Zongzhe Xu, Ming Yu^*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2805-2826, 2025, DOI:10.32604/cmc.2025.066244 - 03 July 2025

Abstract As the group-buying model shows significant progress in attracting new users, enhancing user engagement, and increasing platform profitability, providing personalized recommendations for group-buying users has emerged as a new challenge in the field of recommendation systems. This paper introduces a group-buying recommendation model based on multi-head attention mechanisms and multi-task learning, termed the Multi-head Attention Mechanisms and Multi-task Learning Group-Buying Recommendation (MAMGBR) model, specifically designed to optimize group-buying recommendations on e-commerce platforms. The core dataset of this study comes from the Chinese maternal and infant e-commerce platform “Beibei,” encompassing approximately 430,000 successful group-buying actions and… More >

Ronghua Wang¹, Shibao Sun^1,*, Pengcheng Zhao^1,*, Xianglan Yang², Xingjia Wei¹, Changyang Hu¹

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3519-3539, 2025, DOI:10.32604/cmc.2025.066212 - 03 July 2025

Abstract The capacity to diagnose faults in rolling bearings is of significant practical importance to ensure the normal operation of the equipment. Frequency-domain features can effectively enhance the identification of fault modes. However, existing methods often suffer from insufficient frequency-domain representation in practical applications, which greatly affects diagnostic performance. Therefore, this paper proposes a rolling bearing fault diagnosis method based on a Multi-Scale Fusion Network (MSFN) using the Time-Division Fourier Transform (TDFT). The method constructs multi-scale channels to extract time-domain and frequency-domain features of the signal in parallel. A multi-level, multi-scale filter-based approach is designed to More >

Pengfei Wang^1,2,3, Jiwu Sun², Lu Lu^1,4, Hongchen Li¹, Hongzhe Liu², Cheng Xu², Yongqiang Liu^1,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2883-2903, 2025, DOI:10.32604/cmc.2025.065267 - 03 July 2025

Abstract Flooding and heavy rainfall under extreme weather conditions pose significant challenges to target detection algorithms. Traditional methods often struggle to address issues such as image blurring, dynamic noise interference, and variations in target scale. Conventional neural network (CNN)-based target detection approaches face notable limitations in such adverse weather scenarios, primarily due to the fixed geometric sampling structures that hinder adaptability to complex backgrounds and dynamically changing object appearances. To address these challenges, this paper proposes an optimized YOLOv9 model incorporating an improved deformable convolutional network (DCN) enhanced with a multi-scale dilated attention (MSDA) mechanism. Specifically,… More >

Zhunruo Feng¹, Ruomeng Shi², Yuhan Jiang³, Yiming Han¹, Zeyang Ma¹, Yuheng Ren^4,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3559-3575, 2025, DOI:10.32604/cmc.2025.065152 - 03 July 2025

Abstract In this study, we propose Space-to-Depth and You Only Look Once Version 7 (SPD-YOLOv7), an accurate and efficient method for detecting pests in maize crops, addressing challenges such as small pest sizes, blurred images, low resolution, and significant species variation across different growth stages. To improve the model’s ability to generalize and its robustness, we incorporate target background analysis, data augmentation, and processing techniques like Gaussian noise and brightness adjustment. In target detection, increasing the depth of the neural network can lead to the loss of small target information. To overcome this, we introduce the… More >

Yuchao Xia, Jing Qiu^*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2531-2547, 2025, DOI:10.32604/cmc.2025.064635 - 03 July 2025

Abstract In recent years, fungal diseases affecting grape crops have attracted significant attention. Currently, the assessment of black rot severity mainly depends on the ratio of lesion area to leaf surface area. However, effectively and accurately segmenting leaf lesions presents considerable challenges. Existing grape leaf lesion segmentation models have several limitations, such as a large number of parameters, long training durations, and limited precision in extracting small lesions and boundary details. To address these issues, we propose an enhanced DeepLabv3+ model incorporating Strip Pooling, Content-Guided Fusion, and Convolutional Block Attention Module (SFC_DeepLabv3+), an enhanced lesion segmentation method based… More >

Hao Qiu, Shoudong Ni^*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2677-2697, 2025, DOI:10.32604/cmc.2025.064629 - 03 July 2025

Abstract In response to the missed and false detections that are easily caused by the large variety of and significant differences among aluminum surface defects, a detection algorithm based on an improved You Only Look Once (YOLO)v8n network is proposed. First, a C2f_DWR_DRB module is constructed by introducing a dilation-wise residual (DWR) module and a dilated reparameterization block (DRB) to replace the C2f module at the high level of the backbone network, enriching the gradient flow information and increasing the effective receptive field (ERF). Second, an efficient local attention (ELA) mechanism is fused with the high-level… More >

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

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2699-2716, 2025, DOI:10.32604/cmc.2025.064541 - 03 July 2025

Abstract Infrared imaging technology has been widely adopted in various fields, such as military reconnaissance, medical diagnosis, and security monitoring, due to its excellent ability to penetrate smoke and fog. However, the prevalent low resolution of infrared images severely limits the accurate interpretation of their contents. In addition, deploying super-resolution models on resource-constrained devices faces significant challenges. To address these issues, this study proposes a lightweight super-resolution network for infrared images based on an adaptive attention mechanism. The network’s dynamic weighting module automatically adjusts the weights of the attention and non-attention branch outputs based on the… More >

Chao Li^1,3,#, Quanzhi Feng^1,3,#, Caichang Ding^2,*, Zhiwei Ye^1,3

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3605-3621, 2025, DOI:10.32604/cmc.2025.063703 - 03 July 2025

Abstract The increasing adoption of unmanned aerial vehicles (UAVs) in urban low-altitude logistics systems, particularly for time-sensitive applications like parcel delivery and supply distribution, necessitates sophisticated coordination mechanisms to optimize operational efficiency. However, the limited capability of UAVs to extract state-action information in complex environments poses significant challenges to achieving effective cooperation in dynamic and uncertain scenarios. To address this, we presents an Improved Multi-Agent Hybrid Attention Critic (IMAHAC) framework that advances multi-agent deep reinforcement learning (MADRL) through two key innovations. Firstly, a Temporal Difference Error and Time-based Prioritized Experience Replay (TT-PER) mechanism that dynamically adjusts… 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 >

Yu-Shiuan Tsai^*, Yuk-Hang Sit

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3065-3090, 2025, DOI:10.32604/cmes.2025.064783 - 30 June 2025

Abstract To improve small object detection and trajectory estimation from an aerial moving perspective, we propose the Aerial View Attention-PRB (AVA-PRB) model. AVA-PRB integrates two attention mechanisms—Coordinate Attention (CA) and the Convolutional Block Attention Module (CBAM)—to enhance detection accuracy. Additionally, Shape-IoU is employed as the loss function to refine localization precision. Our model further incorporates an adaptive feature fusion mechanism, which optimizes multi-scale object representation, ensuring robust tracking in complex aerial environments. We evaluate the performance of AVA-PRB on two benchmark datasets: Aerial Person Detection and VisDrone2019-Det. The model achieves 60.9% mAP@0.5 on the Aerial Person… More >