Feng-Cheng Lin^*, Bo-Chiao Jan, Hui-An Wu

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079393 - 08 May 2026

Abstract With the increasing refinement of ornamental fish culture, understanding fish behavioral patterns has become critical. Fish movements not only reflect daily activity ranges but also reveal responses to environmental changes such as water currents and obstacles. However, traditional manual observation is limited by manpower and time, making it difficult to record fish behaviors over long periods stably. Existing automated tracking techniques often suffer from ID switches and track interruptions caused by rapid fish movement, occlusions, or intermingling, which in turn degrade the reliability of subsequent analyses. This paper proposes a deep learning-based multi-object fish tracking… More >

Wenfeng Wang^1,*, Wenjie Fan¹, Fang Dong¹, Bin Zeng¹, Wenxin Yu¹, Xiangping Deng²

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078416 - 08 May 2026

Abstract Unmanned Aerial Vehicle (UAV) image object detection has been widely applied in many fields. However, compared with ordinary natural images, UAV images often exhibit complex backgrounds, a predominance of small objects, and significant variations in target scales, which cause traditional detection algorithms to easily suffer from missed or false detections with insufficient accuracy. To address these issues, this paper proposes a novel UAV image object detection algorithm named DMA-YOLO based on the YOLOv8s model, incorporating a deep diverse branch block and multi-scale auxiliary feature. First, a DF-C2f module integrating a deep diverse branch block and… More >

Xian Wu, Liang Wan^*, Jingxia Ren, Bangfeng Zhang

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077928 - 08 May 2026

Abstract Traditional malware detection models rely on a single feature source for detection, resulting in high false positive or false negative rates due to incomplete information. In addition, conventional models depend on manual feature engineering, which is inefficient and hard to adapt to new malware variants. To address these challenges, this paper proposes a malware detection model called WAFDect based on a self-attention mechanism with multi-source feature fusion. The model consists of two key designs. First, we construct a multi-source feature extraction model that analyzes multi-source data such as API call sequences, registry operation logs, file… More >

Jinjiang Lin, Yuan Lu, Han Li, Xiaolong Cai, Enyi Chen, Jiansheng Guan^*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077916 - 08 May 2026

Abstract Manipulating objects based on verbal commands in cluttered environments remains a critical challenge in robotic arm research. Verbal commands possess high semantic abstraction, while precise grasping and placement actions rely on fine-grained geometric perception. The disparity between these two domains is the primary cause of operational errors. Particularly in certain cluttered scenarios, visual-spatial noise and background redundancy further disrupt attention distribution, significantly degrading the generalization capabilities of existing methods in unseen environments. To address these issues, this paper proposes the Secondary Realignment (SR) framework. It decouples vision-language alignment and vision-action alignment into two stages, mitigating More >

Yuxiao Shi¹, Jinglin Zhang², Yuxia Li^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.078601 - 27 April 2026

Abstract Traffic light detection and fault identification using images from road traffic cameras are important for intelligent traffic management and urban safety monitoring. However, images collected in real traffic environments show clear differences in camera view, lighting conditions, weather, and background complexity. As a result, traffic lights vary greatly in scale, spatial location, and appearance, which reduces detection accuracy in complex scenes. To deal with this problem, this paper presents a multi-scene traffic light detection and fault identification framework based on dual-attention image fusion. Large-scale road camera data from the Chengdu Traffic Management Bureau are used,… More >

Sobhan Manjili¹, Saeid Jafarzadeh Ghoushchi¹, Mohammad Reza Maghami^2,*, Mazlan Mohamed^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.076217 - 27 April 2026

Abstract Accurate electricity load forecasting is crucial for optimizing power distribution networks, especially in rapidly growing cities like Tabriz (annual consumption growth of 7.2%). This study presents a hybrid AI framework integrating the Temporal Fusion Transformer (TFT) and XGBoost for residual error correction. The model is trained and evaluated using actual consumption data from Tabriz’s distribution network (2021–2023). Compared to a baseline TFT model, the proposed framework demonstrates a 11.2% reduction in RMSE (from 0.1249 to 0.1109) and a 10.7% decrease in MAE (from 0.0998 to 0.0891). Attention mechanism analysis reveals temperature (importance coefficient = 0.32), More >

Faten S. Alamri¹, Muhammad Amjad Raza^2,3, Abeer Rashad Mirdad⁴, Adil Ali Saleem², Tanzila Saba^4,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077084 - 09 April 2026

Abstract Rapid expansion of Industrial Internet of Things (IIoT) systems has heightened the vulnerability of critical infrastructure to sophisticated malware attacks. Traditional signature-based detection methods are ineffective against evolving threats, and many machine learning models fail to capture temporal behavior, offer interpretability, or operate efficiently in resource-constrained environments. This study proposes HMF-Net, a Hierarchical Multi-Feature Network, for accurate, interpretable, and efficient IIoT malware detection. HMF-Net combines hierarchical VT-Tag embedding (HVTE) to model semantic behavioral information, temporal detection ratio analysis (TDRA) to capture confidence variations for polymorphic malware, and static structural binary features. These features are fused… More >

Yan-Hao Huang^*, Yu-Tse Huang

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076800 - 09 April 2026

Abstract Diabetic Retinopathy (DR) is a common microvascular complication of diabetes that progressively damages the retinal blood vessels and, without timely treatment, can lead to irreversible vision loss. In clinical practice, DR is typically diagnosed by ophthalmologists through visual inspection of fundus images, a process that is time-consuming and prone to inter- and intra-observer variability. Recent advances in artificial intelligence, particularly Convolutional Neural Networks (CNNs) and Transformer-based models, have shown strong potential for automated medical image classification and decision support. In this study, we propose a Position-Wise Attention-Enhanced Vision Transformer (PWAE-ViT), which integrates a positional attention… More >

Trong-Thua Huynh^1,*, De-Thu Huynh², Du-Thang Phu¹, Hong-Son Nguyen¹, Quoc H. Nguyen³

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076623 - 09 April 2026

Abstract This paper introduces MobiIris, a lightweight deep network for mobile iris recognition that enhances attention and specifically addresses the balance between accuracy and efficiency on devices with limited resources. The proposed model is based on the large version of MobileNetV3 and adds more spatial attention blocks and an embedding-based head that was trained using margin-based triplet learning, enabling fine-grained modeling of iris textures in a compact representation. To further improve discriminability, we design a training pipeline that combines dynamic-margin triplet loss, a staged hard/semi-hard negative mining strategy, and feature-level knowledge distillation from a ResNet-50 teacher.… More >

Qiuxiao Mou, Haoyu Gui, Xianghong Tang^*, Jianguang Lu

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076251 - 09 April 2026

Abstract Electrocardiogram (ECG) is a widely used non-invasive tool for diagnosing cardiovascular diseases. ECG zero-shot classification involves pre-training a model on a large dataset to classify unknown disease categories. However, existing ECG feature extraction networks often neglect key lead signals and spatial topology dependencies during cross-modal alignment. To address these issues, we propose a multimodal channel compression graph attention alignment network (MCCGAA). MCCGAA incorporates a channel attention module (CAM) to effectively integrate key lead features and a graph attention-based alignment network to capture spatial dependencies, enhancing cross-modal alignment. Additionally, MCCGAA employs a log-sum-exp loss function, improving More >