Enhancing quality image fusion was proposed using new algorithms in auto-focus image fusion. The first algorithm is based on determining the standard deviation to combine two images. The second algorithm concentrates on the contrast at edge points and correlation method as the criteria parameter for the resulted image quality. This algorithm considers three blocks with different sizes at the homogenous region and moves it 10 pixels within the same homogenous region. These blocks examine the statistical properties of the block and decide automatically the next step. The resulted combined image is better in the contras

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

Forest cover in Mosul Province experienced significant changes following the 2014 occupation. These changes can be effectively analyzed using multitemporal remote sensing imagery. This study aims to evaluate the ability of multi-temporal Landsat 8 images and the Forest Canopy Density (FCD) model to detect changes in forest canopy density in a protected forest in Mosul Governorate during the period from 2014 to 2025. The remote sensing data used in this research are Landsat 8 images captured on March 21, 2014, and April 4, 2025. The method employed is FCD modeling, which produces pixel-level canopy density estimates. The results of the FCD model are then used to analyze changes in canopy density following the occupation. The findings of this

Segmentation is the process of partition digital images into different parts depending on texture, color, or intensity, and can be used in different fields in order to segment and isolate the area to be partitioned. In this work images of the Moon obtained through observations in Astronomy and space dep. College of science university of Baghdad by ( Toward space telescopes and widespread used of a CCD camera) . Different segmentation methods were used to segment lunar craters. Different celestial objects cause craters when they crash into the surface of the Moon like asteroids and meteorites. Thousands of craters appears on the Moon's surface with ranges in size from meter to many kilometers, it provide insights into the age and ge

Segmentation is the process of partition digital images into different parts depending on texture, color, or intensity, and can be used in different fields in order to segment and isolate the area to be partitioned. In this work images of the Moon obtained through observations in Astronomy and space dep. College of science university of Baghdad by ( Toward space telescopes and widespread used of a CCD camera) . Different segmentation methods were used to segment lunar craters. Different celestial objects cause craters when they crash into the surface of the Moon like asteroids and meteorites. Thousands of craters appears on the Moon's surface with ranges in size from meter to many kilometers, it provide insights into the age and geology

Internal control system is a safety valve that preserves economic units assets and ensure the accuracy of financial data, as well as to obligation in the laws, regulations, administrative policies ,and improve the efficiency, effectiveness and economic of operation, so it has become imperative for these units attention to internal and developed control system The research problem in exposure the economic units when the exercise of their business to many of the risks to growth or hinder the achievement of its objectives and the risks (financial, operational, strategy, risk) and not it rely on risk Assessment according to modern scientific methods, as in Brown's risk Classification, Which led to the weakness of the internal control identif

This study applies a discourse analysis framework to explore the portrayal of women in Maysloon Hadi’s novel (The Black Eyes) (2011), using Critical Discourse Analysis (CDA) and Norman Fairclough’s tri-dimensional model (1989) as the analytical foundation. It investigates the roles and challenges women face in the novel. While there is growing interest in the portrayal of women in literature, Iraqi literature—especially from the perspective of Iraqi women writers remains underexplored. Hadi’s *The Black Eyes* provides a unique case to examine this intersection. Despite the novel’s rich narrative, which offers insight into Iraqi women’s lives, there is a lack of comprehensive CDA to understand how its language constructs

فقدان الزوج يعد حدثًا يغير حياة النساء، مما يضطرهن إلى السباحة في عالم جديد مليء بالحزن والوحدة والشكوك. مع الوقت، تطورت طريقة تصوير الأرامل بشكل كبير تعكس التغييرات في الآراء والقيم الثقافية. تُمثل الأرامل تقليديًا بأنهن ضعيفات ومعتمدات في الأدب، مستندة إلى افتراض أنهن يفتقدن الدعم المالي بعد وفاة شركائهن. ومع ذلك، فإن هذا التصوير لا يعترف بتأثير الأرملة على الرفاهية والهوية الشخصية. يسعى هذا النص إلى

Bacterial contamination of AL-Habania reservoir was studied during the period from February 2005 to January 2006; samples were collected from four stations (AL-Warrar, AL-Theban regulator, middle of the reservoir and the fourth was towards AL-Razzaza reservoir). Coliform bacteria, faecal Coliforms, Streptococci, and faecal Streptococci were used as parameters of bacterial contamination in waters through calculating the most probable number. Highest count of Coliform bacteria (1500 cell/100ml) was recorded at AL-Razaza during August, and the lowest count was less than (300 cell/100ml) in the rest of the collection stations for all months. Fecal Coliform bacteria ranged between less than 300 cells/100ml in all stations for all months to 700 c

This paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance