Applied Fractional Calculus in Machine Learning and Biomedical Engineering
A special issue of Fractal and Fractional (ISSN 2504-3110).
Deadline for manuscript submissions: 31 October 2025 | Viewed by 347
Special Issue Editor
Interests: dynamical systems; systems and control theory; fractional calculus; computational intelligence; optimization; signal processing; machine learning; energy and power engineering; biomedical engineering; data science
Special Issue Information
Dear Colleagues,
The application of fractional calculus in machine learning and biomedical engineering is a novel and rapidly growing area of research. The non-integer-order differentiation and integration offered by FC allow for more accurate modelling of dynamical systems with memory and hereditary properties, which are common in biological systems and complex datasets.
The intersection of FC with ML and BME is an emerging field that promises to revolutionize the way we approach problems in data analysis, signal processing, biomedical system modelling, and control. This Special Issue will provide a comprehensive platform for researchers to present their latest theoretical advances, innovative applications, and practical implementations of FC in these domains.
This special issue aims to bring together cutting-edge research and developments in the application of fractional calculus (FC) to the fields of machine learning (ML) and biomedical engineering (BME). Fractional calculus, an extension of traditional integer-order calculus, offers a powerful framework for describing anomalous dynamics and complex systems. Its non-local and memory-preserving properties have shown significant potential in modelling and solving complex problems that are otherwise intractable with traditional integer-order methods.
- Theoretical advances in fractional calculus and their implications for ML and BME.
- Development of fractional-order algorithms for machine learning models.
- Application of FC in the design of neural networks, including deep learning and reinforcement learning.
- Fractional-order systems in biomedical signal processing and image analysis.
- Modelling of biological systems using fractional-order differential equations (FODEs).
- Fractional-order control systems in biomedical devices and robotics.
- Applications of fractional calculus in physiological modelling and bioinformatics.
- Challenges and future directions in the integration of FC with ML and BME.
The Special Issue targets a multidisciplinary audience that includes researchers, academicians, and professionals working in the fields of applied mathematics, machine learning, biomedical engineering, and systems biology. It will also be of interest to practitioners who are looking to apply fractional calculus methods to solve practical problems in ML and BME.
Dr. Saptarshi Das
Guest Editor
Manuscript Submission Information
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Keywords
- fractional-order machine learning algorithms
- application of fractional calculus in deep learning, including novel architectures and training methods
- fractional calculus in biomedical signal processing (EEG, ECG, MRI, etc.)
- modelling of biological phenomena using fractional differential equations
- fractional-order control systems in biomedical devices
- fractional calculus in the analysis of biomedical data and its integration with ML techniques
- comparative studies between integer-order and fractional-order methods in ML and BME
- practical challenges in applying FC to real-world problems in ML and BME
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