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Artificial Intelligence for Advanced Engineering: Techniques, Methods, and Frameworks

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 18394

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Prof. Dr. Marcello Trovati

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Guest Editor

Department of Computer Science, Edge Hill University, St Helens Road, Ormskirk L39 4QP, Lancashire, UK
Interests: data science; AI; mathematical modelling
Special Issues, Collections and Topics in MDPI journals

Dr. Nonso Nnamoko

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Guest Editor

Department of Computer Science, Edge Hill University, St Helens Road, Ormskirk L39 4QP, Lancashire, UK
Interests: multimodal AI; NLP; software engineering

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has gained enormous popularity, both from a theoretical point of view and for its significant and impactful applications. This Special Issue aims to explore the latest techniques, methods, and frameworks in AI specifically tailored for advanced engineering applications. In particular, it will offer a comprehensive insight into AI techniques, such as machine learning, neural networks, and natural language processing, and their applications in various engineering disciplines, including mechanical, electrical, civil, and aerospace engineering. Topics will include, but are not limited to, AI-based design ptimization, predictive maintenance, autonomous systems, and decision-making processes in complex engineering settings. Furthermore, this special issue will also focus on the ethical considerations and challenges associated with integrating AI into engineering workflows, providing insights into ensuring transparency, fairness, and accountability. We invite contributions that demonstrate novel approaches, theoretical insights, practical implementations, and real-world applications of AI in diverse engineering domains, including (but not limited to) the following:

Predictive maintenance using AI for industrial systems;
Reinforcement learning for autonomous systems in manufacturing;
Deep learning approaches for image recognition in civil engineering;
Optimisation algorithms for energy-efficient building design;
AI-driven robotics for hazardous environment exploration;
Natural language processing for automated code generation in software engineering;
Machine learning techniques for fault detection in electrical power systems;
AI-based predictive modelling for smart transportation systems;
Genetic algorithms for optimisation of structural designs in aerospace engineering;
Intelligent decision support systems for supply chain management in logistics.

Prof. Dr. Marcello Trovati
Dr. Nonso Nnamoko
Guest Editors

Manuscript Submission Information

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Keywords

artificial intelligence
machine learning
engineering
reinforcement learning
deep learning
robotics
autonomous systems

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Published Papers (10 papers)

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Research

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11 pages, 2811 KB

Open AccessArticle

Real-Time Rice Milling Morphology Detection Using Hybrid Framework of YOLOv8 Instance Segmentation and Oriented Bounding Boxes

by Benjamin Ilo, Daniel Rippon, Yogang Singh, Alex Shenfield and Hongwei Zhang

Electronics 2025, 14(18), 3691; https://doi.org/10.3390/electronics14183691 - 18 Sep 2025

Viewed by 276

Abstract

Computer vision and image processing techniques have had great success in the food and drink industry. These technologies are used to analyse images, convert images to greyscale, and extract high-dimensional numerical data from the images; however, when it comes to real-time grain and rice milling processes, this technology has several limitations compared to other applications. Currently, milled rice image samples are collected and separated to avoid one contacting the another during analysis. This approach is not suitable for real-time industrial implementation. However, real-time analysis can be accomplished by utilising artificial intelligence (AI) and machine learning (ML) approaches instead of traditional quality assessment methods, such as manual inspection, which are labour-intensive, time-consuming, and prone to human error. To address these challenges, this paper presents a novel approach for real-time rice morphology analysis during milling by integrating You Only Look Once version 8 (YOLOv8) instance segmentation and Oriented Bounding Box (OBB) detection models. While instance segmentation excels in detecting and classifying both touching and overlapping grains, it underperforms in precise size estimation. Conversely, the object-oriented bounding box detection model provides more accurate size measurements but struggles with touching and overlapping grains. Experiments demonstrate that the hybrid system resolves key limitations of standalone models: instance segmentation alone achieves high detection accuracy (92% mAP@0.5) but struggles with size errors (0.35 mm MAE), while OBB alone reduces the size error to 0.12 mm MAE but falters with complex grain arrangements (88% mAP@0.5). By combining these approaches, our unified pipeline achieves superior performance, improving detection precision (99.5% mAP@0.5), segmentation quality (86% mask IoU), and size estimation (0.10 mm MAE). This represents a 71% reduction in size error compared to segmentation-only models and a 6% boost in detection accuracy over OBB-only methods. This study highlights the potential of advanced deep learning techniques in enhancing the automation and optimisation of quality control in rice milling processes. Full article

(This article belongs to the Special Issue Artificial Intelligence for Advanced Engineering: Techniques, Methods, and Frameworks)

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26 pages, 2329 KB

Open AccessArticle

Federated Learning for Surveillance Systems: A Literature Review and AHP Expert-Based Evaluation

by Yongjoo Shin, Hansung Kim, Jaeyeong Jeong and Dongkyoo Shin

Electronics 2025, 14(17), 3500; https://doi.org/10.3390/electronics14173500 - 1 Sep 2025

Viewed by 740

Abstract

This study explores the application of federated learning (FL) in security camera surveillance systems to overcome the structural limitations inherent in traditional centralized artificial intelligence (AI) training approaches, while simultaneously enhancing operational efficiency and data security. Conventional centralized AI models require the transmission of raw surveillance data from individual security camera units to a central server for model training, which poses significant challenges, including network congestion, a heightened risk of personal data leakage, and inadequate adaptation to localized environmental characteristics. These limitations are particularly critical in high-security environments such as military bases and government facilities, where reliability and real-time processing are paramount. In contrast, FL enables decentralized training by retaining data on local devices and sharing only model parameters with a central aggregator, thereby improving privacy preservation, reducing communication overhead, and facilitating adaptive, context-aware learning. This paper does not present a new federated learning algorithm or original experiment. Instead, it synthesizes existing research findings and applies the Analytic Hierarchy Process (AHP) to evaluate and prioritize critical factors for deploying FL in surveillance systems. By combining literature-based evidence with structured expert judgment, this study provides practical guidelines for real-world application. This paper identifies four key performance metrics—detection accuracy, false alarm rate, response time, and network load—and conducts a comparative analysis of FL and centralized AI-based approaches in the recent literature. In addition, the AHP is employed to evaluate expert survey data, quantitatively prioritizing eight critical factors for effective FL implementation. The results highlight detection accuracy and data security as the most significant concerns, indicating that FL presents a promising solution for future smart surveillance infrastructures. This research contributes to the advancement of AI-powered surveillance systems that are both high-performing and resilient under stringent privacy and operational constraints. Full article

(This article belongs to the Special Issue Artificial Intelligence for Advanced Engineering: Techniques, Methods, and Frameworks)

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22 pages, 4457 KB

Open AccessArticle

From Shore-A 85 to Shore-D 70: Multimaterial Transitions in 3D-Printed Exoskeleton

by Izabela Rojek, Jakub Kopowski, Marek Andryszczyk and Dariusz Mikołajewski

Electronics 2025, 14(16), 3316; https://doi.org/10.3390/electronics14163316 - 20 Aug 2025

Viewed by 574

Abstract

Soft–rigid interfaces in exoskeletons are key to balancing flexibility and structural support, providing both comfort and function. In our experience, combining Bioflex material with a rigid filament improves mechanical properties while allowing the exoskeleton to adapt to complex hand movements. Flexible components provide adaptability, reducing pressure points and discomfort during prolonged use. At the same time, rigid components provide the stability and force transfer necessary to support weakened grip strength. A key challenge in this integration is achieving a smooth transition between materials to prevent stress concentrations that can lead to material failure. Techniques for providing adhesion and mechanical locking are essential to ensure the durability and longevity of soft and rigid interfaces. One issue we have observed is that rigid filaments can restrict movement if not strategically placed, potentially leading to unnatural hand movement. On the other hand, excessive softness can reduce the force output needed for effective rehabilitation or assistance. Optimizing the interface design requires iterative testing to find the perfect balance between flexibility and mechanical support. In some prototypes, material fatigue in soft sections led to early failure, requiring reinforced hybrid structures. Addressing these issues through better material bonding and geometric optimization can significantly improve the performance and comfort of hand exoskeletons. The aim of this study was to investigate the transition between rigid and soft materials for exoskeletons. Full article

(This article belongs to the Special Issue Artificial Intelligence for Advanced Engineering: Techniques, Methods, and Frameworks)

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20 pages, 1226 KB

Open AccessArticle

Diagnostic Signal Acquisition Time Reduction Technique in the Induction Motor Fault Detection and Localization Based on SOM-CNN

by Jeremi Jan Jarosz, Maciej Skowron, Oliwia Frankiewicz, Marcin Wolkiewicz, Sebastien Weisse, Jerome Valire and Krzysztof Szabat

Electronics 2025, 14(12), 2373; https://doi.org/10.3390/electronics14122373 - 10 Jun 2025

Viewed by 504

Abstract

Diagnostic systems for drive with AC motors of key importance for machine safety require the use of limitations related to the processing of measurement information. These limitations result in significant difficulties in assessing the technical condition of the object’s components. The article proposes the use of a combination of artificial intelligence techniques in the form of shallow and convolutional structures in the diagnostics of stator winding damage from an induction motor. The proposed approach ensures a high level of defect detection efficiency while using information preserved in samples from three periods of current signals. The research presents the possibility of combining the data classification capabilities of self-organizing maps (SOMs) with the automatic feature extraction of a convolutional neural network (CNN). The system was verified in steady and transient operating states on a test stand with a 1.5 kW motor. Remarkably, this approach achieves a high detection precision of 97.92% using only 600 samples, demonstrating that this reduced data acquisition does not compromise performance. On the contrary, this efficiency facilitates effective fault detection even in transient operating states, a challenge for traditional methods, and surpasses the 97.22% effectiveness of a reference system utilizing a full 6 s signal. Full article

(This article belongs to the Special Issue Artificial Intelligence for Advanced Engineering: Techniques, Methods, and Frameworks)

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20 pages, 3362 KB

Open AccessArticle

Stability Prediction Model of Transmission Tower Slope Based on ISCSO-SVM

by Zilong Zhang, Xiaoliang Liu, Yanhai Wang, Enyang Li and Yuhao Zhang

Electronics 2025, 14(1), 126; https://doi.org/10.3390/electronics14010126 - 31 Dec 2024

Cited by 2 | Viewed by 897

Abstract

Landslides induced by heavy rainfall are common in southern China and pose significant risks to the safe operation of transmission lines. To ensure the reliability of transmission line operations, this paper presents a stability prediction model for transmission tower slopes based on the Improved Sand Cat Swarm Optimization (ISCSO) algorithm and Support Vector Machine (SVM). The ISCSO algorithm is enhanced with dynamic reverse learning and triangular wandering strategies, which are then used to optimize the kernel and penalty parameters of the SVM, resulting in the ISCSO-SVM prediction model. In this study, a typical transmission tower slope in southern China is used as a case study, with the transmission tower slope database generated through orthogonal experimental design and Geo-studio simulations. In addition to traditional input features, an additional input—transmission tower catchment area—is incorporated, and the stable state of the transmission tower slope is set as the predicted output. The results demonstrate that the ISCSO-SVM model achieves the highest prediction accuracy, with the smallest errors across all metrics. Specifically, compared to the standard SVM, the MAPE, MAE, and RMSE values are reduced by 70.96%, 71.41%, and 57.37%, respectively. The ISCSO-SVM model effectively predicts the stability of transmission tower slopes, thereby ensuring the safe operation of transmission lines. Full article

(This article belongs to the Special Issue Artificial Intelligence for Advanced Engineering: Techniques, Methods, and Frameworks)

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33 pages, 3507 KB

Open AccessArticle

Cognitive Agents Powered by Large Language Models for Agile Software Project Management

by Konrad Cinkusz, Jarosław A. Chudziak and Ewa Niewiadomska-Szynkiewicz

Electronics 2025, 14(1), 87; https://doi.org/10.3390/electronics14010087 - 28 Dec 2024

Cited by 1 | Viewed by 5683

Abstract

This paper investigates the integration of cognitive agents powered by Large Language Models (LLMs) within the Scaled Agile Framework (SAFe) to reinforce software project management. By deploying virtual agents in simulated software environments, this study explores their potential to fulfill fundamental roles in IT project development, thereby optimizing project outcomes through intelligent automation. Particular emphasis is placed on the adaptability of these agents to Agile methodologies and their transformative impact on decision-making, problem-solving, and collaboration dynamics. The research leverages the CogniSim ecosystem, a platform designed to simulate real-world software engineering challenges, such as aligning technical capabilities with business objectives, managing interdependencies, and maintaining project agility. Through iterative simulations, cognitive agents demonstrate advanced capabilities in task delegation, inter-agent communication, and project lifecycle management. By employing natural language processing to facilitate meaningful dialogues, these agents emulate human roles and improve the efficiency and precision of Agile practices. Key findings from this investigation highlight the ability of LLM-powered cognitive agents to deliver measurable improvements in various metrics, including task completion times, quality of deliverables, and communication coherence. These agents exhibit scalability and adaptability, ensuring their applicability across diverse and complex project environments. This study underscores the potential of integrating LLM-powered agents into Agile project management frameworks as a means of advancing software engineering practices. This integration not only refines the execution of project management tasks but also sets the stage for a paradigm shift in how teams collaborate and address emerging challenges. By integrating the capabilities of artificial intelligence with the principles of Agile, the CogniSim framework establishes a foundation for more intelligent, efficient, and adaptable software development methodologies. Full article

(This article belongs to the Special Issue Artificial Intelligence for Advanced Engineering: Techniques, Methods, and Frameworks)

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Review

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32 pages, 4771 KB

Open AccessReview

Industrial Process Automation Through Machine Learning and OPC-UA: A Systematic Literature Review

by Henry O. Velesaca, Juan A. Holgado-Terriza and Jose M. Gutierrez-Guerrero

Electronics 2025, 14(18), 3749; https://doi.org/10.3390/electronics14183749 - 22 Sep 2025

Viewed by 211

Abstract

This systematic literature review examines the integration of Machine Learning techniques within industrial system architectures using OPC-UA for process automation. Through analyzing primary studies published between 2018 and 2024, the review identifies key trends, methodologies, and implementations across various industrial applications. The review identifies a marked increase in research focused on hybrid architectures that integrate Machine Learning with OPC-UA, particularly in applications such as predictive maintenance and quality control. However, despite reported high accuracy rates—often above 95%—in controlled environments, there is limited evidence on the robustness of these solutions in real-world, large-scale deployments. This highlights the need for further empirical validation and benchmarking in diverse industrial contexts. Implementation patterns range from cloud-based deployments to edge computing solutions, with OPC-UA serving as a communication protocol, information modeling framework, and specifically using the finite state machine specification. The review also highlights current challenges and opportunities, providing valuable insights for researchers and practitioners working on intelligent industrial automation. Full article

(This article belongs to the Special Issue Artificial Intelligence for Advanced Engineering: Techniques, Methods, and Frameworks)

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25 pages, 2187 KB

Open AccessReview

Review of Fuzzy Methods Application in IIoT Security—Challenges and Perspectives

by Emanuel Krzysztoń, Dariusz Mikołajewski and Piotr Prokopowicz

Electronics 2025, 14(17), 3475; https://doi.org/10.3390/electronics14173475 - 29 Aug 2025

Viewed by 535

Abstract

Traditional methods often fail when confronted with data characterised by uncertainty, incompleteness, and dynamically evolving threats within the Industrial Internet of Things (IIoT) environment. This paper presents the role of fuzzy set methods as a response to these challenges in ensuring IIoT security. A systematic literature review reveals how fuzzy set methods contribute to supporting and enabling actions ranging from anomaly detection to risk analysis. The work focuses on fuzzy systems such as the Fuzzy Inference System (FIS) and the Adaptive Neuro-Fuzzy Inference System (ANFIS), highlighting their strengths, including their resilience to imperfect data and the intuitiveness of their rules. It also identifies challenges related to optimisation and scalability. The article outlines directions for further research, indicating the potential of fuzzy methods as a cornerstone of future, intelligent IIoT cyber defence systems, capable of effectively responding to complex and changing attack scenarios. Full article

(This article belongs to the Special Issue Artificial Intelligence for Advanced Engineering: Techniques, Methods, and Frameworks)

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28 pages, 1334 KB

Open AccessReview

Evaluating Data Quality: Comparative Insights on Standards, Methodologies, and Modern Software Tools

by Theodoros Alexakis, Evgenia Adamopoulou, Nikolaos Peppes, Emmanouil Daskalakis and Georgios Ntouskas

Electronics 2025, 14(15), 3038; https://doi.org/10.3390/electronics14153038 - 30 Jul 2025

Viewed by 1246

Abstract

In an era of exponential data growth, ensuring high data quality has become essential for effective, evidence-based decision making. This study presents a structured and comparative review of the field by integrating data classifications, quality dimensions, assessment methodologies, and modern software tools. Unlike earlier reviews that focus narrowly on individual aspects, this work synthesizes foundational concepts with formal frameworks, including the Findable, Accessible, Interoperable, and Reusable (FAIR) principles and the ISO/IEC 25000 series on software and data quality. It further examines well-established assessment models, such as Total Data Quality Management (TDQM), Data Warehouse Quality (DWQ), and High-Quality Data Management (HDQM), and critically evaluates commercial platforms in terms of functionality, AI integration, and adaptability. A key contribution lies in the development of conceptual mappings that link data quality dimensions with FAIR indicators and maturity levels, offering a practical reference model. The findings also identify gaps in current tools and approaches, particularly around cost-awareness, explainability, and process adaptability. By bridging theory and practice, the study contributes to the academic literature while offering actionable insights for building scalable, standards-aligned, and context-sensitive data quality management strategies. Full article

(This article belongs to the Special Issue Artificial Intelligence for Advanced Engineering: Techniques, Methods, and Frameworks)

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23 pages, 435 KB

Open AccessSystematic Review

Towards Data-Driven Hydration Monitoring: Insights from Wearable Sensors and Advanced Machine Learning Techniques

by Apparaju Sreeharsha, Sarah McHale, Nonso Nnamoko and Ella Pereira

Electronics 2024, 13(24), 4960; https://doi.org/10.3390/electronics13244960 - 17 Dec 2024

Cited by 4 | Viewed by 5651

Abstract

Advancements in wearable sensors and digital technologies/computational tools (e.g., machine learning (ML), general data analytics, mobile and desktop applications) have been explored in existing studies. However, challenges related to sensor efficacy and the application of digital technology/computational approaches for hydration assessment remain under-explored. Key knowledge gaps include applicable devices and sensors for measuring hydration and/or dehydration, the performance of approaches (e.g., ML algorithms) on sensor-based hydration monitoring; the potential of multi-sensor fusion to enhance measurement accuracy and the limitations posed by experimental datasets. This review aims to address the gaps by examining existing research to provide recommendations for future improvements. A systematic review was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Comprehensive searches across PubMed, Scopus, IEEE Xplore and MDPI databases for academic studies published between 2009 and 2024 were performed using predefined inclusion and exclusion criteria. Two reviewers independently screened and assessed studies, with disagreements resolved by a third reviewer. Data was synthesised narratively or through meta-analysis, where applicable. The database search yielded 1029 articles, with 999 unique studies remaining after duplicate removal. After title and abstract screening, 910 irrelevant studies were excluded. Full-text evaluation of 89 articles led to the inclusion of 20 studies for in-depth analysis. Findings highlight significant progress in hydration monitoring through multi-sensor fusion and advanced ML techniques, which improve accuracy and utility. However, challenges persist, including model complexity, sensor variability under different conditions, and a lack of diverse and representative datasets. This review underscores the need for further research to overcome these challenges and support the development of robust, data-driven hydration monitoring solutions. Full article

(This article belongs to the Special Issue Artificial Intelligence for Advanced Engineering: Techniques, Methods, and Frameworks)

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