CMES | Special Issues: Applied Artificial Intelligence: Advanced Solutions for Engineering Real-World Challenges

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Attention Mechanisms and FFM Feature Fusion Module-Based Modification of the Deep Neural Network for Detection of Structural Cracks
Tao Jin, Zhekun Shou, Hongchao Liu, Yuchun Shao
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.076415
（This article belongs to the Special Issue: Applied Artificial Intelligence: Advanced Solutions for Engineering Real-World Challenges)
Abstract This research centers on structural health monitoring of bridges, a critical transportation infrastructure. Owing to the cumulative action of heavy vehicle loads, environmental variations, and material aging, bridge components are prone to cracks and other defects, severely compromising structural safety and service life. Traditional inspection methods relying on manual visual assessment or vehicle-mounted sensors suffer from low efficiency, strong subjectivity, and high costs, while conventional image processing techniques and early deep learning models (e.g., U-Net, Faster R-CNN) still perform inadequately in complex environments (e.g., varying illumination, noise, false cracks) due to poor perception of fine… More >

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Artificial Neural Network-Based Flow and Heat Transfer Analysis of Williamson Nanofluid over a Moving Wedge: Effects of Thermal Radiation, Viscous Dissipation, and Homogeneous-Heterogeneous
Adnan Ashique, Nehad Ali Shah, Usman Afzal, Yazen Alawaideh, Sohaib Abdal, Jae Dong Chung
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2025.073292
（This article belongs to the Special Issue: Applied Artificial Intelligence: Advanced Solutions for Engineering Real-World Challenges)
Abstract There is a need for accurate prediction of heat and mass transfer in aerodynamically designed, non-Newtonian nanofluids across aerodynamically designed, high-flux biomedical micro-devices for thermal management and reactive coating processes, but existing work is not uncharacteristically remiss regarding viscoelasticity, radiative heating, viscous dissipation, and homogeneous–heterogeneous reactions within a single scheme that is calibrated. This research investigates the flow of Williamson nanofluid across a dynamically wedged surface under conditions that include viscous dissipation, thermal radiation, and homogeneous-heterogeneous reactions. The paper develops a detailed mathematical approach that utilizes boundary layers to transform partial differential equations into ordinary… More >

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ARTICLE

DOEP Framework for Photovoltaic Power Prediction
Yung-Yao Chen, Desri Kristina Silalahi, Atinkut Atinafu Yilma, Chao-Lung Yang
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.075040
（This article belongs to the Special Issue: Applied Artificial Intelligence: Advanced Solutions for Engineering Real-World Challenges)
Abstract Accurate photovoltaic (PV) power generation forecasting is essential for the efficient integration of renewable energy into power grids. However, the nonlinear and non-stationary characteristics of PV power signals, driven by fluctuating weather conditions, pose significant challenges for reliable prediction. This study proposes a DOEP (Decomposition–Optimization–Error Correction–Prediction) framework, a hybrid forecasting approach that integrates adaptive signal decomposition, machine learning, metaheuristic optimization, and error correction. The PV power signal is first decomposed using CEEMDAN to extract multi-scale temporal features. Subsequently, the hyperparameters and window sizes of the LSSVM are optimized using a Segment-based EBQPSO strategy. The main… More >

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ARTICLE

Multimodal Trajectory Generation for Robotic Motion Planning Using Transformer-Based Fusion and Adversarial Learning
Shtwai Alsubai, Ahmad Almadhor, Abdullah Al Hejaili, Najib Ben Aoun, Tahani Alsubait, Vincent Karovič
CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.074687
（This article belongs to the Special Issue: Applied Artificial Intelligence: Advanced Solutions for Engineering Real-World Challenges)
Abstract In Human–Robot Interaction (HRI), generating robot trajectories that accurately reflect user intentions while ensuring physical realism remains challenging, especially in unstructured environments. In this study, we develop a multimodal framework that integrates symbolic task reasoning with continuous trajectory generation. The approach employs transformer models and adversarial training to map high-level intent to robotic motion. Information from multiple data sources, such as voice traits, hand and body keypoints, visual observations, and recorded paths, is integrated simultaneously. These signals are mapped into a shared representation that supports interpretable reasoning while enabling smooth and realistic motion generation. Based… More >

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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, Vol.145, No.3, pp. 4225-4243, 2025, 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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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

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