Weitao Ha¹, Sheng Gang², Yahya D. Navaei³, Abubakar S. Gezawa⁴, Yaser A. Nanehkaran^2,5,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3025-3057, 2025, DOI:10.32604/cmc.2025.061343 - 16 April 2025

Abstract Music recommendation systems are essential due to the vast amount of music available on streaming platforms, which can overwhelm users trying to find new tracks that match their preferences. These systems analyze users’ emotional responses, listening habits, and personal preferences to provide personalized suggestions. A significant challenge they face is the “cold start” problem, where new users have no past interactions to guide recommendations. To improve user experience, these systems aim to effectively recommend music even to such users by considering their listening behavior and music popularity. This paper introduces a novel music recommendation system… More >

Min Yao^1,*, Guangjie Hu¹, Yaozu Zhang²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2751-2771, 2025, DOI:10.32604/cmc.2025.060860 - 16 April 2025

Abstract Semantic segmentation of remote sensing images is a critical research area in the field of remote sensing. Despite the success of Convolutional Neural Networks (CNNs), they often fail to capture inter-layer feature relationships and fully leverage contextual information, leading to the loss of important details. Additionally, due to significant intra-class variation and small inter-class differences in remote sensing images, CNNs may experience class confusion. To address these issues, we propose a novel Category-Guided Feature Collaborative Learning Network (CG-FCLNet), which enables fine-grained feature extraction and adaptive fusion. Specifically, we design a Feature Collaborative Learning Module (FCLM)… More >

Mingwen Bi¹, Hefei Chen^2,*, Zhenghong Yang^3,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 861-876, 2025, DOI:10.32604/cmc.2025.060517 - 26 March 2025

Abstract Entity relation extraction, a fundamental and essential task in natural language processing (NLP), has garnered significant attention over an extended period., aiming to extract the core of semantic knowledge from unstructured text, i.e., entities and the relations between them. At present, the main dilemma of Chinese entity relation extraction research lies in nested entities, relation overlap, and lack of entity relation interaction. This dilemma is particularly prominent in complex knowledge extraction tasks with high-density knowledge, imprecise syntactic structure, and lack of semantic roles. To address these challenges, this paper presents an innovative “character-level” Chinese part-of-speech… More >

Xiaoyan Shao¹, Jiaqi Han^1,*, Lingling Li^1,*, Xuezhuan Zhao^1,2,3,4, Jingjing Yan¹

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 595-617, 2025, DOI:10.32604/cmc.2025.060295 - 26 March 2025

Abstract The primary challenge in weakly supervised semantic segmentation is effectively leveraging weak annotations while minimizing the performance gap compared to fully supervised methods. End-to-end model designs have gained significant attention for improving training efficiency. Most current algorithms rely on Convolutional Neural Networks (CNNs) for feature extraction. Although CNNs are proficient at capturing local features, they often struggle with global context, leading to incomplete and false Class Activation Mapping (CAM). To address these limitations, this work proposes a Contextual Prototype-Based End-to-End Weakly Supervised Semantic Segmentation (CPEWS) model, which improves feature extraction by utilizing the Vision Transformer… More >

Shijie Xiang, Dong Zhou, Dan Tian^*, Zihao Wang

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 497-515, 2025, DOI:10.32604/cmc.2025.060244 - 26 March 2025

Abstract Real-time semantic segmentation tasks place stringent demands on network inference speed, often requiring a reduction in network depth to decrease computational load. However, shallow networks tend to exhibit degradation in feature extraction completeness and inference accuracy. Therefore, balancing high performance with real-time requirements has become a critical issue in the study of real-time semantic segmentation. To address these challenges, this paper proposes a lightweight bilateral dual-residual network. By introducing a novel residual structure combined with feature extraction and fusion modules, the proposed network significantly enhances representational capacity while reducing computational costs. Specifically, an improved compound… 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 >

Wu-Jiu Sun¹, Xiao Fan Liu^1,2,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4117-4136, 2025, DOI:10.32604/cmc.2025.059528 - 06 March 2025

Abstract Predicting information dissemination on social media, specifically users’ reposting behavior, is crucial for applications such as advertising campaigns. Conventional methods use deep neural networks to make predictions based on features related to user topic interests and social preferences. However, these models frequently fail to account for the difficulties arising from limited training data and model size, which restrict their capacity to learn and capture the intricate patterns within microblogging data. To overcome this limitation, we introduce a novel model Adapt pre-trained Large Language model for Reposting Prediction (ALL-RP), which incorporates two key steps: (1)… More >

Xueli Shen, Meng Wang^*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4631-4647, 2025, DOI:10.32604/cmc.2025.059149 - 06 March 2025

Abstract In the field of Weakly Supervised Semantic Segmentation (WSSS), methods based on image-level annotation face challenges in accurately capturing objects of varying sizes, lacking sensitivity to image details, and having high computational costs. To address these issues, we improve the dual-branch architecture of the Conformer as the fundamental network for generating class activation graphs, proposing a multi-scale efficient weakly-supervised semantic segmentation method based on the improved Conformer. In the Convolution Neural Network (CNN) branch, a cross-scale feature integration convolution module is designed, incorporating multi-receptive field convolution layers to enhance the model’s ability to capture long-range… More >

Eliel Martins¹, Javier Bermejo Higuera^2,*, Ricardo Sant’Ana¹, Juan Ramón Bermejo Higuera², Juan Antonio Sicilia Montalvo², Diego Piedrahita Castillo³

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3031-3067, 2025, DOI:10.32604/cmes.2025.061080 - 03 March 2025

Abstract The growing threat of malware, particularly in the Portable Executable (PE) format, demands more effective methods for detection and classification. Machine learning-based approaches exhibit their potential but often neglect semantic segmentation of malware files that can improve classification performance. This research applies deep learning to malware detection, using Convolutional Neural Network (CNN) architectures adapted to work with semantically extracted data to classify malware into malware families. Starting from the Malconv model, this study introduces modifications to adapt it to multi-classification tasks and improve its performance. It proposes a new innovative method that focuses on byte More >