Genome sequencing has significantly improved the understanding of HIV and AIDS through accurate data on viral transmission, evolution and anti-therapeutic processes. Deep learning algorithms, like the Fined-Tuned Gradient Descent Fused Multi-Kernal Convolutional Neural Network (FGD-MCNN), can predict strain behaviour and evaluate complex patterns. Using genotypic-phenotypic data obtained from the Stanford University HIV Drug Resistance Database, the FGD-MCNN created three files covering various antiretroviral medications for HIV predictions and drug resistance. These files include PIs, NRTIs and NNRTIs. FGD-MCNNs classify genetic sequences as vulnerable or resistant to antiretroviral drugs by analyzing chromosomal information and id

Background: Diabetes mellitus is a well
known metabolic and vascular illness associated
with high incidence of bacterial urinary tract
infections especially in diabetic complications
including both micro and macro-vascular types.
Objective: To study the incidence of bacterial
urinary tract infections in type 2 diabetic
patients, the type of micro-organism responsible
in relation to age, sex of patients, duration of the
disease & related micro & macrovascular
diabetic complications.
Methods: A prospective study of the diabetic
patients including 40 males with mean age of
54(±9) years and 50 females, mean age of 51(±7)
years and duration of the and sex matched
controls (27 males and 33

atrogenic atrial septal defect (IASD), post Catheter ablation during electrophysiological study simply can be assess with Echocardiography nowadays ablation consider the main line in the managements of patients with various type of arrhythmia. This study aims to de-termine the outcomes of Iatrogenic Atrial Septal Defect (IASD) six months post radiofrequency ablation (RF) procedure of left atrial arrhythmia using non-invasive Transtho-racic Echocardiography (TTE) parameters (LVEF, E/e` and ASD size) with sheath size as predictors of atrial septal defect closure. Patients and methods: A prospective study was con-ducted in Iraqi Centre for Heart Diseases included 47 patients post Electrophysiology procedure and ablation of left atrial SVT were

Autoría: Muwafaq Obayes Khudhair. Localización: Revista iberoamericana de psicología del ejercicio y el deporte. Nº. 6, 2022. Artículo de Revista en Dialnet.

In information security, fingerprint verification is one of the most common recent approaches for verifying human identity through a distinctive pattern. The verification process works by comparing a pair of fingerprint templates and identifying the similarity/matching among them. Several research studies have utilized different techniques for the matching process such as fuzzy vault and image filtering approaches. Yet, these approaches are still suffering from the imprecise articulation of the biometrics’ interesting patterns. The emergence of deep learning architectures such as the Convolutional Neural Network (CNN) has been extensively used for image processing and object detection tasks and showed an outstanding performance compare

Introduction: Cutaneous leishmaniasis (CL) is a common protozoan disease in Iraq characterized by localized ulcers, primarily on exposed skin. This study aimed to investigate the hematological parameters of infected patients using a complete blood count (CBC) in the endemic area of Diyala Governorate, northeast of Baghdad. This has been studied in newly diagnosed, untreated individuals and patients receiving sodium antimony gluconate. Methodology: Hematological screening was performed on blood samples from 161 patients with microscopically diagnosed cutaneous leishmaniasis before and after treatment. Anti-Leishmania IgG was also assessed by ELISA in seropositive and seronegative subjects. Results: The newly diagnosed, untreated pati

Recent studies have revealed some conflicting results about the health effects of caffeine. These studies are inconsistent in terms of design and population and source of consumed caffeine. In the current study, we aimed to evaluate the possible health effects of dietary caffeine intake among overweight and obese individuals.

In this research the results of applying Artificial Neural Networks with modified activation function to perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of identification strategy consists of a feed-forward neural network with a modified activation function that operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have been trained online and offline have been used, without requiring any previous knowledge about the system to be identified. The activation function that is used in the hidden layer in FFNN is a modified version of the wavelet function. This approach ha