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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 December 2024) | Viewed by 16910

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Prof. Dr. Emilio Soria-Olivas

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IDAL, Electronic Engineering Department, University of Valencia, Av. Universitat, SN, Burjassot, 46100 Valencia, Spain
Interests: deep learning applications
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Prof. Dr. Marcelino Martínez Sober

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IDAL, Electronic Engineering Department, University of Valencia, Av. Universitat, SN, Burjassot, 46100 Valencia, Spain
Interests: artificial intelligence applications
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Dr. Antonio José Serrano López

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IDAL, Electronic Engineering Department, University of Valencia, Av. Universitat, SN, Burjassot, 46100 Valencia, Spain
Interests: medical AI applications
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Dr. Juan Gómez-Sanchís

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IDAL, Electronic Engineering Department, University of Valencia, Av. Universitat, SN, Burjassot, 46100 Valencia, Spain
Interests: AI applications in agriculture
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Dr. Joan Vila-Francés

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IDAL, Electronic Engineering Department, University of Valencia, Av. Universitat, SN, Burjassot, 46100 Valencia, Spain
Interests: NLP applications
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Special Issue Information

Dear Colleagues,

AI technology has undergone significant development, attracting the attention of researchers in the industrial engineering community over the last decades. As such, this Special Issue of Applied Sciences focuses on the application of artificial intelligence (AI) in various sectors, including industry and society. Articles explore how AI is transforming industrial operations, enhancing efficiency and enabling advanced automation. This Special Issue offers a comprehensive view of the challenges and opportunities presented by AI in these crucial areas.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

Industrial and electrical automation;
Metal and food industry;
Data mining;
Artificial neural networks;
Industrial engineering;
Artificial intelligence.

Prof. Dr. Emilio Soria-Olivas
Prof. Dr. Marcelino Martínez Sober
Dr. Antonio José Serrano López
Dr. Juan Gómez-Sanchís
Dr. Joan Vila-Francés
Guest Editors

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Keywords

deep learning
reinforcement learning
visual data mining
advanced data analysis

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Published Papers (11 papers)

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Research

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19 pages, 7019 KiB

Open AccessArticle

Fault Detection Method Using Auto-Associative Shared Nearest Neighbor Kernel Regression for Industrial Processes

by Minseok Kim, Eunkyeong Kim, Seunghwan Jung, Baekcheon Kim, Jinyong Kim and Sungshin Kim

Appl. Sci. 2025, 15(5), 2251; https://doi.org/10.3390/app15052251 - 20 Feb 2025

Viewed by 303

Abstract

As industrial systems grow larger and more interconnected, timely fault detection is essential to minimize downtime, enhance reliability, and reduce costs. However, conventional methods focus on reactive maintenance, limiting their ability to detect faults before escalation. Additionally, fault propagation in large-scale systems can degrade detection performance. To address these challenges, we propose an auto-associative shared nearest neighbor kernel regression method for fault detection in complex industrial processes. Inspired by attention mechanisms, the proposed approach assigns higher weights to relevant training data. Shared nearest neighbor is used to assess similarity between faults and training data, rescaling distances accordingly. These adjusted distances are then utilized in auto-associative kernel regression for fault detection. The performance of the proposed method is evaluated by applying it to benchmark data from the Tennessee Eastman Process and a real-world, unplanned shutdown case concerning a circulating fluidized bed boiler. The experimental results show that the proposed method can detect anomalies up to 2 h earlier than conventional fault detection methods. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Industry)

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18 pages, 3792 KiB

Open AccessArticle

Dynamic Classifier Auditing by Unsupervised Anomaly Detection Methods: An Application in Packaging Industry Predictive Maintenance

by Fernando Mateo, Joan Vila-Francés, Emilio Soria-Olivas, Marcelino Martínez-Sober, Juan Gómez-Sanchis and Antonio José Serrano-López

Appl. Sci. 2025, 15(2), 882; https://doi.org/10.3390/app15020882 - 17 Jan 2025

Viewed by 645

Abstract

Predictive maintenance in manufacturing industry applications is a challenging research field. Packaging machines are widely used in a large number of logistic companies’ warehouses and must be working uninterruptedly. Traditionally, preventive maintenance strategies have been carried out to improve the performance of these machines. However, these kinds of policies do not take into account the information provided by the sensors implemented in the machines. This paper presents an expert system for the automatic estimation of work orders to implement predictive maintenance policies for packaging machines. The central innovation lies in a two-stage process: a classifier generates a binary decision on whether a machine requires maintenance, and an unsupervised anomaly detection module subsequently audits the classifier’s probabilistic output to refine and interpret its predictions. By leveraging the classifier to condense sensor data and applying anomaly detection to its output, the system optimizes the decision reliability. Three anomaly detection methods were evaluated: One-Class Support Vector Machine (OCSVM), Minimum Covariance Determinant (MCD), and a majority (hard) voting ensemble of the two. All anomaly detection methods improved the baseline classifier’s performance, with the majority voting ensemble achieving the highest F1 score. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Industry)

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21 pages, 816 KiB

Open AccessArticle

An Integrated Stacking Ensemble Model for Natural Gas Purchase Prediction Incorporating Multiple Features

by Junjie Wang, Lei Jiang, Le Zhang, Yaqi Liu, Qihong Yu and Yuheng Bu

Appl. Sci. 2025, 15(2), 778; https://doi.org/10.3390/app15020778 - 14 Jan 2025

Viewed by 693

Abstract

Accurate prediction of natural gas purchase volumes is crucial for both the economy and the environment. It not only facilitates the rational allocation of resources for companies but also helps to reduce operational costs. Although existing prediction methods have achieved some success in addressing the nonlinear relationships in natural gas purchases, there remains potential for further improvement. To address this issue, a stacking ensemble learning model was developed to enhance the ability to handle complex nonlinear problems. This model integrates diverse algorithms and incorporates weather factors, while regionalizing characteristics of natural gas usage, thereby achieving accurate forecasts of natural gas purchase volumes. We selected three distinctly different base models—Informer, multiple linear regression (MLR), and support vector regression (SVR)—for our research. By conducting four different feature combination experiments for each base model, including weather, time, regional, and usage features, we constructed 12 foundational models. Subsequently, we integrated these base models using a meta-learner to form the final stacking ensemble model. The experimental results indicate that the stacking ensemble model outperforms individual models across key metrics, including

R^{2}

, MRE, and RMSE. Notably, the

R^{2}

values improved by 4–15% compared to the 12 base models. The model was subsequently applied to predict natural gas purchase volumes in Pi County, Chengdu, China. In November 2024, a side-by-side comparison of the predicted and actual data revealed a maximum error of just 5.39%. This exceptional accuracy effectively meets forecasting requirements, underscoring the model’s predictive strength in the energy sector. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Industry)

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16 pages, 624 KiB

Open AccessArticle

Ensemble Modelling for Predicting Fish Mortality

by Theofanis Aravanis, Ioannis Hatzilygeroudis and Georgios Spiliopoulos

Appl. Sci. 2024, 14(15), 6540; https://doi.org/10.3390/app14156540 - 26 Jul 2024

Viewed by 1015

Abstract

This paper proposes a novel ensemble approach, integrating Artificial Neural Networks (ANNs), Symbolic Regression (SR), and Decision Trees (DTs), to predict fish mortality caused by infectious diseases. The intensifying global burden of fish diseases threatens the sustainability of aquatic ecosystems and the aquaculture industry, necessitating sophisticated modelling strategies for effective disease management and control. The proposed approach capitalizes on the non-linear data modelling strength of ANNs, the explanatory power of SR, and the decision-making efficiency of DTs, offering both predictive accuracy and interpretable insights. The architecture of the proposed ensemble method is developed in two stages. In the intermediate stage, an ANN is employed to learn the complex, non-linear interactions between various biological and environmental factors impacting fish health. Additionally, SR is applied to produce a symbolic equation that effectively maps the input variables to fish mortality rates. In the final stage, a DT model is included to enhance prediction performance by capturing decision rules from the data. This hybrid approach offers superior prediction performance while also revealing meaningful biological/environmental relationships that can guide preventive and reactive interventions in the management of fish health. We evaluate the developed models using extensive real-world datasets acquired from two large Greek fish-farming units, which encompass representative disease types. The results demonstrate that our ensemble approach significantly outperforms traditional standalone models developed in our recent previous work, achieving enhanced predictive accuracy, robustness, and interpretability. Overall, this research has far-reaching implications for improving disease predictions, facilitating optimal decision-making in aquaculture management, and contributing to the sustainability of global fish stocks. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Industry)

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15 pages, 2180 KiB

Open AccessArticle

Utilizing Selected Machine Learning Methods for Conicity Prediction in the Process of Producing Radial Tires for Passenger Cars

by Wojciech Majewski, Ewa Dostatni, Jacek Diakun, Dariusz Mikołajewski and Izabela Rojek

Appl. Sci. 2024, 14(15), 6393; https://doi.org/10.3390/app14156393 - 23 Jul 2024

Viewed by 1164

Abstract

This article presents the current state and development directions of the tire industry. One of the main requirements that a tire must meet before it can leave the factory is achieving values of quantities describing uniformity at a defined level. Of particular importance areconicity and the components of the tire with the greatest impact on its value. This research is based on the possibility of using an ANN to meet contemporary challenges faced by tire manufacturers. In order to achieve a satisfactory level of prediction, we compared the use of a multi-layer perceptron and decision trees XGBoost, LightGbmRegression, and FastTreeRegression. Based on data analysis and similar examples from the literature, metrics were selected to evaluate the models’ ability to solve regression problems in relation to the described problem. We selected the best possible solution, standing at the top of the features covered by the criterion analysis. The proposed solutions can be the basis for acquiring new knowledge and contributions in the field of the computational analysis of industrial data in tire production. These solutions are characterized by the required accuracy and efficiency for online work, and they also contribute to the creation of the best fit elements of complex systems (including computational models). The results of this study will contribute to reducing the volume of waste in the tire industry by eliminating defective tire parts in the early stages of the production process. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Industry)

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20 pages, 5321 KiB

Open AccessArticle

Strip Steel Defect Prediction Based on Improved Immune Particle Swarm Optimisation–Improved Synthetic Minority Oversampling Technique–Stacking

by Zhi Fang, Fan Zhang, Su Yu and Bintao Wang

Appl. Sci. 2024, 14(13), 5849; https://doi.org/10.3390/app14135849 - 4 Jul 2024

Cited by 1 | Viewed by 1103

Abstract

A model framework for the prediction of defects in strip steel is proposed with the objective of enhancing the accuracy of defect detection. Initially, the data are balanced through the utilisation of the Improved Synthetic Minority Oversampling Technique (ISmote), which is based on clustering techniques. Subsequently, further enhancements are made to the inertia weights and learning factors of the immune particle swarm optimisation (IPSO), with additional optimisations in speed updates and population diversity. These enhancements are designed to address the issue of premature convergence at the early stages of the process and local optima at the later stages. Finally, a prediction model is then constructed based on stacking, with its hyperparameters optimised through the improved immune particle swarm optimisation (IIPSO). The results of the experimental trials demonstrate that the IIPSO-ISmote-Stacking model framework exhibits superior prediction performance when compared to other models. The Macro_Precision, Macro_Recall, and Macro_F1 values for this framework are 93.3%, 93.6%, and 92.2%, respectively. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Industry)

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18 pages, 649 KiB

Open AccessArticle

Tool Wear Classification in Chipboard Milling Processes Using 1-D CNN and LSTM Based on Sequential Features

by Jarosław Kurek, Elżbieta Świderska and Karol Szymanowski

Appl. Sci. 2024, 14(11), 4730; https://doi.org/10.3390/app14114730 - 30 May 2024

Cited by 1 | Viewed by 1016

Abstract

The paper presents the comparative analysis of Long short-term memory (LSTM) and one-dimensional convolutional neural networks (1-D CNNs) for tool wear classification in chipboard milling processes. The complexity of sequence data in various fields makes selecting the right model for sequence classification very important. This research aims to show the distinct capabilities and performance nuances of LSTM and 1-D CNN models, leveraging their inherent strengths in understanding temporal dependencies and feature extraction, respectively. Through a series of experiments, the study unveils that while both models demonstrate competencies in handling sequence data, the 1-D CNN model, with its superior feature extraction capabilities, achieved the best performance, boasting an accuracy of 94.5% on the test dataset. The insights gained from this comparison not only help to understand LSTM and 1-D CNN models better, but also open the door for future improvements in using neural networks for complex sequence classification challenges. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Industry)

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15 pages, 8702 KiB

Open AccessArticle

Enhanced Scheduling of AI Applications in Multi-Tenant Cloud Using Genetic Optimizations

by Seokmin Kwon and Hyokyung Bahn

Appl. Sci. 2024, 14(11), 4697; https://doi.org/10.3390/app14114697 - 29 May 2024

Cited by 2 | Viewed by 1830

Abstract

The artificial intelligence (AI) industry is increasingly integrating with diverse sectors such as smart logistics, FinTech, entertainment, and cloud computing. This expansion has led to the coexistence of heterogeneous applications within multi-tenant systems, presenting significant scheduling challenges. This paper addresses these challenges by exploring the scheduling of various machine learning workloads in large-scale, multi-tenant cloud systems that utilize heterogeneous GPUs. Traditional scheduling strategies often struggle to achieve satisfactory results due to low GPU utilization in these complex environments. To address this issue, we propose a novel scheduling approach that employs a genetic optimization technique, implemented within a process-oriented discrete-event simulation framework, to effectively orchestrate various machine learning tasks. We evaluate our approach using workload traces from Alibaba’s MLaaS cluster with over 6000 heterogeneous GPUs. The results show that our scheduling improves GPU utilization by 12.8% compared to Round-Robin scheduling, demonstrating the effectiveness of the solution in optimizing cloud-based GPU scheduling. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Industry)

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19 pages, 2241 KiB

Open AccessArticle

Enhancing Security in Industrial Application Development: Case Study on Self-Generating Artificial Intelligence Tools

by Tomás de J. Mateo Sanguino

Appl. Sci. 2024, 14(9), 3780; https://doi.org/10.3390/app14093780 - 28 Apr 2024

Cited by 1 | Viewed by 2330

Abstract

The emergence of security vulnerabilities and risks in software development assisted by self-generated tools, particularly with regard to the generation of code that lacks due consideration of security measures, could have significant consequences for industry and its organizations. This manuscript aims to demonstrate how such self-generative vulnerabilities manifest in software programming, through a case study. To this end, this work undertakes a methodology that illustrates a practical example of vulnerability existing in the code generated using an AI model such as ChatGPT, showcasing the creation of a web application database, SQL queries, and PHP server-side. At the same time, the experimentation details a step-by-step SQL injection attack process, highlighting the hacker’s actions to exploit the vulnerability in the website’s database structure, through iterative testing and executing SQL commands to gain access to sensitive data. Recommendations on effective prevention strategies include training programs, error analysis, responsible attitude, integration of tools and audits in software development, and collaboration with third parties. As a result, this manuscript discusses compliance with regulatory frameworks such as GDPR and HIPAA, along with the adoption of standards such as ISO/IEC 27002 or ISA/IEC 62443, for industrial applications. Such measures lead to the conclusion that incorporating secure coding standards and guideline—from organizations such as OWASP and CERT training programs—further strengthens defenses against vulnerabilities introduced by AI-generated code and novice programming errors, ultimately improving overall security and regulatory compliance. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Industry)

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Review

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17 pages, 1064 KiB

Open AccessReview

Vocal Communication Between Cobots and Humans to Enhance Productivity and Safety: Review and Discussion

by Yuval Cohen, Maurizio Faccio and Shai Rozenes

Appl. Sci. 2025, 15(2), 726; https://doi.org/10.3390/app15020726 - 13 Jan 2025

Cited by 1 | Viewed by 897

Abstract

This paper explores strategies for fostering efficient vocal communication and collaboration between human workers and collaborative robots (cobots) in assembly processes. Vocal communication enables the division of attention of the worker, as it frees their visual attention and the worker’s hands, dedicated to the task at hand. Speech generation and speech recognition are pre-requisites for effective vocal communication. This study focuses on cobot assistive tasks, where the human is in charge of the work and performs the main tasks while the cobot assists the worker in various peripheral jobs, such as bringing tools, parts, or materials, and returning them or disposing of them, or screwing or packaging the products. A nuanced understanding is necessary for optimizing human–robot interactions and enhancing overall productivity and safety. Through a comprehensive review of the relevant literature and an illustrative example with worked scenarios, this manuscript identifies key factors influencing successful vocal communication and proposes practical strategies for implementation. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Industry)

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32 pages, 850 KiB

Open AccessReview

Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management

by Inmaculada Izquierdo-Bueno, Javier Moraga, Jesús M. Cantoral, María Carbú, Carlos Garrido and Victoria E. González-Rodríguez

Appl. Sci. 2024, 14(22), 10277; https://doi.org/10.3390/app142210277 - 8 Nov 2024

Cited by 2 | Viewed by 4007

Abstract

This review explores the transformative role of artificial intelligence (AI) in the entire winemaking process, from viticulture to bottling, with a particular focus on enhancing food safety and traceability. It discusses AI’s applications in optimizing grape cultivation, fermentation, bottling, and quality control, while emphasizing its critical role in managing microbiological risks such as mycotoxins. The review aims to show how AI technologies not only refine operational efficiencies but also raise safety standards and ensure traceability from vineyard to consumer. Challenges in AI implementation and future directions for integrating more advanced AI solutions into the winemaking industry will also be discussed, providing a comprehensive overview of AI’s potential to revolutionize traditional practices. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Industry)

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