Advanced Machine Learning Analysis and Application in Data Science

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 448

Special Issue Editor


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Guest Editor
Department of Computer Science, California State University, Los Angeles, CA 90032, USA
Interests: machine learning; artificial intelligence; data science; software engineering

Special Issue Information

Dear Colleagues,

Machine learning has become a highly prominent research domain, driven by its extensive application across a wide range of disciplines and communities. As an interdisciplinary field that integrates computer science and mathematics, machine learning offers powerful tools for analyzing large volumes of data, enabling accurate predictions and uncovering complex relationships within diverse datasets.

This Special Issue seeks to publish high-quality review articles, original research papers, and communications that present novel methodologies, applications, data analyses, and case studies. Submissions that explore both theoretical developments and practical implementations of machine learning techniques are encouraged. Topics of interest include, but are not limited to, applications in computer science, economics, industry, medicine, environmental science, finance, education, engineering, marketing, and agriculture.

Prof. Dr. Jiang Guo
Guest Editor

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Keywords

  • machine learning
  • data analysis
  • predictive modeling
  • interdisciplinary applications

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Published Papers (1 paper)

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Research

25 pages, 2139 KB  
Article
MIDS-GAN: Minority Intrusion Data Synthesizer GAN—An ACON Activated Conditional GAN for Minority Intrusion Detection
by Chalerm Klinkhamhom, Pongsarun Boonyopakorn and Pongpisit Wuttidittachotti
Mathematics 2025, 13(21), 3391; https://doi.org/10.3390/math13213391 - 24 Oct 2025
Viewed by 307
Abstract
Intrusion Detection Systems (IDS) are vital to cybersecurity but suffer from severe class imbalance in benchmark datasets such as NSL-KDD and UNSW-NB15. Conventional oversampling methods (e.g., SMOTE, ADASYN) are efficient yet fail to preserve the latent semantics of rare attack behaviors. This study [...] Read more.
Intrusion Detection Systems (IDS) are vital to cybersecurity but suffer from severe class imbalance in benchmark datasets such as NSL-KDD and UNSW-NB15. Conventional oversampling methods (e.g., SMOTE, ADASYN) are efficient yet fail to preserve the latent semantics of rare attack behaviors. This study introduces the Minority-class Intrusion Detection Synthesizer GAN (MIDS-GAN), a divergence-minimization framework for minority data augmentation under structured feature constraints. MIDS-GAN integrates (i) correlation-based structured feature selection (SFS) to reduce redundancy, (ii) trainable ACON activations to enhance generator expressiveness, and (iii) KL-divergence-guided alignment to ensure distributional fidelity. Experiments on NSL-KDD and UNSW-NB15 demonstrate significant improvement on detection, with recall increasing from 2% to 27% for R2L and 1% to 17% for U2R in NSL-KDD, and from 18% to 44% for Worms and 69% to 75% for Shellcode in UNSW-NB15. Weighted F1-scores also improved to 78%, highlighting MIDS-GAN’s effectiveness in enhancing minority-class detection through a principled, divergence-aware approach. Full article
(This article belongs to the Special Issue Advanced Machine Learning Analysis and Application in Data Science)
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