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

Higher education is one of the foundational pillars that contributes to the development of societies and the achievement of social and economic progress. With the accelerating advancements in technological, environmental, and social fields, universities and educational institutions worldwide are facing significant challenges requiring them to adapt to these changes and develop new educational strategies. In this context, directing higher education towards achieving sustainable development goals (SDGs) has become imperative, as these goals are now an integral part of modern societies' vision, particularly in light of global challenges such as climate change, population growth, and unemployment. By the year 2050, educational institutions

In recent years, social media has been increasing widely and obviously as a media for users expressing their emotions and feelings through thousands of posts and comments related to tourism companies. As a consequence, it became difficult for tourists to read all the comments to determine whether these opinions are positive or negative to assess the success of a tourism company. In this paper, a modest model is proposed to assess e-tourism companies using Iraqi dialect reviews collected from Facebook. The reviews are analyzed using text mining techniques for sentiment classification. The generated sentiment words are classified into positive, negative and neutral comments by utilizing Rough Set Theory, Naïve Bayes and K-Nearest Neighbor

The research aims to demonstrate the impact of TDABC as a strategic technology compatible with the rapid developments and changes in the contemporary business environment) on pricing decisions. As TDABC provides a new philosophy in the process of allocating indirect costs through time directives of resources and activities to the goal of cost, identifying unused energy and associated costs, which provides the management of economic units with financial and non-financial information that helps them in the complex and dangerous decision-making process. Of pricing decisions. To achieve better pricing decisions in light of the endeavor to maintain customers in a highly competitive environment and a variety of alternatives, the resear

Examining and comparing the image quality of degenerative cervical spine diseases through the application of three MRI sequences; the Two-Dimension T2 Weighed Turbo Spin Echo (2D T2W TSE), the Three-Dimension T2 Weighted Turbo Spin Echo (3D T2W TSE), and the T2 Turbo Field Echo (T2_TFE). Thirty-three patients who were diagnosed as having degenerative cervical spine diseases were involved in this study. Their age range was 40-60 years old. The images were produced via a 1.5 Tesla MRI device using (2D T2W TSE, 3D T2W TSE, and T2_TFE) sequences in the sagittal plane. The image quality was examined by objective and subjective assessments. The MRI image characteristics of the cervical spines (C4-C5, C5-C6, C6-C7) showed significant difference

Objectives: The study aimed to evaluate health behavior, evaluate Health Action Process Approach, determine the effectiveness of the Health Action Process Approach based the application of program on students’ engaging in regular physical exercise.

Methodology: The research design for this study was a quasi-experimental. The study sample included high school male students, the final sample size was(160 ) Non-probability sampling (convenience sample) are chosen, (80) students study group and (80) students control group.

Results: The results show there was no statistically significant difference in the HAPA constructs among family's socioeconomic class groups and less tha

Abstract

Black paint laser peening (bPLP) technique is currently applied for many engineering materials , especially for aluminum alloys due to high improvement in fatigue life and strength . Constant and variable   bending fatigue tests have been performed at RT and stress ratio R= -1 . The results of the present  work observed that the significance of the surface work hardening which generated high negative residual stresses in bPLP specimens .The fatigue life  improvement factor (FLIF)  for bPLP constant fatigue behavior was from 2.543 to 3.3 compared to untreated  fatigue and the increase in fatigue strength at 10⁷ cycle was 21% . The bPLP cumulative fatigue life behav

The simulation of passively Q-switching is four non – linear first order differential equations. The optimization of passively Q-switching simulation was carried out using the constrained Rosenbrock technique. The maximization option in this technique was utilized to the fourth equation as an objective function; the parameters, γa, γc and β as were dealt with as decision variables. A FORTRAN program was written to determine the optimum values of the decision variables through the simulation of the four coupled equations, for ruby laser Q–switched by Dy +2: CaF2.For different Dy +2:CaF2 molecules number, the values of decision variables was predicted using our written program. The relaxation time of Dy +2: CaF2, used with ruby was