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  • Open Access


    System Reliability Analysis Method Based on T-S FTA and HE-BN

    Qing Xia1, Yonghua Li2,*, Dongxu Zhang2, Yufeng Wang2

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1769-1794, 2024, DOI:10.32604/cmes.2023.030724

    Abstract For high-reliability systems in military, aerospace, and railway fields, the challenges of reliability analysis lie in dealing with unclear failure mechanisms, complex fault relationships, lack of fault data, and uncertainty of fault states. To overcome these problems, this paper proposes a reliability analysis method based on T-S fault tree analysis (T-S FTA) and Hyper-ellipsoidal Bayesian network (HE-BN). The method describes the connection between the various system fault events by T-S fuzzy gates and translates them into a Bayesian network (BN) model. Combining the advantages of T-S fault tree modeling with the advantages of Bayesian network computation, a reliability modeling method… More >

  • Open Access


    Self-Awakened Particle Swarm Optimization BN Structure Learning Algorithm Based on Search Space Constraint

    Kun Liu1,2, Peiran Li3, Yu Zhang1,*, Jia Ren1, Xianyu Wang2, Uzair Aslam Bhatti1

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3257-3274, 2023, DOI:10.32604/cmc.2023.039430

    Abstract To obtain the optimal Bayesian network (BN) structure, researchers often use the hybrid learning algorithm that combines the constraint-based (CB) method and the score-and-search (SS) method. This hybrid method has the problem that the search efficiency could be improved due to the ample search space. The search process quickly falls into the local optimal solution, unable to obtain the global optimal. Based on this, the Particle Swarm Optimization (PSO) algorithm based on the search space constraint process is proposed. In the first stage, the method uses dynamic adjustment factors to constrain the structure search space and enrich the diversity of… More >

  • Open Access


    A Survey on Acute Leukemia Expression Data Classification Using Ensembles

    Abdel Nasser H. Zaied1, Ehab Rushdy2, Mona Gamal3,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1349-1364, 2023, DOI:10.32604/csse.2023.033596

    Abstract Acute leukemia is an aggressive disease that has high mortality rates worldwide. The error rate can be as high as 40% when classifying acute leukemia into its subtypes. So, there is an urgent need to support hematologists during the classification process. More than two decades ago, researchers used microarray gene expression data to classify cancer and adopted acute leukemia as a test case. The high classification accuracy they achieved confirmed that it is possible to classify cancer subtypes using microarray gene expression data. Ensemble machine learning is an effective method that combines individual classifiers to classify new samples. Ensemble classifiers… More >

  • Open Access


    Online Markov Blanket Learning with Group Structure

    Bo Li1, Zhaolong Ling1, Yiwen Zhang1,*, Yong Zhou1, Yimin Hu2, Haifeng Ling3

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 33-48, 2023, DOI:10.32604/iasc.2023.037267

    Abstract Learning the Markov blanket (MB) of a given variable has received increasing attention in recent years because the MB of a variable predicts its local causal relationship with other variables. Online MB Learning can learn MB for a given variable on the fly. However, in some application scenarios, such as image analysis and spam filtering, features may arrive by groups. Existing online MB learning algorithms evaluate features individually, ignoring group structure. Motivated by this, we formulate the group MB learning with streaming features problem, and propose an Online MB learning with Group Structure algorithm, OMBGS, to identify the MB of… More >

  • Open Access


    BN-GEPSO: Learning Bayesian Network Structure Using Generalized Particle Swarm Optimization

    Muhammad Saad Salman1, Ibrahim M. Almanjahie2,3, AmanUllah Yasin1, Ammara Nawaz Cheema1,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4217-4229, 2023, DOI:10.32604/cmc.2023.034960

    Abstract At present Bayesian Networks (BN) are being used widely for demonstrating uncertain knowledge in many disciplines, including biology, computer science, risk analysis, service quality analysis, and business. But they suffer from the problem that when the nodes and edges increase, the structure learning difficulty increases and algorithms become inefficient. To solve this problem, heuristic optimization algorithms are used, which tend to find a near-optimal answer rather than an exact one, with particle swarm optimization (PSO) being one of them. PSO is a swarm intelligence-based algorithm having basic inspiration from flocks of birds (how they search for food). PSO is employed… More >

  • Open Access


    Mean Opinion Score Estimation for Mobile Broadband Networks Using Bayesian Networks

    Ayman A. El-Saleh1, Abdulraqeb Alhammadi2,*, Ibraheem Shayea3, Azizul Azizan4, Wan Haslina Hassan2

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4571-4587, 2022, DOI:10.32604/cmc.2022.024642

    Abstract Mobile broadband (MBB) networks are expanding rapidly to deliver higher data speeds. The fifth-generation cellular network promises enhanced-MBB with high-speed data rates, low power connectivity, and ultra-low latency video streaming. However, existing cellular networks are unable to perform well due to high latency and low bandwidth, which degrades the performance of various applications. As a result, monitoring and evaluation of the performance of these network-supported services is critical. Mobile network providers optimize and monitor their network performance to ensure the highest quality of service to their end-users. This paper proposes a Bayesian model to estimate the minimum opinion score (MOS)… More >

  • Open Access


    Behavioral Intrusion Prediction Model on Bayesian Network over Healthcare Infrastructure

    Mohammad Hafiz Mohd Yusof1,*, Abdullah Mohd Zin2, Nurhizam Safie Mohd Satar2

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2445-2466, 2022, DOI:10.32604/cmc.2022.023571

    Abstract Due to polymorphic nature of malware attack, a signature-based analysis is no longer sufficient to solve polymorphic and stealth nature of malware attacks. On the other hand, state-of-the-art methods like deep learning require labelled dataset as a target to train a supervised model. This is unlikely to be the case in production network as the dataset is unstructured and has no label. Hence an unsupervised learning is recommended. Behavioral study is one of the techniques to elicit traffic pattern. However, studies have shown that existing behavioral intrusion detection model had a few issues which had been parameterized into its common… More >

  • Open Access


    Reliability Modeling and Evaluation of Complex Multi-State System Based on Bayesian Networks Considering Fuzzy Dynamic of Faults

    Fangjun Zuo*, Meiwei Jia, Guang Wen, Huijie Zhang, Pingping Liu

    CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.2, pp. 993-1012, 2021, DOI:10.32604/cmes.2021.016870

    Abstract In the traditional reliability evaluation based on the Bayesian method, the failure probability of nodes is usually expressed by the average failure rate within a period of time. Aiming at the shortcomings of traditional Bayesian network reliability evaluation methods, this paper proposes a Bayesian network reliability evaluation method considering dynamics and fuzziness. The fuzzy theory and the dynamic of component failure probability are introduced to construct the dynamic fuzzy set function. Based on the solving characteristics of the dynamic fuzzy set and Bayesian network, the fuzzy dynamic probability and fuzzy dynamic importance degree of the fault state of leaf nodes… More >

  • Open Access


    Noninherited Factors in Fetal Congenital Heart Diseases Based on Bayesian Network: A Large Multicenter Study

    Yanping Ruan1,#, Xiangyu Liu2,#, Haogang Zhu3,*, Yijie Lu3, Xiaowei Liu1, Jiancheng Han1, Lin Sun1, Ye Zhang1, Xiaoyan Gu1, Ying Zhao1, Lei Li2, Suzhen Ran4, Jingli Chen5, Qiong Yu6, Yan Xu7, Hongmei Xia8, Yihua He1,*

    Congenital Heart Disease, Vol.16, No.6, pp. 529-549, 2021, DOI:10.32604/CHD.2021.015862

    Abstract Background: Current studies have confirmed that fetal congenital heart diseases (CHDs) are caused by various factors. However, the quantitative risk of CHD is not clear given the combined effects of multiple factors. Objective: This cross-sectional study aimed to detect associated factors of fetal CHD using a Bayesian network in a large sample and quantitatively analyze relative risk ratios (RRs). Methods: Pregnant women who underwent fetal echocardiography (N = 16,086 including 3,312 with CHD fetuses) were analyzed. Twenty-six maternal and fetal factors were obtained. A Bayesian network is constructed based on all variables through structural learning and parameter learning methods to… More >

  • Open Access


    A Network Security Risk Assessment Method Based on a B_NAG Model

    Hui Wang1, Chuanhan Zhu1, Zihao Shen1,*, Dengwei Lin2, Kun Liu1, MengYao Zhao3

    Computer Systems Science and Engineering, Vol.38, No.1, pp. 103-117, 2021, DOI:10.32604/csse.2021.014680

    Abstract Computer networks face a variety of cyberattacks. Most network attacks are contagious and destructive, and these types of attacks can be harmful to society and computer network security. Security evaluation is an effective method to solve network security problems. For accurate assessment of the vulnerabilities of computer networks, this paper proposes a network security risk assessment method based on a Bayesian network attack graph (B_NAG) model. First, a new resource attack graph (RAG) and the algorithm E-Loop, which is applied to eliminate loops in the B_NAG, are proposed. Second, to distinguish the confusing relationships between nodes of the attack graph… More >

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