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

       In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial

This study aimed to compare lysyl oxidase-1 level in diabetic patients with and without renal dysfunction, that LOX-1 may be an indicator for the early stage of diabetic nephropathy (DN). In addition to finding it is a relationship with kidney functions in Iraqi diabetic patients with and without renal dysfunction. Blood was obtained from 25 healthy individuals as a control group (G1), 25 diabetic patients with renal dysfunction, and 25 diabetic patients without renal dysfunction. Age range 40-60 years for all subjects. BMI (25-27) Kg/m2 . The serum was used for the analysis of LOX-1, FBG, urea, creatinine and uric acid. Whole blood is used for the determination of HbA1C. Results of FBG and HbA1C revealed a significant increase in G2 and G

Aim: The aim of this study is to determine the correlation between levels of certain seminal biochemical parameters and serum reproductive hormones, on the one hand, and sperm function tests, on the other hand, in asthenospermic patients. Patients and Methods: Sixty asthenospermic patients and twenty fertile men as a control group were included in this study. Semen samples were collected to perform seminal fluid analysis. Total protein, cholesterol, calcium, creatine kinase, and fructose were measured in the seminal plasma. Blood samples were collected for hormonal assay of serum reproductive hormones: testosterone, prolactin, luteinizing hormone, and follicle-stimulating hormone. Results: The results revealed a significant positive correla

This paper considers a new Double Integral transform called Double Sumudu-Elzaki transform DSET. The combining of the DSET with a semi-analytical method, namely the variational iteration method DSETVIM, to arrive numerical solution of nonlinear PDEs of Fractional Order derivatives. The proposed dual method property decreases the number of calculations required, so combining these two methods leads to calculating the solution's speed. The suggested technique is tested on four problems. The results demonstrated that solving these types of equations using the DSETVIM was more advantageous and efficient

This paper considers a new Double Integral transform called Double Sumudu-Elzaki transform DSET. The combining of the DSET with a semi-analytical method, namely the variational iteration method DSETVIM, to arrive numerical solution of nonlinear PDEs of Fractional Order derivatives. The proposed dual method property decreases the number of calculations required, so combining these two methods leads to calculating the solution's speed. The suggested technique is tested on four problems. The results demonstrated that solving these types of equations using the DSETVIM was more advantageous and efficient