Xianghong Cao, Chenxu Li^*, Haoting Zhai

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5487-5515, 2025, DOI:10.32604/cmc.2025.061823 - 19 May 2025

Abstract The behavior safety testing of more and more elderly people living alone has become a hot research topic along with the arrival of an aging society. A YOLO-Abnormal Behaviour (YOLO-AB) algorithm for fusion detection of falling and smoking behaviors of elderly people living alone has been proposed in this paper, which can fully utilize the potential of the YOLOv8 algorithm on object detection and deeply explore the characteristics of different types of behaviors among the elderly, to solve the problems of single detection type, low fusion detection accuracy, and high missed detection rate. Firstly, datasets… More >

Zijian Sun^1,2, Yaqian Li^3,4,*, Haoran Liu^1,2, Haibin Li^3,4, Wenming Zhang^3,4

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3187-3210, 2025, DOI:10.32604/cmc.2025.061920 - 16 April 2025

Abstract In recent years, audio pattern recognition has emerged as a key area of research, driven by its applications in human-computer interaction, robotics, and healthcare. Traditional methods, which rely heavily on handcrafted features such as Mel filters, often suffer from information loss and limited feature representation capabilities. To address these limitations, this study proposes an innovative end-to-end audio pattern recognition framework that directly processes raw audio signals, preserving original information and extracting effective classification features. The proposed framework utilizes a dual-branch architecture: a global refinement module that retains channel and temporal details and a multi-scale embedding… More >

Jianxiang Cao¹, Jinyang Wu¹, Wenqian Shang^1,*, Chunhua Wang¹, Kang Song¹, Tong Yi^2,*, Jiajun Cai¹, Haibin Zhu³

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2659-2675, 2025, DOI:10.32604/cmc.2025.060025 - 16 April 2025

Abstract With the rapid growth of social media, the spread of fake news has become a growing problem, misleading the public and causing significant harm. As social media content is often composed of both images and text, the use of multimodal approaches for fake news detection has gained significant attention. To solve the problems existing in previous multi-modal fake news detection algorithms, such as insufficient feature extraction and insufficient use of semantic relations between modes, this paper proposes the MFFFND-Co (Multimodal Feature Fusion Fake News Detection with Co-Attention Block) model. First, the model deeply explores the More >

Chuanyun Xu^1,#, Die Hu^1,#, Yang Zhang^1,*, Shuaiye Huang¹, Yisha Sun¹, Gang Li²

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 559-574, 2025, DOI:10.32604/cmc.2025.061579 - 26 March 2025

Abstract Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer. However, this task is challenging due to the morphological similarities between abnormal and normal cells and the significant variations in cell size. Pathologists often refer to surrounding cells to identify abnormalities. To emulate this slide examination behavior, this study proposes a Multi-Scale Feature Fusion Network (MSFF-Net) for detecting cervical abnormal cells. MSFF-Net employs a Cross-Scale Pooling Model (CSPM) to effectively capture diverse features and contextual information, ranging from local details to the overall structure. Additionally, a Multi-Scale Fusion Attention (MSFA)… More >

Shaodong Zou¹, Di Wu¹, Jianhou Gan^1,2,*, Juxiang Zhou^1,2, Jiatian Mei^1,2

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1101-1116, 2025, DOI:10.32604/cmc.2025.061376 - 26 March 2025

Abstract The task of student action recognition in the classroom is to precisely capture and analyze the actions of students in classroom videos, providing a foundation for realizing intelligent and accurate teaching. However, the complex nature of the classroom environment has added challenges and difficulties in the process of student action recognition. In this research article, with regard to the circumstances where students are prone to be occluded and classroom computing resources are restricted in real classroom scenarios, a lightweight multi-modal fusion action recognition approach is put forward. This proposed method is capable of enhancing the… More >

Jia Liu¹, Hang Gu¹, Fangmei Liu¹, Hao Chen¹, Zuhe Li¹, Gang Xu², Qidong Liu², Wei Wang^2,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 803-822, 2025, DOI:10.32604/cmc.2025.060966 - 26 March 2025

Abstract In recent years, convolutional neural networks (CNN) and Transformer architectures have made significant progress in the field of remote sensing (RS) change detection (CD). Most of the existing methods directly stack multiple layers of Transformer blocks, which achieves considerable improvement in capturing variations, but at a rather high computational cost. We propose a channel-Efficient Change Detection Network (CE-CDNet) to address the problems of high computational cost and imbalanced detection accuracy in remote sensing building change detection. The adaptive multi-scale feature fusion module (CAMSF) and lightweight Transformer decoder (LTD) are introduced to improve the change detection 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 >

Zhiyong Li, Xinlian Zhou^*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 739-760, 2025, DOI:10.32604/cmc.2025.059807 - 26 March 2025

Abstract Brain tumor classification is crucial for personalized treatment planning. Although deep learning-based Artificial Intelligence (AI) models can automatically analyze tumor images, fine details of small tumor regions may be overlooked during global feature extraction. Therefore, we propose a brain tumor Magnetic Resonance Imaging (MRI) classification model based on a global-local parallel dual-branch structure. The global branch employs ResNet50 with a Multi-Head Self-Attention (MHSA) to capture global contextual information from whole brain images, while the local branch utilizes VGG16 to extract fine-grained features from segmented brain tumor regions. The features from both branches are processed through More >

Dongdong Zhang, Chunping Wang, Huiying Wang, Qiang Fu^*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4319-4338, 2025, DOI:10.32604/cmc.2025.060653 - 06 March 2025

Abstract Video camouflaged object detection (VCOD) has become a fundamental task in computer vision that has attracted significant attention in recent years. Unlike image camouflaged object detection (ICOD), VCOD not only requires spatial cues but also needs motion cues. Thus, effectively utilizing spatiotemporal information is crucial for generating accurate segmentation results. Current VCOD methods, which typically focus on exploring motion representation, often ineffectively integrate spatial and motion features, leading to poor performance in diverse scenarios. To address these issues, we design a novel spatiotemporal network with an encoder-decoder structure. During the encoding stage, an adjacent space-time More >

Naikang Zhong¹, Xiao Lin^1,2,3,4,*, Wen Du⁵, Jin Shi⁶

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5285-5306, 2025, DOI:10.32604/cmc.2025.059102 - 06 March 2025

Abstract Multi-label image classification is a challenging task due to the diverse sizes and complex backgrounds of objects in images. Obtaining class-specific precise representations at different scales is a key aspect of feature representation. However, existing methods often rely on the single-scale deep feature, neglecting shallow and deeper layer features, which poses challenges when predicting objects of varying scales within the same image. Although some studies have explored multi-scale features, they rarely address the flow of information between scales or efficiently obtain class-specific precise representations for features at different scales. To address these issues, we propose… More >