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Computers
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AI in Complex Engineering Systems
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AI in Complex Engineering Systems

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "AI-Driven Innovations".

Deadline for manuscript submissions: 30 August 2026 | Viewed by 11163

Special Issue Editor

Dr. Sandi Baressi Šegota

E-Mail Website
Guest Editor
Faculty of Informatics, Juraj Dobrila University of Pula, Alda Negrija 6, 52100 Pula, Croatia
Interests: robotics; artificial intelligence; regression modeling; convolutional neural networks; data synthetization; evolutionary computing

Special Issue Information

Dear Colleagues,

The increasing demand for the accurate prediction of performance and maintenance metrics in complex engineering systems has led to a rapid expansion in the application of artificial intelligence (AI). AI methodologies are now routinely employed across a wide range of tasks, including the regression modeling of critical values, the detection of events and anomalies in both numerical and image-based datasets, and the optimization of operational processes. In many cases, these AI-driven approaches have demonstrated superior performance and adaptability when compared to traditional analytical or rule-based methods.

This Special Issue is dedicated to exploring and advancing the use of AI in engineering contexts, with a particular emphasis on fostering interdisciplinary collaboration and increasing the familiarity of engineering-oriented researchers with emerging AI paradigms. The topics of interest include but are not limited to

  • Sensor fusion strategies for robust data interpretation;
  • Generative models for data augmentation and simulation;
  • Multi-agent optimization frameworks for complex control environments;
  • Digital twins and AI-enhanced simulation;
  • Edge AI implementations for real-time industrial monitoring;
  • Explainable AI in engineering decision-making;
  • Resilient AI systems for fault-tolerant engineering;
  • The application of large language models for tasks such as documentation automation or semantic parsing.

By presenting innovative research across diverse engineering domains, this Special Issue of Computers aims to highlight the transformative potential of AI in the design, monitoring, and optimization of modern engineering systems.

Dr. Sandi Baressi Šegota
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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. Computers 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
  • engineering data analytics
  • engineering systems
  • fault detection and isolation
  • industrial automation
  • performance modeling
  • predictive maintenance
  • process optimization
  • sensor fusion
  • smart manufacturing

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

Published Papers (4 papers)

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Research

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42 pages, 1583 KB  
Article
Hybrid Sine–Cosine with Hummingbird Foraging Algorithm for Engineering Design Optimisation
by Jamal Zraqou, Ahmad Sami Al-Shamayleh, Riyad Alrousan, Hussam Fakhouri, Faten Hamad and Niveen Halalsheh
Computers 2026, 15(1), 35; https://doi.org/10.3390/computers15010035 - 7 Jan 2026
Viewed by 292
Abstract
We introduce AHA–SCA, a compact hybrid optimiser that alternates the wave-based exploration of the Sine–Cosine Algorithm (SCA) with the exploitation skills of the Artificial Hummingbird Algorithm (AHA) within a single population. Even iterations perform SCA moves with a linearly decaying sinusoidal amplitude to [...] Read more.
We introduce AHA–SCA, a compact hybrid optimiser that alternates the wave-based exploration of the Sine–Cosine Algorithm (SCA) with the exploitation skills of the Artificial Hummingbird Algorithm (AHA) within a single population. Even iterations perform SCA moves with a linearly decaying sinusoidal amplitude to explore widely around the current best solution, while odd iterations invoke guided and territorial hummingbird flights using axial, diagonal, and omnidirectional patterns to intensify the search in promising regions. This simple interleaving yields an explicit and tunable balance between exploration and exploitation and incurs negligible overhead beyond evaluating candidate solutions. The proposed approach is evaluated on the CEC2014, CEC2017, and CEC2022 benchmark suites and on several constrained engineering design problems, including welded beam, pressure vessel, tension/compression spring, speed reducer, and cantilever beam designs. Across these diverse tasks, AHA–SCA demonstrates competitive or superior performance relative to stand-alone SCA, AHA, and a broad panel of recent metaheuristics, delivering faster early-phase convergence and robust final solutions. Statistical analyses using non-parametric tests confirm that improvements are significant on many functions, and the method respects problem constraints without parameter tuning. The results suggest that alternating wave-driven exploration with hummingbird-inspired refinement is a promising general strategy for continuous engineering optimisation. Full article
(This article belongs to the Special Issue AI in Complex Engineering Systems)
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21 pages, 4007 KB  
Article
Computer Vision-Driven Framework for IoT-Enabled Basketball Score Tracking
by Ivan Ćirić, Nikola Ivačko, Miljana Milić, Petar Ristić and Dušan Krstić
Computers 2025, 14(11), 469; https://doi.org/10.3390/computers14110469 - 1 Nov 2025
Viewed by 1777
Abstract
This paper presents the design and implementation of a vision-based score detection system tailored for smart IoT basketball applications. The proposed architecture leverages a compact, low-cost device comprising a high-resolution overhead camera and a Raspberry Pi 5 microprocessor equipped with a hardware accelerator [...] Read more.
This paper presents the design and implementation of a vision-based score detection system tailored for smart IoT basketball applications. The proposed architecture leverages a compact, low-cost device comprising a high-resolution overhead camera and a Raspberry Pi 5 microprocessor equipped with a hardware accelerator for real-time object detection. The detection pipeline integrates convolutional neural networks (YOLO-based) and custom preprocessing techniques to localize the basketball hoop and track the ball trajectory. A scoring event is confirmed when the ball enters the defined scoring zone with downward motion over multiple frames, effectively reducing false positives caused by occlusions, multiple balls, or irregular shot directions. The system is part of a scalable IoT analytics platform known as Koško, which provides real-time statistics, leaderboards, and user engagement tools through a web-based interface. Field tests were conducted using data collected from various public and school courts across Niš, Serbia, resulting in a robust and adaptable solution for automated basketball score monitoring in both indoor and outdoor environments. The methodology supports edge computing, multilingual deployment, and integration with smart coaching and analytics systems. Full article
(This article belongs to the Special Issue AI in Complex Engineering Systems)
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23 pages, 2435 KB  
Article
Explainable Deep Kernel Learning for Interpretable Automatic Modulation Classification
by Carlos Enrique Mosquera-Trujillo, Juan Camilo Lugo-Rojas, Diego Fabian Collazos-Huertas, Andrés Marino Álvarez-Meza and German Castellanos-Dominguez
Computers 2025, 14(9), 372; https://doi.org/10.3390/computers14090372 - 5 Sep 2025
Cited by 1 | Viewed by 1407
Abstract
Modern wireless communication systems increasingly rely on Automatic Modulation Classification (AMC) to enhance reliability and adaptability, especially in the presence of severe signal degradation. However, despite significant progress driven by deep learning, many AMC models still struggle with high computational overhead, suboptimal performance [...] Read more.
Modern wireless communication systems increasingly rely on Automatic Modulation Classification (AMC) to enhance reliability and adaptability, especially in the presence of severe signal degradation. However, despite significant progress driven by deep learning, many AMC models still struggle with high computational overhead, suboptimal performance under low-signal-to-noise conditions, and limited interpretability, factors that hinder their deployment in real-time, resource-constrained environments. To address these challenges, we propose the Convolutional Random Fourier Features with Denoising Thresholding Network (CRFFDT-Net), a compact and interpretable deep kernel architecture that integrates Convolutional Random Fourier Features (CRFFSinCos), an automatic threshold-based denoising module, and a hybrid time-domain feature extractor composed of CNN and GRU layers. Our approach is validated on the RadioML 2016.10A benchmark dataset, encompassing eleven modulation types across a wide signal-to-noise ratio (SNR) spectrum. Experimental results demonstrate that CRFFDT-Net achieves an average classification accuracy that is statistically comparable to state-of-the-art models, while requiring significantly fewer parameters and offering lower inference latency. This highlights an exceptional accuracy–complexity trade-off. Moreover, interpretability analysis using GradCAM++ highlights the pivotal role of the Convolutional Random Fourier Features in the representation learning process, providing valuable insight into the model’s decision-making. These results underscore the promise of CRFFDT-Net as a lightweight and explainable solution for AMC in real-world, low-power communication systems. Full article
(This article belongs to the Special Issue AI in Complex Engineering Systems)
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Review

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29 pages, 2673 KB  
Review
Integrating Large Language Models into Digital Manufacturing: A Systematic Review and Research Agenda
by Chourouk Ouerghemmi and Myriam Ertz
Computers 2025, 14(8), 318; https://doi.org/10.3390/computers14080318 - 7 Aug 2025
Cited by 2 | Viewed by 7049
Abstract
Industries 4.0 and 5.0 are based on technological advances, notably large language models (LLMs), which are making a significant contribution to the transition to smart factories. Although considerable research has explored this phenomenon, the literature remains fragmented and lacks an integrative framework that [...] Read more.
Industries 4.0 and 5.0 are based on technological advances, notably large language models (LLMs), which are making a significant contribution to the transition to smart factories. Although considerable research has explored this phenomenon, the literature remains fragmented and lacks an integrative framework that highlights the multifaceted implications of using LLMs in the context of digital manufacturing. To address this limitation, we conducted a systematic literature review, analyzing 53 papers selected according to predefined inclusion and exclusion criteria. Our descriptive and thematic analyses, respectively, mapped new trends and identified emerging themes, classified into three axes: (1) manufacturing process optimization, (2) data structuring and innovation, and (3) human–machine interaction and ethical challenges. Our results revealed that LLMs can enhance operational performance and foster innovation while redistributing human roles. Our research offers an in-depth understanding of the implications of LLMs. Finally, we propose a future research agenda to guide future studies. Full article
(This article belongs to the Special Issue AI in Complex Engineering Systems)
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