Application and Perspectives of Neural Networks

Dear Colleagues,

Neural networks have revolutionized the way complex data is modeled, interpreted, and acted upon across a vast array of disciplines. From early perceptrons to today’s deep and spiking architectures, advances in network design, training algorithms, and interpretability methods have unlocked new capabilities in image recognition, language understanding, time‑series forecasting, and control. At the same time, emerging application domains—edge‑and‑IoT systems, cybersecurity, cyber‑physical infrastructures, industrial automation, and financial services—pose stringent constraints on latency, power, security, and explainability. Addressing these challenges requires novel lightweight architectures, on‑device learning strategies, and robust, privacy‑preserving deployments. This Special Issue will convene state‑of‑the‑art research that both advances neural network theory and demonstrates high‑impact applications in constrained, security‑sensitive, and industrially relevant settings.

This Special Issue aims to showcase innovative research on neural network methodologies and their real‑world applications, aligning closely with the journal’s scope in both theoretical mathematics and applied computational modeling. We seek a balanced collection of original contributions—spanning foundational algorithmic developments, benchmark comparisons, and rigorous case studies—that illustrate how neural networks can be harnessed to solve complex problems under real‑world constraints. Our goal is to assemble at least ten high‑quality articles; should this threshold be met, the Issue may be consolidated into a standalone book volume. By focusing on both emerging trends and proven solutions, we will provide readers with a comprehensive roadmap for the future of neural network research and deployment.

In this Special Issue, we welcome both original research articles and comprehensive reviews. Suggested topics include, but are not limited to:

• Lightweight and low‑power neural architectures for edge and embedded inference
• On‑device continual and federated learning in IoT systems
• Neural‑network approaches to anomaly detection, intrusion prediction, and secure model deployment in cybersecurity and cyber‑physical systems
• Predictive maintenance, process optimization, and human–robot collaboration in industrial automation
• Neural‑driven financial models: algorithmic trading, credit scoring, risk forecasting, and fraud detection
• Neuromorphic hardware integration and accelerator‑aware network designs
• Hybrid neural–symbolic methods and explainable neural models
• Privacy‑preserving neural training (e.g., differential privacy, secure multiparty computation)
• Benchmark studies, performance evaluations, and comparative analyses

We look forward to receiving your contributions and to assembling a diverse and impactful collection that advances both the theory and practice of neural networks in modern application domains.

Dr. Ali Mehrabi
Guest Editor

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