Mathematical Methods and Applications in Signal Analysis, Machine Learning, and Artificial Intelligence

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

Deadline for manuscript submissions: 7 May 2026 | Viewed by 777

Special Issue Editor


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Guest Editor
Department of Electrical and Computer Engineering, Western Michigan University, 1903 W Michigan Ave, Kalamazoo, MI 49008-5329, USA
Interests: AI, machine learning, and computer vision; image and signal analysis and processing for feature extraction challenges and applications in medicine, autonomous vehicles, and intelligent transportation

Special Issue Information

Dear Colleagues,

The intersection of mathematical methods with signal analysis, machine learning (ML), and artificial intelligence (AI) represents a rapidly evolving and vital research area in contemporary science and technology. The rigorous application of mathematics not only forms the foundation of advancements in these fields but also plays a crucial role in enabling the development of robust algorithms and innovative solutions to complex problems.

We invite you to the Special Issue of Mathematical Methods in Signal Analysis, Machine Learning, and Artificial Intelligence. Your recent advancements and applications in mathematical methods used in signal analysis, machine learning (ML), and artificial intelligence (AI) are crucial to the progress of these fields. This Special Issue aims to provide a platform for you and other researchers and practitioners to present your work, showcasing the integration of mathematical techniques to address complex problems and enhance algorithm performance and accuracy.

The scope of this Special Issue includes, but is not limited to, the following topics:

1. Theoretical developments in signal processing and analysis.

2. Advances in AI driven by mathematical innovations.

3. Signal analysis applications in various domains, such as communications, image and video processing, and merging technologies, including edge computing, real-time processing, quantum signal processing, and AI.

4. AI-driven innovations in autonomous systems and robotics.

5. Optimization methods in ML and AI.

6. Statistical methods for data analysis and interpretation.

7. Hybrid models combining signal processing and AI techniques.

8. Case studies demonstrating the real-world impact of mathematical methods in ML and AI.

9. Computational techniques and software tools for signal analysis and ML.

Prof. Dr. Ikhlas M. Abdel-Qader
Guest Editor

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Keywords

  • mathematical methods
  • signal analysis
  • machine learning
  • artificial intelligence
  • time-frequency analysis
  • deep learning
  • image processing
  • feature extraction
  • pattern recognition
  • quantum signal processing

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Published Papers (1 paper)

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35 pages, 6971 KiB  
Article
Mathematical and Machine Learning Innovations for Power Systems: Predicting Transformer Oil Temperature with Beluga Whale Optimization-Based Hybrid Neural Networks
by Jingrui Liu, Zhiwen Hou, Bowei Liu and Xinhui Zhou
Mathematics 2025, 13(11), 1785; https://doi.org/10.3390/math13111785 - 27 May 2025
Viewed by 265
Abstract
Power transformers are vital in power systems, where oil temperature is a key operational indicator. This study proposes an advanced hybrid neural network model, BWO-TCN-BiGRU-Attention, to predict the top-oil temperature of transformers. The model was validated using temperature data from power transformers in [...] Read more.
Power transformers are vital in power systems, where oil temperature is a key operational indicator. This study proposes an advanced hybrid neural network model, BWO-TCN-BiGRU-Attention, to predict the top-oil temperature of transformers. The model was validated using temperature data from power transformers in two Chinese regions. It achieved MAEs of 0.5258 and 0.9995, MAPEs of 2.75% and 2.73%, and RMSEs of 0.6353 and 1.2158, significantly outperforming mainstream methods like ELM, PSO-SVR, Informer, CNN-BiLSTM-Attention, and CNN-GRU-Attention. In tests conducted in spring, summer, autumn, and winter, the model’s MAPE was 2.75%, 3.44%, 3.93%, and 2.46% for Transformer 1, and 2.73%, 2.78%, 3.07%, and 2.05% for Transformer 2, respectively. These results indicate that the model can maintain low prediction errors even with significant seasonal temperature variations. In terms of time granularity, the model performed well at both 1 h and 15 min intervals: for Transformer 1, MAPE was 2.75% at 1 h granularity and 2.98% at 15 min granularity; for Transformer 2, MAPE was 2.73% at 1 h granularity and further reduced to 2.16% at 15 min granularity. This shows that the model can adapt to different seasons and maintain good prediction performance with high-frequency data, providing reliable technical support for the safe and stable operation of power systems. Full article
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