The analysis of survival and reliability considered of topics and methods of vital statistics at the present time because of their importance in the various demographical, medical, industrial and engineering fields. This research focused generate random data for samples from the probability distribution Generalized Gamma: GG, known as: "Inverse Transformation" Method: ITM, which includes the distribution cycle integration function incomplete Gamma integration making it more difficult classical estimation so will be the need to illustration to the method of numerical approximation and then appreciation of the function of survival function. It was estimated survival function by simulation the way "Monte Carlo". The Entropy method used for the purposes of assessment and estimating and fitting, this along with the use of the classical method. It was to identify the best estimation method through the use of a of comparison criteria: Root of Mean Square Error: RMSE, and the Mean Absolute Percentage Error: MAPE. Sample sizes were selected as (n = 18, 30, 50, 81) which represents the size of data generation n = 18 five-year age groups for the phenomenon being studied and the sample size n = 81 age group represents a unilateral, and replicated the experiment (500) times. The results showed the simulation that the Maximum Likelihood method is the best in the case of small and medium-sized samples where it was applied to the data for five-year age groups suffering from disturbances and confusion of Iraq Household socio-Economic survey: IHSES II2012 while entropy method outperformed in the case of large samples where it was applied to age groups monounsaturated resulting from the use of mathematical method lead to results based on the staging equation data (Formula for Interpolation) placed Sprague (Sprague) and these transactions or what is called Sprague transactions (Sprague multipliers) are used to derive the preparation of deaths and the preparation of the population by unilateral age within the age groups a five-year given the use of the death toll and the preparation of the population in this age group and its environs from a five-year categories by using Excel program where the use of age groups monounsaturated data for accuracy not detect any age is in danger of annihilation.
Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D
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This paper including a gravitational lens time delays study for a general family of lensing potentials, the popular singular isothermal elliptical potential (SIEP), and singular isothermal elliptical density distribution (SIED) but allows general angular structure. At first section there is an introduction for the selected observations from the gravitationally lensed systems. Then section two shows that the time delays for singular isothermal elliptical potential (SIEP) and singular isothermal elliptical density distributions (SIED) have a remarkably simple and elegant form, and that the result for Hubble constant estimations actually holds for a general family of potentials by combining the analytic results with data for the time dela
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In this paper we introduce a new type of functions called the generalized regular
continuous functions .These functions are weaker than regular continuous functions and
stronger than regular generalized continuous functions. Also, we study some
characterizations and basic properties of generalized regular continuous functions .Moreover
we study another types of generalized regular continuous functions and study the relation
among them
Ferritin is a key organizer of protected deregulation, particularly below risky hyperferritinemia, by straight immune-suppressive and pro-inflammatory things. , We conclude that there is a significant association between levels of ferritin and the harshness of COVID-19. In this paper we introduce a semi- parametric method for prediction by making a combination between NN and regression models. So, two methodologies are adopted, Neural Network (NN) and regression model in design the model; the data were collected from مستشفى دار التمريض الخاص for period 11/7/2021- 23/7/2021, we have 100 person, With COVID 12 Female & 38 Male out of 50, while 26 Female & 24 Male non COVID out of 50. The input variables of the NN m
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The research includes the study and calculation of the modulation function of Optical Semiconductor Fractal Modulator and spatial frequency for different values of Silicon modulator transmittance percentage(10%,35%,45%,58%),it found the relation between the modulation function of Silicon and spatial frequency, the exponential relation of all values of the transmittance , the best state of modulation function when the value of transmittance is T=58% ,also the research includes the study of the relation of transmittance with different values of refractive index of Silicon . So the research involves building a computer program of output data which would relate to fractal optical modulation made of semiconductor mate
... Show MoreThe current research seeks to identify mono-multi Vision and its relation to the psychological rebellion and personality traits of university students. To achieve this aim, the researcher has followed all the procedures of the descriptive correlational approach, as it is the closest approach to the objectives of the current research. The researcher has determined his research community for Baghdad University students for the academic year 2019-2020. As for the research sample, it was chosen by the random stratified method with a sample of (500) male and female students. In order to collect data from the research sample, the researcher adopted a mono-multi-dimensional scale
(Othman, 2007), the researcher designed a psychological r
... Show MoreThis paper is concerned with Double Stage Shrinkage Bayesian (DSSB) Estimator for lowering the mean squared error of classical estimator ˆ q for the scale parameter (q) of an exponential distribution in a region (R) around available prior knowledge (q0) about the actual value (q) as initial estimate as well as to reduce the cost of experimentations. In situation where the experimentations are time consuming or very costly, a Double Stage procedure can be used to reduce the expected sample size needed to obtain the estimator. This estimator is shown to have smaller mean squared error for certain choice of the shrinkage weight factor y( ) and for acceptance region R. Expression for
... Show MoreThe aim of our study is to solve a nonlinear epidemic model, which is the COVID-19 epidemic model in Iraq, through the application of initial value problems in the current study. The model has been presented as a system of ordinary differential equations that has parameters that change with time. Two numerical simulation methods are proposed to solve this model as suitable methods for solving systems whose coefficients change over time. These methods are the Mean Monte Carlo Runge-Kutta method (MMC_RK) and the Mean Latin Hypercube Runge-Kutta method (MLH_RK). The results of numerical simulation methods are compared with the results of the numerical Runge-Kutta 4th order method (RK4) from 2021 to 2025 using the absolute error, which prove
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