Peng Xiao¹, Haiyu Xu¹, Peng Xu¹, Zhiwei Guo^1,*, Amr Tolba^2,*, Osama Alfarraj²

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2999-3022, 2025, DOI:10.32604/cmc.2025.066937 - 23 September 2025

Abstract Medical image analysis based on deep learning has become an important technical requirement in the field of smart healthcare. In view of the difficulties in collaborative modeling of local details and global features in multimodal image analysis of ophthalmology, as well as the existence of information redundancy in cross-modal data fusion, this paper proposes a multimodal fusion framework based on cross-modal collaboration and weighted attention mechanism. In terms of feature extraction, the framework collaboratively extracts local fine-grained features and global structural dependencies through a parallel dual-branch architecture, overcoming the limitations of traditional single-modality models in… More >

Zhihua Liu, Shengquan Liu^*, Jian Zhang

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 411-433, 2024, DOI:10.32604/cmc.2023.046237 - 30 January 2024

Abstract Network intrusion detection systems (NIDS) based on deep learning have continued to make significant advances. However, the following challenges remain: on the one hand, simply applying only Temporal Convolutional Networks (TCNs) can lead to models that ignore the impact of network traffic features at different scales on the detection performance. On the other hand, some intrusion detection methods consider multi-scale information of traffic data, but considering only forward network traffic information can lead to deficiencies in capturing multi-scale temporal features. To address both of these issues, we propose a hybrid Convolutional Neural Network that supports… More >

Guohui Song^1,2, Yongbin Wang^1,*, Jianfei Li¹, Hongbin Hu¹

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1621-1641, 2023, DOI:10.32604/cmc.2023.040095 - 30 August 2023

Abstract Predicting the popularity of online news is essential for news providers and recommendation systems. Time series, content and meta-feature are important features in news popularity prediction. However, there is a lack of exploration of how to integrate them effectively into a deep learning model and how effective and valuable they are to the model’s performance. This work proposes a novel deep learning model named Multiple Features Dynamic Fusion (MFDF) for news popularity prediction. For modeling time series, long short-term memory networks and attention-based convolution neural networks are used to capture long-term trends and short-term fluctuations… More >

Najlaa Hindi Alsaedi^*, Emad Sami Jaha

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1283-1311, 2022, DOI:10.32604/cmc.2022.021608 - 03 November 2021

Abstract Biometric recognition refers to the process of recognizing a person’s identity using physiological or behavioral modalities, such as face, voice, fingerprint, gait, etc. Such biometric modalities are mostly used in recognition tasks separately as in unimodal systems, or jointly with two or more as in multimodal systems. However, multimodal systems can usually enhance the recognition performance over unimodal systems by integrating the biometric data of multiple modalities at different fusion levels. Despite this enhancement, in real-life applications some factors degrade multimodal systems’ performance, such as occlusion, face poses, and noise in voice data. In this… More >