tomography; biomedical MRI; dementia; computerizedtomography; CNN; RNN; transformer; large language model; generative AI; multimodal data
Special Issues, Collections and Topics in MDPI journals
Interests: data mining; machine learning; deep learning; statistical analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Probability and statistics in symmetry have become important topics of study in recent years, possessing wide applications in various fields including medicine, biology, economics, engineering, and physics. Specifically, stochastic processes have come to play fundamental roles in the mathematical model of phenomena in wide areas, such as symmetric random walks, random walks in a random environment, Hawkes processes, etc. The study of these phenomena and applications has led to the development of new stochastic processes. Some important probability laws are heavy-tailed distributions, which can be modeled with discretizations of random variables or measured by parameters of either new or old statistical models. The growing data resources have led to the introduction of a variety of distributions and their properties.
The adoption of machine learning and deep learning analytics in the bio-medical field with symmetry properties is progressing at a rapid pace, with some applications already finding use in pre-clinical and clinical settings. In addition, various types of bio-medical data continue to be used, and CNN, RNN, and transform technologies used for analysis continue to undergo further development. There are many types of bio-medical data, such as image data, pathological tissue data, waveform data, natural language data, genetic data, voice data, etc.
The purpose of this Special Issue is to provide a collection of articles that reflect the importance of statistics and probability in symmetry and its applications in several areas, and also to assemble the current research on the latest machine learning and deep learning techniques of various bio-medical data with symmetry properties. Additionally, we would welcome hypotheses for a fusion analysis technique of various bio-medical data.
Dr. Youngsoo Seol
Prof. Dr. Do-Young Kang
Dr. Sangjin Kim
Guest Editors
Manuscript Submission Information
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Keywords
- limit theorems
- probability and statistics
- stochastic processes and its applications
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