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

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

this research seeks to shed light on the study of the impact of Putinism on the Russian-Ukrainian war that broke out in early 2022, by studying the contents of the speeches of Russian President Vladimir Putin before the start of the war. This research uses the structural approach in analyzing the components of the Russian political regime. The research tools used in this research include case studies, content analysis of Russian speeches, and personal meetings of researchers and specialists in Russian affairs. It sums up a number of important conclusions, most notably are : 1) Putinism is a totalitarian regime that includes the political doctrine during the era of Russian President Vladimir Putin. 2) Duginism represents the totality of t

This research aims to demonstrate the nature and concept of the Corona pandemic, its implications for the global economy, and the management and performance of companies in particular. Additionally, the research intends to measure the impact of the Corona pandemic on companies' financial performance. Listed on the Iraqi Stock Exchange, which has finished compiling its year-end financial statements for 2019-2020. The investigation arrived at several findings, the most significant of which was that most businesses were not prepared for such a crisis technologically or to develop human resources to deal with this pandemic. In addition, most companies experienced a decrease in their financial performance as a direct result of the Corona pandemi