Intended for getting good estimates with more accurate results, we must choose the appropriate method of estimation. Most of the equations in classical methods are linear equations and finding analytical solutions to such equations is very difficult. Some estimators are inefficient because of problems in solving these equations. In this paper, we will estimate the survival function of censored data by using one of the most important artificial intelligence algorithms that is called the genetic algorithm to get optimal estimates for parameters Weibull distribution with two parameters. This leads to optimal estimates of the survival function. The genetic algorithm is employed in the method of moment, the least squares method and the weighted least squares method and getting on more efficient estimators than classical methods. Then, a comparison will be made between the methods depending on the experimental side. The best method is evaluated based on mean square error of the survival function and the methods will be applied to real data for patients with lung and bronchia cancer
Normal thyroid function is essential for neonatal growth and brain development. In a newborn infant with severe disease, endocrine regulation of hormones can be affected by abnormal metabolism. The assessment of thyroid parameters results in the recognition of a dysfunction and its association with disease severity
The linear segment with parabolic blend (LSPB) trajectory deviates from the specified waypoints. It is restricted to that the acceleration must be sufficiently high. In this work, it is proposed to engage modified LSPB trajectory with particle swarm optimization (PSO) so as to create through points on the trajectory. The assumption of normal LSPB method that parabolic part is centered in time around waypoints is replaced by proposed coefficients for calculating the time duration of the linear part. These coefficients are functions of velocities between through points. The velocities are obtained by PSO so as to force the LSPB trajectory passing exactly through the specified path points. Also, relations for velocity correction and exact v
... Show MoreBackground: Normal thyroid function is essential for neonatal growth and brain development. In a newborn infant with severe disease, endocrine regulation of hormones can be affected by abnormal metabolism. The assessment of thyroid parameters results in the recognition of a dysfunction and its association with disease severity. Objective: This study aimed to assess thyroid function profiles in critically ill neonates in the neonatal intensive care unit (NICU) compared with healthy controls. Additionally, we aimed to detect the presence of TD and its possible association with critical illness. Methods: A case-control study was performed in 100 neonates, comprising 50 sick neonates and 50 healthy controls. We measured thyroid functio
... Show MoreToxoplasmosis is the term for infection and disease in man and animal caused by a parasite called Toxoplasma gondii. The more susceptible to infect with toxoplasmosis is the Diabetic patients, due to low level of immunity response. The aim of current study is to investigate the immune status of diabetes mellitus type 2. One hundred and seventy five samples of both diabetes mellitus type 2patients and controls which had been tested by ELISA technique to detect anti-Toxoplasma Abs (IgG and IgM). The positive toxoplasmosis samples were tested to detect the level of TNF alpha and MIG. Results for all samples clarified that seronegative for IgM antibodies while 53 (53%) diabetic patients were seropositive for IgG antibodies and for toxoplasmosis
... Show MoreThe main idea of this paper is to define other types of a fuzzy local function and study the advantages and differences between them in addition to discussing some definitions of finding new fuzzy topologies. Also in this research, a new type of fuzzy closure has been defined, where the relation between the new type and different types of fuzzy local function has been studied
The technology of reducing dimensions and choosing variables are very important topics in statistical analysis to multivariate. When two or more of the predictor variables are linked in the complete or incomplete regression relationships, a problem of multicollinearity are occurred which consist of the breach of one basic assumptions of the ordinary least squares method with incorrect estimates results.
There are several methods proposed to address this problem, including the partial least squares (PLS), used to reduce dimensional regression analysis. By using linear transformations that convert a set of variables associated with a high link to a set of new independent variables and unr
... Show MoreThis paper aims to prove an existence theorem for Voltera-type equation in a generalized G- metric space, called the -metric space, where the fixed-point theorem in - metric space is discussed and its application. First, a new contraction of Hardy-Rogess type is presented and also then fixed point theorem is established for these contractions in the setup of -metric spaces. As application, an existence result for Voltera integral equation is obtained.
The great scientific progress has led to widespread Information as information accumulates in large databases is important in trying to revise and compile this vast amount of data and, where its purpose to extract hidden information or classified data under their relations with each other in order to take advantage of them for technical purposes.
And work with data mining (DM) is appropriate in this area because of the importance of research in the (K-Means) algorithm for clustering data in fact applied with effect can be observed in variables by changing the sample size (n) and the number of clusters (K)
... Show MoreCommon walnut (
In this research a new system identification algorithm is presented for obtaining an optimal set of mathematical models for system with perturbed coefficients, then this algorithm is applied practically by an “On Line System Identification Circuit”, based on real time speed response data of a permanent magnet DC motor. Such set of mathematical models represents the physical plant against all variation which may exist in its parameters, and forms a strong mathematical foundation for stability and performance analysis in control theory problems.