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 >

Xiaolu Wang^1,2, Haoyu Sun¹, Aiguo Wang¹, Xin Xia^3,*

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.073991 - 27 January 2026

Abstract To address the issues of insufficient and imbalanced data samples in proton exchange membrane fuel cell (PEMFC) performance degradation prediction, this study proposes a data augmentation-based model to predict PEMFC performance degradation. Firstly, an improved generative adversarial network (IGAN) with adaptive gradient penalty coefficient is proposed to address the problems of excessively fast gradient descent and insufficient diversity of generated samples. Then, the IGAN is used to generate data with a distribution analogous to real data, thereby mitigating the insufficiency and imbalance of original PEMFC samples and providing the prediction model with training data rich More >

Hongwei Wu^*, Guojian Li, Hanyun Zhang, Zi Ye, Chao Ma

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071008 - 12 January 2026

Abstract Federated Learning (FL) protects data privacy through a distributed training mechanism, yet its decentralized nature also introduces new security vulnerabilities. Backdoor attacks inject malicious triggers into the global model through compromised updates, posing significant threats to model integrity and becoming a key focus in FL security. Existing backdoor attack methods typically embed triggers directly into original images and consider only data heterogeneity, resulting in limited stealth and adaptability. To address the heterogeneity of malicious client devices, this paper proposes a novel backdoor attack method named Capability-Adaptive Shadow Backdoor Attack (CASBA). By incorporating measurements of clients’… 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 >

Yifan Zhang¹, Yong Gan^2,*, Mengke Tang¹, Xinxin Gan³

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.068880 - 09 December 2025

Abstract High-resolution remote sensing imagery is essential for critical applications such as precision agriculture, urban management planning, and military reconnaissance. Although significant progress has been made in single-image super-resolution (SISR) using generative adversarial networks (GANs), existing approaches still face challenges in recovering high-frequency details, effectively utilizing features, maintaining structural integrity, and ensuring training stability—particularly when dealing with the complex textures characteristic of remote sensing imagery. To address these limitations, this paper proposes the Improved Residual Module and Attention Mechanism Network (IRMANet), a novel architecture specifically designed for remote sensing image reconstruction. IRMANet builds upon the Super-Resolution… More >