Jianyin Tang, Zhenglin Yu^*, Changshun Shao

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2829-2850, 2025, DOI:10.32604/cmc.2025.063287 - 16 April 2025

Abstract In the field of automated fruit harvesting, precise and efficient fruit target recognition and localization play a pivotal role in enhancing the efficiency of harvesting robots. However, this domain faces two core challenges: firstly, the dynamic nature of the automatic picking process requires fruit target detection algorithms to adapt to multi-view characteristics, ensuring effective recognition of the same fruit from different perspectives. Secondly, fruits in natural environments often suffer from interference factors such as overlapping, occlusion, and illumination fluctuations, which increase the difficulty of image capture and recognition. To address these challenges, this study conducted… More >

Meng Zhu^1,2, Xingyue Wang³, Honge Ren^3,4,*, Abeer Hakeem⁵, Linda Mohaisen^5,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2889-2905, 2025, DOI:10.32604/cmc.2025.061421 - 16 April 2025

Abstract Changing a person’s posture and low resolution are the key challenges for person re-identification (ReID) in various deep learning applications. In this paper, we introduce an innovative architecture using a dual attention network that includes an attention module and a joint measurement module of spatial-temporal information. The proposed approach can be classified into two main tasks. Firstly, the spatial attention feature map is formed by aggregating features in the spatial dimension. Additionally, the same operation is carried out on the channel dimension to form channel attention feature maps. Therefore, the receptive field size is adjusted… More >

Bushra Tayyaba¹, Muhammad Usman Ghani Khan^1,2,3, Talha Waheed², Shaha Al-Otaibi⁴, Tanzila Saba^3,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2851-2864, 2025, DOI:10.32604/cmc.2025.060365 - 16 April 2025

Abstract Reference Evapotranspiration (ETo) is widely used to assess total water loss between land and atmosphere due to its importance in maintaining the atmospheric water balance, especially in agricultural and environmental management. Accurate estimation of ETo is challenging due to its dependency on multiple climatic variables, including temperature, humidity, and solar radiation, making it a complex multivariate time-series problem. Traditional machine learning and deep learning models have been applied to forecast ETo, achieving moderate success. However, the introduction of transformer-based architectures in time-series forecasting has opened new possibilities for more precise ETo predictions. In this study,… More >

Chunlai Du^1,#, Xin Gu^1,#, Yanhui Guo^2,*, Siqi Guo³, Ziwei Pang³, Yi Du³, Guoqing Du^3,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3435-3450, 2025, DOI:10.32604/cmc.2025.060212 - 16 April 2025

Abstract Coronary artery disease is a highly lethal cardiovascular condition, making early diagnosis crucial for patients. Echocardiograph is employed to identify coronary heart disease (CHD). However, due to issues such as fuzzy object boundaries, complex tissue structures, and motion artifacts in ultrasound images, it is challenging to detect CHD accurately. This paper proposes an improved Transformer model based on the Feedback Self-Attention Mechanism (FSAM) for classification of ultrasound images. The model enhances attention weights, making it easier to capture complex features. Experimental results show that the proposed method achieves high levels of accuracy, recall, precision, F1 More >

Kaku Haribabu^1,*, Prasath R¹, Praveen Joe IR²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 413-448, 2025, DOI:10.32604/cmes.2025.059406 - 11 April 2025

Abstract Thyroid nodules, a common disorder in the endocrine system, require accurate segmentation in ultrasound images for effective diagnosis and treatment. However, achieving precise segmentation remains a challenge due to various factors, including scattering noise, low contrast, and limited resolution in ultrasound images. Although existing segmentation models have made progress, they still suffer from several limitations, such as high error rates, low generalizability, overfitting, limited feature learning capability, etc. To address these challenges, this paper proposes a Multi-level Relation Transformer-based U-Net (MLRT-UNet) to improve thyroid nodule segmentation. The MLRT-UNet leverages a novel Relation Transformer, which processes… More >

Tawfeeq Shawly¹, Ahmed A. Alsheikhy^2,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4161-4179, 2025, DOI:10.32604/cmc.2025.061497 - 06 March 2025

Abstract A healthy brain is vital to every person since the brain controls every movement and emotion. Sometimes, some brain cells grow unexpectedly to be uncontrollable and cancerous. These cancerous cells are called brain tumors. For diagnosed patients, their lives depend mainly on the early diagnosis of these tumors to provide suitable treatment plans. Nowadays, Physicians and radiologists rely on Magnetic Resonance Imaging (MRI) pictures for their clinical evaluations of brain tumors. These evaluations are time-consuming, expensive, and require expertise with high skills to provide an accurate diagnosis. Scholars and industrials have recently partnered to implement… More >

Gang Hao, Peng Liang^*, Ziyuan Li, Huimin Zhao, Hong Zhang

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5221-5238, 2025, DOI:10.32604/cmc.2025.055739 - 06 March 2025

Abstract In recent years, the detection of image copy-move forgery (CMFD) has become a critical challenge in verifying the authenticity of digital images, particularly as image manipulation techniques evolve rapidly. While deep convolutional neural networks (DCNNs) have been widely employed for CMFD tasks, they are often hindered by a notable limitation: the progressive reduction in spatial resolution during the encoding process, which leads to the loss of critical image details. These details are essential for the accurate detection and localization of image copy-move forgery. To overcome the limitations of existing methods, this paper proposes a Transformer-based… More >

Manjit Singh, Sunil Kumar Singla^*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3003-3029, 2025, DOI:10.32604/cmes.2025.060863 - 03 March 2025

Abstract Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced security. The performance of existing CNN-based methods is limited by the puny generalization of learned features and deficiency of the finger vein image training data. Considering the concerns of existing methods, in this work, a simplified deep transfer learning-based framework for finger-vein recognition is developed using an EfficientNet model of deep learning with a self-attention mechanism. Data augmentation using various geometrical methods is employed to address the problem of training data shortage required for a… More > Graphic Abstract

Xinlu Zong^*, Fan Yu, Zhen Chen, Xue Xia

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3517-3537, 2025, DOI:10.32604/cmc.2024.057494 - 17 February 2025

Abstract Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a More >

Hongxing Wang¹, Xilai Ju², Hua Zhu^1,*, Huafeng Li^1,*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1417-1437, 2025, DOI:10.32604/cmc.2024.058785 - 03 January 2025

Abstract Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals, which has certain limitations. Conversely, deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency. Recently, utilizing the respective advantages of convolution neural network (CNN) and Transformer in local and global feature extraction, research on cooperating the two have demonstrated promise in the field of fault diagnosis. However, the cross-channel convolution mechanism in CNN and the self-attention calculations in… More > Graphic Abstract