Bouhadeb CE, MENANI MR, Bouguerra H, Derdous O.
Assessing soil loss using GIS based RUSLE methodology. Case of the Bou Namoussa watershed–North-East of Algeria. Journal of Water and Land DevelopmentJournal of Water and Land Development. 2018.
Bouhadeb CE, MENANI MR, Bouguerra H, Derdous O.
Assessing soil loss using GIS based RUSLE methodology. Case of the Bou Namoussa watershed–North-East of Algeria. Journal of Water and Land DevelopmentJournal of Water and Land Development. 2018.
Belkhiri L, Tiri A, Mouni L.
Assessment of heavy metals contamination in groundwater: a case study of the south of Setif area, East Algeria. Achievements and Challenges of Integrated River Basin ManagementAchievements and Challenges of Integrated River Basin Management. 2018 :17-31.
Belkhiri L, Tiri A, Mouni L.
Assessment of heavy metals contamination in groundwater: a case study of the south of Setif area, East Algeria. Achievements and Challenges of Integrated River Basin ManagementAchievements and Challenges of Integrated River Basin Management. 2018 :17-31.
Belkhiri L, Tiri A, Mouni L.
Assessment of heavy metals contamination in groundwater: a case study of the south of Setif area, East Algeria. Achievements and Challenges of Integrated River Basin ManagementAchievements and Challenges of Integrated River Basin Management. 2018 :17-31.
Tafsast A, Ferroudji K, Hadjili ML, Bouakaz A, Benoudjit N.
Automatic microemboli characterization using convolutional neural networks and radio frequency signals. 2018 International Conference on Communications and Electrical Engineering (ICCEE). 2018 :1-4.
Tafsast A, Ferroudji K, Hadjili ML, Bouakaz A, Benoudjit N.
Automatic microemboli characterization using convolutional neural networks and radio frequency signals. 2018 International Conference on Communications and Electrical Engineering (ICCEE). 2018 :1-4.
Tafsast A, Ferroudji K, Hadjili ML, Bouakaz A, Benoudjit N.
Automatic microemboli characterization using convolutional neural networks and radio frequency signals. 2018 International Conference on Communications and Electrical Engineering (ICCEE). 2018 :1-4.
Tafsast A, Ferroudji K, Hadjili ML, Bouakaz A, Benoudjit N.
Automatic microemboli characterization using convolutional neural networks and radio frequency signals. 2018 International Conference on Communications and Electrical Engineering (ICCEE). 2018 :1-4.
Tafsast A, Ferroudji K, Hadjili ML, Bouakaz A, Benoudjit N.
Automatic microemboli characterization using convolutional neural networks and radio frequency signals. 2018 International Conference on Communications and Electrical Engineering (ICCEE). 2018 :1-4.
Tafsast A, Ferroudji K, Hadjijli ML, Bouakaz A, Benoudjit N.
Automatic microemboli classification using convolutional neural networks and RF signals. International Conference on Communications and Electrical Engineering (ICCEE) [Internet]. 2018 :1-4.
Publisher's VersionAbstract
Characterization of microembolic behavior, as solid or gaseous, guides to an efficient treatment protocol. In this study a new methodology to classify microembolic signals by Deep Convolutional Neural Networks (CNN) is implemented. The experimental system is made up of a flow phantom (ATSLaB) with a cylinder of 6 mm in width. Contrast agents composed of bubbles are employed in this investigational study to imitate the ultrasonic characteristics of gaseous emboli. A Doppler liquid which contains particles, have scatter proprieties analogous to red blood cells, is exploited to mimic the ultrasonic characteristics of the solid emboli. In order to optimize the CNN topology in the training phase, an adaptive learning Root Mean Square (RMSProp) algorithm is used. A classification rate of 99.9% is achieved in this experimental study. These results demonstrate that the CNN optimized model can be adequately exploited for microemboli classification using radio frequency (RF) signals compared to artificial neural networks (ANN) models.
Tafsast A, Ferroudji K, Hadjijli ML, Bouakaz A, Benoudjit N.
Automatic microemboli classification using convolutional neural networks and RF signals. International Conference on Communications and Electrical Engineering (ICCEE) [Internet]. 2018 :1-4.
Publisher's VersionAbstract
Characterization of microembolic behavior, as solid or gaseous, guides to an efficient treatment protocol. In this study a new methodology to classify microembolic signals by Deep Convolutional Neural Networks (CNN) is implemented. The experimental system is made up of a flow phantom (ATSLaB) with a cylinder of 6 mm in width. Contrast agents composed of bubbles are employed in this investigational study to imitate the ultrasonic characteristics of gaseous emboli. A Doppler liquid which contains particles, have scatter proprieties analogous to red blood cells, is exploited to mimic the ultrasonic characteristics of the solid emboli. In order to optimize the CNN topology in the training phase, an adaptive learning Root Mean Square (RMSProp) algorithm is used. A classification rate of 99.9% is achieved in this experimental study. These results demonstrate that the CNN optimized model can be adequately exploited for microemboli classification using radio frequency (RF) signals compared to artificial neural networks (ANN) models.
Tafsast A, Ferroudji K, Hadjijli ML, Bouakaz A, Benoudjit N.
Automatic microemboli classification using convolutional neural networks and RF signals. International Conference on Communications and Electrical Engineering (ICCEE) [Internet]. 2018 :1-4.
Publisher's VersionAbstract
Characterization of microembolic behavior, as solid or gaseous, guides to an efficient treatment protocol. In this study a new methodology to classify microembolic signals by Deep Convolutional Neural Networks (CNN) is implemented. The experimental system is made up of a flow phantom (ATSLaB) with a cylinder of 6 mm in width. Contrast agents composed of bubbles are employed in this investigational study to imitate the ultrasonic characteristics of gaseous emboli. A Doppler liquid which contains particles, have scatter proprieties analogous to red blood cells, is exploited to mimic the ultrasonic characteristics of the solid emboli. In order to optimize the CNN topology in the training phase, an adaptive learning Root Mean Square (RMSProp) algorithm is used. A classification rate of 99.9% is achieved in this experimental study. These results demonstrate that the CNN optimized model can be adequately exploited for microemboli classification using radio frequency (RF) signals compared to artificial neural networks (ANN) models.
Tafsast A, Ferroudji K, Hadjijli ML, Bouakaz A, Benoudjit N.
Automatic microemboli classification using convolutional neural networks and RF signals. International Conference on Communications and Electrical Engineering (ICCEE) [Internet]. 2018 :1-4.
Publisher's VersionAbstract
Characterization of microembolic behavior, as solid or gaseous, guides to an efficient treatment protocol. In this study a new methodology to classify microembolic signals by Deep Convolutional Neural Networks (CNN) is implemented. The experimental system is made up of a flow phantom (ATSLaB) with a cylinder of 6 mm in width. Contrast agents composed of bubbles are employed in this investigational study to imitate the ultrasonic characteristics of gaseous emboli. A Doppler liquid which contains particles, have scatter proprieties analogous to red blood cells, is exploited to mimic the ultrasonic characteristics of the solid emboli. In order to optimize the CNN topology in the training phase, an adaptive learning Root Mean Square (RMSProp) algorithm is used. A classification rate of 99.9% is achieved in this experimental study. These results demonstrate that the CNN optimized model can be adequately exploited for microemboli classification using radio frequency (RF) signals compared to artificial neural networks (ANN) models.
Tafsast A, Ferroudji K, Hadjijli ML, Bouakaz A, Benoudjit N.
Automatic microemboli classification using convolutional neural networks and RF signals. International Conference on Communications and Electrical Engineering (ICCEE) [Internet]. 2018 :1-4.
Publisher's VersionAbstract
Characterization of microembolic behavior, as solid or gaseous, guides to an efficient treatment protocol. In this study a new methodology to classify microembolic signals by Deep Convolutional Neural Networks (CNN) is implemented. The experimental system is made up of a flow phantom (ATSLaB) with a cylinder of 6 mm in width. Contrast agents composed of bubbles are employed in this investigational study to imitate the ultrasonic characteristics of gaseous emboli. A Doppler liquid which contains particles, have scatter proprieties analogous to red blood cells, is exploited to mimic the ultrasonic characteristics of the solid emboli. In order to optimize the CNN topology in the training phase, an adaptive learning Root Mean Square (RMSProp) algorithm is used. A classification rate of 99.9% is achieved in this experimental study. These results demonstrate that the CNN optimized model can be adequately exploited for microemboli classification using radio frequency (RF) signals compared to artificial neural networks (ANN) models.