Water level and distribution is very essential in almost all life aspects. Natural and artificial lakes represent a large percentage of these water bodies in Iraq. In this research the changes in water levels are observed by calculating the areas of five different lakes in five different regions and two different marshes in two different regions of the country, in a period of 12 years (2001 - 2012), archived remotely sensed images were used to determine surface areas around lakes and marshes in Iraq for the chosen years . Level of the lakes corresponding to satellite determined surface areas were retrieved from remotely sensed data .These data were collected to give explanations on lake level and surface area fluctuations. It is important to determine these areas at different water levels to know areas which are being flooded in addition to the total area inundated .The behavior of hydrological regime of these lakes during the period was assessed using an integration of remote sensing and GIS techniques which found that the total surface area of the lakes had diminished and their water volumes reduced. The study further revealed that the levels of the lakes surfaces had lowered through these years.
Many isolated rural communities are located in regions where there is an abundant and reliable supply of solar energy, but where the distance to the nearest power station is many tens or even hundreds of kilometre. It is therefore mainly in these areas that rural electrification is now being provided by PV generators. since Stand-Alone PV generator can offer the most cost-effective and reliable option for providing power needed in remote places. Accordingly these isolated rural canters are fitted with PV for lighting, a refrigerator, a television and socket to supply kitchen appliances
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
... Show MoreA total of 335 suspected fecal sample were collected from calf of cattle and buffalo with age in between (3 days to 4 months) from middle area of Iraq between November 2016 to May 2017.
RA Ali, LK Abood, Int J Sci Res, 2017 - Cited by 2
In a world of fierce competition companies of different activities strive to strengthen their competitiveness in order to be able to deliver greater value to their customers and gain a distinct sites in competition with other companies in the market at the local and international levels. Every company seeks to focus on one or more of the competitive capabilities in order to turn it into an obvious advantage or a number of competitive advantages to contribute in improving the performance and superiority over its competitors. Therefore, the management of companies no longer need only useful information for the internal aspects of the environment, but also need to include the external environment that includes various and constantly changin
... Show Moreهدفت الدراسة إلى التعرف على مستوى تقييم الإعلاميين العراقيين المقيمين في الأردن لتغطية الإصلاحات السياسية و الاقتصادية في العراق من قبل الفضائيات العراقية. و هدفت كذلك إلى التعرف على الف
The last few years witnessed great and increasing use in the field of medical image analysis. These tools helped the Radiologists and Doctors to consult while making a particular diagnosis. In this study, we used the relationship between statistical measurements, computer vision, and medical images, along with a logistic regression model to extract breast cancer imaging features. These features were used to tell the difference between the shape of a mass (Fibroid vs. Fatty) by looking at the regions of interest (ROI) of the mass. The final fit of the logistic regression model showed that the most important variables that clearly affect breast cancer shape images are Skewness, Kurtosis, Center of mass, and Angle, with an AUCROC of
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