Longhu Huang, Sheng Zheng^*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082622 - 23 July 2026

Abstract In the field of online automated defect inspection for small-size liquid crystal display modules (LCMs), the accuracy of module loading is crucial for the subsequent lighting inspection. However, due to the physical characteristics of the module’s flexible ribbon cable, the ribbon often exhibits varying degrees of curling, causing conventional monocular vision systems to frequently encounter local underexposure or overexposure when positioning the workpiece, resulting in loss of local details and significantly affecting subsequent positioning and loading. To address the problem of local image degradation caused by abnormal exposure, this study proposes a regional image generation… More >

Hangyu Hu¹, Liangrui Zhang¹, Xiaowei Huang¹, Xingmiao Yao^1,2,*, Youyang Qu³, Xia Wu¹, Guangmin Hu^1,2

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081752 - 23 July 2026

Abstract With the evolution of information technology toward more advanced intelligence and automation, Security Orchestration, Automation, and Response (SOAR) has become a critical foundation for security incident handling, owing to its intelligent orchestration capabilities. Security playbooks, as the core mechanism for automated response in SOAR, require well-designed workflows and precise action matching to ensure efficient and accurate alert handling. However, with the rising sophistication of attacks and the expanding scale of security alerts, traditional expert-driven playbook recommendation approaches often degrade in recommendation quality or completely fail when existing playbook repositories cannot adequately cover unknown or novel… More >

Abolfazl Saghafi^1,*, Soodeh Moallemian², Miray Budak², Rutvik Deshpande²

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082996 - 15 June 2026

Abstract Missing data remain a persistent challenge in statistical analysis and machine learning because many predictive methods require complete observations. Generative Adversarial Imputation Networks (GAIN) offer a flexible deep-learning approach for missing value imputation, but their practical use is limited by convergence instability, sensitivity to hyperparameter selection, and dependence on outdated software implementations. To address these limitations, we propose Enhanced Generative Adversarial Imputation Networks (EGAIN), a modernized extension of GAIN implemented in TensorFlow 2.x. EGAIN incorporates convolution-based generator and discriminator networks, a channel-stacked representation of the data and mask, and checkpoint-based training diagnostics to improve stability More >

Zafer Serin^1,*, Cihan Karakuzu², Uğur Yüzgeç²

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.079254 - 27 May 2026

Abstract This study proposes SWAGE-3D (Spectral Wasserstein Attention Generative Ensemble), an enhanced 3D-VAE-GAN framework for single-view 3D object reconstruction using voxel-based representations. The proposed model integrates RGB-D encoding, Wasserstein adversarial learning with hybrid Lipschitz regularization, and a self-attention–augmented generator to improve structural coherence and training stability. By combining variational latent modeling with stabilized Wasserstein optimization, the framework aims to address common challenges in 3D generative modeling, including mode collapse, unstable convergence, and insufficient global consistency. The encoder employs a depth-aware feature extraction strategy, while the discriminator utilizes a hybrid spectral normalization and gradient penalty mechanism to More > Graphic Abstract

Ziang Wu¹, Xuanyu Zhang², Yinbo Yu³, Qi Zhu³, Jerry Chun-Wei Lin⁴, Chunwei Tian^5,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.078842 - 09 April 2026

Abstract Image super-resolution is a significant area in the field of image processing, with broad applications across multiple domains. In recent years, advancements in Generative Adversarial Networks (GANs) have led to an increased adoption of GAN-based methods in image super-resolution, yielding remarkable results. However, there is still a limited amount of research that systematically and comprehensively summarizes the various GAN-based techniques for image super-resolution. This paper provides a comparative study that elucidates the application differences of GANs in this field. We begin by reviewing the development of GANs and introducing their popular variants used in image… More >

Manh-Tuan Ha¹, Nhu-Nghia Bui², Dinh-Quy Vu^1,*, Thai-Viet Dang^2,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077237 - 09 April 2026

Abstract In the realm of unmanned surface vehicle (USV) operations, leveraging environmental factors to enhance situational awareness has garnered significant academic attention. Developing vision systems for USVs presents considerable challenges, mainly due to variable observational conditions and angular vibrations caused by hydrodynamic forces. The paper proposed a novel MDGAN-DIFI network for end-to-end multi-object tracking (MOT), specifically designed for camera systems mounted on USVs. Beyond enhancing traditional MOT models, the proposed MDGAN-DIFI includes preprocessing modules designed to enhance the efficiency of processing input signal quality. Initially, a Deep Iterative Frame Interpolation (DIFI) module is used to stabilize… More >

Shtwai Alsubai¹, Ahmad Almadhor², Abdullah Al Hejaili³, Najib Ben Aoun^4,5,*, Tahani Alsubait⁶, Vincent Karovič^7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.074687 - 26 February 2026

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 >

Sameera V. Mohd Sagheer^1,#,*, U. Nimitha^2,#, P. M. Ameer², Muneer Parayangat³, Mohamed Abbas³, Krishna Prakash Arunachalam⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2025.067108 - 26 February 2026

Abstract Over the years, Generative Adversarial Networks (GANs) have revolutionized the medical imaging industry for applications such as image synthesis, denoising, super resolution, data augmentation, and cross-modality translation. The objective of this review is to evaluate the advances, relevances, and limitations of GANs in medical imaging. An organised literature review was conducted following the guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The literature considered included peer-reviewed papers published between 2020 and 2025 across databases including PubMed, IEEE Xplore, and Scopus. The studies related to applications of GAN architectures in medical imaging with… More >

Mangala Natampalli^1,2,*, Kruthika Gottikere Channagangaiah³, Bodi Eswara Reddy¹, Rajkumar Buyya⁴, Venugopal Kupanna Rajuk³, Sundaraja Sitharama Iyengar⁵, Lalit Mohan Patnaik⁶

Journal on Internet of Things, Vol.8, pp. 1-30, 2026, DOI:10.32604/jiot.2026.067726 - 24 February 2026

Abstract Contemporary banking focuses on self-service and customer-centric experience by harnessing Internet of Things (IoT) and Cloud Computing. However, these systems remain vulnerable to multifaceted cyberattacks. The IoT-Cloud-based systems can be safeguarded through authentication, confidentiality, integrity, availability, privacy, and non-repudiation. This work proposes a multi-stage security consisting of Generative Adversarial Networks (GAN) for facial authentication, hybrid Curvelet and Least-Significant-Bit (LSB) Steganography for data protection, and Ethereum Blockchain for transactions and storage security to provide complete protection to the self-service pipeline. Customers are authenticated using live images from IoT cameras by GAN facial recognition. Improved data concealment… More >

Su Zhang¹, Song Deng^1,*, Qingsheng Liu²

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072777 - 10 February 2026

Abstract Accurate and reliable power system data are fundamental for critical operations such as grid monitoring, fault diagnosis, and load forecasting, underpinned by increasing intelligentization and digitalization. However, data loss and anomalies frequently compromise data integrity in practical settings, significantly impacting system operational efficiency and security. Most existing data recovery methods require complete datasets for training, leading to substantial data and computational demands and limited generalization. To address these limitations, this study proposes a missing data imputation model based on an improved Generative Adversarial Network (BAC-GAN). Within the BAC-GAN framework, the generator utilizes Bidirectional Long Short-Term… More >