Analysis of image content is important in the classification of images, identification, retrieval, and recognition processes. The medical image datasets for content-based medical image retrieval (  are large datasets that are limited by high computational costs and poor performance. The aim of the proposed method is to enhance this image retrieval and classification by using a genetic algorithm (GA) to choose the reduced features and dimensionality. This process was created in three stages. In the first stage, two algorithms are applied to extract the important features; the first algorithm is the Contrast Enhancement method and the second is a Discrete Cosine Transform algorithm. In the next stage, we used datasets of the medi

A band rationing method is applied to calculate the salinity index (SI) and Normalized Multi-Band Drought Index (NMDI) as pre-processing to take Agriculture decision in these areas is presented. To separate the land from other features that exist in the scene, the classical classification method (Maximum likelihood classification) is used by classified the study area to multi classes (Healthy vegetation (HV), Grasslands (GL), Water (W), Urban (U), Bare Soil (BS)). A Landsat 8 satellite image of an area in the south of Iraq are used, where the land cover is classified according to indicator ranges for each (SI) and (NMDI).

Land Use / Land Cover (LULC) classification is considered one of the basic tasks that decision makers and map makers rely on to evaluate the infrastructure, using different types of satellite data, despite the large spectral difference or overlap in the spectra in the same land cover in addition to the problem of aberration and the degree of inclination of the images that may be negatively affect rating performance. The main objective of this study is to develop a working method for classifying the land cover using high-resolution satellite images using object based method. Maximum likelihood pixel based supervised as well as object approaches were examined on QuickBird satellite image in Karbala, Iraq. This study illustrated that

This hydrochemical study of the surface and groundwater in Khan AL-Baghdadi  area, western Iraq,  included the interpretation of physical, chemical, and biological properties. Water samples were collected from wells (14 samples) and surface water of Euphrates River (6 samples) for the dry and wet periods of October 2018 and April 2019, respectively. The stable isotopes analysis was performed for the dry period only. The surface water samples were characterized by slightly alkaline, fresh, excessively mineralized, Ca-chloride type, and hard to very hard water class. While the groundwater samples were characterized by slightly alkaline, brackish, excessively mineralized, Ca-chloride and Na-Chloride type, and hard to very hard wat

The groundwater represents the main source of water in the study area due to lack of surface water. The Dammam unconfined aquifer represents the main aquifer in the study area and Southern desert because of the regional extent, the quantity and quality of water. Many groundwater wells have been drilled in the study area to coverage the huge demand of water for agricultural purposes. The Geographic Information System (GIS) was used to estimate the volume of water which calculated (25.6964 × 109 m3) within the study area , automate calculation of the area of Al Salman basin using digital elevation models, derive the thickness maps of Al
Dammam unconfined aquifer from Key holes (KH) and Bore holes (

     The coronavirus is a family of viruses that cause different dangerous diseases that lead to death. Two types of this virus have been previously found: SARS-CoV, which causes a severe respiratory syndrome, and MERS-CoV, which causes a respiratory syndrome in the Middle East. The latest coronavirus, originated in the Chinese city of Wuhan, is known as the COVID-19 pandemic. It is a new kind of coronavirus that can harm people and was first discovered in Dec. 2019. According to the statistics of the World Health Organization (WHO), the number of people infected with this serious disease has reached more than seven million people from all over the world. In Iraq, the number of people infected has reached more than tw

In this paper two main stages for image classification has been presented. Training stage consists of collecting images of interest, and apply BOVW on these images (features extraction and description using SIFT, and vocabulary generation), while testing stage classifies a new unlabeled image using nearest neighbor classification method for features descriptor. Supervised bag of visual words gives good result that are present clearly in the experimental part where unlabeled images are classified although small number of images are used in the training process.