Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob

Web testing is very important method for users and developers because it gives the ability to detect errors in applications and check their quality to perform services to users performance abilities, user interface, security and other different types of web testing that may occur in web application. This paper focuses on a major branch of the performance testing, which is called the load testing. Load testing depends on an important elements called request time and response time. From these elements, it can be decided if the performance time of a web application is good or not. In the experimental results, the load testing applied on the website (http://ihcoedu.uobaghdad.edu.iq) the main home page and all the science departments pages. In t

Enhancement of the performance for hybrid solar air conditioning system was presented in this paper. The refrigerant temperature leaving the condenser was controlled using three-way valve, this valve was installed after the compressor to regulate refrigerant flow rate towards the solar system. A control system using data logger, sensors and computer was proposed to set the opening valve ratio. The function of control program using LabVIEW software is to obtain a minimum refrigerant temperature from the condenser outlet to enhance the overall COP of the unit by increasing the degree of subcooled refrigerant. A variable load electrical heater with coiled pipe was used instead of the solar collector and the storage tank to simulate the sola

Human interaction technology based on motion capture (MoCap) systems is a vital tool for human kinematics analysis, with applications in clinical settings, animations, and video games. We introduce a new method for analyzing and estimating dorsal spine movement using a MoCap system. The captured data by the MoCap system are processed and analyzed to estimate the motion kinematics of three primary regions; the shoulders, spine, and hips. This work contributes a non-invasive and anatomically guided framework that enables region-specific analysis of spinal motion which could be used as a clinical alternative to invasive measurement techniques. The hierarchy of our model consists of five main levels; motion capture system settings, marker data

In this paper we describe several different training algorithms for feed forward neural networks(FFNN). In all of these algorithms we use the gradient of the performance function, energy function, to determine how to adjust the weights such that the performance function is minimized, where the back propagation algorithm has been used to increase the speed of training. The above algorithms have a variety of different computation and thus different type of form of search direction and storage requirements, however non of the above algorithms has a global properties which suited to all problems.

Different solvents (light naphtha, n-heptane, and n-hexane) are used to treat Iraqi Atmospheric oil residue by the deasphalting process. Oil residue from Al-Dura refinery with specific gravity 0.9705, API 14.9, and 0.5 wt. % sulfur content was used. Deasphalting oil (DAO) was examined on a laboratory scale by using solvents with different operation conditions (temperature, concentration of solvent, solvent to oil ratio, and duration time). This study investigates the effects of these parameters on asphaltene yield. The results show that an increase in temperature for all solvents increases the extraction of asphaltene yield. The higher reduction in asphaltene content is obtained with hexane solvent at operating conditions of (90 °C, 4/1

Different solvents (light naphtha, n-heptane, and n-hexane) are used to treat Iraqi Atmospheric oil residue by the deasphalting process. Oil residue from Al-Dura refinery with specific gravity 0.9705, API 14.9, and 0.5 wt. % sulfur content was used. Deasphalting oil (DAO) was examined on a laboratory scale by using solvents with different operation conditions (temperature, concentration of solvent, solvent to oil ratio, and duration time). This study investigates the effects of these parameters on asphaltene yield. The results show that an increase in temperature for all solvents increases the extraction of asphaltene yield. The higher reduction in asphaltene content is obtained with hexane solvent at operating conditions of (90 °C

Analyzing sentiment and emotions in Arabic texts on social networking sites has gained wide interest from researchers. It has been an active research topic in recent years due to its importance in analyzing reviewers' opinions. The Iraqi dialect is one of the Arabic dialects used in social networking sites, characterized by its complexity and, therefore, the difficulty of analyzing sentiment. This work presents a hybrid deep learning model consisting of a Convolution Neural Network (CNN) and the Gated Recurrent Units (GRU) to analyze sentiment and emotions in Iraqi texts. Three Iraqi datasets (Iraqi Arab Emotions Data Set (IAEDS), Annotated Corpus of Mesopotamian-Iraqi Dialect (ACMID), and Iraqi Arabic Dataset (IAD)) col