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

This research utilized natural asphalt (NA) deposits from sulfur springs in western Iraq. Laboratory tests were conducted to evaluate the performance of an asphalt mixture incorporating NA and verify its suitability for local pavement applications. To achieve this, a combination of two types of NA, namely soft SNA and hard HNA, was blended to create a binder known as Type HSNA. The resulting HSNA exhibited a penetration grade that adhered to Iraqi specifications. Various percentages of NA (20%, 40%, 60%, and 80%) were added to petroleum asphalt. The findings revealed enhanced physical properties of HSNA, which also satisfied the requirements outlined in the Iraqi specifications for asphalt cement.

Consequently, HS

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 work, the photocatalytic degradation of indigo carmine (IC) using zinc oxide suspension was studied. The effect of influential parameters such as initial indigo carmine concentration and catalyst loading were studied with the effect of Vis irradiation in the presence of reused ZnO was also investigated. The increased in initial dye concentration decreased the photodegradation and the increased catalyst loading increased the degradation percentage and the reused-ZnO exhibits lower photocatalytic activity than the ZnO catalyst. It has been found that the photocatalytic degradation of indigo carmine obeyed the pseudo-first-order kinetic reaction in presence of zinc oxide. This was found from plotting the relationship between ln

The aim of this study was to increasing natural carotenoides production by a locally isolate Rodotorula mucilagenosa M. by determination of the optimal conditions for growth and production of this agents, for encouragest to use it in food application permute artificial pigments which harmfull for consumer health and envieronmental. The optimal condition of carotenoides production from Rhodotorula mucilaginosa M were studied. The results shows the best carbon and nitrogen source were glucose and yeast extract. The carotenoids a mount production was 47430 microgram ̸ litter and 47460 microgram ̸ litter, respectively, and the optimum temperature was 30°C, PH 6, that the carotenoides a mount was 47470 microgram ̸ litter and 47670 microgr

مشكلة البحث   The Problem of the Research

          يعّد التحصيل الدراسي للطلبة عموما من أهم أركان النشاط العقلي في المجال التربوي، فلا تكاد تخلو منه أيه مدرسة، أبتداءً من المدارس الابتدائية وقد تسبقها رياض الأطفال، إلى المدرسة الإعدادية والجامعية ، وقد تمتد إلى مراحل متقدمة في العمر، فكان الطالب وما يزال هو محور العملية ال