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

An Optimal Algorithm for HTML Page Building Process

The main objective of this study is to get a secure broadcasting in real time applications for audio files (precisely the Wave type) and to increase the degree of complexity of the encrypt/decrypt arithmetic operations in the finite . field GF(28) on these files. This objective can be achieved by implementing different proposed architectures, methods and algorithms both in block and stream cipher structure to increase the degree of complexity. For high utilization of resources and fast execution time of operations in cryptographic processes in the proposed cryptographic algorithms, the parallel computing was implemented by using OpenMP directives. The AES Rijndael algorithm has been considered as a base for the developed algorithms in the b