Tao Zhang^1,*, Hailun Wang¹, Tianyue Wang¹, Tian Tian²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072696 - 12 January 2026

Abstract The critical components of gas turbines suffer from prolonged exposure to factors such as thermal oxidation, mechanical wear, and airflow disturbances during prolonged operation. These conditions can lead to a series of issues, including mechanical faults, air path malfunctions, and combustion irregularities. Traditional model-based approaches face inherent limitations due to their inability to handle nonlinear problems, natural factors, measurement uncertainties, fault coupling, and implementation challenges. The development of artificial intelligence algorithms has provided an effective solution to these issues, sparking extensive research into data-driven fault diagnosis methodologies. The review mechanism involved searching IEEE Xplore, ScienceDirect,… More >

Gowrishankar Jayaraman¹, Ashok Kumar Munnangi², Ramesh Sekaran³, Arunkumar Gopu³, Manikandan Ramachandran^4,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071124 - 12 January 2026

Abstract Industrial Cyber-Physical Systems (ICPSs) play a vital role in modern industries by providing an intellectual foundation for automated operations. With the increasing integration of information-driven processes, ensuring the security of Industrial Control Production Systems (ICPSs) has become a critical challenge. These systems are highly vulnerable to attacks such as denial-of-service (DoS), eclipse, and Sybil attacks, which can significantly disrupt industrial operations. This work proposes an effective protection strategy using an Artificial Intelligence (AI)-enabled Smart Contract (SC) framework combined with the Heterogeneous Barzilai–Borwein Support Vector (HBBSV) method for industrial-based CPS environments. The approach reduces run time… More >

Chee Chin Lim^1,2,*, Hui Wen Tiu¹, Qi Wei Oung^1,3, Chiew Chea Lau⁴, Xiao Jian Tan^2,5

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070468 - 12 January 2026

Abstract Breast cancer remains one of the leading causes of cancer mortality world-wide, with accurate molecular subtyping is critical for guiding treatment and improving patient outcomes. Traditional molecular subtyping via immuno-histochemistry (IHC) test is invasive, time-consuming, and may not fully represent tumor heterogeneity. This study proposes a non-invasive approach using digital mammography images and deep learning algorithm for classifying breast cancer molecular subtypes. Four pretrained models, including two Convolutional Neural Networks (MobileNet_V3_Large and VGG-16) and two Vision Transformers (ViT_B_16 and ViT_Base_Patch16_Clip_224) were fine-tuned to classify images into HER2-enriched, Luminal, Normal-like, and Triple Negative subtypes. Hyperparameter tuning,… More >

Rashid Ali^1,*, Alaa Omran Almagrabi^2,3

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070224 - 12 January 2026

Abstract Wi-Fi technology has evolved significantly since its introduction in 1997, advancing to Wi-Fi 6 as the latest standard, with Wi-Fi 7 currently under development. Despite these advancements, integrating machine learning into Wi-Fi networks remains challenging, especially in decentralized environments with multiple access points (mAPs). This paper is a short review that summarizes the potential applications of federated reinforcement learning (FRL) across eight key areas of Wi-Fi functionality, including channel access, link adaptation, beamforming, multi-user transmissions, channel bonding, multi-link operation, spatial reuse, and multi-basic servic set (multi-BSS) coordination. FRL is highlighted as a promising framework for More >

Manaliben Amin^*

Journal on Artificial Intelligence, Vol.8, pp. 1-18, 2026, DOI:10.32604/jai.2026.073895 - 07 January 2026

Abstract Artificial intelligence (AI) is steadily making its way into pharmaceutical validation, where it promises faster documentation, smarter testing strategies, and better handling of deviations. These gains are attractive, but in a regulated environment speed is never enough. Regulators want assurance that every system is reliable, that decisions are explainable, and that human accountability remains central. This paper sets out a Human-in-the-Loop (HITL) AI approach for Computer System Validation (CSV) and Computer Software Assurance (CSA). It relies on explainable AI (XAI) tools but keeps structured human review in place, so automation can be used without creating… More >

Pengwei Guo¹, Xiao Tan^2,3,*, Yiming Liu⁴

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.071317 - 08 January 2026

Abstract Crack detection accuracy in computer vision is often constrained by limited annotated datasets. Although Generative Adversarial Networks (GANs) have been applied for data augmentation, they frequently introduce blurs and artifacts. To address this challenge, this study leverages Denoising Diffusion Probabilistic Models (DDPMs) to generate high-quality synthetic crack images, enriching the training set with diverse and structurally consistent samples that enhance the crack segmentation. The proposed framework involves a two-stage pipeline: first, DDPMs are used to synthesize high-fidelity crack images that capture fine structural details. Second, these generated samples are combined with real data to train… More >

Ans D. Alghamdi^*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-35, 2026, DOI:10.32604/cmc.2025.071210 - 09 December 2025

Abstract Multimodal dialogue systems often fail to maintain coherent reasoning over extended conversations and suffer from hallucination due to limited context modeling capabilities. Current approaches struggle with cross-modal alignment, temporal consistency, and robust handling of noisy or incomplete inputs across multiple modalities. We propose MultiAgent-Chain of Thought (CoT), a novel multi-agent chain-of-thought reasoning framework where specialized agents for text, vision, and speech modalities collaboratively construct shared reasoning traces through inter-agent message passing and consensus voting mechanisms. Our architecture incorporates self-reflection modules, conflict resolution protocols, and dynamic rationale alignment to enhance consistency, factual accuracy, and user engagement. More >

Zhijie Lin¹, Chao Yang^1,2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-33, 2026, DOI:10.32604/cmc.2025.070735 - 09 December 2025

Abstract Sustainable aluminum alloys, renowned for their lower energy consumption and carbon emissions, present a critical path towards a circular materials economy. However, their design is fraught with challenges, including complex performance variability due to impurity elements and the time-consuming, cost-prohibitive nature of traditional trial-and-error methods. The high-dimensional parameter space in processing optimization and the reliance on human expertise for quality control further complicate their development. This paper provides a comprehensive review of Artificial Intelligence (AI) techniques applied to sustainable aluminum alloy design, analyzing their methodologies and identifying key challenges and optimization strategies. We review how… More >

Ahmed Abdel-Wahab¹, Mohammad Alkhatib^2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-34, 2026, DOI:10.32604/cmc.2025.070010 - 09 December 2025

Abstract Deepfake is a sort of fake media made by advanced AI methods like Generative Adversarial Networks (GANs). Deepfake technology has many useful uses in education and entertainment, but it also raises a lot of ethical, social, and security issues, such as identity theft, the dissemination of false information, and privacy violations. This study seeks to provide a comprehensive analysis of several methods for identifying and circumventing Deepfakes, with a particular focus on image-based Deepfakes. There are three main types of detection methods: classical, machine learning (ML) and deep learning (DL)-based, and hybrid methods. There are… More >

Cyreneo Dofitas¹, Yong-Woon Kim², Yung-Cheol Byun^3,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.069374 - 09 December 2025

Abstract Recent advances in deep learning have significantly improved flood detection and segmentation from aerial and satellite imagery. However, conventional convolutional neural networks (CNNs) often struggle in complex flood scenarios involving reflections, occlusions, or indistinct boundaries due to limited contextual modeling. To address these challenges, we propose a hybrid flood segmentation framework that integrates a Vision Transformer (ViT) encoder with a U-Net decoder, enhanced by a novel Flood-Aware Refinement Block (FARB). The FARB module improves boundary delineation and suppresses noise by combining residual smoothing with spatial-channel attention mechanisms. We evaluate our model on a UAV-acquired flood More >