Aims & Scope

The Journal of Computational and Applied Mathematics publishes original papers of high scientific value in all areas of computational and applied mathematics. The main interest of the Journal is in papers that describe and analyze new computational techniques for solving scientific or engineering problems. Also the improved analysis, including the effectiveness and applicability, of existing methods and algorithms is of importance. The computational efficiency (e.g. the convergence, stability, accuracy, ...) should be proved and illustrated by nontrivial numerical examples. Papers describing only variants of existing methods, without adding significant new computational properties are not of interest.

The audience consists of: applied mathematicians, numerical analysts, computational scientists and engineers

This journal has an Open Archive. All published items, including research articles, have unrestricted access and will remain permanently free to read and download 48 months after publication. All papers in the Archive are subject to Elsevier's user license.

Special Issue on Advanced Mathematics of Artificial Intelligence & Biomedical Applications
Submission Date: 2026-05-30

The first thoughts on AI can be traced back to the Turing test. In 1950, Alan Turing, who published “Computing Machinery and Intelligence” (Mind, 49, 1950, pp. 433–460), posed the famous question: “Can automatic calculating machines be said to think?”

The current delay differential equations (DDEs) and fractional-order derivatives are now advanced techniques whose origins date back many years. The DDEs can be traced to Siméon-Denis Poisson (1781–1840) in his 1806 memorandum. The concept of a “non-integer derivative” was first mentioned by Leibniz in a letter dated September 30, 1695, to the French mathematician Guillaume de l’Hospital. A meeting dedicated to this topic took place in 1974 in New Haven.

AI learning techniques also borrow from earlier factor analysis and automatic classification. The complexity of contemporary problems, the uncertainties inherent in behaviors and environments, and the production of massive amounts of data have led to more suitable techniques such as machine learning (ML) and deep learning (DL) in the 2000s, and generative AI in the 2010s.

The advanced mathematics that we regard as foundational to AI include partial differential equations (PDEs), variational approaches, fuzzy sets and systems, vector optimization, tensors, graphs and hypergraphs of complexity, quantum calculus, and game theory. Graph theory and game theory have contributed to the development of biomedical applications discussed in this special issue.

Graphs enable in-depth analysis of ECG and EEG signals, allowing for the detection of pathological anomalies. Mathematical games can represent the competition between tumor and immune cells. Anticipating tumor evolution facilitates medical prognosis and therapy design.

We invite authors to contribute high-quality research to these promising directions that combine advanced mathematics and AI for the benefit of biomedical performance aimed at improving diagnosis, prognosis, and therapies.

Journal editor:

Dr. André A. Keller, University Paris 1 Panthéon-Sorbonne, SAMM (EA4543), Paris, France.

Guest editors:

Dr. Nguyen Van Hung, nvhung@ptit.edu.vn 
Dr. Jen-Chih Yao, National Sun Yat-sen University
Dr. Christiane Tammer, Martin Luther University Halle Wittenberg
Dr. Xiaolong Qin, Hangzhou Normal University
Dr. Hao-Ren Yao, Carnegie Mellon University

Manuscript submission information:

Full scope of the special issue is to present a current state of the art and trends of present research papers on “Advanced Mathematics of Artificial Intelligence & Biomedical Applications”. The topics indicated for illustration include but are not limited to:

● Organoid intelligence of lab-grown mini-brains to learn with AI and compute;
● Brain-computer interface (BCI) for neurological monitoring and therapy;
● Automatic medical diagnosis of diseases with AI machine learning;
● Fuzzy expert systems in medical diagnosis;
● Computer-aided fuzzy medical diagnosis;
● Pattern recognition to medical image analysis;
● Bayesian method to update disease occurrence probability;
● Fractional-order differential equations for epidemic modeling;
● Delay differential equations (DDEs) for modeling diffusion processes in drug pharmacokinetics;
● Feature extraction from ECG (or EEG) signals for structure and amplitude dynamics;
● Graph neural networks (GNNs) to learn from pathological ECG (or EEG) graph structures;
● Deep learning on protein-protein interaction (PPI) to drug target prediction;
● Connectivity pattern and topology of PPI networks to diagnostics, prognostics, and therapeutic designs;
● Brain connectivity graphs in neurological disorders diagnosis;
● Evolutionary games with deep learning in healthcare;
● Tumor-immune dynamics as a competitive game to guide immunotherapy strategies;
● Integrating AI into game-theoretic healthcare models for clinical decisions;

Important date:

    Submission Open Date: October 30, 2025
    Final Manuscript Submission Deadline: May 30, 2026.
    Editorial Acceptance Deadline: August 30, 2026.

Manuscripts should be submitted online through the Editorial Manager (EM) submission system at the following link: https://www.editorialmanager.com/elscam/default.aspx

Authors must select VSI:CAM_Math AI & Biomed Apps when they reach the “Article Type” step in the online submission process. Submitted manuscripts should present original contributions and must not have been previously published nor be currently under consideration for publication elsewhere. All papers will be peer reviewed according to the high standards of the Journal of Computational and Applied Mathematics.

Keywords:

    Artificial Intelligence in Biomedical Engineering; Brain–Computer Interface (BCI); Medical Image Analysis and Pattern Recognition; Fuzzy Logic and Expert Systems in Medicine; Bayesian Inference in Disease Modeling; Fractional and Delay Differential Equations in Biomedicine; Graph Neural Networks for Biomedical Data; Deep Learning for Protein–Protein Interaction Networks; Game Theory and Evolutionary Dynamics in Healthcare; Organoid Intelligence and Computational Neuroscience.