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

The railways network is one of the huge infrastructure projects. Therefore, dealing with these projects such as analyzing and developing should be done using appropriate tools, i.e. GIS tools. Because, traditional methods will consume resources, time, money and the results maybe not accurate. In this research, the train stations in all of Iraq’s provinces were studied and analyzed using network analysis, which is one of the most powerful techniques within GIS. A free trial copy of ArcGIS®10.2 software was used in this research in order to achieve the aim of this study. The analysis of current train stations has been done depending on the road network, because people used roads to reach those train stations. The data layers for this st

The advancement of digital technology has increased the deployment of wireless sensor networks (WSNs) in our daily life. However, locating sensor nodes is a challenging task in WSNs. Sensing data without an accurate location is worthless, especially in critical applications. The pioneering technique in range-free localization schemes is a sequential Monte Carlo (SMC) method, which utilizes network connectivity to estimate sensor location without additional hardware. This study presents a comprehensive survey of state-of-the-art SMC localization schemes. We present the schemes as a thematic taxonomy of localization operation in SMC. Moreover, the critical characteristics of each existing scheme are analyzed to identify its advantages

Frequent data in weather records is essential for forecasting, numerical model development, and research, but data recording interruptions may occur for various reasons. So, this study aims to find a way to treat these missing data and know their accuracy by comparing them with the original data values. The mean method was used to treat daily and monthly missing temperature data. The results show that treating the monthly temperature data for the stations (Baghdad, Hilla, Basra, Nasiriya, and Samawa) in Iraq for all periods (1980-2020), the percentage for matching between the original and the treating values did not exceed (80%). So, the period was divided into four periods.  It was noted that most of the congruence values increased, re

In this paper, ARIMA model was used for Estimating the missing data(air temperature, relative humidity, wind speed) for mean monthly variables in different time series at three stations (Sinjar, Baghdad , AL.Hai) which represented different parts of Iraq from north to south respectively

This research includes structure interpretation of the Yamama Formation (Lower Cretaceous) and the Naokelekan Formation (Jurassic) using 2D seismic reflection data of the Tuba oil field region, Basrah, southern Iraq. The two reflectors (Yamama and Naokelekan) were defined and picked as peak and tough depending on the 2D seismic reflection interpretation process, based on the synthetic seismogram and well log data. In order to obtain structural settings, these horizons were followed over all the regions. Two-way travel-time maps, depth maps, and velocity maps have been produced for top Yamama and top Naokelekan formations. The study concluded that certain longitudinal enclosures reflect anticlines in the east and west of the study ar

In the present study, an attempt has been made to study the change in water quality of the river in terms of turbidity during lockdown associated with COVID-19. Iraq announced the longest-ever lockdown on 25 March 2020 due to COVID-19 pandemic. 

In the absence of ground observations, remote sensing data was adopted, especially during this period. The change in the visible region's spectral reflectance of water in part of the river has been analyzed using the Landsat 8 OLI multispectral remote sensing data at Tigris River, Salah al-Din province (Bayji / near the refinery), Iraq. It was found that the green and red bands are most sensitive and can be used to estimate turbidity. Furthermore, the temporal variation in turbidity was a

A band rationing method is applied to calculate the salinity index (SI) and Normalized Multi-Band Drought Index (NMDI) as pre-processing to take Agriculture decision in these areas is presented. To separate the land from other features that exist in the scene, the classical classification method (Maximum likelihood classification) is used by classified the study area to multi classes (Healthy vegetation (HV), Grasslands (GL), Water (W), Urban (U), Bare Soil (BS)). A Landsat 8 satellite image of an area in the south of Iraq are used, where the land cover is classified according to indicator ranges for each (SI) and (NMDI).

The study area lies in the eastern part of Iraq, within Diyala and small parts of Salah Al-Din and Sulamanyah Governorates. The eastern boundary of the map represents Iraqi-Iranian International borders; it covers about 7001 Km2.The present study depends on two scenes of Thematic Mapper (TM5) data of Landsat and one scene of Multi-Spectral Scanner (MSS) data of Landsat, these data are subset and corrected within the ERDAS 9.2 software using UTM N38 projection. Normalized Difference Vegetation Index (NDVI) was adopted as practical tool for monitoring the surrounding area of Himreen Lake. The obtained result shows the distributions of NDVI for period 1976-1992 were positive pattern of (High vegetation density and Moderate vegetation densit

Chacha 20 is a stream cypher that is used as lightweight on many CPUs that ‎do not have dedicated AES instructions. As stated by Google, that is the reason why they use it on many ‎devices, such as mobile devices, for authentication in TLS protocol. This paper ‎proposes an improvement of chaha20 stream cypher algorithm based on tent and ‎Chebyshev functions (IChacha20). The main objectives of the proposed IChacha20 ‎algorithm are increasing security layer, designing a robust structure of the IChacha20 ‎to be enabled to resist various types of attacks, implementing the proposed ‎algorithm for encryption of colour images, and transiting it in a secure manner. The ‎ test results proved that the MSE, PSNR, UQI and NCC metrics