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

The support vector machine, also known as SVM, is a type of supervised learning model that can be used for classification or regression depending on the datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and time-consuming. SVM was updated in this research by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multi-layer kernels. The non-linear SVM classification model was illustrated and summarized in an algorithm using kernel tricks. The proposed method was examined using three simulation datasets with different sample

In this work, a deep computational study has been conducted to assign several qualities for the graph ⁠. Furthermore, determine the amount of the dihedral subgroups in the Held simple group He through utilizing the attributes of gamma.

In this work, the effect of preparing a composite of copper oxide nanoparticles with carbon on some of its optical properties was studied. The composite preparing process was carried out by exploding graphite electrodes in an aqueous suspension of copper oxide. The properties of the plasma which is formed during the explosion were studied using emission spectroscopy in order to determine the most important elements that are present in the media. The electron’s density and their energy, which is the main factor in the composite process, were determined. The particle properties were studied before and after the exploding process. The XRD showed an additional peak in the copper oxides pattern corresponding to the hexagonal graphite struct

Drilling fluid properties and formulation play a fundamental role in drilling operations. The Classical water-based muds prepared from only the Syrian clay and water without any additives((Organic and industrial polymers) are generally poor in performance. Moreover, The high quantity of Syrian clay (120 gr / l) used in preparing drilling fluids. It leads to a decrease in the drilling speed and thus an increase in the time required to complete the drilling of the well. As a result, the total cost of drilling the well increased, as a result of an increase in the concentration of the solid part in the drilling fluid. In this context, our study focuses on the investigation of the improvement in drilling mud   Prepa

Copper tin sulfide (Cu2SnS3) thin films have been grown on glass
substrate with different thicknesses (500, 750 and 1000) nm by flash
thermal evaporation method after prepare its alloy from their
elements with high purity. The as-deposited films were annealed at
473 K for 1h. Compositional analysis was done using Energy
dispersive spectroscopy (EDS). The microstructure of CTS powder
examined by SEM and found that the large crystal grains are shown
clearly in images. XRD investigation revealed that the alloy was
polycrystalline nature and has cubic structure with preferred
orientation along (111) plane, while as deposited films of different
thickness have amorphous structure and converted to polycrystalline

This study presents a comprehensive set of laboratory works for the examined soil layers extracted from Baghdad city (specifically from Alkadhimya, Alaitaifiya, and Alhurriya) to illustrate their engineering properties. The researchers have adopted the unified soil classification system for soil classification purposes. Also, the direct shear test was performed for soil samples with various degrees of saturation (0%, 25%, 50%, 75%, and 100%). The test results have shown a significant reduction in cohesion property with higher moisture content within soil samples. Also, a noticeable reduction in angle of internal friction value has occurred with such changes. Furthermore, it has been found that the bearing capacity of unsaturated soi

Failure in asphalt mixture and distress in pavement are major issues to roads infrastructure. Selecting an appropriate chemical composition of asphalt cement is a key component in avoiding these issues. This work aimed to investigate the effect of the chemical composition of different polar fractions on the rheological and physical properties of asphalt cement. Four types of asphalt cement with penetration grades of 20/30, 40/50, 60/70 and 85/100 were divided into four fractions. Complex shear modules, rutting resistance and rotational viscosity of the asphalt cement were determined by using a Dynamic Shear Rheometer and a Rotation Viscometer, respectively. The results show that an increase in the asphaltene content and Gastel index resulte