Dae-Kyoo Kim^*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3843-3872, 2025, DOI:10.32604/cmc.2025.062552 - 06 March 2025

Abstract Design patterns offer reusable solutions for common software issues, enhancing quality. The advent of generative large language models (LLMs) marks progress in software development, but their efficacy in applying design patterns is not fully assessed. The recent introduction of generative large language models (LLMs) like ChatGPT and CoPilot has demonstrated significant promise in software development. They assist with a variety of tasks including code generation, modeling, bug fixing, and testing, leading to enhanced efficiency and productivity. Although initial uses of these LLMs have had a positive effect on software development, their potential influence on the… 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 >

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 >

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 >

Alaa Thobhani^1,*, Beiji Zou¹, Xiaoyan Kui^1,*, Amr Abdussalam², Muhammad Asim³, Mohammed ELAffendi³, Sajid Shah³

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3943-3964, 2025, DOI:10.32604/cmc.2025.060788 - 06 March 2025

Abstract Image captioning, the task of generating descriptive sentences for images, has advanced significantly with the integration of semantic information. However, traditional models still rely on static visual features that do not evolve with the changing linguistic context, which can hinder the ability to form meaningful connections between the image and the generated captions. This limitation often leads to captions that are less accurate or descriptive. In this paper, we propose a novel approach to enhance image captioning by introducing dynamic interactions where visual features continuously adapt to the evolving linguistic context. Our model strengthens the… More >

Zhenyang He, Mengjun Tong^*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4301-4317, 2025, DOI:10.32604/cmc.2025.060550 - 06 March 2025

Abstract Tomato plant diseases often first manifest on the leaves, making the detection of tomato leaf diseases particularly crucial for the tomato cultivation industry. However, conventional deep learning models face challenges such as large model sizes and slow detection speeds when deployed on resource-constrained platforms and agricultural machinery. This paper proposes a lightweight model for detecting tomato leaf diseases, named LT-YOLO, based on the YOLOv8n architecture. First, we enhance the C2f module into a RepViT Block (RVB) with decoupled token and channel mixers to reduce the cost of feature extraction. Next, we incorporate a novel Efficient… More >

Woongkyu Park¹, Yeongyu Choi², Mahammad Shareef Mekala³, Gyu Sang Choi¹, Kook-Yeol Yoo¹, Ho-youl Jung^1,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3965-3981, 2025, DOI:10.32604/cmc.2025.059966 - 06 March 2025

Abstract Designing fast and accurate neural networks is becoming essential in various vision tasks. Recently, the use of attention mechanisms has increased, aimed at enhancing the vision task performance by selectively focusing on relevant parts of the input. In this paper, we concentrate on squeeze-and-excitation (SE)-based channel attention, considering the trade-off between latency and accuracy. We propose a variation of the SE module, called squeeze-and-excitation with layer normalization (SELN), in which layer normalization (LN) replaces the sigmoid activation function. This approach reduces the vanishing gradient problem while enhancing feature diversity and discriminability of channel attention. In… More >

Jingjing Yan¹, Xuyang Zhuang^2,*, Xuezhuan Zhao^1,2, Xiaoyan Shao^1,*, Jiaqi Han¹

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5363-5386, 2025, DOI:10.32604/cmc.2025.059709 - 06 March 2025

Abstract The key to the success of few-shot semantic segmentation (FSS) depends on the efficient use of limited annotated support set to accurately segment novel classes in the query set. Due to the few samples in the support set, FSS faces challenges such as intra-class differences, background (BG) mismatches between query and support sets, and ambiguous segmentation between the foreground (FG) and BG in the query set. To address these issues, The paper propose a multi-module network called CAMSNet, which includes four modules: the General Information Module (GIM), the Class Activation Map Aggregation (CAMA) module, the… More >

Wei Liu¹, Sen Liu^2,3,*, Yinchao He², Jiaojiao Wang¹, Yu Gu¹

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4863-4880, 2025, DOI:10.32604/cmc.2025.059295 - 06 March 2025

Abstract To address the issues of slow diagnostic speed, low accuracy, and poor generalization performance in traditional rolling bearing fault diagnosis methods, we propose a rolling bearing fault diagnosis method based on Markov Transition Field (MTF) image encoding combined with a lightweight convolutional neural network that integrates a Convolutional Block Attention Module (CBAM-LCNN). Specifically, we first use the Markov Transition Field to convert the original one-dimensional vibration signals of rolling bearings into two-dimensional images. Then, we construct a lightweight convolutional neural network incorporating the convolutional attention module (CBAM-LCNN). Finally, the two-dimensional images obtained from MTF mapping… More >

Yawar Abbas¹, Aisha Ahmed Alarfaj², Ebtisam Abdullah Alabdulqader³, Asaad Algarni⁴, Ahmad Jalal^1,5, Hui Liu^6,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4759-4776, 2025, DOI:10.32604/cmc.2025.059224 - 06 March 2025

Abstract Human Activity Recognition (HAR) in drone-captured videos has become popular because of the interest in various fields such as video surveillance, sports analysis, and human-robot interaction. However, recognizing actions from such videos poses the following challenges: variations of human motion, the complexity of backdrops, motion blurs, occlusions, and restricted camera angles. This research presents a human activity recognition system to address these challenges by working with drones’ red-green-blue (RGB) videos. The first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while… More >