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

Welcome to International Journal of Research in Social Sciences & Humanities (IJRSSH). It is an international refereed journal of Social Sciences, Humanities & Linguistics in English published quarterly, both print and online.

research objectives to:
1. identify the social, economic and cultural factors affecting consumption.
2. detect the consumption culture among the population in the city of Erbil.
3. Identify the GATT consumer protection and rights.
The most important results:
1. that there is variation in the answers of respondents about keep up with modernity in the basic consumption (necessary), it swallowed the proportion of yes answers about keep up with modernity in food consumption (72%), and is an indication of growing consumer awareness of the individual in the side of nutrition. The clothing on the side of the proportion of yes answers amounted to (85%), in the health field note that the percentage of yes answers (83%), who are abr