One of the challenging and active research topics in the recent years is Facial Expression. This paper presents the method to extract the features from the facial expressions from still images. Feature extraction is very important for classification and recognition process. This paper involve three stages which contain capture the images, pre-processing and feature extractions. This method is very efficient in feature extraction by applying haar wavelet and Karhunen-Loève Transform (KL-T). The database used in this research is from Cohen-Kanade which used six expressions of anger, sadness fear, happiness, disgust and surprise. Features that have been extracted from the image of facial expressions were used as inputs to the neural networ

In this paper, the memorization capability of a multilayer interpolative neural network is exploited to estimate a mobile position based on three angles of arrival. The neural network is trained with ideal angles-position patterns distributed uniformly throughout the region. This approach is compared with two other analytical methods, the average-position method which relies on finding the average position of the vertices of the uncertainty triangular region and the optimal position method which relies on finding the nearest ideal angles-position pattern to the measured angles. Simulation results based on estimations of the mobile position of particles moving along a nonlinear path show that the interpolative neural network approach outperf

In this study, the water treatment plants located on the Tigris River within Baghdad city were subjected to qualitative and quantitative assessments. Based on location, the plants from upstream to downstream are Al-Karkh, East Tigris, Al-Karamah, Al-Wathbah, Al-Wehdah, Al-Kadiseyah, Al-Dora, and Al-Rashid. Data from 2009 to 2020 on the turbidity, total dissolved solids, Alkalinity, hardness, chloride, calcium, and temperature were used in the qualitative assessment while data on the treated water production and population served were used in the quantitative assessment. The above Data was acquired from the Municipality of Baghdad. The turbidity was mainly used as a fair gauge to assess the performance of the water treatment plants in Baghda

     The Water Quality Index (WQI) is an important parameter in describing the water resources' suitability for human uses and is one of the most effective methods of describing water quality and indicative of assessing water quality and suitability for human utilization and the health of ecosystems. WQI of the Canadian Council of Ministers of the Environment (CCME) was used in the study to describe the Shatt al-Arab water quality in Basrah Southern Iraq, and its suitability for drinking use. The data for analyzing river water samples were adopted from five stations along the river every month during the years from 2014 to 2018 by the Iraqi Ministry of Environment, as it included the measurement of acidity function PH, Dissolved Oxyg

Abstract

     Travel Time estimation and reliability measurement is an important issues for improving operation efficiency and safety of traffic roads networks. The aim of this research is the estimation of total travel time and distribution analysis for three selected links in Palestine Arterial Street in Baghdad city. Buffer time index results in worse reliability conditions. Link (2) from Bab Al Mutham intersection to Al-Sakara intersection produced a buffer index of about 36%  and 26 % for Link (1) Al-Mawall intersection to Bab Al- Mutham intersection and finally for link (3) which presented a 24% buffer index. These illustrated that the reliability get worst for link

     This comprehensive study investigates has been made to assess the water quality of Al-Gharraf River, which considered the main branch of Tigris River south of Iraq using the overall Index of Pollution (OIP), depending on 9 physical, chemical, and biological important parameters of water quality were analyzed:  hydrogen ion concentration (pH), turbidity (NTU), total dissolved solid (TDS), dissolved oxygen (DO), biological oxygen demand (BOD5) , total hardness (TH), sulfate (SO4), nitrate (NO3),and fecal coliform (FC), which measured monthly at twenty one stations on the river during 2016-2017. Water quality deterioration  has occurred in the last ten stations, consequently, the health status of the river

<p>Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convol

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