The use of intelligent machines to work and react like humans is vital in emerging smart cities.Computer-aided analysis of complex and huge MRI(Mag-netic Resonance Imaging)scans is very important in healthcare applica...The use of intelligent machines to work and react like humans is vital in emerging smart cities.Computer-aided analysis of complex and huge MRI(Mag-netic Resonance Imaging)scans is very important in healthcare applications.Among AI(Artificial Intelligence)driven healthcare applications,tumor detection is one of the contemporary researchfields that have become attractive to research-ers.There are several modalities of imaging performed on the brain for the pur-pose of tumor detection.This paper offers a deep learning approach for detecting brain tumors from MR(Magnetic Resonance)images based on changes in the division of the training and testing data and the structure of the CNN(Convolu-tional Neural Network)layers.The proposed approach is carried out on a brain tumor dataset from the National Centre of Image-Guided Therapy,including about 4700 MRI images of ten brain tumor cases with both normal and abnormal states.The dataset is divided into test,and train subsets with a ratio of the training set to the validation set of 70:30.The main contribution of this paper is introdu-cing an optimum deep learning structure of CNN layers.The simulation results are obtained for 50 epochs in the training phase.The simulation results reveal that the optimum CNN architecture consists of four layers.展开更多
This study focuses on the deformation characteristics of Kadui-2 Landslide by the influence of reservoir filling-drawdown and precipitation.A three-year monitoring project was implemented in order to observe the short...This study focuses on the deformation characteristics of Kadui-2 Landslide by the influence of reservoir filling-drawdown and precipitation.A three-year monitoring project was implemented in order to observe the short/long-term deformation.The slide mass experienced consistent deformation with a maximum cumulative displacement of 331.34 cm.Based on the recorded data of reservoir water level and precipitation during this period,a two-dimensional(2-D)finite element model using Geostudio software was set up for deformation simulation under different conditions to understand the real influence of these triggering factors on landslide.The numerical simulation results are in consistent with monitoring field data.Both numerical simulation and field monitoring results exhibit that the maximum deformation occurred at the foreside of slumping mass.The slip surface shows significant creep characteristics decreasing as long-term shear strength reducing gradually.Reservoir water level fluctuation is the primary triggering factor to reactivate the landslide mass and has a negative correlation with deformation rate.Displacement rate increases with the reservoir drawdown and decreases with impoundment rise.Compared with reservoir filling-drawdown operation,rainfall has no significant effect on the slide motion of landslide due to limited penetration from the ground surface.展开更多
基金funded and supported by the Taif University Researchers,Taif University,Taif,Saudi Arabia,under Project TURSP-2020/147.
文摘The use of intelligent machines to work and react like humans is vital in emerging smart cities.Computer-aided analysis of complex and huge MRI(Mag-netic Resonance Imaging)scans is very important in healthcare applications.Among AI(Artificial Intelligence)driven healthcare applications,tumor detection is one of the contemporary researchfields that have become attractive to research-ers.There are several modalities of imaging performed on the brain for the pur-pose of tumor detection.This paper offers a deep learning approach for detecting brain tumors from MR(Magnetic Resonance)images based on changes in the division of the training and testing data and the structure of the CNN(Convolu-tional Neural Network)layers.The proposed approach is carried out on a brain tumor dataset from the National Centre of Image-Guided Therapy,including about 4700 MRI images of ten brain tumor cases with both normal and abnormal states.The dataset is divided into test,and train subsets with a ratio of the training set to the validation set of 70:30.The main contribution of this paper is introdu-cing an optimum deep learning structure of CNN layers.The simulation results are obtained for 50 epochs in the training phase.The simulation results reveal that the optimum CNN architecture consists of four layers.
基金supported by to the National Key Research and Development Program of China(No.2018YFC1505401)。
文摘This study focuses on the deformation characteristics of Kadui-2 Landslide by the influence of reservoir filling-drawdown and precipitation.A three-year monitoring project was implemented in order to observe the short/long-term deformation.The slide mass experienced consistent deformation with a maximum cumulative displacement of 331.34 cm.Based on the recorded data of reservoir water level and precipitation during this period,a two-dimensional(2-D)finite element model using Geostudio software was set up for deformation simulation under different conditions to understand the real influence of these triggering factors on landslide.The numerical simulation results are in consistent with monitoring field data.Both numerical simulation and field monitoring results exhibit that the maximum deformation occurred at the foreside of slumping mass.The slip surface shows significant creep characteristics decreasing as long-term shear strength reducing gradually.Reservoir water level fluctuation is the primary triggering factor to reactivate the landslide mass and has a negative correlation with deformation rate.Displacement rate increases with the reservoir drawdown and decreases with impoundment rise.Compared with reservoir filling-drawdown operation,rainfall has no significant effect on the slide motion of landslide due to limited penetration from the ground surface.
