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 >

Yujie Wu¹, Xing Deng^1,*, Haijian Shao¹, Ke Cheng¹, Ming Zhang¹, Yingtao Jiang², Fei Wang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2709-2778, 2025, DOI:10.32604/cmes.2025.066647 - 30 September 2025

Abstract The application of generative artificial intelligence (AI) is bringing about notable changes in anime creation. This paper surveys recent advancements and applications of diffusion and language models in anime generation, focusing on their demonstrated potential to enhance production efficiency through automation and personalization. Despite these benefits, it is crucial to acknowledge the substantial initial computational investments required for training and deploying these models. We conduct an in-depth survey of cutting-edge generative AI technologies, encompassing models such as Stable Diffusion and GPT, and appraise pivotal large-scale datasets alongside quantifiable evaluation metrics. Review of the surveyed literature… More >

Yuzhen Liu^1,2, Jiasheng Yin^1,2, Yixuan Chen^1,2, Jin Wang^1,2, Xiaolan Zhou^1,2, Xiaoliang Wang^1,2,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3965-3980, 2025, DOI:10.32604/cmc.2025.068500 - 23 September 2025

Abstract Multi-instance image generation remains a challenging task in the field of computer vision. While existing diffusion models demonstrate impressive fidelity in image generation, they often struggle with precisely controlling each object’s shape, pose, and size. Methods like layout-to-image and mask-to-image provide spatial guidance but frequently suffer from object shape distortion, overlaps, and poor consistency, particularly in complex scenes with multiple objects. To address these issues, we introduce PolyDiffusion, a contour-based diffusion framework that encodes each object’s contour as a boundary-coordinate sequence, decoupling object shapes and positions. This approach allows for better control over object geometry… More >

Yixin Tang^1,2, Minqing Zhang^1,2,3,*, Peizheng Lai^1,2, Ya Yue^1,2, Fuqiang Di^1,2,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5733-5750, 2025, DOI:10.32604/cmc.2025.064901 - 30 July 2025

Abstract Traditional steganography conceals information by modifying cover data, but steganalysis tools easily detect such alterations. While deep learning-based steganography often involves high training costs and complex deployment. Diffusion model-based methods face security vulnerabilities, particularly due to potential information leakage during generation. We propose a fixed neural network image steganography framework based on secure diffusion models to address these challenges. Unlike conventional approaches, our method minimizes cover modifications through neural network optimization, achieving superior steganographic performance in human visual perception and computer vision analyses. The cover images are generated in an anime style using state-of-the-art diffusion More >

Mariam Ishtiaq^1,2, Jong-Un Won^1,2,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5343-5361, 2025, DOI:10.32604/cmc.2025.061466 - 06 March 2025

Abstract Fire detection has held stringent importance in computer vision for over half a century. The development of early fire detection strategies is pivotal to the realization of safe and smart cities, inhabitable in the future. However, the development of optimal fire and smoke detection models is hindered by limitations like publicly available datasets, lack of diversity, and class imbalance. In this work, we explore the possible ways forward to overcome these challenges posed by available datasets. We study the impact of a class-balanced dataset to improve the fire detection capability of state-of-the-art (SOTA) vision-based models and propose… More >

Sabina Rakhmetulayeva^1,*, Zhandos Zhanabekov², Aigerim Bolshibayeva³

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4503-4523, 2024, DOI:10.32604/cmc.2024.057974 - 19 December 2024

Abstract The applications of machine learning (ML) in the medical domain are often hindered by the limited availability of high-quality data. To address this challenge, we explore the synthetic generation of echocardiography images (echoCG) using state-of-the-art generative models. We conduct a comprehensive evaluation of three prominent methods: Cycle-consistent generative adversarial network (CycleGAN), Contrastive Unpaired Translation (CUT), and Stable Diffusion 1.5 with Low-Rank Adaptation (LoRA). Our research presents the data generation methodology, image samples, and evaluation strategy, followed by an extensive user study involving licensed cardiologists and surgeons who assess the perceived quality and medical soundness of More > Graphic Abstract

Dinh Phu Cuong Le^1,2, Dong Wang¹, Viet-Tuan Le^3,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 37-60, 2024, DOI:10.32604/cmc.2024.050790 - 18 July 2024

Abstract Transformer models have emerged as dominant networks for various tasks in computer vision compared to Convolutional Neural Networks (CNNs). The transformers demonstrate the ability to model long-range dependencies by utilizing a self-attention mechanism. This study aims to provide a comprehensive survey of recent transformer-based approaches in image and video applications, as well as diffusion models. We begin by discussing existing surveys of vision transformers and comparing them to this work. Then, we review the main components of a vanilla transformer network, including the self-attention mechanism, feed-forward network, position encoding, etc. In the main part of More >

S. Balaji^*, S. Karthik

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 727-743, 2023, DOI:10.32604/iasc.2023.037409 - 29 April 2023

Abstract The creation of national energy strategy cannot proceed without accurate projections of future electricity consumption; this is because EC is intimately tied to other forms of energy, such as oil and natural gas. For the purpose of determining and bettering overall energy consumption, there is an urgent requirement for accurate monitoring and calculation of EC at the building level using cutting-edge technology such as data analytics and the internet of things (IoT). Soft computing is a subset of AI that tries to design procedures that are more accurate and reliable, and it has proven to… More >

Asma Baccouche^1,*, Begonya Garcia-Zapirain², Cristian Castillo Olea², Adel S. Elmaghraby¹

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1407-1425, 2021, DOI:10.32604/cmc.2021.018461 - 04 June 2021

Abstract With recent breakthroughs in artificial intelligence, the use of deep learning models achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous applications provided efficient solutions to assist radiologists for medical imaging analysis. For instance, automatic lesion detection and classification in mammograms is still considered a crucial task that requires more accurate diagnosis and precise analysis of abnormal lesions. In this paper, we propose an end-to-end system, which is based on You-Only-Look-Once (YOLO) model, to simultaneously localize and classify suspicious breast lesions from entire mammograms. The proposed system first preprocesses the raw images,… More >

J.R. Zabadal¹, B. Bodmann¹, V. G. Ribeiro², A. Silveira², S. Silveira²

CMES-Computer Modeling in Engineering & Sciences, Vol.103, No.4, pp. 215-227, 2014, DOI:10.3970/cmes.2014.103.215

Abstract This work presents a new analytical method to transform exact solutions of linear diffusion equations into exact ones for nonlinear advection-diffusion models. The proposed formulation, based on Bäcklund transformations, is employed to obtain velocity fields for the unsteady two-dimensional Helmholtz equation, starting from analytical solutions of a heat conduction type model. More >