Ghadah Naif Alwakid¹, Samabia Tehsin^2,*, Mamoona Humayun^3,*, Asad Farooq², Ibrahim Alrashdi¹, Amjad Alsirhani¹

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.069162 - 10 November 2025

Abstract Skin diseases affect millions worldwide. Early detection is key to preventing disfigurement, lifelong disability, or death. Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and severe class imbalance, and occasional imaging artifacts can create ambiguity for state-of-the-art convolutional neural networks (CNNs). We frame skin lesion recognition as graph-based reasoning and, to ensure fair evaluation and avoid data leakage, adopt a strict lesion-level partitioning strategy. Each image is first over-segmented using SLIC (Simple Linear Iterative Clustering) to produce perceptually homogeneous superpixels. These superpixels form the nodes of a region-adjacency graph whose edges encode… More >

Muhammad Asif Khan¹, Dildar Hussain², Bhuyan Kaibalya Prasad³, Irfan Ullah⁴, Inayat Khan⁵, Jawad Khan^6,*, Yeong Hyeon Gu^2,*, Pavlos Kefalas⁷

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5451-5468, 2025, DOI:10.32604/cmc.2025.069134 - 23 October 2025

Abstract Dialogue State Tracking (DST) is a critical component of task-oriented spoken dialogue systems (SDS), tasked with maintaining an accurate representation of the conversational state by predicting slots and their corresponding values. Recent advances leverage Large Language Models (LLMs) with prompt-based tuning to improve tracking accuracy and efficiency. However, these approaches often incur substantial computational and memory overheads and typically address slot extraction implicitly within prompts, without explicitly modeling the complex dependencies between slots and values. In this work, we propose PUGG, a novel DST framework that constructs schema-driven prompts to fine-tune GPT-2 and utilizes its tokenizer… More >

Jun Li^#,*, Kai Xu^#,*, Baozhu Chen, Xiaohan Yang, Mengting Sun, Guojun Li, HaoJie Du

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3349-3368, 2025, DOI:10.32604/cmc.2025.067316 - 23 September 2025

Abstract Pedestrian trajectory prediction is pivotal and challenging in applications such as autonomous driving, social robotics, and intelligent surveillance systems. Pedestrian trajectory is governed not only by individual intent but also by interactions with surrounding agents. These interactions are critical to trajectory prediction accuracy. While prior studies have employed Convolutional Neural Networks (CNNs) and Graph Convolutional Networks (GCNs) to model such interactions, these methods fail to distinguish varying influence levels among neighboring pedestrians. To address this, we propose a novel model based on a bidirectional graph attention network and spatio-temporal graphs to capture dynamic interactions. Specifically,… More >

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 >

Mei Chen, Yunsheng Xu, Nan Wu, Ying Pan^*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5307-5324, 2025, DOI:10.32604/cmc.2024.060993 - 06 March 2025

Abstract With the development of the Semantic Web, the number of ontologies grows exponentially and the semantic relationships between ontologies become more and more complex, understanding the true semantics of specific terms or concepts in an ontology is crucial for the matching task. At present, the main challenges facing ontology matching tasks based on representation learning methods are how to improve the embedding quality of ontology knowledge and how to integrate multiple features of ontology efficiently. Therefore, we propose an Ontology Matching Method Based on the Gated Graph Attention Model (OM-GGAT). Firstly, the semantic knowledge related… 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 >

Zhihong Lin¹, Zeng Zeng², Yituan Yu², Yinlin Ren¹, Xuesong Qiu^1,*, Jinqian Chen¹

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1641-1665, 2024, DOI:10.32604/cmc.2024.055802 - 15 October 2024

Abstract For permanent faults (PF) in the power communication network (PCN), such as link interruptions, the time-sensitive networking (TSN) relied on by PCN, typically employs spatial redundancy fault-tolerance methods to keep service stability and reliability, which often limits TSN scheduling performance in fault-free ideal states. So this paper proposes a graph attention residual network-based routing and fault-tolerant scheduling mechanism (GRFS) for data flow in PCN, which specifically includes a communication system architecture for integrated terminals based on a cyclic queuing and forwarding (CQF) model and fault recovery method, which reduces the impact of faults by simplified… More >

Yu Song, Pengcheng Wu, Dongming Dai, Mingyu Gui, Kunli Zhang^*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4767-4790, 2024, DOI:10.32604/cmc.2024.053506 - 12 September 2024

Abstract The growing prevalence of knowledge reasoning using knowledge graphs (KGs) has substantially improved the accuracy and efficiency of intelligent medical diagnosis. However, current models primarily integrate electronic medical records (EMRs) and KGs into the knowledge reasoning process, ignoring the differing significance of various types of knowledge in EMRs and the diverse data types present in the text. To better integrate EMR text information, we propose a novel intelligent diagnostic model named the Graph ATtention network incorporating Text representation in knowledge reasoning (GATiT), which comprises text representation, subgraph construction, knowledge reasoning, and diagnostic classification. In the… More >