Mathematical Applications and Computational Intelligence in Medicine and Biology

Abstract submission deadline

31 January 2026

Manuscript submission deadline

31 March 2026

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Topic Information

Dear Colleagues,

Mathematical modeling, triaged in an optimized and controlled manner through applicable computational methods with regard to uncertain phenomena, is pertinent for describing and computing complex behaviors, which entails the viable mathematical formulations and applications of neural dynamics across the temporal and spatial scales in medical and biological systems. Systems based on computational intelligence are capable of learning data, manifesting an evolutionary process based on the alterations in the environment while considering uncertainty, complexities and risks and ensuring the characterization of clinical, medical, synthetic, fragmented, omics, imaging, signal, sensor data, amongst other elements to improve the processing of a multitude of data in healthcare toward the development of timely and intelligent solutions to diagnose, prognose, treat, manage and analyze the course of complex dynamic diseases. Accordingly, computational analyses based on advanced mathematical models and computational intelligence, as biologically inspired computational algorithms, constituting the use of genetic algorithms, fuzzy systems, neural networks, probabilistic methods, and so forth, address the employment of computational techniques, methods and algorithms geared towards voluminous data analysis, control, as well as problem-solving in various realms spanning and progressing across advancing applied sciences, engineering applications and scientific research in which mathematical and computational analyses include computational models, simulations, predictions, numerical analyses, processes, etc.

The theory of mathematics and computational theory constitute the essential foundational structure for comprehending computation and its applications in medicine and biology along with their inherent intricate details to analyze the capabilities concerning computer systems, neural networks and algorithms, with relevant implications ranging across various disciplines including mathematics, natural sciences, life sciences, applied sciences, data science, computer science, engineering, bioengineering, etc.

Based on these considerations, mathematical applications and computational intelligence entail the techniques, algorithms, approaches and methods for customized problems and solutions so that mathematical and computational power can be enhanced to process data and conduct advanced implementations regarding real-world complex problems, diseases, biological, epidemiological, environmental factors and other conceivable elements by means of simulations, experimental and/or computational processes, numerical methods, optimization, control and so forth.

Potential topics include, but are not limited to, the following:

• Stochastic disease dynamics of complex networks
• Neural network complexity
• Computational intelligence in oncology
• Complex feedback circuitry
• Gene regulatory networks
• Advanced biomedical data analysis
• Predictive mathematical modeling in medicine/biology
• Medical / Clinical decision-support systems
• Systems biology and medicine
• Precision medicine
• Applied mathematics modeling in treatment regimens
• Soft computing with advanced applications for clinical purposes
• Fractional calculus and its applications in systems medicine and biology
• Difference and differential equations (PDEs, ODEs, FDEs, SDEs, and so on) with applications for diagnostics
• Multidrug-resistant treatment regiments
• Advanced mathematical methods in intelligence systems
• Mathematical modeling, control systems and optimization methods
• Chaos and nonlinear systems
• Fractal physiology and chaos in medicine/biology
• Image and/or signal processing
• Applied complexity, computational complexity and complex systems
• Applied statistics and probability
• Advances in computational modeling in engineering and science applications
• Application of advanced AI (machine learning, deep learning, quantum, and so forth)
• Artificial Intelligence of Things (AIoT) in medicine/biology

Prof. Dr. Yeliz Karaca
Prof. Dr. Majaz Moonis (MD)
Prof. Dr. Yudong Zhang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Algorithms algorithms	1.8	4.1	2008	18.9 Days	CHF 1600	Submit
AppliedMath appliedmath	-	-	2021	25.3 Days	CHF 1000	Submit
Mathematics mathematics	2.3	4.0	2013	18.3 Days	CHF 2600	Submit
Bioengineering bioengineering	3.8	4.0	2014	16.4 Days	CHF 2700	Submit
AI ai	3.1	7.2	2020	18.9 Days	CHF 1600	Submit

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Published Papers (2 papers)

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All Algorithms AppliedMath Mathematics Bioengineering AI

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19 pages, 2030 KiB  

Open AccessArticle

Non-Linear Synthetic Time Series Generation for Electroencephalogram Data Using Long Short-Term Memory Models

by Bakr Rashid Alqaysi, Manuel Rosa-Zurera and Ali Abdulameer Aldujaili

AI 2025, 6(5), 89; https://doi.org/10.3390/ai6050089 - 25 Apr 2025

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Abstract

Background/Objectives: The implementation of artificial intelligence-based systems for disease detection using biomedical signals is challenging due to the limited availability of training data. This paper deals with the generation of synthetic EEG signals using deep learning-based models, to be used in future research [...] Read more.

Background/Objectives: The implementation of artificial intelligence-based systems for disease detection using biomedical signals is challenging due to the limited availability of training data. This paper deals with the generation of synthetic EEG signals using deep learning-based models, to be used in future research for training Parkinson’s disease detection systems. Methods: Linear models, such as AR, MA, and ARMA, are often inadequate due to the inherent non-linearity of time series. To overcome this drawback, long short-term memory (LSTM) networks are proposed to learn long-term dependencies in non-linear EEG time series and subsequently generate synthetic signals to enhance the training of detection systems. To learn the forward and backward time dependencies in the EEG signals, a Bidirectional LSTM model has been implemented. The LSTM model was trained on the UC San Diego Resting State EEG Dataset, which includes samples from two groups: individuals with Parkinson’s disease and a healthy control group. Results: To determine the optimal number of cells in the model, we evaluated the mean squared error (MSE) and cross-correlation between the original and synthetic signals. This method was also applied to select the length of the hidden state vector. The number of hidden cells was set to 14, and the length of the hidden state vector for each cell was fixed at 4. Increasing these values did not improve MSE or cross-correlation and unnecessarily increased computational complexity. The proposed model’s performance was evaluated using the mean-squared error (MSE), Pearson’s correlation coefficient, and the power spectra of the synthetic and original signals, demonstrating the suitability of the proposed method for this application. Conclusions: The proposed model was compared to Autoregressive Moving Average (ARMA) models, demonstrating superior performance. This confirms that deep learning-based models, such as LSTM, are strong alternatives to statistical models like ARMA for handling non-linear, multifrequency, and non-stationary signals. Full article

(This article belongs to the Topic Mathematical Applications and Computational Intelligence in Medicine and Biology)

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27 pages, 2859 KiB  

Open AccessArticle

A New Multidimensional Computerized Testing Approach: On-the-Fly Assembled Multistage Adaptive Testing Based on Multidimensional Item Response Theory

by Jingwen Li, Jianan Sun, Mingyu Shao, Yinghui Lai and Chen Chen

Mathematics 2025, 13(4), 594; https://doi.org/10.3390/math13040594 - 11 Feb 2025

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Abstract

Unidimensional on-the-fly assembled multistage adaptive testing (OMST), a flexible testing method, integrates the strengths of the adaptive test assembly of computerized adaptive testing (CAT) and the modular test administration of multistage adaptive testing (MST). Since numerous latent trait structures in practical applications are [...] Read more.

Unidimensional on-the-fly assembled multistage adaptive testing (OMST), a flexible testing method, integrates the strengths of the adaptive test assembly of computerized adaptive testing (CAT) and the modular test administration of multistage adaptive testing (MST). Since numerous latent trait structures in practical applications are inherently multidimensional, extending the realm from unidimensional to multidimensional is necessary. Multidimensional item response theory (MIRT), a branch of mathematical and statistical latent variable modeling research, has an important position in the international testing field. Based on MIRT, this study proposes an approach of multidimensional OMST (OMST-M), and on-the-fly automated test assembly algorithms are proposed based on point estimation and confidence ellipsoid, respectively. OMST-M can effectively and flexibly measure multidimensional latent traits through stage-by-stage adaptive testing. The simulation results indicated that under different settings of latent trait structures, module lengths, and module contents, the OMST-M approach demonstrated good performance in terms of ability estimation accuracy and item exposure control. The empirical research revealed that the OMST-M approach was comparable to both multidimensional MST and CAT in ability estimation accuracy and exhibited remarkable flexibility in adjusting the length and content across its test stages. In summary, the proposed OMST-M features relatively high measurement accuracy, efficiency, convenient implementation, and practical feasibility. Full article

(This article belongs to the Topic Mathematical Applications and Computational Intelligence in Medicine and Biology)

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