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Advances in Artificial Intelligence Algorithms for Anomaly Detection and Pattern...
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Advances in Artificial Intelligence Algorithms for Anomaly Detection and Pattern Recognition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 1 January 2027 | Viewed by 760

Editor

Dr. Karl O. Jones

E-Mail Website
Guest Editor
Applied Forensic Technology Research Group, School of Engineering, Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool L3 3AF, UK
Interests: audio forensics; video forensics; deepfakes and the law; optimisation; artificial intelligence

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) continues to revolutionise the way we analyse, interpret, and respond to complex data patterns. AI offers new tools for numerous areas such as, but not limited to, cyber security, fault detection, healthcare diagnostics, financial modelling and forensic analysis. This Special Issue aims to bring together cutting-edge research and innovative applications in the development of AI algorithms for anomaly detection and pattern recognition across diverse domains.

We invite original research articles, reviews, case studies, and technical notes that address the development, deployment, and evaluation of AI methods in forensic contexts. Authors should submit original, unpublished work that is not currently under review elsewhere.

Join us in shaping the future of forensic investigation through intelligent technologies. We look forward to your contributions to this timely and impactful Special Issue.

Topics include, but are not limited to, the following:

  • Biometric identification (e.g., face, voice, gait etc.);
  • Explainable AI (XAI) in decision-making;
  • Cybersecurity and fraud detection;
  • Industrial fault detection and predictive maintenance;
  • Healthcare diagnostics and medical imaging;
  • Financial anomaly detection and risk modelling;
  • Smart cities and IoT sensor data analysis;
  • Remote sensing and environmental monitoring;
  • Bioinformatics and genomics;
  • Forensic evidence analysis;
  • Image and video analysis;
  • Deepfake detection.

Dr. Karl O. Jones
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-anonymized 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

  • feature extraction
  • dimensionality reduction
  • clustering algorithms
  • artificial intelligence in forensics
  • forensic AI technologies
  • multimedia forensic analysis
  • feature extraction
  • dimensionality reduction
  • clustering algorithms
  • biometric identification

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Published Papers (1 paper)

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Research

32 pages, 1929 KB  
Article
Sequential Multiple Concept Drifts and Change Point Detection for Regression Problems
by Edgard M. Maboudou-Tchao and Randyll Pandohie
Mathematics 2026, 14(12), 2116; https://doi.org/10.3390/math14122116 - 13 Jun 2026
Viewed by 235
Abstract
This research advances the study of learning under non-stationary conditions by proposing a unified framework for concept drift detection and adaptive regression in evolving data streams. Unlike traditional batch models that assume static data distributions, the proposed approach operates sequentially, enabling real-time adaptation [...] Read more.
This research advances the study of learning under non-stationary conditions by proposing a unified framework for concept drift detection and adaptive regression in evolving data streams. Unlike traditional batch models that assume static data distributions, the proposed approach operates sequentially, enabling real-time adaptation to drifting concepts in both time series and regression tasks. The method integrates Least Squares Support Vector Regression (LS-SVR) with Least Squares Support Vector Data Description (LS-SVDD) to jointly perform prediction and drift monitoring within a single kernel-based structure. LS-SVDD serves as a distributional drift detector, while LS-SVR incrementally updates model parameters to maintain predictive accuracy as data evolves. The framework accommodates both abrupt and gradual drifts, making it suitable for dynamic, high-dimensional environments. Experimental evaluations on synthetic data show that this proposal is able to outperform conventional batch and static methods in accuracy, responsiveness and computational efficiency. This method was compared using a real-world dataset, namely the high-dimensional Drosophila microarray time series, to demonstrate that the proposed approach is able to detect the meaningful change points using the whole data which is not doable using existing methods. Existing methods only used subsets of the dataset. These results highlight the potential of LS-SVR and LS-SVDD integration for real-time, adaptive learning across diverse domains where data distributions change over time. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence Algorithms for Anomaly Detection and Pattern Recognition)
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