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 CNNs have shown improved accuracy in the classification of ASD compared to traditional machine learning algorithms, on all these datasets with higher accuracy of 99.45%, 98.66%, and 90% for Autistic Spectrum Disorder Screening in Data for Adults, Children, and Adolescents respectively as they are better suited for the analysis of time series data commonly used in the diagnosis of this disorder
Background: Autism is a complex developmental disability that typically appears during the first three years of life. Autism affects the normal development of the brain in the areas of social interaction and communication skills.Objectives: To identify risk factors for Autism among a sample of autistic children in Baghdad city. Type of the study: this is a case – control, study. Methods: This study was conducted during the period of data collection extended from first of November 2010 until the first of April 2011,The total number of children involved was 100children with diagnosis of autism. Handred children who are free from autism were taken as the control sample. Results: there was a significant association between paternal age and
... Show MoreThe study using Nonparametric methods for roubust to estimate a location and scatter it is depending minimum covariance determinant of multivariate regression model , due to the presence of outliear values and increase the sample size and presence of more than after the model regression multivariate therefore be difficult to find a median location .
It has been the use of genetic algorithm Fast – MCD – Nested Extension and compared with neural Network Back Propagation of multilayer in terms of accuracy of the results and speed in finding median location ,while the best sample to be determined by relying on less distance (Mahalanobis distance)has the stu
... Show MoreThis paper presents a proposed neural network algorithm to solve the shortest path problem (SPP) for communication routing. The solution extends the traditional recurrent Hopfield architecture introducing the optimal routing for any request by choosing single and multi link path node-to-node traffic to minimize the loss. This suggested neural network algorithm implemented by using 20-nodes network example. The result shows that a clear convergence can be achieved by 95% valid convergence (about 361 optimal routes from 380-pairs). Additionally computation performance is also mentioned at the expense of slightly worse results.
Abstract:
The aim of the research is to demonstrate the impact of the professional specialization of the audit companies in the detection of fraud in the financial statements of the economic units listed in the Iraqi market for securities for the period 2014-2015 through the application of the model (Carcello) to test the hypothesis of research on the impact of professional specialization of audit companies in the detection of fraud in lists The effect of the variables was revealed through the use of statistical models of logistic regression model and correlation coefficient. After testing the hypotheses of the research, a number of conclusions were reached. The most important was the existence of a signi
... Show MoreIn this work laser detection and tracking system (LDTS) is designed and implemented using a fuzzy logic controller (FLC). A 5 mW He-Ne laser system and an array of nine PN photodiodes are used in the detection system. The FLC is simulated using MATLAB package and the result is stored in a lock up table to use it in the real time operation of the system. The results give a good system response in the target detection and tracking in the real time operation.