Early detection of eye diseases can forestall visual deficiency and vision loss. There are several types of human eye diseases, for example, diabetic retinopathy, glaucoma, arteriosclerosis, and hypertension. Diabetic retinopathy (DR) which is brought about by diabetes causes the retinal vessels harmed and blood leakage in the retina. Retinal blood vessels have a huge job in the detection and treatment of different retinal diseases. Thus, retinal vasculature extraction is significant to help experts for the finding and treatment of systematic diseases. Accordingly, early detection and consequent treatment are fundamental for influenced patients to protect their vision. The aim of this paper is to detect blood vessels from

In this work laser detection and tracking system (LDTS) is designed and implemented using a fuzzy logic controller (FLC). A 5 mW He-Ne laser system and an array of nine PN photodiodes are used in the detection system. The FLC is simulated using MATLAB package and the result is stored in a lock up table to use it in the real time operation of the system. The results give a good system response in the target detection and tracking in the real time operation.

The problem of research is to identify the uses of Iraqi women for Facebook and the motives of these uses and the innovations that have been achieved from them, in view of the increasing role-played by social networking sites in the lives of individuals in general and women in particular.The research seeks to identify the following: Identify the extent to which Iraqi women use Facebook and the usage motives and innovations achieved by the interviewees When they using Facebook. This study belongs to the descriptive studies that are interested in monitoring the characteristics of a particular phenomenon to identify its characteristics and characteristics.This study adopted the survey methodology to test the hypotheses of the study and how

Background. Najaf governorate was recorded as one of the most polluted Iraqi governorates with increased cancer, autoimmune, and abortion cases. Study Groups. A total of 88 adult volunteers from three test groups were divided based on their inhabitance in different geographical regions in Najaf governorate. Group 1 (G1; n, 29) inhabitants of Al‐Ansar, Al‐Abbaseyeh, and Al‐Manathera districts, Group 2 (G2; n, 27) inhabitants of 22 different scattered districts of the governorate, Group 3 (G3; n, 32) inhabitants of Kufa city and center districts in the old Najaf city. According to previous author

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