Dang Hung Tran, Van Tinh Nguyen^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4679-4698, 2025, DOI:10.32604/cmc.2025.063228 - 19 May 2025

Abstract It is known that long non-coding RNAs (lncRNAs) play vital roles in biological processes and contribute to the progression, development, and treatment of various diseases. Obviously, understanding associations between diseases and lncRNAs significantly enhances our ability to interpret disease mechanisms. Nevertheless, the process of determining lncRNA-disease associations is costly, labor-intensive, and time-consuming. Hence, it is expected to foster computational strategies to uncover lncRNA-disease relationships for further verification to save time and resources. In this study, a collaborative filtering and graph attention network-based LncRNA-Disease Association (CFGANLDA) method was nominated to expose potential lncRNA-disease associations. First, it… 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 >

Kamil Yasen^1,#, Heyan Jin^2,#, Sijie Yang², Li Zhan², Xuyang Zhang², Ke Qin^1,3, Ye Li^2,3,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3277-3301, 2025, DOI:10.32604/cmc.2025.061037 - 16 April 2025

Abstract Research on mass gathering events is critical for ensuring public security and maintaining social order. However, most of the existing works focus on crowd behavior analysis areas such as anomaly detection and crowd counting, and there is a relative lack of research on mass gathering behaviors. We believe real-time detection and monitoring of mass gathering behaviors are essential for migrating potential security risks and emergencies. Therefore, it is imperative to develop a method capable of accurately identifying and localizing mass gatherings before disasters occur, enabling prompt and effective responses. To address this problem, we propose… More >

Tariq Mahmood^1,2, Tanzila Saba¹, Faten S. Alamri^3,*, Alishba Tahir⁴, Noor Ayesha⁵

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1173-1193, 2025, DOI:10.32604/cmc.2025.061150 - 26 March 2025

Abstract Innovation in learning algorithms has made retinal vessel segmentation and automatic grading techniques crucial for clinical diagnosis and prevention of diabetic retinopathy. The traditional methods struggle with accuracy and reliability due to multi-scale variations in retinal blood vessels and the complex pathological relationship in fundus images associated with diabetic retinopathy. While the single-modal diabetic retinopathy grading network addresses class imbalance challenges and lesion representation in fundus image data, dual-modal diabetic retinopathy grading methods offer superior performance. However, the scarcity of dual-modal data and the lack of effective feature fusion methods limit their potential due to… More >

Zhen-Yu Chen¹, Feng-Chi Liu², Xin Wang³, Cheng-Hsiung Lee¹, Ching-Sheng Lin^1,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4287-4300, 2025, DOI:10.32604/cmc.2025.061661 - 06 March 2025

Abstract In the domain of knowledge graph embedding, conventional approaches typically transform entities and relations into continuous vector spaces. However, parameter efficiency becomes increasingly crucial when dealing with large-scale knowledge graphs that contain vast numbers of entities and relations. In particular, resource-intensive embeddings often lead to increased computational costs, and may limit scalability and adaptability in practical environments, such as in low-resource settings or real-world applications. This paper explores an approach to knowledge graph representation learning that leverages small, reserved entities and relation sets for parameter-efficient embedding. We introduce a hierarchical attention network designed to refine More >

Dat Tran-Anh¹, Hoai Nam Vu^2,3,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2703-2722, 2025, DOI:10.32604/cmc.2025.061086 - 17 February 2025

Abstract Counterfeit agricultural products pose a significant challenge to global food security and economic stability, necessitating advanced detection mechanisms to ensure authenticity and quality. To address this pressing issue, we introduce iGFruit, an innovative model designed to enhance the detection of counterfeit agricultural products by integrating multimodal data processing. Our approach utilizes both image and text data for comprehensive feature extraction, employing advanced backbone models such as Vision Transformer (ViT), Normalizer-Free Network (NFNet), and Bidirectional Encoder Representations from Transformers (BERT). These extracted features are fused and processed using a Graph Attention Network (GAT) to capture intricate More >

Ming Liu, Qichao Yang, Wenqing Wang, Shengli Liu^*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2985-3004, 2025, DOI:10.32604/cmc.2024.059417 - 17 February 2025

Abstract The proliferation of internet traffic encryption has become a double-edged sword. While it significantly enhances user privacy, it also inadvertently shields cyber-attacks from detection, presenting a formidable challenge to cybersecurity. Traditional machine learning and deep learning techniques often fall short in identifying encrypted malicious traffic due to their inability to fully extract and utilize the implicit relational and positional information embedded within data packets. This limitation has led to an unresolved challenge in the cybersecurity community: how to effectively extract valuable insights from the complex patterns of traffic packet transmission. Consequently, this paper introduces the… More >

Haitao He¹, Bingjian Yan¹, Ke Xu^1,*, Lu Yu^1,2

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2077-2108, 2025, DOI:10.32604/cmc.2024.058779 - 17 February 2025

Abstract Software defect prediction aims to use measurement data of code and historical defects to predict potential problems, optimize testing resources and defect management. However, current methods face challenges: (1) Coarse-grained file level detection cannot accurately locate specific defects. (2) Fine-grained line-level defect prediction methods rely solely on local information of a single line of code, failing to deeply analyze the semantic context of the code line and ignoring the heuristic impact of line-level context on the code line, making it difficult to capture the interaction between global and local information. Therefore, this paper proposes a… More >

Hina Bhanbhro^1,*, Yew Kwang Hooi¹, Mohammad Nordin Bin Zakaria¹, Worapan Kusakunniran², Zaira Hassan Amur¹

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2243-2259, 2024, DOI:10.32604/cmc.2024.052138 - 18 November 2024

Abstract Object detection has made a significant leap forward in recent years. However, the detection of small objects continues to be a great difficulty for various reasons, such as they have a very small size and they are susceptible to missed detection due to background noise. Additionally, small object information is affected due to the downsampling operations. Deep learning-based detection methods have been utilized to address the challenge posed by small objects. In this work, we propose a novel method, the Multi-Convolutional Block Attention Network (MCBAN), to increase the detection accuracy of minute objects aiming to… More >

Bolin Guo^1,2, Shi Qiu^1,*, Pengchang Zhang¹, Xingjia Tang³

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1809-1833, 2024, DOI:10.32604/cmc.2024.056706 - 15 October 2024

Abstract Mural paintings hold significant historical information and possess substantial artistic and cultural value. However, murals are inevitably damaged by natural environmental factors such as wind and sunlight, as well as by human activities. For this reason, the study of damaged areas is crucial for mural restoration. These damaged regions differ significantly from undamaged areas and can be considered abnormal targets. Traditional manual visual processing lacks strong characterization capabilities and is prone to omissions and false detections. Hyperspectral imaging can reflect the material properties more effectively than visual characterization methods. Thus, this study employs hyperspectral imaging… More >