IRAQI JOURNAL OF STATISTICAL SCIENCES最新文献_第4页

Enhancing the ARIMAX model by using the bivariate wavelet denoising: Application on road traffic accidents 利用双变量小波去噪增强 ARIMAX 模型：在道路交通事故中的应用

IRAQI JOURNAL OF STATISTICAL SCIENCES Pub Date : 2023-12-01 DOI: 10.33899/iqjoss.2023.0181146

nawroz Ahmed, Q. Abdulqader

引用次数: 0

Comparison of Logistic regression, Convolution Neural Network, and Kernel Approaches for Classifying the Caenorhabditis Elegans Motion 比较逻辑回归、卷积神经网络和核方法对优雅鼠运动进行分类

IRAQI JOURNAL OF STATISTICAL SCIENCES Pub Date : 2023-12-01 DOI: 10.33899/iqjoss.2023.181225

O. Shukur, Omar Malaa

{"title":"Comparison of Logistic regression, Convolution Neural Network, and Kernel Approaches for Classifying the Caenorhabditis Elegans Motion","authors":"O. Shukur, Omar Malaa","doi":"10.33899/iqjoss.2023.181225","DOIUrl":"https://doi.org/10.33899/iqjoss.2023.181225","url":null,"abstract":": Time series data are widely used in many fields including microbiology data. It is necessary to know how to classify the category to which observation belongs by using statistical classification methods and machine learning and deep learning algorithms. The study of the movement of some types of nematodes as one of the types of microorganisms including Caenorhabditis elegans (CE) is important to determine the actions and their impact on the life of the worms. In this study the CE motion time series data were represented by its wave motion angles which would be the study case. the non-linearity and uncertainty will be among the most common problems in this type of data that may lead to classifications that are not accurate. Convolutional Neural Network (CNN) will be used as one of the deep learning techniques and it is a non-linear method used to classify CE movement as a dependent variable in binary cases based on images of wave motion angles as an independent variable and its use will lead to accurate results because it is a suitable non-linear method to deal with Study data to solve nonlinearity and uncertainty problems through digital data visualization. Logistic regression (LR) and kernel method were also used to classify CE angles of movement. The AR(p) rank was used to determine the structure of the used methods. And by comparing the results between the methods used, it was found that the CNN method is superior to the other methods used. Therefore, it is possible to conclude that the use of the CNN method, which is based on pictorial classification, leads to accurate classification results compared to other methods based on numerical classification.","PeriodicalId":351789,"journal":{"name":"IRAQI JOURNAL OF STATISTICAL SCIENCES","volume":"199 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139013514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using Elastic-Net for High Dimensional Time Variables Selection of Autoregressive Model Series of Caenorhabditis Elegans Motion 使用弹性网对高维时间变量进行自回归模型序列选择

IRAQI JOURNAL OF STATISTICAL SCIENCES Pub Date : 2023-12-01 DOI: 10.33899/iqjoss.2023.181216

Mohammed Rasheed, O. Shukur

引用次数: 0

A New Family Of Distributions: Exponential Power-X Family Of Distributions And Its Some Properties 一个新的分布族指数 Power-X 分布族及其一些特性

IRAQI JOURNAL OF STATISTICAL SCIENCES Pub Date : 2023-12-01 DOI: 10.33899/iqjoss.2023.181258

Noorsl Zeenalabiden, Buğra Saraçoğlu

引用次数: 0

Using Wavelet Shrinkage to Deal with Contamination Problem in Survival Function for Weibull Distribution 利用小波收缩处理魏布尔分布生存函数中的污染问题

IRAQI JOURNAL OF STATISTICAL SCIENCES Pub Date : 2023-12-01 DOI: 10.33899/iqjoss.2023.0181139

B. Sedeeq

引用次数: 0

Application of Poisson's Hierarchical Regression Model to the Deaths of Covid-19 in Mosul City Hospitals Poisson分层回归模型在摩苏尔市医院Covid-19死亡中的应用

IRAQI JOURNAL OF STATISTICAL SCIENCES Pub Date : 2022-12-02 DOI: 10.33899/iqjoss.2022.176199

Ban Al ani, Mahmmood Altai

引用次数: 0

Using ARIMA and Random Forest Models for Climatic Datasets Forecasting 利用ARIMA和随机森林模型进行气候数据集预报

IRAQI JOURNAL OF STATISTICAL SCIENCES Pub Date : 2022-12-01 DOI: 10.33899/iqjoss.2022.176203

Oday Aljuborey, O. Shukur

引用次数: 0

Study of the two-parameter Weibull distribution and Estimation of the scale and shape parameter application to the voltage data of the cement material (review) 双参数威布尔分布及其尺度和形状参数估计在水泥材料电压数据中的应用研究(综述)

IRAQI JOURNAL OF STATISTICAL SCIENCES Pub Date : 2022-12-01 DOI: 10.33899/iqjoss.2022.176206

Safwan Rashed, Farok Thafer, Ali Abdallah

引用次数: 0

Use the robust RFCH method with a polychoric correlation matrix in structural equation modeling When you are ordinal data 当您是有序数据时，在结构方程建模中使用具有多周期相关矩阵的鲁棒RFCH方法

IRAQI JOURNAL OF STATISTICAL SCIENCES Pub Date : 2022-12-01 DOI: 10.33899/iqjoss.2022.176201

O. Salim, M. Mohammed

引用次数: 0

Locally weighted regression for sunspots estimation and prediction 太阳黑子估计与预报的局部加权回归

IRAQI JOURNAL OF STATISTICAL SCIENCES Pub Date : 2022-12-01 DOI: 10.33899/iqjoss.2022.176200

I. Fadel, M. Al-Hashimi

{"title":"Locally weighted regression for sunspots estimation and prediction","authors":"I. Fadel, M. Al-Hashimi","doi":"10.33899/iqjoss.2022.176200","DOIUrl":"https://doi.org/10.33899/iqjoss.2022.176200","url":null,"abstract":"Locally weighted regression (LOESS) is a modern non-parametric regression method designed for treating cases where classical procedures are not highly efficient or cannot applied efficiently. Sunspots are the darker areas of the solar sphere's surface relative to other regions and are an important indicator of solar activity .The aim of this paper is to model and predict the number of sunspots because of their very importance to understanding the terrestrial consequences of solar activity and its direct impact on weather and communication systems on Earth, which may lead to damage to satellites. In this paper, the number of sunspots represented by annual data for the period from 1900 to 2021 (122 years) as well as monthly data for the period from January 1900 to January 2022 (1465 months) was obtained from the global data center (Sunspot Index and Long-term Solar Observations) (SILSO). The LOESS regression used for estimating and predicting the number of monthly and annual sunspots. The smoothing parameter, as well as the degree of the polynomial that fulfills the lowest for Akaike corrected information criterion. The analysis showed the ability of the LOESS to represent sunspot data by passing diagnostic tests as well as its high predictive ability. From the predictive values for the monthly data, it found that the maximum average number of sunspots will be 123.7 in July 2022, and the lowest average will be in February with 61.3 sunspots. Regarding the annual data, it found from the predictive values that the maximum average number of sunspots will be in the year 2023 with an average of 161.7 sunspots, and the lowest average will be in the year 2029 with an average of 16.1.","PeriodicalId":351789,"journal":{"name":"IRAQI JOURNAL OF STATISTICAL SCIENCES","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127923139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0