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On a Novel Extended Lomax Distribution with Asymmetric Properties and Its Statistical Applications

Computer Modeling in Engineering & Sciences 2023年第9期136卷 2371-2403页

作者： Aisha Fayomi Christophe Chesneau farrukh jamal Ali Algarni Department of Statistics Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia Laboratory Mathematics Nicolas Oresme Universitéde Caen NormandieCampus IICaen14032France Department of Statistics The Islamia University of BahawalpurPunjab63100Pakistan

In this article,we highlight a new three-parameter heavy-tailed lifetime distribution that aims to extend the modeling possibilities of the Lomax *** is called the extended Lomax *** considered distribution naturally appears as the distribution of a transformation of a random variable following the logweighted power distribution recently introduced for percentage or proportion data analysis *** a result,its cumulative distribution has the same functional basis as that of the Lomax distribution,but with a novel special logarithmic term depending on several *** modulation of this logarithmic term reveals new types of asymetrical shapes,implying a modeling horizon beyond that of the Lomax *** the first part,we examine several of its mathematical properties,such as the shapes of the related probability and hazard rate functions;stochastic comparisons;manageable expansions for various moments;and quantile *** particular,based on the quantile functions,various actuarial measures are *** the second part,the distribution’s applicability is investigated with the use of themaximumlikelihood *** behavior of the obtained parameter estimates is validated by a simulation *** claim data are *** show that the proposed distribution outperforms eight well-known distributions,including the Lomax distribution and several extended Lomax *** addition,we demonstrate that it gives preferable inferences from these competitor distributions in terms of risk measures.

关键词： Lomax distribution extended Lomax distribution asymmetry actuarial measures maximum likelihood estimation data analysis

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Topp-Leone Odd Fréchet Generated Family of Distributions with Applications to COVID-19 Data Sets

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Computer Modeling in Engineering & Sciences 2020年第10期125卷 437-458页

作者： Sanaa Al-Marzouki farrukh jamal Christophe Chesneau Mohammed Elgarhy Statistics Department Faculty of ScienceKing AbdulAziz UniversityJeddah21551Saudi Arabia Department of Statistics Govt.S.A Postgraduate College Dera Nawab SahibBahawalpur63100Pakistan Universitéde Caen Normandie LMNOCampus IIScience 3Caen14032France Valley High Institute for Management Finance and Information Systems Obour11828Egypt

Recent studies have pointed out the potential of the odd Fréchet family(or class)of continuous distributions in fitting data of all *** this article,we propose an extension of this family through the so-called“Topp-Leone strategy”,aiming to improve its overall flexibility by adding a shape *** main objective is to offer original distributions with modifiable properties,from which adaptive and pliant statistical models can be *** the new family,these aspects are illustrated by the means of comprehensive mathematical and numerical *** particular,we emphasize a special distribution with three parameters based on the exponential *** related model is shown to be skillful to the fitting of various lifetime data,more or less *** all the possible applications,we consider two data sets of current interest,linked to the COVID-19 *** concern daily cases confirmed and recovered in Pakistan from March 24 to April 28,*** a result of our analyzes,the proposed model has the best fitting results in comparison to serious challengers,including the former odd Fréchet model.

关键词： General family of distributions asymmetric distributions probabilistic properties parametric estimation confidence intervals COVID-19 pandemic data analysis

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Generalized Truncated Fréchet Generated Family Distributions and Their Applications

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Computer Modeling in Engineering & Sciences 2021年第2期126卷 791-819页

作者： Ramadan A.ZeinEldin Christophe Chesneau farrukh jamal Mohammed Elgarhy Abdullah M.Almarashi Sanaa Al-Marzouki Deanship of Scientific Research King AbdulAziz UniversityJeddah21589Saudi Arabia Faculty of Graduate Studies for Statistical Research Cairo UniversityAl OrmanGiza Governorate12613Egypt Universite de Caen LMNOCampus IIScience 3Caen14032France Department of Statistics The Islamia University of BahawalpurPunjab63100Pakistan The Higher Institute of Commercial Sciences Al Mahalla Al KubraAlgarbia31951Egypt Statistics Department Faculty of ScienceKing AbdulAziz UniversityJeddah21551Saudi Arabia

Understanding a phenomenon from observed data requires contextual and efficient statistical *** models are based on probability distributions having sufficiently flexible statistical properties to adapt to a maximum of *** examples include the distributions of the truncated Fréchet generated *** this paper,we go even further by introducing a more general family,based on a truncated version of the generalized Fréchet *** generalization involves a new shape parameter modulating to the extreme some central and dispersion parameters,as well as the skewness and weight of the *** also investigate the main functions of the new family,stress-strength parameter,diverse functional series expansions,incomplete moments,various entropy measures,theoretical and practical parameters estimation,bivariate extensions through the use of copulas,and the estimation of the model *** considering a special member of the family having the Weibull distribution as the parent,we fit two data sets of interest,one about waiting times and the other about *** statistical criteria attest that the proposed model is superior over other extended Weibull models,including the one derived to the former truncated Fréchet generated family.

关键词： Truncated distribution general family of distributions incomplete moments entropy copula data analysis

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Bayesian Analysis in Partially Accelerated Life Tests for Weighted Lomax Distribution

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Computers, Materials & Continua 2021年第9期68卷 2859-2875页

作者： Rashad Bantan Amal S.Hassan Ehab Almetwally M.Elgarhy farrukh jamal Christophe Chesneau Mahmoud Elsehetry Deanship of Scientific Research King Abdulaziz UniversityJeddah21589Kingdom of Saudi Arabia Faculty of Graduate Studies for Statistical Research Cairo UniversityGiza12613Egypt Faculty of Business Administration Delta University of Science and TechnologyMansoura11152Egypt The Higher Institute of Commercial Sciences Al Mahalla Al Kubra31951AlgarbiaEgypt Department of Statistics The Islamia University of BahawalpurPunjab 63100Pakistan Department of Mathematics LMNOUniversity of CaenCaen14032France Deanship of Information Technology King Abdulaziz UniversityJeddah21589Kingdom of Saudi Arabia

Accelerated life testing has been widely used in product life testing experiments because it can quickly provide information on the lifetime distributions by testing products or materials at higher than basic conditional levels of stress,such as pressure,temperature,vibration,voltage,or load to induce early *** this paper,a step stress partially accelerated life test(SSPALT)is regarded under the progressive type-II censored data with random *** removals from the test are considered to have the binomial *** life times of the testing items are assumed to follow lengthbiased weighted Lomax *** maximum likelihood method is used for estimating the model parameters of length-biased weighted *** asymptotic confidence interval estimates of the model parameters are evaluated using the Fisher information *** Bayesian estimators cannot be obtained in the explicit form,so the Markov chain Monte Carlo method is employed to address this problem,which ensures both obtaining the Bayesian estimates as well as constructing the credible interval of the involved *** precision of the Bayesian estimates and the maximum likelihood estimates are compared by *** addition,to compare the performance of the considered confidence intervals for different parameter values and sample *** Bootstrap confidence intervals give more accurate results than the approximate confidence intervals since the lengths of the former are less than the lengths of latter,for different sample sizes,observed failures,and censoring schemes,in most ***,the percentile Bootstrap confidence intervals give more accurate results than Bootstrap-t since the lengths of the former are less than the lengths of latter for different sample sizes,observed failures,and censoring schemes,in most *** performance comparison is conducted by the experiments with real data.

关键词： Partially accelerated life testing progressive type-II censoring length-biased weighted Lomax Bayesian and bootstrap confidence intervals

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Machine Learning-based USD/PKR Exchange Rate Forecasting Using Sentiment Analysis of Twitter Data

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Computers, Materials & Continua 2021年第6期67卷 3451-3461页

作者： Samreen Naeem Wali Khan Mashwani Aqib Ali M.Irfan Uddin Marwan Mahmoud farrukh jamal Christophe Chesneau Department of Computer Science&IT Glim Institute of Modern StudiesBahawalpur63100Pakistan Institute of Numerical Sciences Kohat University of Science&TechnologyKohat26000Pakistan Department of Computer Science Concordia College BahawalpurBahawalpur63100Pakistan Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Faculty of Applied Studies King Abdulaziz UniversityJeddah21577Saudi Arabia Department of Statistics The Islamia University of BahawalpurBahawalpur63100Pakistan Department of Mathematics Universitéde CaenLMNOCampus IIScience 3Caen14032France

This study proposes an approach based on machine learning to forecast currency exchange rates by applying sentiment analysis to messages on Twitter(called tweets).A dataset of the exchange rates between the United States Dollar(USD)and the Pakistani Rupee(PKR)was formed by collecting information from a forex website as well as a collection of tweets from the business community in Pakistan containing finance-related *** dataset was collected in raw form,and was subjected to natural language processing by way of data *** variable labeling was then applied to the standardized dataset,where the response variables were divided into two classes:“1”indicated an increase in the exchange rate and“−1”indicated a decrease in *** better represent the dataset,we used linear discriminant analysis and principal component analysis to visualize the data in three-dimensional vector *** that were obtained using a sampling approach were then used for data *** machine learning classifiers—the simple logistic classifier,the random forest,bagging,naïve Bayes,and the support vector machine—were applied to the optimized *** results show that the simple logistic classifier yielded the highest accuracy of 82.14%for the USD and the PKR exchange rates forecasting.

关键词： Machine learning exchange rate sentiment analysis linear discriminant analysis principal component analysis simple logistic

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COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach

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Computers, Materials & Continua 2021年第7期68卷 391-407页

作者： Aqib Ali Wali Khan Mashwani Samreen Naeem Muhammad Irfan Uddin Wiyada Kumam Poom Kumam Hussam Alrabaiah farrukh jamal Christophe Chesneau Department of Computer Science Concordia College BahawalpurBahawalpur63100Pakistan Department of Computer Science&IT Glim Institute of Modern StudiesBahawalpur63100Pakistan Institute of Numerical Sciences Kohat University of Science&TechnologyKohat26000Pakistan Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Program in Applied Statistics Department of Mathematics and Computer ScienceFaculty of Science and TechnologyRajamangala University of Technology ThanyaburiThanyaburi12110Thailand Departments of Mathematics Faculty of ScienceCenter of Excellence in Theoretical and Computational Science(TaCS-CoE)&KMUTT Fixed Point Research LaboratoryRoom SCL 802 Fixed Point LaboratoryScience Laboratory BuildingKing Mongkut’s University of Technology Thonburi(KMUTT)Bangkok10140Thailand Department of Medical Research China Medical University HospitalTaichung40402Taiwan College of Engineering Al Ain UniversityAl Ain64141United Arab Emirates Department of Mathematics Tafila Technical UniversityTafila66110Jordan Department of Statistics The Islamia University of BahawalpurBahawalpur63100Pakistan 11Department of MathematicsUniversitéde CaenLMNOCaen14032France

The purpose of this research is the segmentation of lungs computed tomography(CT)scan for the diagnosis of COVID-19 by using machine learning *** dataset contains data from patients who are prone to the *** contains three types of lungs CT images(Normal,Pneumonia,and COVID-19)collected from two different sources;the first one is the Radiology Department of Nishtar Hospital Multan and Civil Hospital Bahawalpur,Pakistan,and the second one is a publicly free available medical imaging database known as *** the preprocessing,a novel fuzzy c-mean automated region-growing segmentation approach is deployed to take an automated region of interest(ROIs)and acquire 52 hybrid statistical features for each ***,12 optimized statistical features are selected via the chi-square feature reduction *** the classification,five machine learning classifiers named as deep learning J4,multilayer perceptron,support vector machine,random forest,and naive Bayes are deployed to optimize the hybrid statistical features *** is observed that the deep learning J4 has promising results(sensitivity and specificity:0.987;accuracy:98.67%)among all the deployed *** a complementary study,a statistical work is devoted to the use of a new statistical model to fit the main datasets of COVID-19 collected in Pakistan.

关键词： COVID-19 machine learning fuzzy c-mean deep learning J4

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