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 >

Yi Zuo, Zhenping Chen^*, Jing Feng, Yunhao Fan

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4649-4667, 2025, DOI:10.32604/cmc.2025.059472 - 06 March 2025

Abstract Image classification is crucial for various applications, including digital construction, smart manufacturing, and medical imaging. Focusing on the inadequate model generalization and data privacy concerns in few-shot image classification, in this paper, we propose a federated learning approach that incorporates privacy-preserving techniques. First, we utilize contrastive learning to train on local few-shot image data and apply various data augmentation methods to expand the sample size, thereby enhancing the model’s generalization capabilities in few-shot contexts. Second, we introduce local differential privacy techniques and weight pruning methods to safeguard model parameters, perturbing the transmitted parameters to ensure More >

Konstantinos I. Roumeliotis^1,*, Nikolaos D. Tselikas², Dimitrios K. Nasiopoulos³

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2769-2792, 2025, DOI:10.32604/cmc.2025.059567 - 17 February 2025

Abstract In the rapidly evolving landscape of natural language processing (NLP) and sentiment analysis, improving the accuracy and efficiency of sentiment classification models is crucial. This paper investigates the performance of two advanced models, the Large Language Model (LLM) LLaMA model and NLP BERT model, in the context of airline review sentiment analysis. Through fine-tuning, domain adaptation, and the application of few-shot learning, the study addresses the subtleties of sentiment expressions in airline-related text data. Employing predictive modeling and comparative analysis, the research evaluates the effectiveness of Large Language Model Meta AI (LLaMA) and Bidirectional Encoder… More >

Yujiao Tang¹, Yadong Wu^1,*, Yuanmei He², Jilin Liu¹, Weihan Zhang¹

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2331-2352, 2025, DOI:10.32604/cmc.2024.059115 - 17 February 2025

Abstract Emotion recognition plays a crucial role in various fields and is a key task in natural language processing (NLP). The objective is to identify and interpret emotional expressions in text. However, traditional emotion recognition approaches often struggle in few-shot cross-domain scenarios due to their limited capacity to generalize semantic features across different domains. Additionally, these methods face challenges in accurately capturing complex emotional states, particularly those that are subtle or implicit. To overcome these limitations, we introduce a novel approach called Dual-Task Contrastive Meta-Learning (DTCML). This method combines meta-learning and contrastive learning to improve emotion… More >

ByeongYeol An¹, JeaHyuk Yang², Seoyeon Kim², Taeguen Kim^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 563-584, 2024, DOI:10.32604/cmes.2024.052403 - 20 August 2024

Abstract This paper proposes a new approach to counter cyberattacks using the increasingly diverse malware in cyber security. Traditional signature detection methods that utilize static and dynamic features face limitations due to the continuous evolution and diversity of new malware. Recently, machine learning-based malware detection techniques, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), have gained attention. While these methods demonstrate high performance by leveraging static and dynamic features, they are limited in detecting new malware or variants because they learn based on the characteristics of existing malware. To overcome these limitations, malware… More >

Ronghao Pan, José Antonio García-Díaz^*, Rafael Valencia-García

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2849-2868, 2024, DOI:10.32604/cmes.2024.049631 - 08 July 2024

Abstract Large Language Models (LLMs) are increasingly demonstrating their ability to understand natural language and solve complex tasks, especially through text generation. One of the relevant capabilities is contextual learning, which involves the ability to receive instructions in natural language or task demonstrations to generate expected outputs for test instances without the need for additional training or gradient updates. In recent years, the popularity of social networking has provided a medium through which some users can engage in offensive and harmful online behavior. In this study, we investigate the ability of different LLMs, ranging from zero-shot… More >

Tao Yi^1,3, Xingshu Chen^1,2,*, Mingdong Yang³, Qindong Li¹, Yi Zhu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 929-955, 2024, DOI:10.32604/cmes.2024.048793 - 16 April 2024

Abstract Due to the rapid evolution of Advanced Persistent Threats (APTs) attacks, the emergence of new and rare attack samples, and even those never seen before, make it challenging for traditional rule-based detection methods to extract universal rules for effective detection. With the progress in techniques such as transfer learning and meta-learning, few-shot network attack detection has progressed. However, challenges in few-shot network attack detection arise from the inability of time sequence flow features to adapt to the fixed length input requirement of deep learning, difficulties in capturing rich information from original flow in the case… More >

Shoukun Xu¹, Lujun Zhang¹, Guangqi Jiang¹, Yining Hua², Yi Liu^1,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3021-3039, 2024, DOI:10.32604/cmc.2023.045853 - 26 March 2024

Abstract This paper focuses on the task of few-shot 3D point cloud semantic segmentation. Despite some progress, this task still encounters many issues due to the insufficient samples given, e.g., incomplete object segmentation and inaccurate semantic discrimination. To tackle these issues, we first leverage part-whole relationships into the task of 3D point cloud semantic segmentation to capture semantic integrity, which is empowered by the dynamic capsule routing with the module of 3D Capsule Networks (CapsNets) in the embedding network. Concretely, the dynamic routing amalgamates geometric information of the 3D point cloud data to construct higher-level feature… More >

Yanzhao Zhou, Binghao Liu, Yiran Liu, Jianbin Jiao^*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 647-668, 2023, DOI:10.32604/cmes.2023.026745 - 23 April 2023

Abstract Deep Convolution Neural Networks (DCNNs) can capture discriminative features from large datasets. However, how to incrementally learn new samples without forgetting old ones and recognize novel classes that arise in the dynamically changing world, e.g., classifying newly discovered fish species, remains an open problem. We address an even more challenging and realistic setting of this problem where new class samples are insufficient, i.e., Few-Shot Class-Incremental Learning (FSCIL). Current FSCIL methods augment the training data to alleviate the overfitting of novel classes. By contrast, we propose Filter Bank Networks (FBNs) that augment the learnable filters to… More >

Qaisar Abbas¹, Abdul Rauf Baig^1,*, Ayyaz Hussain²

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1483-1499, 2023, DOI:10.32604/cmc.2023.036366 - 06 February 2023

Abstract The diagnosis of eye disease through deep learning (DL) technology is the latest trend in the field of artificial intelligence (AI). Especially in diagnosing pathologic myopia (PM) lesions, the implementation of DL is a difficult task because of the classification complexity and definition system of PM. However, it is possible to design an AI-based technique that can identify PM automatically and help doctors make relevant decisions. To achieve this objective, it is important to have adequate resources such as a high-quality PM image dataset and an expert team. The primary aim of this research is… More >