The proposal of nonlinear models is one of the most important methods in time series analysis, which has a wide potential for predicting various phenomena, including physical, engineering and economic, by studying the characteristics of random disturbances in order to arrive at accurate predictions.

In this, the autoregressive model with exogenous variable was built using a threshold as the first method, using two proposed approaches that were used to determine the best cutting point of [the predictability forward (forecasting) and the predictability in the time series (prediction), through the threshold point indicator]. B-J seasonal models are used as a second method based on the principle of the two proposed approaches in dete

Support vector machines (SVMs) are supervised learning models that analyze data for classification or regression. For classification, SVM is widely used by selecting an optimal hyperplane that separates two classes. SVM has very good accuracy and extremally robust comparing with some other classification methods such as logistics linear regression, random forest, k-nearest neighbor and naïve model. However, working with large datasets can cause many problems such as time-consuming and inefficient results. In this paper, the SVM has been modified by using a stochastic Gradient descent process. The modified method, stochastic gradient descent SVM (SGD-SVM), checked by using two simulation datasets. Since the classification of different ca

  The Covid-19 virus disease has been shown to affect numerous organs and systems including the liver. The study aimed to compare lipid profiles and liver enzyme levels in individuals who had recovered from Covid-19 infection. To achieve the study objectives, liver Aspartate Aminotransferase (AST), Alanine Aminotransferase (ALT), Alkaline phosphatase (ALP),  Random Blood Sugar (RBS) and Lipid profile which include cholesterol, High-Density Lipoprotein (HDL), Triglycerides (T.G), Low-Density Lipoprotein (LDL), and Very low-density Lipoprotein (VLDL) were determined.

One hundred twenty serum samples were obtained, of which fifty samples were utilized as the control healthy persons (not affected by COVID) and seventy samples came f

This study aimed to evaluate the effect of the COVID-19 outbreak on emergencies and pain among orthodontic patients attending a teaching hospital. The study was conducted among orthodontic patients receiving active orthodontic treatment or in a retention period at the College of Dentistry, University of Baghdad, Iraq. Their participation was voluntary, and they filled out an Arabic-translated questionnaire. The survey included general information, orthodontic problems, and a numerical rating scale for pain assessment. We used descriptive and inferential statistics (frequencies and intersecting frequencies), chi-square test and linear regression. Out of 75 orthodontic patients, only 54 (15 males and 39 females) were included in the s

<p>Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convol

<p>Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convol

This study aims to find the chemosensitive dysfunction incidence in COVID-19-positive patients and its recovery.

We collected the data from sixty-five patients, all COVID-19 positive, quarantined in-hospital between 5 April 2020 and 17 May 2020, by a questionnaire distributed in the quarantine ward.

Polycystic ovary syndrome (PCOS) is the most endocrine problem in women of regenerative age. PCOS women typically belong to an age and sex group which is at higher risk for severe coronavirus disease (COVID-19). COVID-19 targets cells through angiotensin-converting enzyme 2 (ACE2) receptor presents on cells in veins, lungs, heart, digestion tracts, and kidneys. Renin-Angiotensin System (RAS) over activity has likewise been described in metabolic disorders; type 2 diabetes mellitus (T2DM), and conditions shared by women with polycystic ovary condition. The point of this study is to know the job of renin and ACE2 in PCOS and coronavirus and its relationship with hormones and other metabolic parameters related. The study groups consist of 1