Studies of Symmetry and Asymmetry in Big Data

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 746

Special Issue Editor


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Guest Editor
Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 2B5, Canada
Interests: deep machine learning; artificial intelligence and computer vision problems; signal processing; medical imaging; computer-aided detection (CADe); computer-aided diagnosis (CADx)

Special Issue Information

Dear Colleagues,

This Special Issue explores structural and statistical patterns in large-scale datasets, with emphasis on medical imaging, artificial intelligence, and healthcare analytics. Symmetry and asymmetry play vital roles in deep learning models by influencing feature extraction, model interpretability, and data imbalance—factors critical for disease classification and clinical decision making, including cancer detection. We welcome theoretical, computational, and application-focused contributions that investigate how recognizing and leveraging symmetry or asymmetry can improve diagnostic accuracy, robustness, and generalizability in AI systems applied to real-world biomedical and healthcare challenges.

Dr. Liton Devnath
Guest Editor

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Keywords

  • symmetry
  • asymmetry
  • big data
  • deep learning
  • feature extraction
  • artificial intelligence
  • medical imaging
  • cancer detection
  • data imbalance
  • model interpretability

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Published Papers (1 paper)

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Research

26 pages, 1222 KB  
Article
PA-FRIM: An Adaptive Hybrid FOX–RUN Framework with Adaptive Intensive Mutation for Multi-Metric Big Data Anonymization
by M. Faruk Şahin and Can Eyüpoğlu
Symmetry 2026, 18(5), 734; https://doi.org/10.3390/sym18050734 - 25 Apr 2026
Viewed by 295
Abstract
Background/Objectives: Privacy preservation in big data environments is an NP-hard optimization task that requires the satisfaction of k-anonymity and l-diversity constraints to ensure data utility. Methods: This study proposes a novel hybrid optimization approach, adaptive hybrid FOX–RUN Intensive Mutation (PA-FRIM), to address the [...] Read more.
Background/Objectives: Privacy preservation in big data environments is an NP-hard optimization task that requires the satisfaction of k-anonymity and l-diversity constraints to ensure data utility. Methods: This study proposes a novel hybrid optimization approach, adaptive hybrid FOX–RUN Intensive Mutation (PA-FRIM), to address the privacy–utility trade-off in anonymization process. The proposed approach integrates FOX-based global exploration with RUN-based local search using a hybrid adaptive control strategy and intensive mutation search to improve solution diversity in highly constrained solution spaces. Results: The experimental study on the Adult and Bank Marketing datasets shows that PA-FRIM exhibits stable convergence behavior compared to competing methods. The results indicate that full privacy is achieved on the Adult dataset with a violation value of 0.00, and information loss is minimized with an NIL measure of 0.5686. From the analytical utility perspective, PA-FRIM ensures data usability, even in the constrained region, achieving classification accuracies of 89.61% on the Bank Marketing dataset and 84.90% on the Adult dataset. Conclusions: By using a multi-metric evaluation strategy, PA-FRIM provides a robust optimization framework that eliminates privacy violations while maintaining high analytical performance. Full article
(This article belongs to the Special Issue Studies of Symmetry and Asymmetry in Big Data)
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