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Algorithms
Special Issues
AI Applications and Modern Industry
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AI Applications and Modern 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 January 2026 | Viewed by 1004

Special Issue Editors

Dr. Mateus Mendes

E-Mail Website
Guest Editor
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. Annemieke Meghoe

E-Mail Website
Guest Editor Assistant
Faculty of Engineering Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
Interests: rails; physics of failure; maintenance; remaining useful life; mechanical engineering; wear; fatigue; friction; material science

Special Issue Information

Dear Colleagues,

Modern industries require more sensors, computing power, and algorithms to run smoothly and be competitive in an increasingly global market. Artificial Intelligence algorithms contribute to optimizing processes and the safety of people and assets. Hence, investment in edge AI, classical and deep machine learning systems, business intelligence, and other engineering tools is key to competitiveness and sustainability. Those systems facilitate the automation of tasks and detection of events and patterns at the same time as they offer prognostics, greatly contributing to a deeper understanding of modern complex systems and more informed decisions.

This Special Issue aims to cover the latest research so that decisions made based on the proposed algorithms are sound and adequate, effectively contributing to facilitating management and operational decisions. Original contributions on the above aspects and related topics are encouraged.

Dr. Mateus Mendes
Guest Editor

Dr. Annemieke Meghoe
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 1800 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

  • artificial intelligence
  • asset management
  • clustering
  • data analysis
  • data mining
  • decision-support systems
  • deep learning
  • digital transition
  • edge AI
  • 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.

Published Papers (2 papers)

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Research

19 pages, 2117 KB  
Article
Point-Wise Full-Field Physics Neural Mapping Framework via Boundary Geometry Constrained for Large Thermoplastic Deformation
by Jue Wang, Xinyi Xu, Changxin Ye and Wei Huangfu
Algorithms 2025, 18(10), 651; https://doi.org/10.3390/a18100651 - 16 Oct 2025
Viewed by 244
Abstract
Computation modeling for large thermoplastic deformation of plastic solids is critical for industrial applications like non-invasive assessment of engineering components. While deep learning-based methods have emerged as promising alternatives to traditional numerical simulations, they often suffer from systematic errors caused by geometric mismatches [...] Read more.
Computation modeling for large thermoplastic deformation of plastic solids is critical for industrial applications like non-invasive assessment of engineering components. While deep learning-based methods have emerged as promising alternatives to traditional numerical simulations, they often suffer from systematic errors caused by geometric mismatches between predicted and ground truth meshes. To overcome this limitation, we propose a novel boundary geometry-constrained neural framework that establishes direct point-wise mappings between spatial coordinates and full-field physical quantities within the deformed domain. The key contributions of this work are as follows: (1) a two-stage strategy that separates geometric prediction from physics-field resolution by constructing direct, point-wise mappings between coordinates and physical quantities, inherently avoiding errors from mesh misalignment; (2) a boundary-condition-aware encoding mechanism that ensures physical consistency under complex loading conditions; and (3) a fully mesh-free approach that operates on point clouds without structured discretization. Experimental results demonstrate that our method achieves a 36–98% improvement in prediction accuracy over deep learning baselines, offering a efficient alternative for high-fidelity simulation of large thermoplastic deformations. Full article
(This article belongs to the Special Issue AI Applications and Modern Industry)
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29 pages, 1730 KB  
Article
Explaining Corporate Ratings Transitions and Defaults Through Machine Learning
by Nazário Augusto de Oliveira and Leonardo Fernando Cruz Basso
Algorithms 2025, 18(10), 608; https://doi.org/10.3390/a18100608 - 28 Sep 2025
Viewed by 453
Abstract
Credit rating transitions and defaults are critical indicators of corporate creditworthiness, yet their accurate modeling remains a persistent challenge in risk management. Traditional models such as logistic regression (LR) and structural approaches (e.g., Merton’s model) offer transparency but often fail to capture nonlinear [...] Read more.
Credit rating transitions and defaults are critical indicators of corporate creditworthiness, yet their accurate modeling remains a persistent challenge in risk management. Traditional models such as logistic regression (LR) and structural approaches (e.g., Merton’s model) offer transparency but often fail to capture nonlinear relationships, temporal dynamics, and firm heterogeneity. This study proposes a hybrid machine learning (ML) framework to explain and predict corporate rating transitions and defaults, addressing key limitations in existing literature. We benchmark four classification algorithms—LR, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machines (SVM)—on a structured corporate credit dataset. Our approach integrates segment-specific modeling across rating bands, out-of-time validation to simulate real-world applicability, and SHapley Additive exPlanations (SHAP) values to ensure interpretability. The results demonstrate that ensemble methods, particularly XGBoost and RF, significantly outperform LR and SVM in predictive accuracy and early warning capability. Moreover, SHAP analysis reveals differentiated drivers of rating transitions across credit quality segments, highlighting the importance of tailored monitoring strategies. This research contributes to the literature by bridging predictive performance with interpretability in credit risk modeling and offers practical implications for regulators, rating agencies, and financial institutions seeking robust, transparent, and forward-looking credit assessment tools. Full article
(This article belongs to the Special Issue AI Applications and Modern Industry)
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