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  • Open Access

    ARTICLE

    Lightweight Cross-Modal Multispectral Pedestrian Detection Based on Spatial Reweighted Attention Mechanism

    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 >

  • Open Access

    ARTICLE

    Data Secure Storage Mechanism for IIoT Based on Blockchain

    Jin Wang1,2, Guoshu Huang1, R. Simon Sherratt3, Ding Huang4, Jia Ni4,*

    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 >

  • Open Access

    ARTICLE

    Predicting Traffic Flow Using Dynamic Spatial-Temporal Graph Convolution Networks

    Yunchang Liu1,*, 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 >

  • Open Access

    ARTICLE

    Fake News Detection Based on Text-Modal Dominance and Fusing Multiple Multi-Model Clues

    Lifang Fu1, Huanxin Peng2,*, Changjin Ma2, Yuhan Liu2

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4399-4416, 2024, DOI:10.32604/cmc.2024.047053

    Abstract In recent years, how to efficiently and accurately identify multi-model fake news has become more challenging. First, multi-model data provides more evidence but not all are equally important. Secondly, social structure information has proven to be effective in fake news detection and how to combine it while reducing the noise information is critical. Unfortunately, existing approaches fail to handle these problems. This paper proposes a multi-model fake news detection framework based on Tex-modal Dominance and fusing Multiple Multi-model Cues (TD-MMC), which utilizes three valuable multi-model clues: text-model importance, text-image complementary, and text-image inconsistency. TD-MMC is dominated by textural content and… More >

  • Open Access

    ARTICLE

    A Holistic Secure Communication Mechanism Using a Multilayered Cryptographic Protocol to Enhanced Security

    Fauziyah1, Zhaoshun Wang1,*, Mujahid Tabassum2

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4417-4452, 2024, DOI:10.32604/cmc.2024.046797

    Abstract In an era characterized by digital pervasiveness and rapidly expanding datasets, ensuring the integrity and reliability of information is paramount. As cyber threats evolve in complexity, traditional cryptographic methods face increasingly sophisticated challenges. This article initiates an exploration into these challenges, focusing on key exchanges (encompassing their variety and subtleties), scalability, and the time metrics associated with various cryptographic processes. We propose a novel cryptographic approach underpinned by theoretical frameworks and practical engineering. Central to this approach is a thorough analysis of the interplay between Confidentiality and Integrity, foundational pillars of information security. Our method employs a phased strategy, beginning… More >

  • Open Access

    ARTICLE

    A Cover-Independent Deep Image Hiding Method Based on Domain Attention Mechanism

    Nannan Wu1, Xianyi Chen1,*, James Msughter Adeke2, Junjie Zhao2

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3001-3019, 2024, DOI:10.32604/cmc.2023.045311

    Abstract Recently, deep image-hiding techniques have attracted considerable attention in covert communication and high-capacity information hiding. However, these approaches have some limitations. For example, a cover image lacks self-adaptability, information leakage, or weak concealment. To address these issues, this study proposes a universal and adaptable image-hiding method. First, a domain attention mechanism is designed by combining the Atrous convolution, which makes better use of the relationship between the secret image domain and the cover image domain. Second, to improve perceived human similarity, perceptual loss is incorporated into the training process. The experimental results are promising, with the proposed method achieving an… More >

  • Open Access

    ARTICLE

    Intelligent Fault Diagnosis Method of Rolling Bearings Based on Transfer Residual Swin Transformer with Shifted Windows

    Haomiao Wang1, Jinxi Wang2, Qingmei Sui2,*, Faye Zhang2, Yibin Li1, Mingshun Jiang2, Phanasindh Paitekul3

    Structural Durability & Health Monitoring, Vol.18, No.2, pp. 91-110, 2024, DOI:10.32604/sdhm.2023.041522

    Abstract Due to their robust learning and expression ability for complex features, the deep learning (DL) model plays a vital role in bearing fault diagnosis. However, since there are fewer labeled samples in fault diagnosis, the depth of DL models in fault diagnosis is generally shallower than that of DL models in other fields, which limits the diagnostic performance. To solve this problem, a novel transfer residual Swin Transformer (RST) is proposed for rolling bearings in this paper. RST has 24 residual self-attention layers, which use the hierarchical design and the shifted window-based residual self-attention. Combined with transfer learning techniques, the… More >

  • Open Access

    ARTICLE

    MECHANISMS AND APPLICATIONS OF CATALYTIC COMBUSTION OF NATURAL GAS*

    Shihong Zhang#, Ning Li, Zhihua Wang

    Frontiers in Heat and Mass Transfer, Vol.2, No.3, pp. 1-5, 2011, DOI:10.5098/hmt.v2.3.3004

    Abstract This article discussed the thermal efficiency, stability and pollutant emissions characteristics of the combustion of lean natural gas-air mixtures in Pd metal based honeycomb monoliths by means of experiments on a practical burner V. The chemistry at work in the monoliths was then investigated by the stagnation point flow reactor( SPFR), a fundamental experimental reactor. It was found that catalytic combustion inhibited the extent of gas-phase oxidation and increased the surface temperature of homogeneous ignition. According to the applications of catalytic combustion in the condenser boiler, the data of catalytic combustion condenser boiler V were measured at atmospheric temperature and… More >

  • Open Access

    REVIEW

    Exploring the molecular mechanisms and potential therapeutic strategies of ferroptosis in ovarian cancer

    LISHA MA1,#, WANQI SHAO1,#, WEILI ZHU2,*

    BIOCELL, Vol.48, No.3, pp. 379-386, 2024, DOI:10.32604/biocell.2024.047812

    Abstract The morbidity rate of ovarian cancer, a malignant tumour in gynaecological tumours, is rising, and it is considered to be the most lethal cancer. The majority of patients are typically diagnosed during the advanced stages of the illness due to the elusive characteristics of ovarian cancer and an absence of highly sensitive and specific diagnostic indicators. Surgical excision of the lesions, along with chemotherapy, is the conventional treatment for ovarian cancer; however, resistance to platinum-based chemotherapeutic drugs and molecular targeted therapies frequently arises. Improving the survival rate and prognosis of patients with end-stage or recurring ovarian cancer requires the identification… More >

  • Open Access

    REVIEW

    MicroRNAs modulation in lung cancer: exploring dual mechanisms and clinical prospects

    SHAHID HUSSAIN1,*, HABIB BOKHARI1, XINGXING FAN2, SHAUKAT IQBAL MALIK3, SUNDAS IJAZ1, MUHAMMAD ADNAN SHEREEN4, AIMAN FATIMA3

    BIOCELL, Vol.48, No.3, pp. 403-413, 2024, DOI:10.32604/biocell.2024.044801

    Abstract The global incidence of lung cancer is marked by a considerably elevated mortality rate. MicroRNAs (miRNAs) exert pivotal influence in the intricate orchestration of gene regulation, and their dysregulation can precipitate dire consequences, notably cancer. Within this context, miRNAs encapsulated in exosomes manifest a diversified impact on the landscape of lung cancer, wherein their actions may either foster angiogenesis, cell proliferation, and metastasis, or counteract these processes. This comprehensive review article discerns potential targets for the prospective development of therapeutic agents tailored for lung cancer. Tumor-suppressive miRNAs, such as miR-204, miR-192, miR-30a, miR-34a, miR-34b, miR-203, and miR-212, exhibit heightened expression… More >

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