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

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 1854

Special Issue Editors

Prof. Dr. Igor Aizenberg

E-Mail Website
Guest Editor
Department of Computer Science, Manhattan University, New York, NY 10471, USA
Interests: complex-valued neural networks; classification; pattern recognition; intelligent image processing; spectral techniques
Dr. Antonio Luchetta

E-Mail Website
Guest Editor
Department of Information Engineering (DINFO), University of Florence, 50139 Florence, Italy
Interests: circuit theory; neural networks; fault diagnosis of electronic circuits and symbolic analysis of analog circuits
Special Issues, Collections and Topics in MDPI journals
Dr. Marco Bindi

E-Mail Website
Guest Editor
Department of Information Engineering (DINFO), University of Florence, 50139 Florence, Italy
Interests: artificial intelligence algorithms; fault diagnosis of electrical power systems; symbolic analysis, simulation of analog circuits; monitoring of electrical distribution lines; Renewable Energy Communities (RECs)
Special Issues, Collections and Topics in MDPI journals
Dr. Matteo Intravaia

E-Mail Website
Guest Editor
Department of Information Engineering (DINFO), University of Florence, 50139 Florence, Italy
Interests: optimization; machine learning; electrical systems; power electronics

Special Issue Information

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

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.

Keywords

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

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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22 pages, 3570 KiB  
Article
High-Performance Computing and Parallel Algorithms for Urban Water Demand Forecasting
by Georgios Myllis, Alkiviadis Tsimpiris, Stamatios Aggelopoulos and Vasiliki G. Vrana
Algorithms 2025, 18(4), 182; https://doi.org/10.3390/a18040182 - 22 Mar 2025
Viewed by 353
Abstract
This paper explores the application of parallel algorithms and high-performance computing (HPC) in the processing and forecasting of large-scale water demand data. Building upon prior work, which identified the need for more robust and scalable forecasting models, this study integrates parallel computing frameworks [...] Read more.
This paper explores the application of parallel algorithms and high-performance computing (HPC) in the processing and forecasting of large-scale water demand data. Building upon prior work, which identified the need for more robust and scalable forecasting models, this study integrates parallel computing frameworks such as Apache Spark for distributed data processing, Message Passing Interface (MPI) for fine-grained parallel execution, and CUDA-enabled GPUs for deep learning acceleration. These advancements significantly improve model training and deployment speed, enabling near-real-time data processing. Apache Spark’s in-memory computing and distributed data handling optimize data preprocessing and model execution, while MPI provides enhanced control over custom parallel algorithms, ensuring high performance in complex simulations. By leveraging these techniques, urban water utilities can implement scalable, efficient, and reliable forecasting solutions critical for sustainable water resource management in increasingly complex environments. Additionally, expanding these models to larger datasets and diverse regional contexts will be essential for validating their robustness and applicability in different urban settings. Addressing these challenges will help bridge the gap between theoretical advancements and practical implementation, ensuring that HPC-driven forecasting models provide actionable insights for real-world water management decision-making. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithms for Prediction, Control, Classification, Regression, and Intelligent Signal Processing in Industry)
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11 pages, 877 KiB  
Article
Beyond Spectrograms: Rethinking Audio Classification from EnCodec’s Latent Space
by Jorge Perianez-Pascual, Juan D. Gutiérrez, Laura Escobar-Encinas, Álvaro Rubio-Largo and Roberto Rodriguez-Echeverria
Algorithms 2025, 18(2), 108; https://doi.org/10.3390/a18020108 - 16 Feb 2025
Viewed by 636
Abstract
This paper presents a novel approach to audio classification leveraging the latent representation generated by Meta’s EnCodec neural audio codec. We hypothesize that the compressed latent space representation captures essential audio features more suitable for classification tasks than the traditional spectrogram-based approaches. We [...] Read more.
This paper presents a novel approach to audio classification leveraging the latent representation generated by Meta’s EnCodec neural audio codec. We hypothesize that the compressed latent space representation captures essential audio features more suitable for classification tasks than the traditional spectrogram-based approaches. We train a vanilla convolutional neural network for music genre, speech/music, and environmental sound classification using EnCodec’s encoder output as input to validate this. Then, we compare its performance training with the same network using a spectrogram-based representation as input. Our experiments demonstrate that this approach achieves comparable accuracy to state-of-the-art methods while exhibiting significantly faster convergence and reduced computational load during training. These findings suggest the potential of EnCodec’s latent representation for efficient, faster, and less expensive audio classification applications. We analyze the characteristics of EnCodec’s output and compare its performance against traditional spectrogram-based approaches, providing insights into this novel approach’s advantages. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithms for Prediction, Control, Classification, Regression, and Intelligent Signal Processing in Industry)
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18 pages, 2326 KiB  
Article
Batch-to-Batch Optimization Control of Fed-Batch Fermentation Process Based on Recursively Updated Extreme Learning Machine Models
by Alex Moore and Jie Zhang
Algorithms 2025, 18(2), 87; https://doi.org/10.3390/a18020087 - 6 Feb 2025
Viewed by 831
Abstract
This paper presents a new method of batch-to-batch optimization control for a fed-batch fermentation process. A recursively updated extreme learning machine (ELM) neural network model is used to model a fed-batch fermentation process. ELM models have advantages over other neural networks in that [...] Read more.
This paper presents a new method of batch-to-batch optimization control for a fed-batch fermentation process. A recursively updated extreme learning machine (ELM) neural network model is used to model a fed-batch fermentation process. ELM models have advantages over other neural networks in that they can be trained very fast and have good generalization performance. However, the ELM model loses its predictive abilities in the presence of batch-to-batch process variations or disturbances, which lead to a process–model mismatch. The recursive least squares (RLS) technique takes the model prediction error from the previous batch and uses it to update the model parameters for the next batch. This improves the performance of the model and helps it to respond to any changes in process conditions or disturbances. The updated model is used in an optimization control procedure, which generates an improved control profile for the next batch. The update of the RLS model enables the optimization control strategy to maintain a high final product quality in the presence of disturbances. The proposed batch-to-batch optimization control method is demonstrated on a simulated fed-batch fermentation process. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithms for Prediction, Control, Classification, Regression, and Intelligent Signal Processing in Industry)
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