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  • Open Access

    ARTICLE

    On a New Version of Weibull Model: Statistical Properties, Parameter Estimation and Applications

    Hassan Okasha1,2, Mazen Nassar1,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2219-2241, 2023, DOI:10.32604/cmes.2023.028783 - 03 August 2023

    Abstract In this paper, we introduce a new four-parameter version of the traditional Weibull distribution. It is able to provide seven shapes of hazard rate, including constant, decreasing, increasing, unimodal, bathtub, unimodal then bathtub, and bathtub then unimodal shapes. Some basic characteristics of the proposed model are studied, including moments, entropies, mean deviations and order statistics, and its parameters are estimated using the maximum likelihood approach. Based on the asymptotic properties of the estimators, the approximate confidence intervals are also taken into consideration in addition to the point estimators. We examine the effectiveness of the maximum More >

  • Open Access

    ARTICLE

    Stock Market Prediction Using Generative Adversarial Networks (GANs): Hybrid Intelligent Model

    Fares Abdulhafidh Dael1,*, Ömer Çağrı Yavuz2, Uğur Yavuz1

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 19-35, 2023, DOI:10.32604/csse.2023.037903 - 26 May 2023

    Abstract The key indication of a nation’s economic development and strength is the stock market. Inflation and economic expansion affect the volatility of the stock market. Given the multitude of factors, predicting stock prices is intrinsically challenging. Predicting the movement of stock price indexes is a difficult component of predicting financial time series. Accurately predicting the price movement of stocks can result in financial advantages for investors. Due to the complexity of stock market data, it is extremely challenging to create accurate forecasting models. Using machine learning and other algorithms to anticipate stock prices is an More >

  • Open Access

    ARTICLE

    Analysis of the Lost Circulation Problem

    Xingquan Zhang1, Renjun Xie1, Kuan Liu2,*, Yating Li2, Yuqiang Xu2,*

    FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.6, pp. 1721-1733, 2023, DOI:10.32604/fdmp.2023.025578 - 30 January 2023

    Abstract The well-known “lost circulation” problem refers to the uncontrolled flow of whole mud into a formation. In order to address the problem related to the paucity of available data, in the present study, a model is introduced for the lost-circulation risk sample profile of a drilled well. The model is built taking into account effective data (the Block L). Then, using a three-dimensional geological modeling software, relying on the variation function and sequential Gaussian simulation method, a three-dimensional block lost-circulation risk model is introduced able to provide relevant information for regional analyses. More >

  • Open Access

    ARTICLE

    Functional Nonparametric Predictions in Food Industry Using Near-Infrared Spectroscopy Measurement

    Ibrahim M. Almanjahie1,2,*, Omar Fetitah3, Mohammed Kadi Attouch3, Tawfik Benchikh3,4

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6307-6319, 2023, DOI:10.32604/cmc.2023.033441 - 28 December 2022

    Abstract Functional statistics is a new technique for dealing with data that can be viewed as curves or images. Parallel to this approach, the Near-Infrared Reflectance (NIR) spectroscopy methodology has been used in modern chemistry as a rapid, low-cost, and exact means of assessing an object’s chemical properties. In this research, we investigate the quality of corn and cookie dough by analyzing the spectroscopic technique using certain cutting-edge statistical models. By analyzing spectral data and applying functional models to it, we could predict the chemical components of corn and cookie dough. Kernel Functional Classical Estimation (KFCE),… More >

  • Open Access

    ARTICLE

    A Novel Modified Alpha Power Transformed Weibull Distribution and Its Engineering Applications

    Refah Alotaibi1, Hassan Okasha2,3, Mazen Nassar2,4, Ahmed Elshahhat5,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2065-2089, 2023, DOI:10.32604/cmes.2023.023408 - 23 November 2022

    Abstract This paper suggests a new modified version of the traditional Weibull distribution by adding a new shape parameter utilising the modified alpha power transformed technique. We refer to the new model as modified alpha power transformed Weibull distribution. The attractiveness and significance of the new distribution lie in its power to model monotone and non-monotone failure rate functions, which are quite familiar in environmental investigations. Its hazard rate function can be decreasing, increasing, bathtub and upside-down then bathtub shaped. Diverse structural properties of the proposed model are acquired including quantile function, moments, entropies, order statistics, More >

  • Open Access

    ARTICLE

    A New Three-Parameter Inverse Weibull Distribution with Medical and Engineering Applications

    Refah Alotaibi1, Hassan Okasha2,3, Hoda Rezk4, Mazen Nassar2,5,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1255-1274, 2023, DOI:10.32604/cmes.2022.022623 - 27 October 2022

    Abstract The objective of this article is to provide a novel extension of the conventional inverse Weibull distribution that adds an extra shape parameter to increase its flexibility. This addition is beneficial in a variety of fields, including reliability, economics, engineering, biomedical science, biological research, environmental studies, and finance. For modeling real data, several expanded classes of distributions have been established. The modified alpha power transformed approach is used to implement the new model. The data matches the new inverse Weibull distribution better than the inverse Weibull distribution and several other competing models. It appears to More >

  • Open Access

    ARTICLE

    Change Point Detection for Process Data Analytics Applied to a Multiphase Flow Facility

    Rebecca Gedda1,*, Larisa Beilina2, Ruomu Tan3

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1737-1759, 2023, DOI:10.32604/cmes.2022.019764 - 20 September 2022

    Abstract Change point detection becomes increasingly important because it can support data analysis by providing labels to the data in an unsupervised manner. In the context of process data analytics, change points in the time series of process variables may have an important indication about the process operation. For example, in a batch process, the change points can correspond to the operations and phases defined by the batch recipe. Hence identifying change points can assist labelling the time series data. Various unsupervised algorithms have been developed for change point detection, including the optimisation approach which minimises a… More > Graphic Abstract

    Change Point Detection for Process Data Analytics Applied to a Multiphase Flow Facility

  • Open Access

    ARTICLE

    Numerical Analysis of the Erosion Mechanism inside the Tube Sockets of Main Steam Thermometers in a Coal-Fired Power Plant

    Yukun Lv1, Fan Yang1,*, Zi’an Wei1, Quan Lu2

    FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.2, pp. 379-397, 2023, DOI:10.32604/fdmp.2022.020373 - 29 August 2022

    Abstract Leakage occurring in the tube sockets of the main steam thermometers can seriously threaten the safe operation of coal-fired power plants. Here, assuming a 300 MW unit as a relevant testbed, this problem is investigated numerically through solution of the equations of fluid-dynamics in synergy with the mathematical treatment of relevant statistics. The results indicate that the steam can form a large-scale spiral flow inside the tube socket and continuously scour the inner wall. In the model with the protective casing setting angle of 60°, the average tangential fluid velocity can reach up to 4.8 m/s,… More > Graphic Abstract

    Numerical Analysis of the Erosion Mechanism inside the Tube Sockets of Main Steam Thermometers in a Coal-Fired Power Plant

  • Open Access

    ARTICLE

    Spatial Heterogeneity of Selected Soil Nutrients Related to Torreya grandis cv. Merrillii Plantation in Southeastern China

    Longlong Bai1,#, Yong Zhang2,#, Min Wang1, Ying He1, Tao Ye1, Keli Zhao1,*

    Phyton-International Journal of Experimental Botany, Vol.91, No.10, pp. 2221-2233, 2022, DOI:10.32604/phyton.2022.021422 - 30 May 2022

    Abstract Chinese Torreya grandis (Torreya grandis cv. Merrillii) is a unique economic tree species in China. Intensive management related to application of chemical fertilizer and herbicides caused serious soil quality degradation of Chinese Torreya grandis plantations. Totally, 120 soil samples were collected from the main disbtributed areas of Chinese Torreya grandis in Southeastern China. In this area, soil pH values varied from 3.68 to 6.78, with a median value of 4.91, implying a trend of acidification. The average concentrations of organic matter, available nitrogen, available phosphorus and available potassium were 27.52 g kg−1, 135.77 mg kg−1, 15.12 mg kg−1, and 153.43 mg kg−1, More >

  • Open Access

    ARTICLE

    A Novel Soft Clustering Approach for Gene Expression Data

    E. Kavitha1,*, R. Tamilarasan2, Arunadevi Baladhandapani3, M. K. Jayanthi Kannan4

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 871-886, 2022, DOI:10.32604/csse.2022.021215 - 09 May 2022

    Abstract Gene expression data represents a condition matrix where each row represents the gene and the column shows the condition. Micro array used to detect gene expression in lab for thousands of gene at a time. Genes encode proteins which in turn will dictate the cell function. The production of messenger RNA along with processing the same are the two main stages involved in the process of gene expression. The biological networks complexity added with the volume of data containing imprecision and outliers increases the challenges in dealing with them. Clustering methods are hence essential to… More >

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