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
As a result of the significance of image compression in reducing the volume of data, the requirement for this compression permanently necessary; therefore, will be transferred more quickly using the communication channels and kept in less space in memory. In this study, an efficient compression system is suggested; it depends on using transform coding (Discrete Cosine Transform or bi-orthogonal (tap-9/7) wavelet transform) and LZW compression technique. The suggested scheme was applied to color and gray models then the transform coding is applied to decompose each color and gray sub-band individually. The quantization process is performed followed by LZW coding to compress the images. The suggested system was applied on a set of seven stand
... Show MoreCanonical correlation analysis is one of the common methods for analyzing data and know the relationship between two sets of variables under study, as it depends on the process of analyzing the variance matrix or the correlation matrix. Researchers resort to the use of many methods to estimate canonical correlation (CC); some are biased for outliers, and others are resistant to those values; in addition, there are standards that check the efficiency of estimation methods.
In our research, we dealt with robust estimation methods that depend on the correlation matrix in the analysis process to obtain a robust canonical correlation coefficient, which is the method of Biwe
... Show MoreThis paper describes a newly modified wind turbine ventilator that can achieve highly efficient ventilation. The new modification on the conventional wind turbine ventilator system may be achieved by adding a Savonius wind turbine above the conventional turbine to make it work more efficiently and help spinning faster. Three models of the Savonius wind turbine with 2, 3, and 4 blades' semicircular arcs are proposed to be placed above the conventional turbine of wind ventilator to build a hybrid ventilation turbine. A prototype of room model has been constructed and the hybrid turbine is placed on the head of the room roof. Performance's tests for the hybrid turbine with a different number of blades and different values o
... Show MoreGreen nanotechnology is a thrilling and rising place of technology and generation that bracesthe ideas of inexperienced chemistry with ability advantages for sustainability, protection, andthe general protection from the race human. The inexperienced chemistry method introduces aproper technique for the production, processing, and alertness of much less dangerous chemicalsubstances to lessen threats to human fitness and the environment. The technique calls for inintensity expertise of the uncooked materials, particularly in phrases in their creation intonanomaterials and the resultant bioactivities that pose very few dangerous outcomes for peopleand the environment. In the twenty-first century, nanotechnology has become a systematic
... Show MoreThe fiscal position of governments in rentier economies depends heavily on oil revenues. The relationship between oil prices and the budget surplus or deficit is often nonlinear and characterized by complex temporal dependencies, which may limit the predictive capability of conventional econometric models. Accordingly, this study aims to forecast the Iraqi budget surplus and deficit and compare the predictive performance of the ARDL, NARDL, LSTM, 1D-CNN, and hybrid 1D-CNN-LSTM models using oil prices as the primary predictive variable. The hybrid model integrates the feature-extraction capability of One-Dimensional Convolutional Neural Networks (1D-CNN) with the ability of Long Short-Term Memory (LSTM) networks to capture long-term
... Show MoreThe research mainly seeks to predict the amounts of non- oil Iraqi exports which concludes ) Food & Animals , Raw materials and non- tanned Leather and fur , Mineral fuels and Lubricating Oil , Chemical substances and amounts , Manufactured goods , Electrical and non - electrical machines , Supplies and Total non- Oil exports ) by using Markov Chain as one of Statistical approach to forecasting in future . In this search We estimate the transliteration probabilities matrix according to Maximum Likelihood on a data collected from central organization for Statistics and information technology represents an index numbers of non- Oil exports amount in Iraq from 2004 to 2015 depending on 2007 as a basic year . Results shown that trend of index
... Show MoreA variety of single-engine driven files and inematics have been introduced to improve the clinical performance of NiTi rotary files. The purpose of this in vitro study was to measure and compare the incidence of dentinal defects after root canal preparation with different single file systems.