Artificial Intelligence Algorithms for Prediction, Control, Classification, Regression, and Intelligent Signal Processing in Industry

Dear Colleagues,

In recent years, the application of Artificial Intelligence (AI), Computational Intelligence (CI), and Machine Learning (ML) algorithms has significantly transformed industrial practices, driving advancements in predictive analytics, intelligent control, classification, and regression techniques.

We invite contributions that focus on the use of AI for the prediction of time series and critical variables relevant to specific industrial sectors. Time series forecasting plays a crucial role in optimizing processes, anticipating maintenance needs, and enhancing decision-making through data-driven insights. We welcome research that showcases novel ML models and deep learning architectures for accurate and efficient forecasting in dynamic industrial environments.

Additionally, this Special Issue will focus on AI-driven classification techniques to assess the health status of devices and equipment. This includes studies that implement diagnostic algorithms to identify faults, predict potential failures, and recommend corrective actions. Papers highlighting advancements in anomaly detection and predictive maintenance strategies are of particular interest.

Regression analysis using AI algorithms is another key area of this Special Issue, where researchers are encouraged to share their findings on modeling and predicting system behavior under varying conditions. Emphasis will be placed on methods that improve precision and reliability in estimating continuous outcomes for industrial processes.

Intelligent signal processing has become a popular tool for solving various problems. Signal and image filtering using artificial neural networks, solving various pattern recognition and classification problems using intelligent signal analysis (image recognition, in particular), are just some of the applications which have been rapidly developed during the last decade. Papers devoted to new algorithmic solutions in signal and image processing using neural networks are of particular interest.

Finally, this Special Issue will cover the intelligent control of industrial tools and processes. Contributions should address how AI can optimize control strategies, enhance automation, and ensure adaptability in response to evolving production demands. We are particularly interested in control solutions that integrate reinforcement learning, adaptive systems, and real-time data processing.

This Special Issue covers, but is not limited to, the following topics:

• Parametric fault diagnosis in the industrial sector;
• Catastrophic fault prevention;
• Prediction of non-measurable quantities;
• Smart metering and soft computing techniques applied to the intelligent control of industrial processes;
• Regression and classification techniques of complex systems;
• Maintenance, fault prevention, fault resolution, and fault-tolerant approaches;
• Non-intrusive monitoring techniques;
• CAD and simulation techniques oriented to fault diagnosis;
• Intelligent signal and image processing and recognition.

This Special Issue aims to bring together researchers, practitioners, and industry experts who are pushing the boundaries of AI applications in industrial settings. We seek to create a comprehensive platform that fosters knowledge exchange, stimulates innovation, and drives the adoption of AI solutions for industrial challenges.

Prof. Dr. Igor Aizenberg
Dr. Antonio Luchetta
Dr. Marco Bindi
Dr. Matteo Intravaia
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• AI-based system control
• industrial computational intelligence
• intelligent signal and image processing
• time series forecasting
• fault classification
• regression of complex systems