Innovative Applications of Fractional Modeling and AI for Real-World Problems

Submission Deadline: 01 March 2026 View: 2891 Submit to Special Issue

Guest Editors

Prof. Mehmet Yavuz, Department of Mathematics and Computer Sciences, Faculty of Science, Necmettin Erbakan University, Konya-42090, Turkey
Prof. Hardik Joshi, Department of Mathematics, LJ Institute of Engineering and Technology, LJ University, Ahmedabad, Gujarat-382210, India

Summary

Artificial Intelligence (AI) is transforming science and engineering by providing advanced tools and techniques to tackle complex problems. AI accelerates simulations in almost every field of science and engineering by predicting the outcomes of experiments and the behavior of complex systems. Optimizes the design of engineering systems and enhances the control of complex systems. AI is revolutionizing science and engineering by providing powerful tools for data analysis, automation, simulation, and innovation. Fractional modeling, optimal control, and adaptive control are advanced concepts in control theory and modeling in engineering and science problems. Fractional order modeling and optimal control handle systems with memory effects and achieve optimal performance. This involves defining an optimal control problem where the system dynamics are described by fractional differential equations. Combines fractional modeling, optimal and adaptive control, and advanced AI techniques represent cutting-edge approaches in control theory, offering sophisticated tools to address complex dynamic systems.

We welcome original research that is devoted to studying the problems of natural science, computer and mathematics modeling, and real-world application of science and engineering by combining fractional modeling, optimal and adaptive control, and advanced AI techniques.

Keywords

Artificial intelligence
AI-inspired computer modeling
Deep learning models
Neural networks models
Machine learning
Modeling fractional differential equations
Numerical analysis for differential equations
Control theory
Nonlinear dynamics
Advanced computational intelligence models
State-of-the-art multiscale model
Machine learning and deep learning techniques in computer modeling
Modeling, optimization, simulation, and control of real-world problem

Published Papers

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ARTICLE

A Hybrid Machine Learning and Fractional-Order Dynamical Framework for Multi-Scale Prediction of Breast Cancer Progression
David Amilo, Khadijeh Sadri, Evren Hincal, Mohamed Hafez
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2189-2222, 2025, DOI:10.32604/cmes.2025.070298
（This article belongs to the Special Issue: Innovative Applications of Fractional Modeling and AI for Real-World Problems)
Abstract Breast cancer’s heterogeneous progression demands innovative tools for accurate prediction. We present a hybrid framework that integrates machine learning (ML) and fractional-order dynamics to predict tumor growth across diagnostic and temporal scales. On the Wisconsin Diagnostic Breast Cancer dataset, seven ML algorithms were evaluated, with deep neural networks (DNNs) achieving the highest accuracy (97.72%). Key morphological features (area, radius, texture, and concavity) were identified as top malignancy predictors, aligning with clinical intuition. Beyond static classification, we developed a fractional-order dynamical model using Caputo derivatives to capture memory-driven tumor progression. The model revealed clinically interpretable patterns: More >

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ARTICLE

Systematic Analysis of Latent Fingerprint Patterns through Fractionally Optimized CNN Model for Interpretable Multi-Output Identification
Mubeen Sabir, Zeshan Aslam Khan, Muhammad Waqar, Khizer Mehmood, Muhammad Junaid Ali Asif Raja, Naveed Ishtiaq Chaudhary, Khalid Mehmood Cheema, Muhammad Asif Zahoor Raja, Muhammad Farhan Khan, Syed Sohail Ahmed
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 807-855, 2025, DOI:10.32604/cmes.2025.068131
（This article belongs to the Special Issue: Innovative Applications of Fractional Modeling and AI for Real-World Problems)
Abstract Fingerprint classification is a biometric method for crime prevention. For the successful completion of various tasks, such as official attendance, banking transactions, and membership requirements, fingerprint classification methods require improvement in terms of accuracy, speed, and the interpretability of non-linear demographic features. Researchers have introduced several CNN-based fingerprint classification models with improved accuracy, but these models often lack effective feature extraction mechanisms and complex multineural architectures. In addition, existing literature primarily focuses on gender classification rather than accurately, efficiently, and confidently classifying hands and fingers through the interpretability of prominent features. This research seeks to… More >

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ARTICLE

Dynamic Multi-Graph Spatio-Temporal Graph Traffic Flow Prediction in Bangkok: An Application of a Continuous Convolutional Neural Network
Pongsakon Promsawat, Weerapan Sae-dan, Marisa Kaewsuwan, Weerawat Sudsutad, Aphirak Aphithana
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 579-607, 2025, DOI:10.32604/cmes.2024.057774
（This article belongs to the Special Issue: Innovative Applications of Fractional Modeling and AI for Real-World Problems)
Abstract The ability to accurately predict urban traffic flows is crucial for optimising city operations. Consequently, various methods for forecasting urban traffic have been developed, focusing on analysing historical data to understand complex mobility patterns. Deep learning techniques, such as graph neural networks (GNNs), are popular for their ability to capture spatio-temporal dependencies. However, these models often become overly complex due to the large number of hyper-parameters involved. In this study, we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks (DMST-GNODE), a framework based on ordinary differential equations (ODEs) that autonomously discovers effective spatial-temporal… More >

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831
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ARTICLE

Global Piecewise Analysis of HIV Model with Bi-Infectious Categories under Ordinary Derivative and Non-Singular Operator with Neural Network Approach
Ghaliah Alhamzi, Badr Saad T. Alkahtani, Ravi Shanker Dubey, Mati ur Rahman
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 609-633, 2025, DOI:10.32604/cmes.2024.056604
（This article belongs to the Special Issue: Innovative Applications of Fractional Modeling and AI for Real-World Problems)
Abstract This study directs the discussion of HIV disease with a novel kind of complex dynamical generalized and piecewise operator in the sense of classical and Atangana Baleanu (AB) derivatives having arbitrary order. The HIV infection model has a susceptible class, a recovered class, along with a case of infection divided into three sub-different levels or categories and the recovered class. The total time interval is converted into two, which are further investigated for ordinary and fractional order operators of the AB derivative, respectively. The proposed model is tested separately for unique solutions and existence on… More >

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Innovative Applications of Fractional Modeling and AI for Real-World Problems

Guest Editors

Summary

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196

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609

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1619

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937

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