Data-Driven Artificial Intelligence and Optimization for Real-World Applications
A special issue of Mathematics (ISSN 2227-7390).
Deadline for manuscript submissions: 30 June 2026 | Viewed by 91
Special Issue Editor
Interests: deep learning; computer vision; intelligent driving; anomaly detection; biomedical signal analysis; remote sensing image processing and analysis
Special Issue Information
Dear Colleagues,
In the era of pervasive data and intelligent connectivity, the integration of data-driven artificial intelligence with optimization and control strategies is revolutionizing system perception, reasoning, and decision-making. This Special Issue aims to showcase advances in AI models and optimization frameworks that combine deep learning, predictive control, graph-based reasoning, and multimodal data fusion for real-world applications such as autonomous driving, precision agriculture, and remote sensing.
We invite contributions focused on theoretical innovation and practical deployment, particularly those leveraging tools such as moving horizon strategies, joint chance-constrained optimization, self-organizing maps (SOMs), and attention-based neural representations. Topics include anomaly detection, real-time perception, spatiotemporal prediction, and decision-making under uncertainty.
Topics of Interest
- Data-driven deep learning for real-time inference.
- Fault diagnosis using sparse or noisy inputs.
- Predictive control with joint constraints.
- Graph neural networks and self-organizing maps.
- Attention-based multimodal fusion and masking.
- Optimization for TSP, scheduling, and adaptive planning.
- Hyperspectral image analysis and wearable sensing.
This Special Issue provides a multidisciplinary platform bridging machine learning, operations research, remote sensing, and control engineering. We welcome work that is robust, scalable, cross-domain, and interpretable, driving forward the intelligent AI-powered optimization landscape.
Dr. Xing Hu
Guest Editor
Manuscript Submission Information
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Keywords
- data-driven artificial intelligence
- predictive control
- anomaly detection
- optimization strategies
- graph neural networks (GNNs)
- multimodal sensor fusion
- moving horizon estimation
- self-organizing maps (SOMs)
- attention mechanisms
- spatiotemporal modeling
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