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  • Open Access

    ARTICLE

    Recommendation Method for Contrastive Enhancement of Neighborhood Information

    Hairong Wang, Beijing Zhou*, Lisi Zhang, He Ma

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 453-472, 2024, DOI:10.32604/cmc.2023.046560

    Abstract Knowledge graph can assist in improving recommendation performance and is widely applied in various personalized recommendation domains. However, existing knowledge-aware recommendation methods face challenges such as weak user-item interaction supervisory signals and noise in the knowledge graph. To tackle these issues, this paper proposes a neighbor information contrast-enhanced recommendation method by adding subtle noise to construct contrast views and employing contrastive learning to strengthen supervisory signals and reduce knowledge noise. Specifically, first, this paper adopts heterogeneous propagation and knowledge-aware attention networks to obtain multi-order neighbor embedding of users and items, mining the high-order neighbor information of users and items. Next,… More >

  • Open Access

    ARTICLE

    Network Configuration Entity Extraction Method Based on Transformer with Multi-Head Attention Mechanism

    Yang Yang1, Zhenying Qu1, Zefan Yan1, Zhipeng Gao1,*, Ti Wang2

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 735-757, 2024, DOI:10.32604/cmc.2023.045807

    Abstract Nowadays, ensuring the quality of network services has become increasingly vital. Experts are turning to knowledge graph technology, with a significant emphasis on entity extraction in the identification of device configurations. This research paper presents a novel entity extraction method that leverages a combination of active learning and attention mechanisms. Initially, an improved active learning approach is employed to select the most valuable unlabeled samples, which are subsequently submitted for expert labeling. This approach successfully addresses the problems of isolated points and sample redundancy within the network configuration sample set. Then the labeled samples are utilized to train the model… More >

  • Open Access

    REVIEW

    A Survey of Knowledge Graph Construction Using Machine Learning

    Zhigang Zhao1, Xiong Luo1,2,3,*, Maojian Chen1,2,3, Ling Ma1

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 225-257, 2024, DOI:10.32604/cmes.2023.031513

    Abstract Knowledge graph (KG) serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured framework. This framework facilitates a transformation in information retrieval, transitioning it from mere string matching to far more sophisticated entity matching. In this transformative process, the advancement of artificial intelligence and intelligent information services is invigorated. Meanwhile, the role of machine learning method in the construction of KG is important, and these techniques have already achieved initial success. This article embarks on a comprehensive journey through the last strides in the field of KG via machine learning. With a profound amalgamation… More >

  • Open Access

    ARTICLE

    Application Research on Two-Layer Threat Prediction Model Based on Event Graph

    Shuqin Zhang, Xinyu Su*, Yunfei Han, Tianhui Du, Peiyu Shi

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3993-4023, 2023, DOI:10.32604/cmc.2023.044526

    Abstract Advanced Persistent Threat (APT) is now the most common network assault. However, the existing threat analysis models cannot simultaneously predict the macro-development trend and micro-propagation path of APT attacks. They cannot provide rapid and accurate early warning and decision responses to the present system state because they are inadequate at deducing the risk evolution rules of network threats. To address the above problems, firstly, this paper constructs the multi-source threat element analysis ontology (MTEAO) by integrating multi-source network security knowledge bases. Subsequently, based on MTEAO, we propose a two-layer threat prediction model (TL-TPM) that combines the knowledge graph and the… More >

  • Open Access

    ARTICLE

    Fuzzy Logic Inference System for Managing Intensive Care Unit Resources Based on Knowledge Graph

    Ahmad F Subahi*, Areej Athama

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3801-3816, 2023, DOI:10.32604/cmc.2023.034522

    Abstract With the rapid growth in the availability of digital health-related data, there is a great demand for the utilization of intelligent information systems within the healthcare sector. These systems can manage and manipulate this massive amount of health-related data and encourage different decision-making tasks. They can also provide various sustainable health services such as medical error reduction, diagnosis acceleration, and clinical services quality improvement. The intensive care unit (ICU) is one of the most important hospital units. However, there are limited rooms and resources in most hospitals. During times of seasonal diseases and pandemics, ICUs face high admission demand. In… More >

  • Open Access

    ARTICLE

    Threat Modeling and Application Research Based on Multi-Source Attack and Defense Knowledge

    Shuqin Zhang, Xinyu Su*, Peiyu Shi, Tianhui Du, Yunfei Han

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 349-377, 2023, DOI:10.32604/cmc.2023.040964

    Abstract Cyber Threat Intelligence (CTI) is a valuable resource for cybersecurity defense, but it also poses challenges due to its multi-source and heterogeneous nature. Security personnel may be unable to use CTI effectively to understand the condition and trend of a cyberattack and respond promptly. To address these challenges, we propose a novel approach that consists of three steps. First, we construct the attack and defense analysis of the cybersecurity ontology (ADACO) model by integrating multiple cybersecurity databases. Second, we develop the threat evolution prediction algorithm (TEPA), which can automatically detect threats at device nodes, correlate and map multi-source threat information,… More >

  • Open Access

    ARTICLE

    Traffic Scene Captioning with Multi-Stage Feature Enhancement

    Dehai Zhang*, Yu Ma, Qing Liu, Haoxing Wang, Anquan Ren, Jiashu Liang

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2901-2920, 2023, DOI:10.32604/cmc.2023.038264

    Abstract Traffic scene captioning technology automatically generates one or more sentences to describe the content of traffic scenes by analyzing the content of the input traffic scene images, ensuring road safety while providing an important decision-making function for sustainable transportation. In order to provide a comprehensive and reasonable description of complex traffic scenes, a traffic scene semantic captioning model with multi-stage feature enhancement is proposed in this paper. In general, the model follows an encoder-decoder structure. First, multi-level granularity visual features are used for feature enhancement during the encoding process, which enables the model to learn more detailed content in the… More >

  • Open Access

    ARTICLE

    Combining Deep Learning with Knowledge Graph for Design Knowledge Acquisition in Conceptual Product Design

    Yuexin Huang1,2, Suihuai Yu1, Jianjie Chu1,*, Zhaojing Su1,3, Yangfan Cong1, Hanyu Wang1, Hao Fan4

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 167-200, 2024, DOI:10.32604/cmes.2023.028268

    Abstract The acquisition of valuable design knowledge from massive fragmentary data is challenging for designers in conceptual product design. This study proposes a novel method for acquiring design knowledge by combining deep learning with knowledge graph. Specifically, the design knowledge acquisition method utilises the knowledge extraction model to extract design-related entities and relations from fragmentary data, and further constructs the knowledge graph to support design knowledge acquisition for conceptual product design. Moreover, the knowledge extraction model introduces ALBERT to solve memory limitation and communication overhead in the entity extraction module, and uses multi-granularity information to overcome segmentation errors and polysemy ambiguity… More >

  • Open Access

    ARTICLE

    Multi-Domain Malicious Behavior Knowledge Base Framework for Multi-Type DDoS Behavior Detection

    Ouyang Liu, Kun Li*, Ziwei Yin, Deyun Gao, Huachun Zhou

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2955-2977, 2023, DOI:10.32604/iasc.2023.039995

    Abstract Due to the many types of distributed denial-of-service attacks (DDoS) attacks and the large amount of data generated, it becomes a challenge to manage and apply the malicious behavior knowledge generated by DDoS attacks. We propose a malicious behavior knowledge base framework for DDoS attacks, which completes the construction and application of a multi-domain malicious behavior knowledge base. First, we collected malicious behavior traffic generated by five mainstream DDoS attacks. At the same time, we completed the knowledge collection mechanism through data pre-processing and dataset design. Then, we designed a malicious behavior category graph and malicious behavior structure graph for… More >

  • Open Access

    ARTICLE

    Construction Method of Equipment Defect Knowledge Graph in IoT

    Huafei Yang1, Wenqing Yang1, Nan Zhang1, Shanming Wei2,*, Yingnan Shang1

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2745-2765, 2023, DOI:10.32604/iasc.2023.036614

    Abstract Equipment defect detection is essential to the security and stability of power grid networking operations. Besides the status of the power grid itself, environmental information is also necessary for equipment defect detection. At the same time, different types of intelligent sensors can monitor environmental information, such as temperature, humidity, dust, etc. Therefore, we apply the Internet of Things (IoT) technology to monitor the related environment and pervasive interconnections to diverse physical objects. However, the data related to device defects in the existing Internet of Things are complex and lack uniform association hence building a knowledge graph is proposed to solve… More >

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