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 >

Zitong Zhao¹, Zixuan Zhang², Zhenxing Niu^3,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-16, 2026, DOI:10.32604/cmc.2025.069752 - 10 November 2025

Abstract Reliable traffic flow prediction is crucial for mitigating urban congestion. This paper proposes Attention-based spatiotemporal Interactive Dynamic Graph Convolutional Network (AIDGCN), a novel architecture integrating Interactive Dynamic Graph Convolution Network (IDGCN) with Temporal Multi-Head Trend-Aware Attention. Its core innovation lies in IDGCN, which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs, and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data. For 15- and 60-min forecasting on METR-LA, AIDGCN achieves MAEs of 0.75% and 0.39%, and RMSEs More >

Hamza Murad Khan¹, Shakila Basheer², Mohammad Tabrez Quasim³, Raja`a Al-Naimi⁴, Vijaykumar Varadarajan⁵, Anwar Khan^1,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-25, 2026, DOI:10.32604/cmc.2025.069327 - 10 November 2025

Abstract With the increasing growth of online news, fake electronic news detection has become one of the most important paradigms of modern research. Traditional electronic news detection techniques are generally based on contextual understanding, sequential dependencies, and/or data imbalance. This makes distinction between genuine and fabricated news a challenging task. To address this problem, we propose a novel hybrid architecture, T5-SA-LSTM, which synergistically integrates the T5 Transformer for semantically rich contextual embedding with the Self-Attention-enhanced (SA) Long Short-Term Memory (LSTM). The LSTM is trained using the Adam optimizer, which provides faster and more stable convergence compared… More >

Qiang Zhang¹, Shuo Feng¹, Shanxiong Chen², Teng Wan¹, Ying Qi^1,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-22, 2026, DOI:10.32604/cmc.2025.068247 - 10 November 2025

Abstract Human motion modeling is a core technology in computer animation, game development, and human-computer interaction. In particular, generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge. Existing methods typically rely on dense keyframe inputs or complex prior structures, making it difficult to balance motion quality and plausibility under conditions such as sparse constraints, long-term dependencies, and diverse motion styles. To address this, we propose a motion generation framework based on a frequency-domain diffusion model, which aims to better model complex motion distributions and enhance generation… More >

Qinzhen Fang^1,2, Dongliang Peng^1,2, Lu Zeng^1,2,*, Zixuan Jiang^1,2

Journal on Artificial Intelligence, Vol.7, pp. 469-484, 2025, DOI:10.32604/jai.2025.073016 - 06 November 2025

Abstract To address the issues of small target miss detection, false positives in complex scenarios, and insufficient real-time performance in maglev train foreign object intrusion detection, this paper proposes a multi-module fusion improvement algorithm, YOLO11-FADA (Fusion of Augmented Features and Dynamic Attention), based on YOLO11. The model achieves collaborative optimization through three key modules: The Local Feature Augmentation Module (LFAM) enhances small target features and mitigates feature loss during down-sampling through multi-scale feature parallel extraction and attention fusion. The Dynamically Tuned Self-Attention (DTSA) module introduces learnable parameters to adjust attention weights dynamically, and, in combination with More >

Peng Xiao¹, Haiyu Xu¹, Peng Xu¹, Zhiwei Guo^1,*, Amr Tolba^2,*, Osama Alfarraj²

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2999-3022, 2025, DOI:10.32604/cmc.2025.066937 - 23 September 2025

Abstract Medical image analysis based on deep learning has become an important technical requirement in the field of smart healthcare. In view of the difficulties in collaborative modeling of local details and global features in multimodal image analysis of ophthalmology, as well as the existence of information redundancy in cross-modal data fusion, this paper proposes a multimodal fusion framework based on cross-modal collaboration and weighted attention mechanism. In terms of feature extraction, the framework collaboratively extracts local fine-grained features and global structural dependencies through a parallel dual-branch architecture, overcoming the limitations of traditional single-modality models in… More >

Charles Munyao^*, John Ndia

Journal on Artificial Intelligence, Vol.7, pp. 329-346, 2025, DOI:10.32604/jai.2025.069226 - 22 September 2025

Abstract The natural language processing (NLP) domain has witnessed significant advancements with the emergence of transformer-based models, which have reshaped the text understanding and generation landscape. While their capabilities are well recognized, there remains a limited systematic synthesis of how these models perform across tasks, scale efficiently, adapt to domains, and address ethical challenges. Therefore, the aim of this paper was to analyze the performance of transformer-based models across various NLP tasks, their scalability, domain adaptation, and the ethical implications of such models. This meta-analysis paper synthesizes findings from 25 peer-reviewed studies on NLP transformer-based models,… More >

Heng Wang¹, Jian-Hua Qin^2,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1037-1052, 2025, DOI:10.32604/cmc.2025.067650 - 29 August 2025

Abstract Vehicle detection is a crucial aspect of intelligent transportation systems (ITS) and autonomous driving technologies. The complexity and diversity of real-world road environments, coupled with traffic congestion, pose significant challenges to the accuracy and real-time performance of vehicle detection models. To address these challenges, this paper introduces a fast and accurate vehicle detection algorithm named BES-Net. Firstly, the BoTNet module is integrated into the backbone network to bolster the model’s long-distance dependency, address the complexities and diversity of road environments, and accelerate the detection speed of the BES-Net network. Secondly, to accommodate the varying sizes… More >

Yasir Khan Jadoon¹, Yasir Noman Khalid¹, Muhammad Attique Khan², Jungpil Shin^3,*, Fatimah Alhayan⁴, Hee-Chan Cho⁵, Byoungchol Chang^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1143-1164, 2025, DOI:10.32604/cmes.2025.066984 - 31 July 2025

Abstract Real-time surveillance is attributed to recognizing the variety of actions performed by humans. Human Action Recognition (HAR) is a technique that recognizes human actions from a video stream. A range of variations in human actions makes it difficult to recognize with considerable accuracy. This paper presents a novel deep neural network architecture called Attention RB-Net for HAR using video frames. The input is provided to the model in the form of video frames. The proposed deep architecture is based on the unique structuring of residual blocks with several filter sizes. Features are extracted from each… More >

Jinrong Li^1,2, Chunhua Wei², Lei Liang^2,3,*, Zhisheng Gao^1,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3259-3280, 2025, DOI:10.32604/cmc.2025.063547 - 03 July 2025

Abstract The Pressure Sensitive Paint Technique (PSP) has gained attention in recent years because of its significant benefits in measuring surface pressure on wind tunnel models. However, in the post-processing process of PSP images, issues such as pressure taps, paint peeling, and contamination can lead to the loss of pressure data on the image, which seriously affects the subsequent calculation and analysis of pressure distribution. Therefore, image inpainting is particularly important in the post-processing process of PSP images. Deep learning offers new methods for PSP image inpainting, but some basic characteristics of convolutional neural networks (CNNs)… More >