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

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with

In this study, we investigate the behavior of the estimated spectral density function of stationary time series in the case of missing values, which are generated by the second order Autoregressive (AR (2)) model, when the error term for the AR(2) model has many of continuous distributions. The Classical and Lomb periodograms used to study the behavior of the estimated spectral density function by using  the simulation.

Community detection is useful for better understanding the structure of complex networks. It aids in the extraction of the required information from such networks and has a vital role in different fields that range from healthcare to regional geography, economics, human interactions, and mobility. The method for detecting the structure of communities involves the partitioning of complex networks into groups of nodes, with extensive connections within community and sparse connections with other communities. In the literature, two main measures, namely the Modularity (Q) and Normalized Mutual Information (NMI) have been used for evaluating the validation and quality of the detected community structures. Although many optimization algo

Objectives: To determine the (QoL) for patients with permanent pacemaker and to find-out the relationship between
these patients’ (QoL) and their sociodemographic characteristics such as age, gender, level of education, and
occupation.
Methodology: ٨ purposive non-probability” sample of (62) patient with permanent pacemaker was involved in this
study. The developed questionnaire consists of (4) parts which include !.demographic data form, 2.disease-related
information form, 3.socioeconomic data form, and 4.Permanent pacemaker patient’s quality of life questionnaire data
form. The validity and reliability of the questionnaire were determined through the application of a pilot study. ٨
descriptive statistical a

Dermatophytes are species with slight genetic variation, and are yet several uncertainties about the differences among species. This study aims to isolate and diagnose the Trichophyton interdigitale by molecular technique and to reveal the phylogenetic distance and similarity of the Iraqi isolates to other isolates from the globe, in addition, to submit the obtained sequences to the NCBI database. This study included 86 with multiple lesions on different parts of the body. The results showed different variations within the ITS gene between the isolates. It was concluded that Trichophyton interdigitale in Iraqi isolates had two types of substitution variations (Transition and Transversion) different than global isolates. Moreover, it

Introduction and Aim: Beta-thalassemia is a serious inherited genetic disorder and an increasing health burden globally. Beta -thalassemia is caused by genetic globin abnormalities within the hemoglobin beta (HBB) gene. This study aimed to characterize the HBB gene mutations in beta -thalassemia among southern Iraqi patients. Materials and Methods: The study included 30 beta -thalassemia patients referred to the Thi-Qar Center for Genetic Diseases, Iraq and 15 control samples from a random group of apparently healthy individuals. Genomic DNA was isolated from blood sample collected from each individual. The DNA was amplified for specific regions of the HBB gene and the amplified products sequenced. The sequences generated were analysed for