Md Minhazul Islam^1,2, Yunfei Yin^1,2,*, Md Tanvir Islam^1,2, Zheng Yuan^1,2, Argho Dey^1,2

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072550 - 12 January 2026

Abstract Automatic segmentation of landslides from remote sensing imagery is challenging because traditional machine learning and early CNN-based models often fail to generalize across heterogeneous landscapes, where segmentation maps contain sparse and fragmented landslide regions under diverse geographical conditions. To address these issues, we propose a lightweight dual-stream siamese deep learning framework that integrates optical and topographical data fusion with an adaptive decoder, guided multimodal fusion, and deep supervision. The framework is built upon the synergistic combination of cross-attention, gated fusion, and sub-pixel upsampling within a unified dual-stream architecture specifically optimized for landslide segmentation, enabling efficient… More >

Tianzhe Jiao, Yuming Chen, Xiaoyue Feng, Chaopeng Guo, Jie Song^*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2681-2700, 2025, DOI:10.32604/cmc.2025.069235 - 23 September 2025

Abstract Visible-infrared object detection leverages the day-night stable object perception capability of infrared images to enhance detection robustness in low-light environments by fusing the complementary information of visible and infrared images. However, the inherent differences in the imaging mechanisms of visible and infrared modalities make effective cross-modal fusion challenging. Furthermore, constrained by the physical characteristics of sensors and thermal diffusion effects, infrared images generally suffer from blurred object contours and missing details, making it difficult to extract object features effectively. To address these issues, we propose an infrared-visible image fusion network that realizes multimodal information fusion… More >

Ronghua Wang¹, Shibao Sun^1,*, Pengcheng Zhao^1,*, Xianglan Yang², Xingjia Wei¹, Changyang Hu¹

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3519-3539, 2025, DOI:10.32604/cmc.2025.066212 - 03 July 2025

Abstract The capacity to diagnose faults in rolling bearings is of significant practical importance to ensure the normal operation of the equipment. Frequency-domain features can effectively enhance the identification of fault modes. However, existing methods often suffer from insufficient frequency-domain representation in practical applications, which greatly affects diagnostic performance. Therefore, this paper proposes a rolling bearing fault diagnosis method based on a Multi-Scale Fusion Network (MSFN) using the Time-Division Fourier Transform (TDFT). The method constructs multi-scale channels to extract time-domain and frequency-domain features of the signal in parallel. A multi-level, multi-scale filter-based approach is designed to More >

Yingyong Zou^*, Xingkui Zhang, Tao Liu, Yu Zhang, Long Li, Wenzhuo Zhao

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4699-4723, 2025, DOI:10.32604/cmc.2025.062625 - 19 May 2025

Abstract High-speed train engine rolling bearings play a crucial role in maintaining engine health and minimizing operational losses during train operation. To solve the problems of low accuracy of the diagnostic model and unstable model due to the influence of noise during fault detection, a rolling bearing fault diagnosis model based on cross-attention fusion of WDCNN and BILSTM is proposed. The first layer of the wide convolutional kernel deep convolutional neural network (WDCNN) is used to extract the local features of the signal and suppress the high-frequency noise. A Bidirectional Long Short-Term Memory Network (BILSTM) is… More >

Yumei Hao^1,2, Haiyan Wang^1,2,*, Dong Zhang³

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2989-3005, 2025, DOI:10.32604/cmc.2025.063008 - 16 April 2025

Abstract Named entity recognition (NER) in musk deer domain is the extraction of specific types of entities from unstructured texts, constituting a fundamental component of the knowledge graph, Q&A system, and text summarization system of musk deer domain. Due to limited annotated data, diverse entity types, and the ambiguity of Chinese word boundaries in musk deer domain NER, we present a novel NER model, CAELF-GP, which is based on cross-attention mechanism enhanced lexical features (CAELF). Specifically, we employ BERT as a character encoder and advocate the integration of external lexical information at the character representation layer.… More >

Lihua Xie¹, Ni Tang¹, Qing Chen^1,*, Jun Li^2,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3381-3397, 2025, DOI:10.32604/cmc.2024.058950 - 17 February 2025

Abstract In the age of information explosion and artificial intelligence, sentiment analysis tailored for the tobacco industry has emerged as a pivotal avenue for cigarette manufacturers to enhance their tobacco products. Existing solutions have primarily focused on intrinsic features within consumer reviews and achieved significant progress through deep feature extraction models. However, they still face these two key limitations: (1) neglecting the influence of fundamental tobacco information on analyzing the sentiment inclination of consumer reviews, resulting in a lack of consistent sentiment assessment criteria across thousands of tobacco brands; (2) overlooking the syntactic dependencies between Chinese… More >

Jianwei Zhang^1,2,*, Zhishang Zhao³, Zengyu Cai³, Yuan Feng⁴, Liang Zhu³, Yahui Sun³

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 977-996, 2025, DOI:10.32604/cmc.2024.058438 - 03 January 2025

Abstract The rapid development of short video platforms poses new challenges for traditional recommendation systems. Recommender systems typically depend on two types of user behavior feedback to construct user interest profiles: explicit feedback (interactive behavior), which significantly influences users’ short-term interests, and implicit feedback (viewing time), which substantially affects their long-term interests. However, the previous model fails to distinguish between these two feedback methods, leading it to predict only the overall preferences of users based on extensive historical behavior sequences. Consequently, it cannot differentiate between users’ long-term and short-term interests, resulting in low accuracy in describing… More >

Chengzhang Zhu^1,2, Renmao Zhang¹, Yalong Xiao^1,2,*, Beiji Zou¹, Xian Chai¹, Zhangzheng Yang¹, Rong Hu³, Xuanchu Duan⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1103-1128, 2024, DOI:10.32604/cmes.2024.048453 - 16 April 2024

Abstract Automatic segmentation of medical images provides a reliable scientific basis for disease diagnosis and analysis. Notably, most existing methods that combine the strengths of convolutional neural networks (CNNs) and Transformers have made significant progress. However, there are some limitations in the current integration of CNN and Transformer technology in two key aspects. Firstly, most methods either overlook or fail to fully incorporate the complementary nature between local and global features. Secondly, the significance of integrating the multi-scale encoder features from the dual-branch network to enhance the decoding features is often disregarded in methods that combine… More >