In this paper,an advanced and optimized Light Gradient Boosting Machine(LGBM)technique is proposed to identify the intrusive activities in the Internet of Things(IoT)network.The followings are the major contributions:...In this paper,an advanced and optimized Light Gradient Boosting Machine(LGBM)technique is proposed to identify the intrusive activities in the Internet of Things(IoT)network.The followings are the major contributions:i)An optimized LGBM model has been developed for the identification of malicious IoT activities in the IoT network;ii)An efficient evolutionary optimization approach has been adopted for finding the optimal set of hyper-parameters of LGBM for the projected problem.Here,a Genetic Algorithm(GA)with k-way tournament selection and uniform crossover operation is used for efficient exploration of hyper-parameter search space;iii)Finally,the performance of the proposed model is evaluated using state-of-the-art ensemble learning and machine learning-based model to achieve overall generalized performance and efficiency.Simulation outcomes reveal that the proposed approach is superior to other considered methods and proves to be a robust approach to intrusion detection in an IoT environment.展开更多
The accuracy of Digital Surface Models(DSMs)generated using stereo matching methods varies due to the varying acquisition conditions and configuration parameters of stereo images.It has been a good practice to fuse th...The accuracy of Digital Surface Models(DSMs)generated using stereo matching methods varies due to the varying acquisition conditions and configuration parameters of stereo images.It has been a good practice to fuse these DSMs generated from various stereo pairs to achieve enhanced,in which multiple DSMs are combined through computational approaches into a single,more accurate,and complete DSM.However,accurately characterizing detailed objects and their boundaries still present a challenge since most boundary-ware fusion methods still struggle to achieve sharpened depth discontinuities due to the averaging effects of different DSMs.Therefore,we propose a simple and efficient adaptive image-guided DSM fusion method that applies k-means clustering on small patches of the orthophoto to guide the pixel-level fusion adapted to the most consistent and relevant elevation points.The experiment results show that our proposed method has outperformed comparing methods in accuracy and the ability to preserve sharpened depth edges.展开更多
The National Health Interview Survey(NHIS)shows that there are 13.2%of children at the age of 11 to 17 who are suffering from Attention Deficit Hyperactivity Disorder(ADHD),globally.The treatment methods for ADHD are ...The National Health Interview Survey(NHIS)shows that there are 13.2%of children at the age of 11 to 17 who are suffering from Attention Deficit Hyperactivity Disorder(ADHD),globally.The treatment methods for ADHD are either psycho-stimulant medications or cognitive therapy.These traditional methods,namely therapy,need a large number of visits to hospitals and include medication.Neurogames could be used for the effective treatment of ADHD.It could be a helpful tool in improving children and ADHD patients’cognitive skills by using Brain–Computer Interfaces(BCI).BCI enables the user to interact with the computer through brain activity using Electroencephalography(EEG),which can be used to control different computer applications by processing acquired brain signals.This paper proposes a system based on neurofeedback that can improve cognitive skills such as attention level,mediation level,and spatial memory.The proposed system consists of a puzzle game where its complexity increases with each level.EEG signals were acquired using the Neurosky headset;then sent the signals to the designed gaming environment.This neurofeedback system was tested on 10 different subjects,and their performance was calculated using different evaluation measures.The results show that this game improves player overall performance from 74%to 98%by playing each game level.展开更多
Relativistic electron injections are one of the mechanisms of relativistic(≥0.5 MeV) electron enhancements in the Earth’s outer radiation belt. In this study, we present a statistical observation of 600 keV electron...Relativistic electron injections are one of the mechanisms of relativistic(≥0.5 MeV) electron enhancements in the Earth’s outer radiation belt. In this study, we present a statistical observation of 600 keV electron injections in the outer radiation belt by using data from the Van Allen Probes. On the basis of the characteristics of different injections, 600 keV electron injections in the outer radiation belt were divided into pulsed electron injections and nonpulsed electron injections. The 600 keV electron injections were observed at 4.5 < L <6.4 under the geomagnetic conditions of 450 nT < AE < 1,450 nT. An L of ~4.5 is an inward limit for 600 keV electron injections. Before the electron injections, a flux negative L shell gradient for ≤0.6 MeV electrons or low electron fluxes in the injected region were observed. For600 keV electron injections at different L shells, the source populations from the Earth’s plasma sheet were different. For 600 keV electron injections at higher L shells, the source populations were higher energy electrons(~200 keV at X ~–9 R_(E)), whereas the source populations for 600 keV electron injections at lower L shells were lower energy electrons(~80 keV at X ~–9 R_(E)). These results are important to further our understanding of electron injections and rapid enhancements of 600 keV electrons in the Earth’s outer radiation belt.展开更多
The agriculture sector has an immense potential to improve the requirement of food and supplies healthy and nutritious food.Crop insect detection is a challenging task for farmers as a significant portion of the crops...The agriculture sector has an immense potential to improve the requirement of food and supplies healthy and nutritious food.Crop insect detection is a challenging task for farmers as a significant portion of the crops are damaged,and the quality is degraded due to the pest attack.Traditional insect identification has the drawback of requiring well-trained tax-onomists to identify insects based on morphological features accurately.Experiments were conducted for classification on nine and 24 insect classes of Wang and Xie dataset using the shape features and applying machine learning techniques such as artificial neural net-works(ANN),support vector machine(SVM),k-nearest neighbors(KNN),naive bayes(NB)and convolutional neural network(CNN)model.This paper presents the insect pest detec-tion algorithm that consists of foreground extraction and contour identification to detect the insects for Wang,Xie,Deng,and IP102 datasets in a highly complex background.The 9-fold cross-validation was applied to improve the performance of the classification mod-els.The highest classification rate of 91.5%and 90%was achieved for nine and 24 class insects using the CNN model.The detection performance was accomplished with less com-putation time for Wang,Xie,Deng,and IP102 datasets using insect pest detection algo-rithm.The comparison results with the state-of-the-art classification algorithms exhibited considerable improvement in classification accuracy,computation time perfor-mance while apply more efficiently in field crops to recognize the insects.The results of classification accuracy are used to recognize the crop insects in the early stages and reduce the time to enhance the crop yield and crop quality in agriculture.展开更多
In this paper,we present a case study that performs an unmanned aerial vehicle(UAV)based fine-scale 3D change detection and monitoring of progressive collapse performance of a building during a demolition event.Multi-...In this paper,we present a case study that performs an unmanned aerial vehicle(UAV)based fine-scale 3D change detection and monitoring of progressive collapse performance of a building during a demolition event.Multi-temporal oblique photogrammetry images are collected with 3D point clouds generated at different stages of the demolition.The geometric accuracy of the generated point clouds has been evaluated against both airborne and terrestrial LiDAR point clouds,achieving an average distance of 12 cm and 16 cm for roof and façade respectively.We propose a hierarchical volumetric change detection framework that unifies multi-temporal UAV images for pose estimation(free of ground control points),reconstruction,and a coarse-to-fine 3D density change analysis.This work has provided a solution capable of addressing change detection on full 3D time-series datasets where dramatic scene content changes are presented progressively.Our change detection results on the building demolition event have been evaluated against the manually marked ground-truth changes and have achieved an F-1 score varying from 0.78 to 0.92,with consistently high precision(0.92–0.99).Volumetric changes through the demolition progress are derived from change detection and have been shown to favorably reflect the qualitative and quantitative building demolition progression.展开更多
文摘In this paper,an advanced and optimized Light Gradient Boosting Machine(LGBM)technique is proposed to identify the intrusive activities in the Internet of Things(IoT)network.The followings are the major contributions:i)An optimized LGBM model has been developed for the identification of malicious IoT activities in the IoT network;ii)An efficient evolutionary optimization approach has been adopted for finding the optimal set of hyper-parameters of LGBM for the projected problem.Here,a Genetic Algorithm(GA)with k-way tournament selection and uniform crossover operation is used for efficient exploration of hyper-parameter search space;iii)Finally,the performance of the proposed model is evaluated using state-of-the-art ensemble learning and machine learning-based model to achieve overall generalized performance and efficiency.Simulation outcomes reveal that the proposed approach is superior to other considered methods and proves to be a robust approach to intrusion detection in an IoT environment.
基金John Hopkins University Applied Physics Lab to support the Imagery of the 2019 DFC datasets
文摘The accuracy of Digital Surface Models(DSMs)generated using stereo matching methods varies due to the varying acquisition conditions and configuration parameters of stereo images.It has been a good practice to fuse these DSMs generated from various stereo pairs to achieve enhanced,in which multiple DSMs are combined through computational approaches into a single,more accurate,and complete DSM.However,accurately characterizing detailed objects and their boundaries still present a challenge since most boundary-ware fusion methods still struggle to achieve sharpened depth discontinuities due to the averaging effects of different DSMs.Therefore,we propose a simple and efficient adaptive image-guided DSM fusion method that applies k-means clustering on small patches of the orthophoto to guide the pixel-level fusion adapted to the most consistent and relevant elevation points.The experiment results show that our proposed method has outperformed comparing methods in accuracy and the ability to preserve sharpened depth edges.
基金funding for this study under Technology Development Fund(TDF-02-228).supported by AIDA Lab CCIS Prince Sultan University Riyadh Saudi Arabia and authors would also like to acknowledge the support of Prince Sultan University for paying the Article Processing Charges(APC)of this publication.
文摘The National Health Interview Survey(NHIS)shows that there are 13.2%of children at the age of 11 to 17 who are suffering from Attention Deficit Hyperactivity Disorder(ADHD),globally.The treatment methods for ADHD are either psycho-stimulant medications or cognitive therapy.These traditional methods,namely therapy,need a large number of visits to hospitals and include medication.Neurogames could be used for the effective treatment of ADHD.It could be a helpful tool in improving children and ADHD patients’cognitive skills by using Brain–Computer Interfaces(BCI).BCI enables the user to interact with the computer through brain activity using Electroencephalography(EEG),which can be used to control different computer applications by processing acquired brain signals.This paper proposes a system based on neurofeedback that can improve cognitive skills such as attention level,mediation level,and spatial memory.The proposed system consists of a puzzle game where its complexity increases with each level.EEG signals were acquired using the Neurosky headset;then sent the signals to the designed gaming environment.This neurofeedback system was tested on 10 different subjects,and their performance was calculated using different evaluation measures.The results show that this game improves player overall performance from 74%to 98%by playing each game level.
基金supported by the National Natural Science Foundation of China under grant 41974188。
文摘Relativistic electron injections are one of the mechanisms of relativistic(≥0.5 MeV) electron enhancements in the Earth’s outer radiation belt. In this study, we present a statistical observation of 600 keV electron injections in the outer radiation belt by using data from the Van Allen Probes. On the basis of the characteristics of different injections, 600 keV electron injections in the outer radiation belt were divided into pulsed electron injections and nonpulsed electron injections. The 600 keV electron injections were observed at 4.5 < L <6.4 under the geomagnetic conditions of 450 nT < AE < 1,450 nT. An L of ~4.5 is an inward limit for 600 keV electron injections. Before the electron injections, a flux negative L shell gradient for ≤0.6 MeV electrons or low electron fluxes in the injected region were observed. For600 keV electron injections at different L shells, the source populations from the Earth’s plasma sheet were different. For 600 keV electron injections at higher L shells, the source populations were higher energy electrons(~200 keV at X ~–9 R_(E)), whereas the source populations for 600 keV electron injections at lower L shells were lower energy electrons(~80 keV at X ~–9 R_(E)). These results are important to further our understanding of electron injections and rapid enhancements of 600 keV electrons in the Earth’s outer radiation belt.
文摘The agriculture sector has an immense potential to improve the requirement of food and supplies healthy and nutritious food.Crop insect detection is a challenging task for farmers as a significant portion of the crops are damaged,and the quality is degraded due to the pest attack.Traditional insect identification has the drawback of requiring well-trained tax-onomists to identify insects based on morphological features accurately.Experiments were conducted for classification on nine and 24 insect classes of Wang and Xie dataset using the shape features and applying machine learning techniques such as artificial neural net-works(ANN),support vector machine(SVM),k-nearest neighbors(KNN),naive bayes(NB)and convolutional neural network(CNN)model.This paper presents the insect pest detec-tion algorithm that consists of foreground extraction and contour identification to detect the insects for Wang,Xie,Deng,and IP102 datasets in a highly complex background.The 9-fold cross-validation was applied to improve the performance of the classification mod-els.The highest classification rate of 91.5%and 90%was achieved for nine and 24 class insects using the CNN model.The detection performance was accomplished with less com-putation time for Wang,Xie,Deng,and IP102 datasets using insect pest detection algo-rithm.The comparison results with the state-of-the-art classification algorithms exhibited considerable improvement in classification accuracy,computation time perfor-mance while apply more efficiently in field crops to recognize the insects.The results of classification accuracy are used to recognize the crop insects in the early stages and reduce the time to enhance the crop yield and crop quality in agriculture.
基金supported by the National Science Foundation[grant number 2036193]supported in part by Office of Naval Research[grant numbers N00014-17-l-2928,N00014-20-1-2141].
文摘In this paper,we present a case study that performs an unmanned aerial vehicle(UAV)based fine-scale 3D change detection and monitoring of progressive collapse performance of a building during a demolition event.Multi-temporal oblique photogrammetry images are collected with 3D point clouds generated at different stages of the demolition.The geometric accuracy of the generated point clouds has been evaluated against both airborne and terrestrial LiDAR point clouds,achieving an average distance of 12 cm and 16 cm for roof and façade respectively.We propose a hierarchical volumetric change detection framework that unifies multi-temporal UAV images for pose estimation(free of ground control points),reconstruction,and a coarse-to-fine 3D density change analysis.This work has provided a solution capable of addressing change detection on full 3D time-series datasets where dramatic scene content changes are presented progressively.Our change detection results on the building demolition event have been evaluated against the manually marked ground-truth changes and have achieved an F-1 score varying from 0.78 to 0.92,with consistently high precision(0.92–0.99).Volumetric changes through the demolition progress are derived from change detection and have been shown to favorably reflect the qualitative and quantitative building demolition progression.
