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

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the bes

The paper include study the effect thickness of the polymeric sample which is manufactured by thermo press way. The sample was used as an active tunable R6G laser media. The remarks show that, when the thickness of the samples is increased, with the same concentration, the spectrum will shift towards the short wavelength, & the quantum fluorescence yield will increased. The best result we obtained for the quantum fluorescence yield is (0.68) at the sample, with thickness (0.304mm) in Ethanol solvent, while when we used the Pure Water as a solvent, we found that the best quantum fluorescence yield is (0.63) at (0.18mm) thickness of the sample.

The paper include studies the effect of solvent of dye doped in polymeric laser sample which manufactured in primo press way, which is used as an active (R6G) tunable dye lasers. The remarks show that, when the viscosity of the solvent (from Pure Water to Ethanol), for the same concentration and thickness of the performance polymeric sample is increased, the absorption spectrum is shifts towards the long wave length (red shift), & towards short wave length (blue shift) for fluorescence spectrum, also increased the quantum fluorescence yield. The best result we obtained for the quantum fluorescence yield is (0.882) with thickness (0.25mm) in Ethanol solvent in concentration (2*10-3mole/liter), while when we used the Pure Water as a solvent,

The paper presents the results of precise of the calculations of the diffusion of slow electrons in ionospheric gases, such as, (Argon – Hydrogen mixture, pure Nitrogen and Argon – Helium – Nitrogen) in the presence of a uniform electric field and temperature 300 Kelvin. Such calculations lead to the value Townsend's energy coefficient (KT) as a function of E/P (electric field strength/gas pressure), electric field (E), electric drift velocity (Vd), momentum transfer collision frequency ( ), energy exchange collision frequency ( ) and characteristic energy (D/?). The following physical quantities are deduced as function s E/P: mean free path of the electrons at unit pressure, mean energy lost by an electron per collision, mean velocit

Recently, several concepts and expressions have emerged that have often preoccupied the world . around the concept of environment and sustainability. This is due to the negative and irresponsible impact of man and his innovations in various industrial and technological fieldsthat have damaged the natural environment. Architecture and cities at the broader level are some of the man made components that caused these negative impacts and in the same time affected by them. What distinguishes architectural and urban projects is the consumption of large . quantities of natural resources and production larger amounts of waste and pollution, along the life of these projects. At the end of the twentieth century and the beginning of the twenty-fir

Document source identification in printer forensics involves determining the origin of a printed document based on characteristics such as the printer model, serial number, defects, or unique printing artifacts. This process is crucial in forensic investigations, particularly in cases involving counterfeit documents or unauthorized printing. However, consistent pattern identification across various printer types remains challenging, especially when efforts are made to alter printer-generated artifacts. Machine learning models are often used in these tasks, but selecting discriminative features while minimizing noise is essential. Traditional KNN classifiers require a careful selection of distance metrics to capture relevant printing