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Algorithms
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Machine Learning Algorithms and Optimization in the Digital Transition (2nd...
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Machine Learning Algorithms and Optimization in the Digital Transition (2nd Edition)

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1419

Special Issue Editors

Dr. Mateus Mendes

E-Mail Website
Guest Editor
1. Coimbra Institute of Engineering, Polytechnic University of Coimbra, Rua Pedro Nunes-Quinta da Nora, 3030-199 Coimbra, Portugal
2. RCM2+ Research Centre for Asset Management and Systems Engineering, Polytechnic University of Coimbra, Rua Pedro Nunes, 3030-199 Coimbra, Portugal
Interests: machine learning; artificial intelligence; computer vision; data mining
Special Issues, Collections and Topics in MDPI journals
Dr. Balduíno Mateus

E-Mail Website
Guest Editor Assistant
1. Faculty of Engineering, Lusófona University, 1749-024 Lisboa, Portugal
2. Research Centre in Asset Management and Systems Engineering, RCM2+ Lusófona University, CampoGrande, 376, 1749-024 Lisboa, Portugal
Interests: production optimization; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Nuno Lavado

E-Mail Website
Guest Editor Assistant
1. Coimbra Institute of Engineering, Polytechnic University of Coimbra, Rua Pedro Nunes-Quinta da Nora, 3030-199 Coimbra, Portugal
2. RCM2+ Research Centre for Asset Management and Systems Engineering, Polytechnic University of Coimbra, Rua Pedro Nunes, 3030-199 Coimbra, Portugal
Interests: machine learning; artificial intelligence; computer vision; NLP; statistics

Special Issue Information

Dear Colleagues,

To optimize and manage modern industrial systems as well as facilities, many factors must be taken into account. Modern technologies in all sectors of activity depend on large amounts of sensors and data that facilitate multivariable analyses via algorithms to support decision making in the short, middle, and long terms. Classical and deep learning machine models have boosted the capacity to analyze large volumes of data and extract patterns that greatly contribute to informed decisions, making intelligent systems more prevalent and an important part of all organizations.

Industries and large institutions are always concerned about adjusting capacity and minimizing costs, in order to meet demand without delays or the excessive use of resources. This drives research focusing on models that can provide consistent support in decision-making processes. Prediction techniques based on time series models and artificial intelligence are being used more frequently to meet these challenges and contribute to more informed decisions.

This Special Issue aims to cover the latest research so decisions made based on the proposed algorithms are sound and adequate and contribute to facilitate management as well as operational decisions. Original contributions on the above aspects, and related topics, are encouraged.

Dr. Mateus Mendes
Guest Editor

Dr. Balduíno Mateus
Dr. Nuno Lavado
Guest Editor Assistant

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

  • asset management
  • clustering
  • data analysis
  • data mining
  • decision-support systems
  • deep learning
  • fault detection
  • knowledge-based systems
  • machine learning
  • object detection
  • optimization
  • predictive maintenance
  • time series

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

Related Special Issue

Published Papers (5 papers)

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Research

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27 pages, 6303 KiB  
Article
Detecting and Analyzing Botnet Nodes via Advanced Graph Representation Learning Tools
by Alfredo Cuzzocrea, Abderraouf Hafsaoui and Carmine Gallo
Algorithms 2025, 18(5), 253; https://doi.org/10.3390/a18050253 - 26 Apr 2025
Viewed by 158
Abstract
Private consumers, small businesses, and even large enterprises are all at risk from botnets. These botnets are known for spearheading Distributed Denial-Of-Service (DDoS) attacks, spamming large populations of users, and causing critical harm to major organizations. The development of Internet of Things (IoT) [...] Read more.
Private consumers, small businesses, and even large enterprises are all at risk from botnets. These botnets are known for spearheading Distributed Denial-Of-Service (DDoS) attacks, spamming large populations of users, and causing critical harm to major organizations. The development of Internet of Things (IoT) devices led to the use of these devices for cryptocurrency mining, in-transit data interception, and sending logs containing private data to the master botnet. Different techniques were developed to identify these botnet activities, but only a few use Graph Neural Networks (GNNs) to analyze host activity by representing their communications with a directed graph. Although GNNs are intended to extract structural graph properties, they risk causing overfitting, which leads to failure when attempting to do so from an unidentified network. In this study, we test the notion that structural graph patterns might be used for efficient botnet detection. In this study, we also present SIR-GN, a structural iterative representation learning methodology for graph nodes. Our approach is built to work well with untested data, and our model is able to provide a vector representation for every node that captures its structural information. Finally, we demonstrate that, when the collection of node representation vectors is incorporated into a neural network classifier, our model outperforms the state-of-the-art GNN-based algorithms in the detection of bot nodes within unknown networks. Full article
(This article belongs to the Special Issue Machine Learning Algorithms and Optimization in the Digital Transition (2nd Edition))
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22 pages, 4631 KiB  
Article
ChurnKB: A Generative AI-Enriched Knowledge Base for Customer Churn Feature Engineering
by Maryam Shahabikargar, Amin Beheshti, Wathiq Mansoor, Xuyun Zhang, Eu Jin Foo, Alireza Jolfaei, Ambreen Hanif and Nasrin Shabani
Algorithms 2025, 18(4), 238; https://doi.org/10.3390/a18040238 - 21 Apr 2025
Viewed by 304
Abstract
Customers are the cornerstone of business success across industries. Companies invest significant resources in acquiring new customers and, more importantly, retaining existing ones. However, customer churn remains a major challenge, leading to substantial financial losses. Addressing this issue requires a deep understanding of [...] Read more.
Customers are the cornerstone of business success across industries. Companies invest significant resources in acquiring new customers and, more importantly, retaining existing ones. However, customer churn remains a major challenge, leading to substantial financial losses. Addressing this issue requires a deep understanding of customers’ cognitive status and behaviours, as well as early signs of churn. Predictive and Machine Learning (ML)-based analysis, when trained with appropriate features indicative of customer behaviour and cognitive status, can be highly effective in mitigating churn. A robust ML-driven churn analysis depends on a well-developed feature engineering process. Traditional churn analysis studies have primarily relied on demographic, product usage, and revenue-based features, overlooking the valuable insights embedded in customer–company interactions. Recognizing the importance of domain knowledge and human expertise in feature engineering and building on our previous work, we propose the Customer Churn-related Knowledge Base (ChurnKB) to enhance feature engineering for churn prediction. ChurnKB utilizes textual data mining techniques such as Term Frequency-Inverse Document Frequency (TF-IDF), cosine similarity, regular expressions, word tokenization, and stemming to identify churn-related features within customer-generated content, including emails. To further enrich the structure of ChurnKB, we integrate Generative AI, specifically large language models, which offer flexibility in handling unstructured text and uncovering latent features, to identify and refine features related to customer cognitive status, emotions, and behaviours. Additionally, feedback loops are incorporated to validate and enhance the effectiveness of ChurnKB.Integrating knowledge-based features into machine learning models (e.g., Random Forest, Logistic Regression, Multilayer Perceptron, and XGBoost) improves predictive performance of ML models compared to the baseline, with XGBoost’s F1 score increasing from 0.5752 to 0.7891. Beyond churn prediction, this approach potentially supports applications like personalized marketing, cyberbullying detection, hate speech identification, and mental health monitoring, demonstrating its broader impact on business intelligence and online safety. Full article
(This article belongs to the Special Issue Machine Learning Algorithms and Optimization in the Digital Transition (2nd Edition))
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20 pages, 3991 KiB  
Article
A New GIS-Based Detection Technique for Urban Heat Islands Using the Fuzzy C-Means Clustering Algorithm: A Case Study of Naples, (Italy)
by Rosa Cafaro, Barbara Cardone, Valeria D’Ambrosio, Ferdinando Di Martino and Vittorio Miraglia
Algorithms 2025, 18(4), 228; https://doi.org/10.3390/a18040228 - 15 Apr 2025
Viewed by 226
Abstract
This study proposes a novel urban heat island detection method implemented in a GIS-based framework, designed to identify the most critical urban areas during heatwave events. The framework employs the fuzzy C-means clustering algorithm with remotely sensed land surface temperature and normalized difference [...] Read more.
This study proposes a novel urban heat island detection method implemented in a GIS-based framework, designed to identify the most critical urban areas during heatwave events. The framework employs the fuzzy C-means clustering algorithm with remotely sensed land surface temperature and normalized difference vegetation index data to delineate and visualize hotspots. The proposed approach is compared with other established methods for urban heat island detection to evaluate their relative accuracy and effectiveness. This methodology integrates advanced spatial analysis with environmental indicators such as vegetation cover and permeable open spaces to assess urban vulnerability. The city of Naples, Italy, serves as a case study for testing the framework. The results from the case study indicate that the proposed method outperforms alternative methods in identifying heat hotspots, providing higher accuracy and suggesting potential adaptability to other urban contexts. This GIS-based approach not only provides a robust tool for urban climate assessment but also serves as a decision support framework that enables urban planners and policymakers to identify critical areas and prioritize interventions for climate adaptation and mitigation. Full article
(This article belongs to the Special Issue Machine Learning Algorithms and Optimization in the Digital Transition (2nd Edition))
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25 pages, 6410 KiB  
Article
Intelligent Multi-Fault Diagnosis for a Simplified Aircraft Fuel System
by Jiajin Li, Steve King and Ian Jennions
Algorithms 2025, 18(2), 73; https://doi.org/10.3390/a18020073 - 1 Feb 2025
Cited by 2 | Viewed by 675
Abstract
Machine learning (ML) techniques are increasingly used to diagnose faults in aerospace applications, but diagnosing multiple faults in aircraft fuel systems (AFSs) remains challenging due to complex component interactions. This paper evaluates the accuracy and introduces an innovative approach to quantify and compare [...] Read more.
Machine learning (ML) techniques are increasingly used to diagnose faults in aerospace applications, but diagnosing multiple faults in aircraft fuel systems (AFSs) remains challenging due to complex component interactions. This paper evaluates the accuracy and introduces an innovative approach to quantify and compare the interpretability of four ML classification methods—artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and logistic regressions (LRs)—for diagnosing fault combinations present in AFSs. While the ANN achieved the highest diagnostic accuracy at 90%, surpassing other methods, its interpretability was limited. By contrast, the decision tree model showed an 82% consistency between global explanations and engineering insights, highlighting its advantage in interpretability despite the lower accuracy. Interpretability was assessed using two widely accepted tools, LIME and SHAP, alongside engineering understanding. These findings underscore a trade-off between prediction accuracy and interpretability, which is critical for trust in ML applications in aerospace. Although an ANN can deliver high diagnostic accuracy, a decision tree offers more transparent results, facilitating better alignment with engineering expectations even at a slight cost to accuracy. Full article
(This article belongs to the Special Issue Machine Learning Algorithms and Optimization in the Digital Transition (2nd Edition))
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Review

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37 pages, 980 KiB  
Review
Digital Transformation in Aftersales and Warranty Management: A Review of Advanced Technologies in I4.0
by Vicente González-Prida, Carlos Parra Márquez, Pablo Viveros Gunckel, Fredy Kristjanpoller Rodríguez and Adolfo Crespo Márquez
Algorithms 2025, 18(4), 231; https://doi.org/10.3390/a18040231 - 17 Apr 2025
Viewed by 352
Abstract
This research examines how Industry 4.0 technologies such as artificial intelligence (AI), the Internet of Things (IoT), and digital twins (DT) are used in the digital transformation process of warranty management. This research focuses on converting traditional warranty management practices from reactive systems [...] Read more.
This research examines how Industry 4.0 technologies such as artificial intelligence (AI), the Internet of Things (IoT), and digital twins (DT) are used in the digital transformation process of warranty management. This research focuses on converting traditional warranty management practices from reactive systems to predictive and proactive ones, improving operational performance and customer experiences. Based on an already established eight-phase framework for warranty management, this paper reviews machine learning (ML), natural language processing (NLP), and predictive analytics, among other advanced technologies, to enhance warranty optimization processes. Best practices in the automotive sector, as well as in the railway and aeronautics industries, have experienced substantial achievements, including optimized resource utilization and savings, together with tailored services. This study describes the limitations of capital investments, labor training requirements, and data protection issues. Therefore, it suggests implementation sequencing and staff education approaches as solutions. In addition to the current evolution of Industry 4.0, this research’s conclusion highlights how digital warranty management advancements optimize resources and reduce costs while adhering to international standards and ethical data practices. Full article
(This article belongs to the Special Issue Machine Learning Algorithms and Optimization in the Digital Transition (2nd Edition))
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