In this article, a Website for medical healthcare system was designing and developing. It consists of two major sides: client- server side (front end side and back end side). The client-side is everything involved with what the user sees, it has been designed a web using HTML, CSS and JavaScript languages. The server-side is mainly how the site doing modifications and updates which indicates to the entire user can't see everything in the browser such as servers and databases. The web has been implemented and developed using ASP MVC5 and C# programming language. SQL Server languages used for the database part and it make simple ease of use for patients to their health registrations. Consequently, it has simple and straight accessibility through a group of physicians for patient records. The article interested with auspices to the patient appointments combination, billing, timetable, physical, date, and information of medication in single overall system. The results of website designed provide accessibility with easy manner of pertinent information to the management organizations for instance the Medicaid and Medicare. Furthermore, the website reduces the mistake in healthcare, and reduce the cost of delivery of healthcare. Consequently, the website prepared for utilize by nurses, physician, pharmacists and another healthcare professionals, and by patients and monitor patients using medical devices.
Remote sensing techniques used in many studies for classfying and measuring of wildfires. Satellite Landsat8(OLI) imagery is used in the presented work. The satellite is considered as a near-polar orbit, with a high multispectral resolution for covering Wollemi National Park in Australia. The work aims to study and measure wildfire natural resources prior to and throughout fire breakout which occurred in Wollemi National Park in Australia for a year (October, 2019), as well as analyzing the harm resulting from such wildfires and their effects on earth and environment through recognizing satellite images for studied region prior to and throughout wildfires. A discussion of methods for computing the affecred area is covered regarding each one of the classes and lessening or limiting the quickly-spreading wildfires damage. This paper propose a 2-phases techniques: training and classifying. In the training phase, the number of clustering is computed by using C# Programming Language and feature extracted and clustered as a group and stored in the dataset. The classification used the moments with (K-Means) classification approach in RS (Remote Sensing) for classified image. The results of classification showed 5 distinctive classes (trees, rivers, bare earth, buildings with no trees, and buildings with trees) in which it might be indicates that the region is secured via each one of the classes prior to and throughout wildfires as well as the changed pixels with regard to all the classes. Also, the classification experimental methods results indicate an excellent performance recision with a good classifying and result analysis about the harms caused by fires in the study area.
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