Haocheng Sun, Ping Li, Ying Li^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4899-4914, 2025, DOI:10.32604/cmc.2025.063029 - 19 May 2025

Abstract Traffic flow prediction is a key component of intelligent transportation systems, particularly in data-scarce regions where traditional models relying on complete datasets often fail to provide accurate forecasts. These regions are characterized by limited sensor coverage and sparse data collection, pose significant challenges for existing prediction methods. To address this, we propose a novel transfer learning framework called transfer learning with deep knowledge distillation (TL-DKD), which combines graph neural network (GNN) with deep knowledge distillation to enable effective knowledge transfer from data-rich to data-scarce domains. Our contributions are three-fold: (1) We introduce, for the first… More >

Huyong Yan¹, Huidong Zhou^2,*, Jian Zheng¹, Zhaozhe Zhou¹

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4659-4677, 2025, DOI:10.32604/cmc.2025.062807 - 19 May 2025

Abstract As smart manufacturing and Industry 4.0 continue to evolve, fault diagnosis of mechanical equipment has become crucial for ensuring production safety and optimizing equipment utilization. To address the challenge of cross-domain adaptation in intelligent diagnostic models under varying operational conditions, this paper introduces the CNN-1D-KAN model, which combines a 1D Convolutional Neural Network (1D-CNN) with a Kolmogorov–Arnold Network (KAN). The novelty of this approach lies in replacing the traditional 1D-CNN’s final fully connected layer with a KANLinear layer, leveraging KAN’s advanced nonlinear processing and function approximation capabilities while maintaining the simplicity of linear transformations. Experimental… More >

Pengfei Zhang¹, Rui Xin¹, Xing Xu¹, Yuzhen Wang¹, Xiaodong Li², Xiao Zhang², Meina Song², Zhonghong Ou^3,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5413-5428, 2025, DOI:10.32604/cmc.2025.062618 - 19 May 2025

Abstract Session-based recommendation systems (SBR) are pivotal in suggesting items by analyzing anonymized sequences of user interactions. Traditional methods, while competent, often fall short in two critical areas: they fail to address potential inter-session item transitions, which are behavioral dependencies that extend beyond individual session boundaries, and they rely on monolithic item aggregation to construct session representations. This approach does not capture the multi-scale and heterogeneous nature of user intent, leading to a decrease in modeling accuracy. To overcome these limitations, a novel approach called HMGS has been introduced. This system incorporates dual graph architectures to… More >

Shaoming Qiu, Xinchen Huang^*, Liangyu Liu, Bicong E, Jingfeng Ye

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5657-5678, 2025, DOI:10.32604/cmc.2025.062482 - 19 May 2025

Abstract Most existing knowledge graph relationship prediction methods are unable to capture the complex information of multi-relational knowledge graphs, thus overlooking key details contained in different entity pairs and making it difficult to aggregate more complex relational features. Moreover, the insufficient capture of multi-hop relational information limits the processing capability of the global structure of the graph and reduces the accuracy of the knowledge graph completion task. This paper uses graph neural networks to construct new message functions for different relations, which can be defined as the rotation from the source entity to the target entity… More >

Tajinder Kumar¹, Sarbjit Kaur², Purushottam Sharma^3,*, Ankita Chhikara⁴, Xiaochun Cheng^5,*, Sachin Lalar⁶, Vikram Verma⁷

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5219-5234, 2025, DOI:10.32604/cmc.2025.062010 - 19 May 2025

Abstract During its growth stage, the plant is exposed to various diseases. Detection and early detection of crop diseases is a major challenge in the horticulture industry. Crop infections can harm total crop yield and reduce farmers’ income if not identified early. Today’s approved method involves a professional plant pathologist to diagnose the disease by visual inspection of the afflicted plant leaves. This is an excellent use case for Community Assessment and Treatment Services (CATS) due to the lengthy manual disease diagnosis process and the accuracy of identification is directly proportional to the skills of pathologists.… More >

Jiling Wan, Lifeng Cao^*, Jinlong Bai, Jinhui Li, Xuehui Du

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4775-4794, 2025, DOI:10.32604/cmc.2025.061964 - 19 May 2025

Abstract Blockchain platforms with the unique characteristics of anonymity, decentralization, and transparency of their transactions, which are faced with abnormal activities such as money laundering, phishing scams, and fraudulent behavior, posing a serious threat to account asset security. For these potential security risks, this paper proposes a hybrid neural network detection method (HNND) that learns multiple types of account features and enhances fusion information among them to effectively detect abnormal transaction behaviors in the blockchain. In HNND, the Temporal Transaction Graph Attention Network (T2GAT) is first designed to learn biased aggregation representation of multi-attribute transactions among More >

Daoming Wei¹, Fangyan Ge², Bopeng Zhang¹, Zhiqiang Zhao³, Dequan Li^3,*, Lizong Xi⁴, Jinrong Hu^5,*, Xin Wang⁶

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5729-5746, 2025, DOI:10.32604/cmc.2025.061402 - 19 May 2025

Abstract Accurate cloud classification plays a crucial role in aviation safety, climate monitoring, and localized weather forecasting. Current research has been focusing on machine learning techniques, particularly deep learning based model, for the types identification. However, traditional approaches such as convolutional neural networks (CNNs) encounter difficulties in capturing global contextual information. In addition, they are computationally expensive, which restricts their usability in resource-limited environments. To tackle these issues, we present the Cloud Vision Transformer (CloudViT), a lightweight model that integrates CNNs with Transformers. The integration enables an effective balance between local and global feature extraction. To… More >

Prashanth B. S^1,*, Manoj Kumar M. V.^2,*, Nasser Almuraqab³, Puneetha B. H⁴

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4979-4998, 2025, DOI:10.32604/cmc.2025.060881 - 19 May 2025

Abstract Ever since the research in machine learning gained traction in recent years, it has been employed to address challenges in a wide variety of domains, including mechanical devices. Most of the machine learning models are built on the assumption of a static learning environment, but in practical situations, the data generated by the process is dynamic. This evolution of the data is termed concept drift. This research paper presents an approach for predicting mechanical failure in real-time using incremental learning based on the statistically calculated parameters of mechanical equipment. The method proposed here is applicable… More >

Ridha Djebali^1,*, Bernard Pateyron², Mokhtar Ferhi¹, Mohamed Ouerhani³, Karim Khemiri¹, Montassar Najari¹, M. Ammar Abbassi⁴, Chohdi Amri⁵, Ridha Ennetta⁶, Zied Driss⁷

Frontiers in Heat and Mass Transfer, Vol.23, No.2, pp. 441-461, 2025, DOI:10.32604/fhmt.2025.063375 - 25 April 2025

Abstract This paper investigates the application of Direct Current Atmospheric Plasma Spraying (DC-APS) as a versatile thermal spray technique for the application of coatings with tailored properties to various substrates. The process uses a high-speed, high-temperature plasma jet to melt and propel the feedstock powder particles, making it particularly useful for improving the performance and durability of components in renewable energy systems such as solar cells, wind turbines, and fuel cells. The integration of nanostructured alumina (Al₂O₃) thin films into multilayer coatings is considered a promising advancement that improves mechanical strength, thermal stability, and environmental resistance. The More >

Qingpeng Wei^1,#, Wenai Song^1,#, Lizhen Fu¹, Yi Lei², Qing Wang^2,*

Journal on Artificial Intelligence, Vol.7, pp. 39-53, 2025, DOI:10.32604/jai.2025.063188 - 23 April 2025

Abstract Pneumoconiosis, a prevalent occupational lung disease characterized by fibrosis and impaired lung function, necessitates early and accurate diagnosis to prevent further progression and ensure timely clinical intervention. This study investigates the potential application of the Vision Transformer (ViT) deep learning model for automated pneumoconiosis classification using digital radiography (DR) images. We utilized digital X-ray images from 934 suspected pneumoconiosis patients. A U-Net model was applied for lung segmentation, followed by Canny edge detection to divide the lungs into six anatomical regions. The segmented images were augmented and used to train the ViT model. Model component… More >