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

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

Deadline for manuscript submissions: 31 July 2025 | Viewed by 6377

Special Issue Editors

Prof. Dr. Marcello Trovati

E-Mail Website
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

E-Mail Website
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

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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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
  • machine learning
  • engineering
  • reinforcement learning
  • deep learning
  • robotics
  • autonomous systems

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

Published Papers (3 papers)

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Research

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20 pages, 3362 KiB  
Article
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 626
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 [...] Read more.
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 KiB  
Article
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 2619
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 [...] Read more.
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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Other

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23 pages, 435 KiB  
Systematic 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 2 | Viewed by 2518
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. [...] Read more.
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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