In order to develop an urban GIS of Fez, we began with the scoping and feasibility study of the system. This strategic review has identified the characteristics of the urban areas, their problems, needs and existing potential, both in terms of data, equipment or personnel. This contribution focuses on a conceptual modeling of the geospatial database, the proposed template for the system and the quality assurance plan for the project. According to the feasibility study conducted during the first phase, the system must have an architecture respecting the intervention of actors in the city. Therefore, the implementation of the system requires an adequate hardware and software platform for optimum operation of the proposed urban GIS. After identifying technical choices, the financing plan of this scenario has been proposed. This arrangement is structured in three parts: Hardware, Software and Development/Training
Brain imaging techniques play an important role in determining the causes of brain cell injury. Therefore, earlier diagnosis of these diseases can be led to give rise to bring huge benefits in improving treatment possibilities and avoiding any potential complications that may occur to the patient. Recently, brain tumor segmentation has become a common task in medical image analysis due to its efficacy in diagnosing the type, size, and location of the tumor in automatic methods. Several researches have developed new methods in order to obtain the best results in brain tumor segmentation, including using deep learning techniques such as the convolutional neural network (CNN). The goal of this survey is to present a brief overview of MRI modalities and discuss common methods of brain tumor segmentation from MRI images, including brain tumor segmentation using deep learning techniques, as well as the most important contributions in this field, which have shown significant improvements in recent years. Finally, we focused in summary on the building blocks of the convolutional neural network (CNN) algorithms used for image segmentation. the entire survey methodology, it has been observed that hybrid techniques and CNN-based segmentation are more effective for brain tumor segmentation from MRI images.
Saïss plain contains an important aquifer. This complex groundwater system plays an important socio-economic role. Managing the groundwater resources requests a good understanding of resources availability and human needs satisfaction. These two components are estimated to get annual water balance of Saïss aquifer. Resources availability depends on meteorological parameters (rainfall, wind speed, temperature, etc.), soil types, topography, water table, and land use. Previous studies do not consider all of these parameters in assessing the water resources availability in this region. In this study, we have identified the relationship between all Saïss hydro-meteorological parameters and we have organized all data in Geographic Information System (GIS) to assess inputs of the water balance. Meteorological and hydrological data and landuse/landcover maps obtained from Landsat imageries classification were used in Wet-Spass (water and energy transfer between soil, plants, and atmosphere under quasi-steady state) model and Soil Conservation Service Curve Number (CN-SCS) method to assess annual runoff, potential evapotranspiration, interception, actual evapotranspiration, and natural recharge of shallow aquifer of Saïss plain. The results of the water balance calculation indicate significant fluctuations in the aquifer recharge: 280 Mm 3 (1987) and 418 Mm 3 (2018). The annual direct runoff increased from 344 Mm 3 (1987) to 638 Mm 3 (2018).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.