The support vector machine, also known as SVM, is a type of supervised learning model that can be used for classification or regression depending on the datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and time-consuming. SVM was updated in this research by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multi-layer kernels. The non-linear SVM classification model was illustrated and summarized in an algorithm using kernel tricks. The proposed method was examined using three simulation datasets with different sample

      The alterations in glyoxylate reductase and hydroxy-pyruvate reductase concentrations in the sera and the genetic alterations associated with calcium oxalate kidney stones in Iraqi patients were not studied previously so this study aimed to focus on these points. This study included 80 subjects; they were 50 patients with calcium oxalate stones compared to 30 apparently healthy controls. Biochemical investigations for kidney functions (creatinine, urea, and uric acid), were performed on the sera of both groups. Also, complete blood count, random blood sugar, and blood group tests. Furthermore, urine had been collected for General Urine Examination to visualize oxalate crystals in the urine of the patient. Also, the GRHPR

Hepatitis B virus (HBV) infection is a serious disease of the liver and signifies a major worldwide health concern. HBV Genotyping is vital for further epidemiological study, predicting the disease outcome and response to treatment. The current study aimed to determine hepatitis B virus genotypes in patients with chronic hepatitis B, and to validate possible associations with the baseline characteristics of the disease. A total of 90 patients with chronic hepatitis B infection were enrolled in this study. Liver function tests, hepatitis B virus markers and DNA viral load were done using routine standardized procedures. HBV genotyping was performed using real time PCR. Genotype D was the most predominant in 64 (71.1%) of samples, while

Worldwide, hundreds of millions of people have been infected with COVID-19 since December 2019; however, about 20% or less developed severe symptoms. The main aim of the current study was to  assess  the  relationship  between  the  severity of Covid-19 and different clinical and laboratory parameters. A total number of 466 Arabs have willingly joined this prospective cohort. Out of the total number, 297 subjects (63.7%) had negative COVID-19 tests, and thus, they were recruited as controls, while 169 subjects (36.3%) who tested positive for COVID-19 were enrolled as cases. Out of the total number of COVID-19 patients, 127 (75.15%) presented with mild symptoms, and 42 (24.85%) had severe symptoms. The age range for the partic

The high temperature superconductor’s compounds are one of the hot spot field of science, due to their applications in industries. Hg0.8Sb0.2Ba2Ca2Cu3O8+δ and Hg0.8Sb0.2Ba2Ca1Cu2O6+δ, were manufactured using a doable-step of solid state reaction method. The samples were sintered at 800 ° C. The transition temperatures Tc are found from electrically resistively by using four probe techniques. The resistivity become zero when the transition temperature Tc(offset) have 131 and 119 K, and the onset temperature Tc(onset) have 139 K for Hg0.8Sb0.2Ba2Ca2Cu3O8+δ and 132 K for Hg0.8Sb0.2Ba2Ca1Cu2O6+δ. Analysis of X-ray diffraction showed a tetragonal structure with lattice parameters changes for all samples.

HTH Ali Tarik Abdulwahid , Ahmed Dheyaa Al-Obaidi , Mustafa Najah Al-Obaidi, eNeurologicalSci, 2023

This paper proposed a new  method to study functional non-parametric regression data analysis with conditional expectation in the case that the covariates  are functional and the Principal Component Analysis was utilized to de-correlate the multivariate response variables. It  utilized the formula of the Nadaraya Watson estimator (K-Nearest Neighbour (KNN)) for prediction with different types of the semi-metrics, (which are based on Second Derivative and Functional Principal Component Analysis (FPCA))  for measureing the closeness between curves.  Root Mean Square Errors is used for the  implementation of this model which is then compared to the independent response method. R program is used for analysing data. Then, when  the cov