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  • Open Access


    Computational Analysis of Novel Extended Lindley Progressively Censored Data

    Refah Alotaibi1, Mazen Nassar2,3, Ahmed Elshahhat4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2571-2596, 2024, DOI:10.32604/cmes.2023.030582

    Abstract A novel extended Lindley lifetime model that exhibits unimodal or decreasing density shapes as well as increasing, bathtub or unimodal-then-bathtub failure rates, named the Marshall-Olkin-Lindley (MOL) model is studied. In this research, using a progressive Type-II censored, various inferences of the MOL model parameters of life are introduced. Utilizing the maximum likelihood method as a classical approach, the estimators of the model parameters and various reliability measures are investigated. Against both symmetric and asymmetric loss functions, the Bayesian estimates are obtained using the Markov Chain Monte Carlo (MCMC) technique with the assumption of independent gamma… More >

  • Open Access


    SRI-XDFM: A Service Reliability Inference Method Based on Deep Neural Network

    Yang Yang1,*, Jianxin Wang1, Zhipeng Gao1, Yonghua Huo2, Xuesong Qiu1

    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1459-1475, 2020, DOI:10.32604/iasc.2020.011688

    Abstract With the vigorous development of the Internet industry and the iterative updating of web service technologies, there are increasing web services with the same or similar functions in the ocean of platforms on the Internet. The issue of selecting the most reliable web service for users has received considerable critical attention. Aiming to solve this task, we propose a service reliability inference method based on deep neural network (SRI-XDFM) in this article. First, according to the pattern of the raw data in our scenario, we improve the performance of embedding by extracting self-correlated information with More >

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