Al-Rafidain University College For Sciences

Studying population distribution is of great importance in understanding demographic and economic patterns in Iraq. It is also of great importance in understanding and determining the nature of the geographical distribution of population in all regions and its impact on economic resources. where Some areas will attract large amounts of the population and some will be unattractive to the population. To find out the most important factors affecting the distribution of the population in Iraq, a group of factors affecting the distribution of the population were studied and analyzed using one of the methods for selecting variables, which is the Elastic Net method. where Monthly data was collected for five years (2018-2022). Using the R language program, it was concluded that the most important factors affecting population distribution are basically five factors (Job opportunities and economic activity, geographical location and terrain, technological development and availability of infrastructure, political stability and security, population growth rates and migration).

Population societies are affected by vital factors. They increase due to births and decrease due to deaths. The immigration factor also affects the size of the population in two different directions, as the population increases due to the influence of immigration entering the country and decreases due to the influence of immigration leaving the country. The three factors: births, deaths, and migration of both types are the only reasons for the change in the size of the world's population in its various parts and regions. The increasing number of births compared to deaths leads to a net natural increase. Time series are among the most important statistical methods through which it is possible to know the nature of the changes that occur in the values of the phenomenon over time and predict what changes will occur in the values of the phenomenon in the future in light of what happened to it in the past. The most famous modern time series method (Box-Jenkins) is a paradigm shift in data modeling analysis and a modern introduction to time series analysis and forecasting. Death statistics are an essential element in vital statistics and in estimating population numbers and population growth rates. Therefore, the importance lies in building a model used to predict the number of deaths to provide accurate indicators for the planner that will enable him to make future plans. The research dealt with the time series, the components of the series, and the stages of building the Box–Jenkins model, with a practical application of three-time series, represented by deaths of children under one year, deaths of children under the age of five, and total deaths for all of Diyala Governorate, and forecasting the number of deaths during the year 2024

The multiple linear regression model is one of the most widely used statistical methods in many scientific fields. The model parameters are estimated using the ordinary least squares method, which gives the best unbiased linear estimate when its assumptions are met. One of these assumptions is that the distribution of the random error term is normal with a mean of zero and a constant variance. The presence of outliers leads to a violation of this assumption, and therefore the least squares method cannot be applied. Robust methods have emerged as a suitable alternative when outliers appear, such as the M method, LMS, LTS, and the S estimator. However, the appearance of leverage points as another type of outlier in the space of explanatory variables has led to inaccuracy in the estimates of these methods. Very distinctive methods have been proposed in the statistical literature, such as the MM estimator method and the GM6 method, but the emergence of the problem of high leverage points, the reason for whose existence is attributed to the emergence of the phenomena of (Masking and Swamping). These two phenomena indicate inaccuracy in diagnosing leverage points, which causes either a loss of information or failure to completely treat bad leverage points. The proposal of this research seeks to improve the performance of the GM6 method, even in the presence of these two phenomena, by employing a high-precision diagnostic algorithm called IDRGP (RMVN) in order to obtain high-precision weights to reduce the impact of leverage points in the GM6 method. We have put the accurate diagnosis and GM6 method into a single algorithmic framework that we called GM6.IDRGP(RMVN), which showed outstanding performance compared to previous methods through the results of simulation studies and real data.

The Integrated mixed model (ARIMA) is a popular model for forecasting unstationary linear time series that combines the autoregressive (AR) model and the moving average (MA) model by including the integral part that changes the time series to make it stable. At the same time, prediction with hidden artificial neural networks (HANNs) is an alternative to linear methods. But often we encounter complex data, which means that there are two or more main components in the data, a linear component and a non-linear component, and these components can be interwoven together so that the overall effect is a mixture of the two effects, with each component having its own effect on the data. Also, we may find time series observations in the real world that suffer from inaccuracy in their measurements, and one of these phenomena is the population numbers in Iraq from 1978 to 2023, the majority of which depends on estimation and no accurate measurements are available. In this research, a fuzzy hybrid time series forecasting method (FHHAA) was proposed, a mixture of the (FARIMA) model and the (HANNs) model, then forecasting the population numbers in Iraq and comparing the proposed model with the (FARIMA) model, the (HANNs) model, (HANNs) and the non-fuzzy hybrid model using comparison criteria, Root Mean Square Error (RMSE), Bayesian Akaike Information Criterion (BIC). It was concluded that the proposed Fuzzy Hybrid Model (FHHAA) is better than the basic models because it recorded the lowest criteria's. The estimated values of population numbers were more consistent according to the (FHHAA) model. The population numbers in Iraq were predicted for the years (2024-2028), and it was shows an increase in the population from the year (2023) by approximately (6,940,432) people by the year (2028).

The kernel density estimator is a nonparametric method to estimate the probability density function for any variable. Therefore, it is a statistical technique used to smooth each point in the data of the variable to be studied. As for nonparametric confidence intervals, they are also one of the estimation methods, which determines an interval that contains a set of values based on the sample data and is defined by an upper and lower bound. In this research, nonparametric confidence intervals were used to treat the noisy image with Gaussian noise, an pre-processing where the noise was removed from the image and filtered from the noise to give a clearly defined image free of noise, then the pre-processed image was segmented using the confidence interval method of the kernel density estimator, which smoothed the image data, as the important features of the image were identified by separating homogeneous areas. It was noted that the processed images on which the segmentation process was performed were better in showing Features from segmented images that contain noise. It was concluded that the bandwidth parameter, which controls the amount of data homogeneity, has an impact on the work of both the nonparametric confidence interval method and the kernel estimator. Also, in the pre-processing process, the Epanechnikov function was chosen because it gives better and clearer features than others.

In this paper , the method of spatial discriminant analysis was used for the purpose of distinguishing population structures in the governorates of Iraq by selecting groups of residents according to the governorates in Iraq across their geographical regions and spatial lines for the purpose of distinguishing between the spatial patterns of the population and their growth. Using the Matlab program, it was concluded that the method of spatial discriminant analysis Fisher's on-site discriminant analysis is better than the rest of the discriminant analysis methods, which are (Fisher's linear discriminant analysis - core discriminant analysis - on-site discriminant analysis), and it was also found that there are governorates with more population growth than the rest of the governorates, which are (Baghdad - Anbar - Basra - Babylon - Karbala ) Relative to the geographical locations of the governorates and the spatial points of the population projections.

Population censuses suffer from different behavior in data, especially those classified according to age groups. Due to the scarcity of research and special studies on survival analysis of grouped complete data, the empirical method and the Kaplan-Meier method with a developed method of the Kaplan-Meier formula were employed in estimating the survival function for the grouped complete data. It was using the data of population censuses for 1997 as a base year, Due to the lack of subsequent population censuses we relied on population projection tables for the period (2017-2022) in analyzing survival behavior during this period.

Accordingly, the results were explained through tables and figures, analyzing and interpreting the behavior and survival functions of the three methods by age groups and according to gender and the total population, which showed that the developed method has an effect the estimator by the rank of the age group.

Statistical studies are the primary focus of the independent planning method in any country, as the state and the private sector use population projections in planning. Another essential element is the provision of comprehensive services, which requires knowing how many people there will be in the future, and what they are divided by age, gender and characteristics, such as ethnicity. Geography, etc. Population forecasts are also widely used in the private sector for strategic planning, as population projections are considered one of the most important indicators used by its maker and population analyzes over time. In this research, the machine Learning by using neural networks, in addition to using a classical method, which is the ARIMA time series model, to predict population projections. The internal data was scanned in both methods, up to 70% training and 30% testing, and a five-year forecast for the Iraqi population. The research found that machine learning has an advantage in estimation because it has the lowest MAPE and higher R2. The population forecast of Iraq in 2028 is 47,516.3113 (per thousand).

The life expectancy index is one of the most important demographic indicators and reflects the reality of institutions that provide services and take into account the population’s economic, social and health conditions. The average life expectancy index for the old age group of 60 years and over is an important development indicator in assessing the reality of health and economic services for the population of Iraq, as it is noted that the old age in Iraq, suffer from several economic and social problems, which led some of them to reside in social care homes as a result of the complexities of contemporary lifestyle, the difficulty of economic conditions and the living conditions of families, and the widening of the housing crisis, which was an excuse for the old age’s families to transfer them to care homes. The care home is a service institution that aims to care for the old age and provide social, health, psychological, cultural and recreational services to enable them to overcome the effects resulting from their disability and ensure a dignified and quiet life for them during their stay in this institution, in addition to the decline in extended family coexistence represented by the large family (family) and the increase of the nuclear family. This has negative repercussions on the elderly. Elderly lifestyle, especially after the loss of a husband or wife, widowhood, and feelings of loneliness and isolation.

The topic of regression analysis is receiving increasing and clear attention in most studies, especially economic and medical ones. The nonparametric regression model in general and the multiple nonparametric regression model in particular is one of the most important and prominent regression models used in recent years, which have witnessed great expansion, especially in the economic and environmental aspects. The Multivariate Nadaraya-Watson estimator is one of the most important estimators used in the multiple nonparametric regression model. In estimating the multiple nonparametric regression model, this estimator, in turn, relies on a matrix of parameters called smoothing parameters, the estimation of which is of great importance in achieving good fit of the estimated curve in the multiple nonparametric regression model. In this research, it was proposed to employ an algorithm inspired by nature, represented by the invasive weed optimization algorithm, in the process of estimating the smoothing parameter matrix (Bandwidth matrix) in the Ndaria-Watson multiple estimator. The Monte Carlo simulation method was also used to generate data following a number of multiple nonparametric regression models. The simulation results showed the superiority of the proposed method compared to other estimation methods, using the mean square error as a standard for comparison.

Sometimes it is difficult to obtain the data and information required for research for reasons that may be related to time and effort. In addition, in many applied issues, obtaining real measurements of the variable of interest may be very expensive. To solve these problems, the sampling method is used, which ensures that the researcher achieves research objectives with the least time, effort, and cost. One of the important sampling methods in this aspect is the Ranked Set Sampling (RSS) method. From this standpoint, the main goal of the research was to find the probability function of the Lomax distribution by estimating the scale and shape parameters, in addition to estimating the reliability function of the distribution. All of this will be done based on the Double Ranked Set Sampling (DRSS), using both estimation methods, which are the Maximum likelihood Estimation (MLE) and the Maximum Product Spacing (MPS). For the purpose of comparing the two estimation methods of the reliability function, the Monte-Carlo simulation method was used to generate data for the random variable that follows the Lomax distribution with the use of the statistical criterion represented by the mean square error (MSE). The research found that the (MLE) gives the best estimate of the reliability function of the Lomax distribution when using the (DRSS) method with different sample sizes and number of cycles. This has been proven through experimental results, as the larger the sample size, the better the estimation results, as the (MLE) method achieved a percentage of preference (72.22%). To demonstrate the importance of the topic from an applied and realistic perspective, real data was used on bladder cancer patients, in which the random variable represented the disease remission times in months (beginning of complete recovery), recorded from the time the patients entered the hospital until they left it. It was tested that the data follows a Lomax distribution using goodness of fit tests (Anderson-Darling, Kolmogorov-Smirnov and Chi-square). Using the (DRSS) method, a sample size of 96 was drawn from the total sample through four groups, and the process was repeated 24 times. Using the (MLE) method as the best way to estimate the reliability function, it was observed that the reliability of recovery from bladder cancer decreases with increasing patient stay times in the hospital when using Treatment and recovery protocol.

Population studies are the knowledge they provide about the population, its distribution, and characteristics, and it is one of the very important matters. The human element is one of the basic dimensions or inputs in planning, whether at the national level or the level of smaller spatial units. Recording the numbers of infant and child deaths faces a problem in correct enumeration. This is because some deaths are not recorded, which leads to the numbers of deaths being inaccurate. In this research, it is necessary for us to know the population projections that depend on the total population census, the age composition, the fertility rate, the death rate, and to estimate the numbers of infant and child deaths because of their importance in the demographic analysis of the population. Through the use of direct and indirect methods, which is represented by the Brass method, to estimate the number of deaths of children in Wasit Governorate, related to the distribution of the population, their geographical movements, and their various characteristics, whether these studies are demographic, economic, or social.

Factor Analysis is a statistical technique used for dimensions reduction and reduced redundant data by extracting the correlations between individuals to reveal the hidden structure of the studied problem, So, in this research we suggested using Factor Analysis as a statistical technique to reduce image dimensions for reconstruction a new image form multispectral image, The results proved the efficiency of this technique in providing high quality low dimension images based on the value of Peak Signal to Noise Ratio (PSNR) that measured to the new image, At the same time, ability of this technique to reduce dimensions while preserving the basic characteristics of the image has been confirmed.

The study provides an estimate of the nonparametric binary logistic regression model with the likelihood of chronic lymphocytic leukemia depending on the factors affecting the injury (patient sex, patient's age, white blood cells, hemoglobinemia, hematoglocytes, platelets) and lymphocytic leukemia is one of the most dangerous diseases for human life, it may result in many complications that may lead to exposure to other types of blood Irrigation of cancer, uncontrollable. The study aims to identify the most important statistical methods in analyzing cancer patient data and identifying the most important influences on it, which are categorical variables, and their importance lies in how to implement predictions after obtaining the best logistical model. Using the mixed method of core estimator and smoothed slide to estimate an optimal smoothing parameter using legitimate transit and generalized legitimate transit, it was concluded that the mixed method of core estimator and smoothed slide (LBS) is the best through the comparison criterion average squared error.

The productive efficiency of any industrial organization depends on the success of maintenance operations that are based on organizing maintenance management, and preventive maintenance is part of this success, which is used to maintain the continuity of machine operation. In this research, the optimal time for preventive maintenance of the second South Baghdad Gas Electricity Distribution Station was determined using a method The reliability function, where the optimal maintenance period at the lowest cost was determined by estimating the parameters of the Type – II Generalized Log Logistic Distribution using the maximum likelihood method, and the best time was approximately only sixteen days according to the first shape parameter, and only a month and a half according to the second shape parameter.

The nonlinear regression model is one of the regression models used when the relationship between explanatory variables and the response variable is nonlinear. It is considered one of the most common and widely used models in analyzing the impact of explanatory variables on the response variable. Two nonlinear regression models, MEYER and MEYER5, were studied. The study's problem lies in the difficulty of obtaining parameter estimates of the models directly using classical methods because they are nonlinear. To obtain accurate parameter estimates, traditional methods were used as iterative methods to estimate these parameters (nonlinear least squares method and maximum likelihood method) and employed one of the artificial intelligence algorithms, the Whale Optimization Algorithm (WOA). Using simulation techniques to compare estimation methods with different sample sizes and the mean squared error criterion to obtain the best method, the results showed that the nonlinear least squares method employing the Whale Optimization Algorithm (NLSWOA) is the best for both models.

The increase in the number of patients around the world and what this phenomenon causes in terms of human and material losses and the subsequent social and psychological effects that include all segments of society is the main motive and the main goal that directed us for the purpose of conducting this research for this phenomenon and making predictions about it، and the issue of time series is one of the important topics that It deals with the behavior of phenomena and their interpretation over certain time periods، and the importance of time series analysis lies in obtaining an accurate description of the series and defining an appropriate model for prediction and using the results for future planning purposes. In Diyala Governorate for the coming periods so that those in charge can identify the expected numbers in the future to take the necessary precautions to confront this phenomenon and develop ways to address it. On the practical side، the methods mentioned in the theoretical side were applied to a sample of people with kidney disease in the first series، as well as a sample of the artificial kidney (people with kidney failure) for the second series، with a volume of (55) for the period from December 2018 to July 2022، and they are not They are stable according to the values of the autocorrelation coefficients، and both series have a general trend. The first difference was taken in both cases to make them stable and obtain predictions close to the true values. The time square variable in the quadratic model of the series of kidney diseases had an inverse effect because its sign was negative، as the estimated parameter reached (0.644). ) but its effect is slight due to the decrease in the value of the estimated parameter، and that the ARIMA (3،1،3) model is the only significant and best for a series of kidney diseases that can be relied upon in making predictions of the number of kidney patients، and the same is the case، the best model for the industrial kidney series is the ARIMA model (2، 1،2) among the rest of the other intangible models can be relied upon in making predictions about the numbers of the Industrial kidney in Diyala Governorate

This study seeks to examine the relationship between two economic variables represented by the import and export chains of products and services in Iraq using a vector autoregressive model. Using the cointegration test, it was concluded that there is a short-term equilibrium relationship between the two variables, and through estimating the first-order autoregressive vector model that was chosen based on several statistical criteria to determine the rank of the model, as well as using the Granger M test, using the economic program Eviews10, it was found that That the two variables are independent of each other. By estimating the response function for shock analysis, it was discovered that both variables have positive and negative effects for both parties, that is, there is no significant effect of either variable on the other.

The research aimed to study the development of demographic variables and study the development of human capital indicators in Iraq during the time period from (2012-2021), and design a mathematical model to link demographic variables and human capital indicators and analyze the impact of demographic variables on human capital to benefit from this model in guiding... The process of human, social and economic development in Iraq and the employment of human capital in the development process. The researcher adopted the descriptive analytical method to study the development of the study variables, and designed a model to study the relationship between (demographic) variables and human capital indicators in Iraq, using the autocorrelation coefficient and the multiple linear regression model. The analysis was done using the statistical program (SPSS - V26). The study reached the most important The following results: Demographic variables affect human capital indicators by (99.8%). There is a positive effect of the variable fertility rate and infant mortality rate and a negative effect of the variable of the crude birth rate and the mortality rate of children under five in the legal composite. The Human Development Index variables also affect the percentage of students graduating from university education (public and private universities) and the percentage of students enrolling in university education (government + National Bank) has a positive effect on the legal component, while the unemployment rate has a negative effect on the legal component. The percentage of (47.3%) of the variation occurring in the human capital variables is due to the change occurring in the demographic variables. The percentage of (74.8%) of the variation occurring In demographic variables, the reason is due to the change in human capital indicators. Through statistical analysis of the data, the alternative hypothesis (H1) was accepted, which indicates a statistically significant effect of demographic variables on human capital indicators in Iraq, and the null hypothesis was rejected.

Prediction is one of the important topics in statistical sciences to help leaders develop future plans and make appropriate decisions for them. This research includes one of the modern prediction methods represented by artificial neural network (ANN) models, specifically the multi-layer network, as the back propagation (BP) algorithm was adopted and trained several times to get the lowest error value.

The research aims to predict an important indicator of the Iraqi Stock Exchange, which is the traded value index, using one of the scientific methods, which is artificial neural networks, applying the back propagation algorithm and using the (Matlab 2018) program.

The researcher concluded that the artificial neural network model gives accurate results by spreading the data in the regression line, and the value of the coefficient of determination reached (R = 0.99928), and the mean square error reached (0.0000001), which is very small, and this indicates the accuracy of the prediction. Daily data was collected for the traded value index for the period from January 2, 2018, until October 31, 2021.

In this paper, we present the Ramos-Louzada Lomax distribution, which is an expansion of the Lomax distribution. Important statistical properties such as the arithmetic mean moments, the variance, the coefficient of skewness, and the coefficient of kurtosis were determined. The parameters of this distribution was estimated using the maximum likelihood method, and part of a simulation study was presented to determine the efficiency of this method. One of the most important conclusions was knowing the efficiency of the maximum potential method and the extent of its capabilities in estimating the parameters of this distribution.

The problem of environmental pollution is one of the most important and dangerous problems facing humans at present because of its direct impact on human health and other living organisms. In recent years, it has been observed that there has been an increase in environmental pollution rates, which has greatly affected human health and led to the emergence of many diseases such as cancer, pneumonia, poisoning, birth defects, and others. Its impact has not only been limited to humans but has extended beyond its impact on animals and plants as well.

Given the importance of the topic, its seriousness, and its direct impact on human life, this research was conducted to determine the proportion of the amount of pollution based on two of the most important factors on air pollution, which are both gases (CO and ), based on the explanatory variables, which are the average temperature, the average dew point, and the average humidity: the average wind speed and the average amount of crude oil used in the filtering process.

In this research, the risk function was estimated using the Log-Logistic regression model using estimation methods: the Chi-square minimization method, the weighted least squares method, and the genetic algorithm method. These methods were applied to air pollution data from the Central Refineries Company in Baghdad (Al-Dora Refinery). which represents daily measurements of environmental pollution compounds, which are based on time for the period from 2018 to 2022.

The Zero Truncated Poisson Distribution (ZTPD) is a special case of the Poisson distribution that occurs when the Poisson distribution is devoid of zero values for certain phenomena, resulting in a change in its probability function and statistical properties. The aim of this study is to find a modeling approach that accommodates the impact of data values without the zero value for any phenomenon and to use it to build a Zero Truncated Poisson Regression Model (ZTPRM) using the Iteratively Reweighted Least Squares (IRWLS) method. This model is used to estimate the regression model and parameters for the Zero Truncated Poisson Distribution, with some distribution properties mentioned. In the applied side, a sample of 68 patients with renal failure undergoing renal dialysis at Al-Karama Hospital/Life Kidney Center was drawn.

The Gamma Regression Model is considered a non-linear regression model when the dependent variable does not follow a normal distribution. It differs from linear regression in that the value of for the dependent variable Y is replaced by a link function g( ) = η, as η is a linear combination of independent variables, and the purpose of using this is The function is to reduce the contrast value and make it more stable. The problem of multicollinearity between explanatory variables is considered one of the most common problems facing researchers and negatively affects the estimated parameters of the model in the regression analysis. In addition to the presence of restrictions imposed on the parameters that directly affect the estimation of the model parameters, the research dealt with estimating the parameters of the gamma regression model in the presence of The problem of multicollinearity by using three estimation methods, which is the Restricted Maximum Likelihood RMLE and the Gamma Ridge Regression GRR estimator, in addition to the Built in Estimator RGRRE, which combines the Restricted Maximum Likelihood estimator with the Gamma Ridge Regression estimator. The mean square error (MSE) was relied upon as a standard for comparison between the estimation methods for model parameters. The results showed that the built-in estimator It was superior to other estimation methods because it gave the lowest MSE and was applied experimentally to real data related to the subject of money supply and the factors affecting it for a sample size of 48, and the results of estimating the parameters were appropriate for the model.

Linear regression analysis is one of the most important statistical techniques in many fields such as economics, survival studies, business administration, medicine, engineering, etc. To estimate the coefficients of a linear regression model, the least squares method is often used because of its good advantages and ease of calculation. However, the presence of single or multiple outliers in a data set can destroy OLS estimates. Many researchers have mentioned that a real data set typically contains 1% to 10% of anomalous observations. Outliers in explanatory variables, called high leverage points (HLPs), have more serious effects on OLS estimates than outliers in the variable y. High leverage values ​​are responsible for the problem of masking and swapping in linear regression. HLPs also cause the problem of multi-co-linearity and have a significant impact on the accuracy of the estimates. Therefore, it is necessary to detect these unusual observations. Also, the estimates of the least squares method can suffer from a significant deterioration in the presence of the auto-correlation problem. To address this problem, a group of methods have been proposed, including the Cochrane-Orcutt-Price-Winston iterative method, but unfortunately most of these traditional methods fail individually to address the problem of autocorrelation between errors in the presence of outliers. In this study, a robust method was proposed to address the complex problem of autocorrelation in a multiple linear regression model in the presence of outliers and high leverage values. The performance of the proposed method was evaluated using real data.

Compound probability models have important roles in modeling real life events, in this paper, a new class of continuous distributions was presented named Truncated [0,1] Log logistic -Log logistic distribution ([0,1] TLL-LLD). And we presented the important properties of the new distribution model, which are the r-th moment function, reliability function, the function of hazard, the function stress strength function , and the function of Shannon entropy.

In The current time witnesses a significant surge in data, fueled by technology's rapid advancement. This increase in data volume has led to the emergence of high-dimensional data (where the number of variables exceeds the sample size), creating challenges in precision and target identification. Consequently, binary response variable classification becomes intricate due to the multicollinearity in explanatory variables. To tackle this, response variable classification has prompted the utilization of penalization techniques, reduced variables and selecting best variables in the model. This aids in simplifying the model complexity to attain the specific binary outcome (0,1). In this paper, penalization methods were applied, including Adaptive Least Absolute Shrinkage and Selection Operator, With the Adjusted Adaptive Elastic Net Penalty with logistic regression model. The application involved a set of real data. A sample collected by the researcher (p=49, n=41), and it produced positive results for classification in the sample collected by the researcher, in resulted we found these methods have achieving high classification accuracy while efficiently selecting an optimal number of variables using a range of packages and functions in the R programming language.

This paper aims to study some properties of the three-parameter Lindley distribution TPLD and the proposed new formula for the TPLD in studying the life-time distribution, survival function, and hazard function. We proposed a new formula for the TPLD by considering the new mixing weights of the mixing function that were used in constructing the TPLD and by replacing the mixing weights in the mixing equation to get a new formula for the TPLD that is called the proposed three-parameter Lindley distribution, and we denoted it by PTPLD. We study some properties of PTPLD in the lifetime distribution. We use three methods of estimation, the method of moments , the MLH method and Standard Bayesian method, to estimate the parameters and the survival functions for the two distributions (TPLD and PTPLD) and compare the survival functions estimates for both distributions using the above three methods of estimation, and we select the best estimate based on some criteria of error. In addition, we used real data from the AL-Hussain Teaching Hospital in Nasiriya City to estimate the survival time of infected patients with COVID-19 using TPLD and PTPLD. It was concluded that the proposed formula for the distribution is the best for all selected sample sizes.

Face recognition is a computer vision approach that analyzes facial features to identify or verify a person's identity. it may be required to combine different biometric identification methods to improve security and recognition accuracy.

To identify an individual, facial recognition systems simulate human face understanding and recognition abilities management, there is difficulty in detecting data when using various patterns of facial image capture, especially when there is a large overlap between the patterns, it takes time to determine a person's membership, Many statistical methods have been proposed in recent years, and in this study, will be employing the Support Vector Machine (SVM), which is a statistical model for face recognition that has been used for classification and regression tasks, so this model will be compared with logistic regression, one of the significant statistical models used in prediction, classification, and facial recognition. Various methods will be evaluated with a group of datasets and varied samples (number of images); this group represented the real data, which consisted of 100 individuals. The results showed that SVM had the lowest mean square error (MSE) for the dataset, followed by LR, and all the methods reached the highest accuracy rate of 100% for facial image recognition on real data.

The accuracy of forecasting time series is quite a popular subject for researchers in the past and at present. However, the lack of ability of conventional analysis methods to forecast time series leads scientists and researchers to apply various forecasting models that have different mathematical backgrounds such as artificial neural networks, fuzzy predictors, evolutionary and genetic algorithms. In this paper, we will use different versions of Grey models such as GM(1,1), Expanded Grey model, the Modified Grey model using Fourier ,Grey Verhulst model, and the Discrete Grey Model. The monthly numbers of respiratory patients lying in the emergency lobby of Dhi Qar hospital were used to compare the accuracy of the models. We note that the best model among the five Grey models is the Grey model whose errors were corrected by adopting the Fourier series as it achieved the best value for the four comparison criteria.

Some time series are characterized by their great volatility over time, especially time series related to the movement of the economy, and those related to the change in stock prices or the movement of financial transactions and stock markets, which are characterized by being non- stationary over time due to the change in the behavior of observations, making them suffer from the problem of Heteroscedasticity . The paper aims the use of predictive models that a time series can adapt to with large fluctuations and with long memory over time, a number of important models used to deal with FIGARCH time series when the error distribution follows the t-distribution were studied and reviewed, which were used For the first time by Researcher Engel in 1982 and developed by other researchers, the characteristics of these models were reviewed and applied for the purpose of forecasting daily oil prices according to the prices approved by OPEC for the period from 2004 to 2022, where the practical analysis of oil price data showed that the best prediction model is the ARMA(2,2)-FIGARCH(1,d,2) model in which the error follows the t-distribution, and the best predictor performance is out of sample .

The Artificial Bee Colony (ABC) algorithm was used in this study as a representation of a developmental artificial intelligence technique to identify the best class boundaries for modeling data on the number of daily infections with the Coronavirus (Covid-19) epidemic and for the three governorates of Iraq (Baghdad, Erbil, and Basra). The number of daily infection cases during the outbreak of a particular epidemic disease, such as the emerging acute respiratory syndrome coronavirus, frequently exhibits multiple trends: a steady increase during the epidemic's growth phase or outbreak, followed by a stable phase in the number of daily infection cases (referred to as the stabilization phase by some sources), which involves controlling the epidemic to eradicate it, and a subsequent decline, decline, or disappearance phase. In order to handle this type of data, a random stochastic model known as the geometric stochastic process model with an intelligent threshold was put forth. This model uses an intelligent technique to identify the data's turning points, or inversions.

In this paper we going to comparison using Mean Absolute Error (MAE), among three nonparametric methods: Nadaraya-Watson (NW), B-Splines (BS) and Smoothing Spline (SS) which use to estimate quantile functions depending on the model of time-varying coefficient under the concept of depth in the bivariate case, to prepare for construct a better contours.

We used simulation study for construct two stages: first stage is to construct the simulation models for the three nonparametric methods and then comparison among them by using MAE, where the method that gives the least MAE is the best method, and the second stage is to plot the best method which given the best contour shape.

Where it was concluded that the BS is the best method because it's given a suitable contouring shape and less MAE which equal to 0.048 which will be used to plot best bivariate growth charts.

Identifying the important variables in building regression models is considered one of the fundamental challenges in constructing a good model. Therefore, selecting potential independent variables that have a direct impact on the dependent variable provides us with accurate explanations of the capabilities of our study model. Additionally, the chosen model should be capable of generalizing the phenomenon under study. And with a high predictive capability. There are classic methods used in selecting optimal variables, but these methods have drawbacks, including the need for a significant amount of time and the lack of accuracy in results due to dealing with a large number of regression models. However, recently, a set of variable selection methods has been proposed that focus on estimating model characteristics and selecting variables simultaneously, within a very short period of time. One of these methods is the Lasso method. To obtain a regression model characterized by a set of features compared to the traditional regression model, ridge regression can be used. To achieve variable selection in the ridge regression model, the Lasso method can be integrated to obtain a Lasso quantile regression model. In estimating and selecting the factors associated with a phenomenon that threatens Iraqi population communities, namely the phenomenon of divorce. The phenomenon of divorce is the process of separation between spouses and the final termination of the legal marriage contract. Divorce is a common social phenomenon in many cultures and societies around the world, posing challenges to individuals, families, and society as a whole.The causes of divorce can be diverse and complex. These reasons may include marital incompatibility, ongoing family conflicts, communication difficulties, marital infidelity, domestic violence, cultural and religious differences, financial problems, lack of social support, and other factors. The phenomenon of divorce affects all parties involved in the marital relationship. It can have psychological and emotional effects on spouses and children, if they have any, including anxiety, depression, sadness, anger, and feelings of disappointment. Children may struggle to adapt to family changes and may experience division and feelings of guilt or rejection. In current paper. We identify the important variables that effecting on phenomenon of divorce by using one of important variable selection approach

The significant increase in population growth rates is the largest in most developing countries, including Iraq, the increase is accompanied by a change in the characteristics of the population in terms of size, composition and distribution, this leads to a review of existing population policies, to be able to cope with the pressures that overpopulation poses on sustainable development efforts and future plans. Hence, there is always a need to make estimates of future population numbers and the changes that are likely to accompany the composition and distribution of the population at the level of different activities and areas of life. The methods of estimation differ from each other on the basis of each applied in completely different circumstances from the other, the reason for the difference is due to the level of accuracy of the data that are used as the basis for making these estimates and predictions indicate an increase or decrease in the number of future populations. This research deals with the use of some of the estimation methods that will be applied to the population in Iraq during a certain period of time, to estimate the numbers of the population in the future and these methods like (Exponential Modified, Logistics), the population numbers during this period (1989-2023) were obtained from the Central Bureau of Statistics, and by using algorithms prepared by researchers for each method of estimation and During the extracted results, it is clear that the population forecasts of the population of Iraq will be increasing or decreasing in the future.

Studying the motion of some roundworms types such as Caenorhabditis elegans (CE) is important to identify the actions and reactions and their effects of worm’s life. In this study, the time series of CE motion represented by the angles of wave-motion between 1 to 177 degrees will be the case study. Each observation of this time series is a recorded frame (0.5 second) of 2.5 hours video of CE motion. A convolutional neural network (CNN) as one of deep learning techniques will be used to classify CE motion as dependent variable in binary cases based on the images of the angles of wave-motion as explanatory variable. The images of motion angles are imagined and designed by two dimensions image corresponding to every observation. These images combined into 4-d image (four dimensions matrix) to represent univariate explanatory variable. Support vector machine (SVM) will be also used to classify the angles of CE. In these types of data, the nonlinearity and uncertainty will be the most probably problems as reasons for in accurate classifications. CNN and SVM used with this type of dataset to improve the classification results. The results of comparisons explain that CNN approach outperforms SVM absolutely. In conclusion, CNN approach can be used to classify this type of time series with accurate results.

The transition matrix estimators of the Markov chain are not accurate and the transition matrix is considered given. There are many methods that are used to estimate the transition probabilities matrix for different cases, the most famous of which is the Maximum Likelihood Method, in order to find a good and new estimator for the transition probabilities matrix of the Markov chain, a method was proposed, which is a modification of the Bayes method, to reach the transition probabilities with the least variance. This method assumes that the values of in the initial probability are estimated by two methods: Maximum Likelihood Method (MLE), and the algorithm of particle swarm (PSO), The Escherichia Coli (E.Coli) gene chain was chosen as an applied aspect of the study due to its importance in medical research and for the purpose of discovering and manufacturing treatments by knowing the final form of its gene chain. After testing the E.Coli gene chain, it was found that is represents a Markov chain, and then both the transition probabilities matrix and the transition probabilities variance were estimated, and it was found that the proposed method for transitional probabilities is better than the method of greatest possibility depending on the variance.

In this paper, four different estimation methods are presented to estimate distribution parameters of A New Generalized Gamma-Weibull Distribution (NGGW) it represented Maximum Likelihood Method (ML), Weighted Least Square Method (WLS) , Power Density Method (PD), and Mean Standard Deviation Method (MSD). In order to obtain the best results, the study compared the estimation methods by applying the Monte Carlo simulation method using the Wolfram Mathematica 13 program. Several experiments were conducted with different sample sizes (n=20,50,100 and 250), and the results showed the superiority of the maximum potential method in finding estimates of distribution parameters compared to the rest of the methods used in this paper.

Noise affects images by distorting the features or reducing the required information. Gaussian noise is one of many types of noise that are characterized by normal distribution's statistical properties. Removing or reducing this noise is an essential step in image processing. James Webb Space Telescope (JWST) is a crucial tool for advancing our understanding of the universe across various domains. The images taken by the (JWST) are not only scientifically valuable for advancing our understanding but also have the potential to captivate and inspire people around the world. In this paper, we introduce several nonlinear filters, including Non-Local Mean (NLM) which gives weights to the pixels based on the distance from the noisy pixel. A Bilateral filter that gives weights for each pixel and then calculates the weighted distance. Propose a nonlinear filter depends on obtaining an appropriate smoothing parameter for the image by using the plug-in method and using it to estimate the image's density function, then using an appropriate noise reduction method on the estimated density function to extract the denoised image by removing the Gaussian noise from the Carina Nebula Image, the first image taken by (JWST) on 12 July 2022. The importance of this image lies in its potential to advance scientific knowledge, showcase technological prowess, inspire the public, and contribute to the broader mission of exploring and understanding the cosmos, Also, since it is the farthest point in the universe that humanity has been able to reach or take pictures of, it is therefore essential to preserve its quality to study all its elements or details. These nonlinear filters were therefore selected to highlight the significance of selecting the right technique that can handle, process, and preserve as many details as possible. They also elucidate the degree of advancement achieved in denoising and the distinction between the classical filters and the more sophisticated ones that have evolved to handle finer details. These filters consider the similarities and distances between the central pixel and its neighbours, they preserve the edges of the image as advanced features. Based on quality measurements Peak Signal to Noise Ratio (PSNR) and Structural similarity index measure (SSIM), the filter results were compared and show that the proposed filter gives high performance in restoring images under different Gaussian noise densities. Where it gives values of (42.51) and (0.99) for (PSNR) and (SSIM) respectively, then the bilateral filter gives (30.65) and (0.93) respectively.

The optimal population and the right political approach to achieving it are today a big concern of governments, especially developing countries. The uncontrolled increase in population can pose a variety of challenges for any country. In the meantime, unlike educational and health restrictions, one of the most important issues on which population change can be problematic is the economic path. Iraq will be a developing country due to the war and the problems it has faced in recent decades. So in this article, we examined the impact of population age structure on economic growth and in the period 2019-2021. The time series was used to do this research. The results of this study indicate that the ratio of population dependence, which indicates the state of population structure, has a negative and significant impact on economic growth.

In this paper, the effect of the spatial variable of kidney failure in Baghdad is studied using the general spatial autoregressive model (SAC), where the model was applied to the regular and modified spatial adjacency matrices and according to the adjacency criterion (Rook), using the estimation method represented by the maximum likelihood method, as it included the dependent variable (Y) is the number of people with creatinine in the blood, and the explained variables represent their levels after agreement with the specialized doctors as follows: (X1: Age, X2: Diabetes , X3:High blood pressure, X4: Gender, X5: Smoking). The most important results were that the data suffers from the effect of the spatial variable. It turns out that the modified spatial adjacency matrix (Rook) (Wadj) is the best in order to obtain the lowest standard value, and that the variables (X2, X3, X5) have a significant impact on Kidney failure disease in Baghdad.

The classification is one of the essential tools used in medical statistics to classify the nutritional status of children under five years. A good nutritional status is necessary for children's health and has a sicnificant role in the child's normal development. Likewise, poor nutritional status is considered a serious disease for this age group and needs attention from child health specialists. Doctors face great difficulty in determining the nutritional status due to the state of uncertainty in the incidence of this disease or the ambiguity in the variables and characteristics of that phenomenon. The fuzzy sets theory was applied to classify the nutritional status of children under the age of five years using fuzzy logic, where the fuzzy decision tree method was employed, and a tree was created for each age group (male, female) based on the linguistic variables (weight, height), and body mass index for the classification categories. In this study, the nutritional status of children was classified and the groups were identified more accurately to reduce the chances of misdiagnosis and provide them with more accurate appropriate treatment to improve the health level of children to build a more immune society with a good health level.

The study dealt with a sample of (16487) Iraqi children under 5 years of age, the number of males (8427) and the number of females (8060), divided according to age into 12 categories or groups. The results showed the age groups She was of normal weight in varying proportions.

This paper estimates the median lethal dose of a two-replicate multivariate biological experiment through a new statistical formula related to the best model among eleven models constructed to describe the relationship of dose-response and time based on the inverse Weibull. The real biological experiment focused on using actual data to biologically cure breast cancer using therapeutic zinc selenide, produced either by a plasma-physically approach or by employing an environmental plant extract (Calgan plant). The unknown parameters associated with the eleven models are estimated using two traditional estimation methods: ordinary least squares and maximum likelihood. Because these estimates could not be determined directly, the Newton-Raphson iterative technique is used. The mean squared error is used to choose the best model. Then, at successive time points, the breast cancer's median lethal dose is calculated. The results clearly show that the mean square error values of maximum likelihood are always lower than those of the ordinary least squares, and the median lethal dose exhibits a decreasing relationship over time. Further, the new procedure for measuring the median lethal dose allows for several estimates with subsequent periods, which allows for several effective concentrations of 50% in the experimental units.

In this article the spectral envelope function for a nonstationary time series is estimated by using the Dirichlet kernel function. we divide the time series into a number of segmentations, then the nonstationary problem is solved to reduce its effect. The simulation and the sequence of the nitrogenous bases of the DNA of Escherichia coli bacteria are used to implement the goal of this article.

The anomalous diffusion is characterized by deviation from Gaussian statistics and the absence of a linear time dependency in the mean square displacement. This study investigates anomalous diffusion processes that exhibit a power-law growth in mean square displacement as time progresses.

The first stage is to create the model using random methods, that is, by using random walks. The following statement describes a continuous-time random walk model represented by a series of convolution-type integral equations depicting probability density functions. Fractional differential equations for time and space are derived from the master equations by choosing probability density functions with infinite first and/or second moments.

The obtained model equations are analyzed with respect to elementary boundary value problems in constrained fields. The main focus is on studying elementary boundary value problems related to the generalized fractional time diffusion equation, especially using the fractional Caputo derivative.

This equation applies a well-established maximum principle to stochastic partial differential equations of the elliptic and parabolic type (SPDEs). This concept is used to make preliminary estimates of the answer before using it to prove the uniqueness of the solution. To prove the existence of a solution, a clearly defined generalized solution is first generated using the spectroscopic method. Under certain additional circumstances, a comprehensive solution may be considered a solution in the traditional sense.

Iraq is considered one of the countries that depend mainly on oil production, so the revenues from these resources are assigned in various sectors and fields within the country is the biggest problem. The importance of this research comes from the importance of this sector in terms of production and profits (in wars and epidemics Crises) and its impact on the global oil market and the international economy, this paper examines the effect of changes in oil production and values on Iraq's economic growth to assess its reliance on the crude oil sector. as well as on the local Iraqi economy to shed light on both cases (Corona epidemic in 2020 and the Russian-Ukrainian war in 2022), By analyzing the time series of Iraqi oil (production and prices) separately, and prices and production lines for (Basra and Ceyhan separately and totally) for the extended period (from January-2019 to April-2022) and on a monthly basis using different statistical forecasting methods in terms of stability in production and prices as well as seasonality and forecasting oil prices in the next 2 years where (R) programming language with multi Packages and functions have been used to find best fitted for the selected Box Jenkins (ARIMA) model and studying the homogeneity or heterogeneity of Iraqi oil influence locally by (GDP) and globally on international oil prices, and what are the proposed solutions and decision making to reduce Risks (or losses) and maximize profits as much as and stability in the Iraqi oil policy compared to neighboring countries or the world.

In this paper, we discussed two distributions (the Lindley Distribution and the Transmuted Lindley Distribution) , in addition to some functions such as the PDF, CDF function, and we discussed some statistical properties such as Moments, the Moment Generating Function, the Coefficient of Variation, Skewness, Kurtosis, and Ordered Statistics. The parameters are estimated via the maximum likelihood method. This paper concluded the importance of the Transmuted Lindley Distribution on the Lindley distribution and on all sample sizes in the simulation side.

Control charts are an effective tool for statistical control of production quality processes by drawing samples from the production randomly and at specific time periods. In this research, different control charts were used for the purpose of monitoring the quality of resistant cement production in the Kubaisa Cement Factory, which is located in Al - Anbar Governorate. The weights of bags of cement (resistant cement) (which are measured in kilograms) were monitored using a new packing device, and after monitoring production for a period of (50) days in the months of November and December for a sample size of (5) units per day. The data was recorded on a regular and continuous period, , and the quality of production was examined by applying deferent statistical control charts . these charts are the arithmetic mean chart, the range chart, the standard deviation chart, the moving average chart, the Geometric Moving Average(GMA) chart . and Individual-Moving Range chart .The charts were applied using the statistical program (Minitab), and the results showed that the most of the charts were in control, with some notes.

This paper assesses the impact of changes and fluctuations in

bank deposits on the money supply in Iraq. Employing the research constructs an Error Correction Model (ECM) using monthly time series data from 2010 to 2015. The analysis begins with the Phillips-Perron unit root test to ascertain the stationarity of the time series and the Engle and Granger cointegration test to examine the existence of a long-term relationship. Nonparametric regression functions are estimated using two methods: Smoothing Spline and M-smoothing. The results indicate that the M-smoothing approach is the most effective, achieving the shortest adjustment period and the highest adjustment ratio for short-term disturbances, thereby facilitating a return to the long-term equilibrium.