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

RA Ali, LK Abood, Int J Sci Res, 2017 - Cited by 2

Water pollution is an issue that can be exacerbated by drought as increased concentrations of unwanted substances are a consequence of lower water levels. Polluted water that flows into natural marshlands leads to the deposition of pollutants in the interior of the marsh. Here we present evidence that the interior of the Central Marsh (CM) in southern Iraq suffers from higher levels of pollution than areas closer to the source of water entering the marsh (the Euphrates River). A 1.7m embankment that halts the flow of the Euphrates is only infrequently breached and so the CM is effectively the terminal destination of the waters (and their associated pollutants and agricultural waste) flowing from the West of Iraq.

A range of water

Al-Chibayish Marsh (CM)  is considered as the major part of Central Marshes area of this marsh is 1050 Km². The water quality of these marshes is suffering from salt accumulation due to intensive dam construction, limited supply of water from sources,  climate change impacts, and the absence of outlet flow from these marshes, specifically at low flow periods. So, the current research aims to assess and improve these marshes' hydraulic behavior and water quality and define the best location for outlet drains.  Field measurements and laboratory tests were conducted for two periods (November 2020 and February 2021) to define the (TDS) concentrations at nine different locations. Samples were also examined for water's phy

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

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

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