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

Digital image manipulation has become increasingly prevalent due to the widespread availability of sophisticated image editing tools. In copy-move forgery, a portion of an image is copied and pasted into another area within the same image. The proposed methodology begins with extracting the image's Local Binary Pattern (LBP) algorithm features. Two main statistical functions, Stander Deviation (STD) and Angler Second Moment (ASM), are computed for each LBP feature, capturing additional statistical information about the local textures. Next, a multi-level LBP feature selection is applied to select the most relevant features. This process involves performing LBP computation at multiple scales or levels, capturing textures at different

One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services through our ca

Intrusion Detection Systems (IDS) is the main defense mechanism deployed by the current networks to prevent cyber threats. Recurrent Neural Network (RNN) are also a novel IDS structure that replaces the conventional training and testing mechanism. The strategy encodes network traffic data as biological sequences using amino acid codons in such a fashion that the RNN is capable of effectively analyzing temporal and sequence data patterns. RNN architecture design adopts embedding layers to handle codon representations and Long Short-Term Memory (LSTM) layers to perform sequential data learning, which is followed by a fully connected network to perform classification functions, which preserve high feature extraction and classification

One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services th

The subject of children's rights in the modern era of the priorities of educators , because the child in the community is the future , which is held by the hope which is the basis for society in the renaissance and progress so quickly communities to prove these rights through its various institutions and issued legislation and held numerous conferences and taking specialists the field of childhood Aatron aspects of this rights and Aasalunha including a benefit for children until tomorrow for the rights of children , psychological and social health and educational . Therefore, the current study focused on the awareness of parents and educators of children's rights as individuals most closely satisfying their needs and rights. The

Aim of the present study is Identification of specific gene for GPCR using specific primers .and identification of difference in PCR analysis in patients with heart thrombosis and compared with healthy, Sequencing of PCR product regarding GPCR compared for all three subject, Identification the similarity of human GPCR with local strain of yeast fifty healthy control and fifty patients with thrombosis which diagnosed medically with cardiac specific troponin t, troponin 1 levels and electro myocardiogram ECG. The aged for all subjects ranged (39-75) years patients were lying in cardiac care unit at Ibn- al- Nafees teaching hospital and Sheikh Zayed teaching hospital. Genomic DNA of whole blood was extracted from buffy coat and cell cu