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Mathematics
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Advanced Machine Learning Analysis and Application in Data Science
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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: closed (30 April 2026) | Viewed by 1796

Special Issue Editor

Prof. Dr. Jiang Guo

E-Mail Website
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

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 250 words) can be sent to the Editorial Office for assessment.

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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

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

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Published Papers (2 papers)

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Research

19 pages, 1121 KB  
Article
Comparing ARIMA, Holt–Winters and TimeGPT Models for Municipal Water Consumption Forecasting: Evidence from Vouzela, Portugal
by Júlio Rocha, Salviano Soares, António Valente and Filipe Cabral Pinto
Mathematics 2026, 14(10), 1740; https://doi.org/10.3390/math14101740 - 19 May 2026
Abstract
This study presents a methodology for forecasting municipal water consumption to support efficient resource management. Using monthly data from 2018 to 2022 for the municipality of Vouzela, Portugal, three forecasting approaches were evaluated: SARIMA, Holt–Winters, and TimeGPT. Data preparation included logarithmic transformation and [...] Read more.
This study presents a methodology for forecasting municipal water consumption to support efficient resource management. Using monthly data from 2018 to 2022 for the municipality of Vouzela, Portugal, three forecasting approaches were evaluated: SARIMA, Holt–Winters, and TimeGPT. Data preparation included logarithmic transformation and stationarity assessment using the KPSS test, ensuring appropriate conditions for statistical modelling. The SARIMA model was selected automatically based on the Akaike Information Criterion (AIC), while the Holt–Winters method was fitted with additive components and a Box–Cox transformation. In addition, TimeGPT was employed as a state-of-the-art foundation model for time series forecasting. The three methods were used to predict water consumption for the 12 months of 2023, and their performance was assessed using MAE, MSE, RMSE and MAPE. Results indicate that although all methods perform adequately, Holt–Winters and TimeGPT better capture recent consumption dynamics, providing more accurate forecasts in several periods. Overall, this study shows that combining classical statistical models with advanced forecasting techniques offers local authorities reliable and computationally accessible tools to support water supply planning and sustainability. Full article
(This article belongs to the Special Issue Advanced Machine Learning Analysis and Application in Data Science)
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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
Cited by 1 | Viewed by 1370
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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