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 >

Hefei Wang, Ruichun Gu^*, Jingyu Wang, Xiaolin Zhang, Hui Wei

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.069251 - 10 November 2025

Abstract Graph Federated Learning (GFL) has shown great potential in privacy protection and distributed intelligence through distributed collaborative training of graph-structured data without sharing raw information. However, existing GFL approaches often lack the capability for comprehensive feature extraction and adaptive optimization, particularly in non-independent and identically distributed (NON-IID) scenarios where balancing global structural understanding and local node-level detail remains a challenge. To this end, this paper proposes a novel framework called GFL-SAR (Graph Federated Collaborative Learning Framework Based on Structural Amplification and Attention Refinement), which enhances the representation learning capability of graph data through a dual-branch… More >

Zhiyong Deng¹, Yanchen Ye², Jiangling Guo^1,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-18, 2026, DOI:10.32604/cmc.2025.069090 - 10 November 2025

Abstract With the rapid expansion of drone applications, accurate detection of objects in aerial imagery has become crucial for intelligent transportation, urban management, and emergency rescue missions. However, existing methods face numerous challenges in practical deployment, including scale variation handling, feature degradation, and complex backgrounds. To address these issues, we propose Edge-enhanced and Detail-Capturing You Only Look Once (EHDC-YOLO), a novel framework for object detection in Unmanned Aerial Vehicle (UAV) imagery. Based on the You Only Look Once version 11 nano (YOLOv11n) baseline, EHDC-YOLO systematically introduces several architectural enhancements: (1) a Multi-Scale Edge Enhancement (MSEE) module… More >

Longfei Gao^*, Weidong Wang, Dieyun Ke

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-15, 2026, DOI:10.32604/cmc.2025.068873 - 10 November 2025

Abstract At present, energy consumption is one of the main bottlenecks in autonomous mobile robot development. To address the challenge of high energy consumption in path planning for autonomous mobile robots navigating unknown and complex environments, this paper proposes an Attention-Enhanced Dueling Deep Q-Network (AD-Dueling DQN), which integrates a multi-head attention mechanism and a prioritized experience replay strategy into a Dueling-DQN reinforcement learning framework. A multi-objective reward function, centered on energy efficiency, is designed to comprehensively consider path length, terrain slope, motion smoothness, and obstacle avoidance, enabling optimal low-energy trajectory generation in 3D space from the… More >

Yanting Zhang¹, Qiyue Liu^1,2, Chuanzhao Tian^1,2,*, Xuewen Li¹, Na Yang¹, Feng Zhang¹, Hongyue Zhang³

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-25, 2026, DOI:10.32604/cmc.2025.068403 - 10 November 2025

Abstract High-resolution remote sensing images (HRSIs) are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies. However, their significant scale changes and wealth of spatial details pose challenges for semantic segmentation. While convolutional neural networks (CNNs) excel at capturing local features, they are limited in modeling long-range dependencies. Conversely, transformers utilize multihead self-attention to integrate global context effectively, but this approach often incurs a high computational cost. This paper proposes a global-local multiscale context network (GLMCNet) to extract both global and local multiscale contextual information from HRSIs.… More >

Shuangyuan Li^1,*, Zhengwei Wang², Jiaming Liang³, Yichen Wang⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3993-4011, 2025, DOI:10.32604/cmes.2025.073896 - 23 December 2025

Abstract Object detection plays a crucial role in the field of computer vision, and small object detection has long been a challenging issue within this domain. In order to improve the performance of object detection on small targets, this paper proposes an enhanced structure for YOLOv5, termed ATC-YOLOv5. Firstly, a novel structure, AdaptiveTrans, is introduced into YOLOv5 to facilitate efficient communication between the encoder and the detector. Consequently, the network can better address the adaptability challenge posed by objects of different sizes in object detection. Additionally, the paper incorporates the CBAM (Convolutional Block Attention Module) attention More >

Zheng Yin, Zhao Zhang^*

Journal on Artificial Intelligence, Vol.7, pp. 633-645, 2025, DOI:10.32604/jai.2025.074450 - 24 December 2025

Abstract Power load forecasting load forecasting is a core task in power system scheduling, operation, and planning. To enhance forecasting performance, this paper proposes a dual-input deep learning model that integrates Convolutional Neural Networks, Gated Recurrent Units, and a self-attention mechanism. Based on standardized data cleaning and normalization, the method performs convolutional feature extraction and recurrent modeling on load and meteorological time series separately. The self-attention mechanism is then applied to assign weights to key time steps, after which the two feature streams are flattened and concatenated. Finally, a fully connected layer is used to generate More >

Palak Bari, Gurnur Bedi, Khushi Joshi, Anupama Jawale^*

Journal on Artificial Intelligence, Vol.7, pp. 499-508, 2025, DOI:10.32604/jai.2025.072531 - 17 November 2025

Abstract This study investigates sarcasm detection in text using a dataset of 8095 sentences compiled from MUStARD and HuggingFace repositories, balanced across sarcastic and non-sarcastic classes. A sequential baseline model (LSTM) is compared with transformer-based models (RoBERTa and XLNet), integrated with attention mechanisms. Transformers were chosen for their proven ability to capture long-range contextual dependencies, whereas LSTM serves as a traditional benchmark for sequential modeling. Experimental results show that RoBERTa achieves 0.87 accuracy, XLNet 0.83, and LSTM 0.52. These findings confirm that transformer architectures significantly outperform recurrent models in sarcasm detection. Future work will incorporate multimodal More >

Qiang Ma^1,2,3,4, Zepeng Li^1,2, Kai Yang^1,2,*, Shaofeng Zhang^1,2, Zhuopei Wei^1,2

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1607-1634, 2025, DOI:10.32604/sdhm.2025.069876 - 17 November 2025

Abstract Effective fault identification is crucial for bearings, which are critical components of mechanical systems and play a pivotal role in ensuring overall safety and operational efficiency. Bearings operate under variable service conditions, and their diagnostic environments are complex and dynamic. In the process of bearing diagnosis, fault datasets are relatively scarce compared with datasets representing normal operating conditions. These challenges frequently cause the practicality of fault detection to decline, the extraction of fault features to be incomplete, and the diagnostic accuracy of many existing models to decrease. In this work, a transfer-learning framework, designated DSCNN-HA-TL,… More >

Yating Xu¹, Mansheng Xiao^1,*, Mengxing Gao¹, Zhenzhen Liu¹, Zeyu Xiao²

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1635-1656, 2025, DOI:10.32604/sdhm.2025.068822 - 17 November 2025

Abstract During the operation, maintenance and upkeep of concrete buildings, surface cracks are often regarded as important warning signs of potential damage. Their precise segmentation plays a key role in assessing the health of a building. Traditional manual inspection is subjective, inefficient and has safety hazards. In contrast, current mainstream computer vision–based crack segmentation methods still suffer from missed detections, false detections, and segmentation discontinuities. These problems are particularly evident when dealing with small cracks, complex backgrounds, and blurred boundaries. For this reason, this paper proposes a lightweight building surface crack segmentation method, HL-YOLO, based on… More >