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

Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD

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

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

Background. Diabetes mellitus (DM) is a prevalent disease that, if not appropriately managed, can lead to a variety of problems, including diabetic foot. Glycated hemoglobin A1c (HbA1c), FBS, amylase, and lipase are important diabetic management indicators now employed as diagnostic tests. Objective. This study aimed to evaluate the value of amylase and lipase as predictive markers in patients with diabetic foot. Patients and methods. This study included 50 patients who reported to Baghdad Hospital with diabetic feet between November 2023 and February 2025. All patients had their HbA1c, amylase, lipase, and FBS levels tested. Means, independent t-tests, and the F-test were used in the statistical analysis. Results. The study evaluat

Hepatitis B virus (HBV) infection is a serious disease of the liver and signifies a major worldwide health concern. HBV Genotyping is vital for further epidemiological study, predicting the disease outcome and response to treatment. The current study aimed to determine hepatitis B virus genotypes in patients with chronic hepatitis B, and to validate possible associations with the baseline characteristics of the disease. A total of 90 patients with chronic hepatitis B infection were enrolled in this study. Liver function tests, hepatitis B virus markers and DNA viral load were done using routine standardized procedures. HBV genotyping was performed using real time PCR. Genotype D was the most predominant in 64 (71.1%) of samples, while

Elevated C-Reactive Protein (CRP) level in serum is a risk factor for type 2 diabetes ,this relationship is likely to be the cause it means elevated CRP leads to T2D in future . Our objective was to examine CRP in male Type 2 Diabetes(T2D) patients in different age ,we studied 120 male subjects divided to two groups according to their age. First group A age (31 - 40) year old ,60 person )30 control & 30 T2D patients(,3 person for each same age: second group B age (41 – 50) years old ,60 person )30 control & 30 T2D patients(,3 person for each same age. We examined blood sugar ,cholesterol and CRP in each group. and we toke the mean of samples in the same age in each data in all the 4 groups. Our data shows that CRP