Fangfang Shan^1,2,*, Huifang Sun^1,2, Mengyi Wang^1,2

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 581-605, 2024, DOI:10.32604/cmc.2024.046202

Abstract As social networks become increasingly complex, contemporary fake news often includes textual descriptions of events accompanied by corresponding images or videos. Fake news in multiple modalities is more likely to create a misleading perception among users. While early research primarily focused on text-based features for fake news detection mechanisms, there has been relatively limited exploration of learning shared representations in multimodal (text and visual) contexts. To address these limitations, this paper introduces a multimodal model for detecting fake news, which relies on similarity reasoning and adversarial networks. The model employs Bidirectional Encoder Representation from Transformers (BERT) and Text Convolutional Neural… More >

EFFAT ALEMZADEH¹, LEILA ALLAHQOLI², AFROOZ MAZIDIMORADI³, ESMAT ALEMZADEH^1,4, FAHIMEH GHASEMI^4,5, HAMID SALEHINIYA⁶, IBRAHIM ALKATOUT^7,*

Oncology Research, Vol.32, No.5, pp. 831-847, 2024, DOI:10.32604/or.2024.031006

Abstract Ovarian cancer is among the most lethal gynecological cancers, primarily due to the lack of specific symptoms leading to an advanced-stage diagnosis and resistance to chemotherapy. Drug resistance (DR) poses the most significant challenge in treating patients with existing drugs. The Food and Drug Administration (FDA) has recently approved three new therapeutic drugs, including two poly (ADP-ribose) polymerase (PARP) inhibitors (olaparib and niraparib) and one vascular endothelial growth factor (VEGF) inhibitor (bevacizumab) for maintenance therapy. However, resistance to these new drugs has emerged. Therefore, understanding the mechanisms of DR and exploring new approaches to overcome them is crucial for effective… 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

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 of insufficient samples, and the… More >

Lyuchao Liao^1,2, Hankun Xiao^2,*, Pengqi Xing², Zhenhua Gan^1,2, Youpeng He², Jiajun Wang²

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 557-576, 2024, DOI:10.32604/cmes.2024.047452

Abstract Autonomous driving has witnessed rapid advancement; however, ensuring safe and efficient driving in intricate scenarios remains a critical challenge. In particular, traffic roundabouts bring a set of challenges to autonomous driving due to the unpredictable entry and exit of vehicles, susceptibility to traffic flow bottlenecks, and imperfect data in perceiving environmental information, rendering them a vital issue in the practical application of autonomous driving. To address the traffic challenges, this work focused on complex roundabouts with multi-lane and proposed a Perception Enhanced Deep Deterministic Policy Gradient (PE-DDPG) for Autonomous Driving in the Roundabouts. Specifically, the model incorporates an enhanced variational… More >

Qing Zhu^1,*, Linlin Gu^1,2, Huijie Lin^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 577-591, 2024, DOI:10.32604/cmes.2023.043307

Abstract With the rapid development of electric power systems, load estimation plays an important role in system operation and planning. Usually, load estimation techniques contain traditional, time series, regression analysis-based, and machine learning-based estimation. Since the machine learning-based method can lead to better performance, in this paper, a deep learning-based load estimation algorithm using image fingerprint and attention mechanism is proposed. First, an image fingerprint construction is proposed for training data. After the data preprocessing, the training data matrix is constructed by the cyclic shift and cubic spline interpolation. Then, the linear mapping and the gray-color transformation method are proposed to… More >

Zhuoqun Xia¹, Hangyu Hu¹, Wenjing Li^2,3, Qisheng Jiang¹, Lan Pu¹, Yicong Shu¹, Arun Kumar Sangaiah^4,5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 409-430, 2024, DOI:10.32604/cmes.2024.030052

Abstract Early screening of diabetes retinopathy (DR) plays an important role in preventing irreversible blindness. Existing research has failed to fully explore effective DR lesion information in fundus maps. Besides, traditional attention schemes have not considered the impact of lesion type differences on grading, resulting in unreasonable extraction of important lesion features. Therefore, this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator (MPAG) and a lesion localization module (LLM). Firstly, MPAG is used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained… More >

JIANGBO WANG^*

Journal of Polymer Materials, Vol.36, No.4, pp. 381-389, 2019, DOI:10.32381/JPM.2019.36.04.7

Abstract In this study, a novel flame retardant polyvinylphenylsilsesquioxane (PVP) was added into epoxy resin (EP) to prepare EP/PVP (FREP) composites. The results of cone calorimeter measurement showed that in comparison with virgin EP, the peak heat release rate (PHRR) and total heat release (THR) of FREP were reduced by 27.3% and 10.4%, respectively. Moreover, the thermal degradation behavior of FREP was studied by the Kissinger and Ozawa-Flynn-Wall methods. The results suggested that the addition of PVP greatly enhanced the thermal stability of EP in the final stage, which could be attributed to that the branched silicone with vinyl and phenyl… More >

Lujuan Deng, Ruochong Fu^*, Zuhe Li, Boyi Liu, Mengze Xue, Yuhao Cui

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4071-4089, 2024, DOI:10.32604/cmc.2024.048200

Abstract Multispectral pedestrian detection technology leverages infrared images to provide reliable information for visible light images, demonstrating significant advantages in low-light conditions and background occlusion scenarios. However, while continuously improving cross-modal feature extraction and fusion, ensuring the model’s detection speed is also a challenging issue. We have devised a deep learning network model for cross-modal pedestrian detection based on Resnet50, aiming to focus on more reliable features and enhance the model’s detection efficiency. This model employs a spatial attention mechanism to reweight the input visible light and infrared image data, enhancing the model’s focus on different spatial positions and sharing the… More >

Jin Wang^1,2, Guoshu Huang¹, R. Simon Sherratt³, Ding Huang⁴, Jia Ni^4,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4029-4048, 2024, DOI:10.32604/cmc.2024.047468

Abstract With the development of Industry 4.0 and big data technology, the Industrial Internet of Things (IIoT) is hampered by inherent issues such as privacy, security, and fault tolerance, which pose certain challenges to the rapid development of IIoT. Blockchain technology has immutability, decentralization, and autonomy, which can greatly improve the inherent defects of the IIoT. In the traditional blockchain, data is stored in a Merkle tree. As data continues to grow, the scale of proofs used to validate it grows, threatening the efficiency, security, and reliability of blockchain-based IIoT. Accordingly, this paper first analyzes the inefficiency of the traditional blockchain… More >

Yunchang Liu^1,*, Fei Wan¹, Chengwu Liang²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4343-4361, 2024, DOI:10.32604/cmc.2024.047211

Abstract Traffic flow prediction plays a key role in the construction of intelligent transportation system. However, due to its complex spatio-temporal dependence and its uncertainty, the research becomes very challenging. Most of the existing studies are based on graph neural networks that model traffic flow graphs and try to use fixed graph structure to deal with the relationship between nodes. However, due to the time-varying spatial correlation of the traffic network, there is no fixed node relationship, and these methods cannot effectively integrate the temporal and spatial features. This paper proposes a novel temporal-spatial dynamic graph convolutional network (TSADGCN). The dynamic… More >