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

Submission Deadline: 30 June 2025 View: 463 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

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

Guest Editors

Summary

Keywords

Published Papers

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181

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2

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