Recently Genetic Algorithms (GAs) have frequently been used for optimizing the solution of estimation problems. One of the main advantages of using these techniques is that they require no knowledge or gradient information about the response surface. The poor behavior of genetic algorithms in some problems, sometimes attributed to design operators, has led to the development of other types of algorithms. One such class of these algorithms is compact Genetic Algorithm (cGA), it dramatically reduces the number of bits reqyuired to store the poulation and has a faster convergence speed. In this paper compact Genetic Algorithm is used to optimize the maximum likelihood estimator of the first order moving avergae model MA(1). Simulation results based on MSE were compared with those obtained from the moments method and showed that the Canonical GA and compact GA can give good estimator of θ for the MA(1) model. Another comparison has been conducted to show that the cGA method has less number of function evaluations, minimum searched space percentage, faster convergence speed and has a higher optimal precision than that of the Canonical GA.
In this paper, third order non-polynomial spline function is used to solve 2nd kind Volterra integral equations. Numerical examples are presented to illustrate the applications of this method, and to compare the computed results with other known methods.
The study attempts to measure the level of shyness; the level of psychological isolation; to identify the relationship between shyness and psychological isolation; and to identify the differences between shyness and psychological isolation among first-intermediate students. To this end, a random sample comprised (187) male and female students was chosen for the academic year (2016-2017) from Baghdad \ Al-Rasafa. To measure the shyness and psychological isolation, the researcher designed two scales: one to measure the shyness composed of (37) items divided into four domains; and the other to measure the psychological isolation made of (56) items divided into three domains. The study concluded that the sample has a medium level of shyness;
... Show MoreA new spectrophotometric method for individual and simultaneous determination of cefixime and cephalexin depending on the first and second derivative mode techniques. The first and second derivative spectra of these compounds permitted individual and simultaneous determination of cefixime and cephalexin in concentration interval of (4– 24μg.ml-1 ) by measuring the amplitude of peak-to-base line, pea to peak at certain wavelengths and the area under peak at selected spectrum intervals. The methods showed reasonable precision and accuracy and have been applied to determine cefixime and cephalexin in two different pharmaceutical preparations.
This article comprehensively examines the history, diagnosis, genetics, diversity, and treatment of SARS-CoV-2. It details the emergence of coronaviruses over the past 50 years, including the coronavirus from 2019 and its subsequent mutations, along with updated information about this virus. This review explains the development and nomenclature of coronaviruses, their cellular invasion through glycoprotein spikes binding to ACE-2 receptors, and the mechanism of cell entry via endocytosis. Diagnosis methods for COVID-19, including nucleic acid amplification, serology, and imaging techniques like chest X-ray and CT scan tests, are discussed. Treatment approaches for COVID-19 are outlined, emphasizing healthcare, antiviral medications like Rem
... Show MoreA three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an
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