Applied Artificial Intelligence: Advanced Solutions for Engineering Real-World Challenges

Submission Deadline: 30 November 2025 (closed) View: 1543 Submit to Special Issue

Guest Editors

Prof. Siamak TalatAhari, Macquarie University, Australia
Prof. Amin Beheshti, Macquarie University, Australia

Summary

This special issue, “Applied Artificial Intelligence: Advanced Solutions for Engineering Real-World Challenges,” showcases the transformative power of artificial intelligence (AI) in various industries, emphasizing its role in addressing complex engineering real-world problems. As we navigate an era where data and technology shape decision-making, this issue highlights the practical applications of AI that enhance efficiency, resource management, and sustainable development across different engineering sectors.

The application of AI is not just about technological enhancement but also about a shift in our approach to problem-solving and decision-making. Integrating AI with technologies like machine learning, natural language processing, computer vision, robotics, big data, and the internet of things, organizations are fostering innovation, adding value, and gaining a competitive edge.

This issue provides a platform for presenting leading-edge research, case studies, and real-world AI implementations for engineering problems. Featured topics include, but not limited to:

• Biomedical and Healthcare Engineering: AI in medical diagnostics, patient care, biomedical engineering, and healthcare management systems.

• Manufacturing Engineering: Enhancements through AI in automation, predictive maintenance, quality control, and supply chain management.

• Transportation Engineering: Innovations in autonomous vehicles, traffic systems, logistics, and maintenance forecasting.

• Civil and Structural Engineering: Applications of AI in structural analysis, design, maintenance, and smart infrastructure.

• Environmental Science: AI applications in climate modeling, resource management, environmental monitoring, and sustainability efforts.

• Energy Engineering: Optimization of renewable energy systems, smart grids, energy management, and efficiency improvements using AI technologies.

In essence, this special issue aims to deepen the understanding of AI’s capabilities to solve intricate challenges, thereby fostering progress, innovation, and beneficial outcomes in today’s sophisticated world.

Published Papers

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ARTICLE

Federated Learning for Vision-Based Applications in 6G Networks: A Simulation-Based Performance Study
Manuel J. C. S. Reis, Nishu Gupta
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.073366
（This article belongs to the Special Issue: Applied Artificial Intelligence: Advanced Solutions for Engineering Real-World Challenges)
Abstract The forthcoming sixth generation (6G) of mobile communication networks is envisioned to be AI-native, supporting intelligent services and pervasive computing at unprecedented scale. Among the key paradigms enabling this vision, Federated Learning (FL) has gained prominence as a distributed machine learning framework that allows multiple devices to collaboratively train models without sharing raw data, thereby preserving privacy and reducing the need for centralized storage. This capability is particularly attractive for vision-based applications, where image and video data are both sensitive and bandwidth-intensive. However, the integration of FL with 6G networks presents unique challenges, including communication… More >

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ARTICLE

Effects of Normalised SSIM Loss on Super-Resolution Tasks
Adéla Hamplová, Tomáš Novák, Miroslav Žáček, Jiří Brožek
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3329-3349, 2025, DOI:10.32604/cmes.2025.066025
（This article belongs to the Special Issue: Applied Artificial Intelligence: Advanced Solutions for Engineering Real-World Challenges)
Abstract This study proposes a new component of the composite loss function minimised during training of the Super-Resolution (SR) algorithms—the normalised structural similarity index loss , which has the potential to improve the natural appearance of reconstructed images. Deep learning-based super-resolution (SR) algorithms reconstruct high-resolution images from low-resolution inputs, offering a practical means to enhance image quality without requiring superior imaging hardware, which is particularly important in medical applications where diagnostic accuracy is critical. Although recent SR methods employing convolutional and generative adversarial networks achieve high pixel fidelity, visual artefacts may persist, making the design of… More >

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ARTICLE

Pareto Multi-Objective Reconfiguration of IEEE 123-Bus Unbalanced Power Distribution Networks Using Metaheuristic Algorithms: A Comprehensive Analysis of Power Quality Improvement
Nisa Nacar Çıkan
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3279-3327, 2025, DOI:10.32604/cmes.2025.065442
（This article belongs to the Special Issue: Applied Artificial Intelligence: Advanced Solutions for Engineering Real-World Challenges)
Abstract This study addresses the critical challenge of reconfiguration in unbalanced power distribution networks (UPDNs), focusing on the complex 123-Bus test system. Three scenarios are investigated: (1) simultaneous power loss reduction and voltage profile improvement, (2) minimization of voltage and current unbalance indices under various operational cases, and (3) multi-objective optimization using Pareto front analysis to concurrently optimize voltage unbalance index, active power loss, and current unbalance index. Unlike previous research that oftensimplified system components, this work maintains all equipment, including capacitor banks, transformers, and voltage regulators, to ensure realistic results. The study evaluates twelve metaheuristic More >

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ARTICLE

Enhancing Emotional Expressiveness in Biomechanics Robotic Head: A Novel Fuzzy Approach for Robotic Facial Skin’s Actuators
Nguyen Minh Trieu, Nguyen Truong Thinh
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 477-498, 2025, DOI:10.32604/cmes.2025.061339
（This article belongs to the Special Issue: Applied Artificial Intelligence: Advanced Solutions for Engineering Real-World Challenges)
Abstract In robotics and human-robot interaction, a robot’s capacity to express and react correctly to human emotions is essential. A significant aspect of the capability involves controlling the robotic facial skin actuators in a way that resonates with human emotions. This research focuses on human anthropometric theories to design and control robotic facial actuators, addressing the limitations of existing approaches in expressing emotions naturally and accurately. The facial landmarks are extracted to determine the anthropometric indicators for designing the robot head and is employed to the displacement of these points to calculate emotional values using Fuzzy… More >

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ARTICLE

DaC-GANSAEBF: Divide and Conquer-Generative Adversarial Network—Squeeze and Excitation-Based Framework for Spam Email Identification
Tawfeeq Shawly, Ahmed A. Alsheikhy, Yahia Said, Shaaban M. Shaaban, Husam Lahza, Aws I. AbuEid, Abdulrahman Alzahrani
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3181-3212, 2025, DOI:10.32604/cmes.2025.061608
（This article belongs to the Special Issue: Applied Artificial Intelligence: Advanced Solutions for Engineering Real-World Challenges)
Abstract Email communication plays a crucial role in both personal and professional contexts; however, it is frequently compromised by the ongoing challenge of spam, which detracts from productivity and introduces considerable security risks. Current spam detection techniques often struggle to keep pace with the evolving tactics employed by spammers, resulting in user dissatisfaction and potential data breaches. To address this issue, we introduce the Divide and Conquer-Generative Adversarial Network Squeeze and Excitation-Based Framework (DaC-GANSAEBF), an innovative deep-learning model designed to identify spam emails. This framework incorporates cutting-edge technologies, such as Generative Adversarial Networks (GAN), Squeeze and… More >

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Applied Artificial Intelligence: Advanced Solutions for Engineering Real-World Challenges

Guest Editors

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Published Papers

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