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 918
Special Issue Editor
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
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 100 words) can be sent to the Editorial Office for announcement on this website.
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
- 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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