Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot interaction.However,recognizing actions f...Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot interaction.However,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera angles.This research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)videos.The first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale images.The YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further processing.The joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are included.These features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity classification.Real-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing methods.In particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos.展开更多
Background:In the field of genetic diagnostics,DNA sequencing is an important tool because the depth and complexity of this field have major implications in light of the genetic architectures of diseases and the ident...Background:In the field of genetic diagnostics,DNA sequencing is an important tool because the depth and complexity of this field have major implications in light of the genetic architectures of diseases and the identification of risk factors associated with genetic disorders.Methods:Our study introduces a novel two-tiered analytical framework to raise the precision and reliability of genetic data interpretation.It is initiated by extracting and analyzing salient features from DNA sequences through a CNN-based feature analysis,taking advantage of the power inherent in Convolutional neural networks(CNNs)to attain complex patterns and minute mutations in genetic data.This study embraces an elite collection of machine learning classifiers interweaved through a stern voting mechanism,which synergistically joins the predictions made from multiple classifiers to generate comprehensive and well-balanced interpretations of the genetic data.Results:This state-of-the-art method was further tested by carrying out an empirical analysis on a variants'dataset of DNA sequences taken from patients affected by breast cancer,juxtaposed with a control group composed of healthy people.Thus,the integration of CNNs with a voting-based ensemble of classifiers returned outstanding outcomes,with performance metrics accuracy,precision,recall,and F1-scorereaching the outstanding rate of 0.88,outperforming previous models.Conclusions:This dual accomplishment underlines the transformative potential that integrating deep learning techniques with ensemble machine learning might provide in real added value for further genetic diagnostics and prognostics.These results from this study set a new benchmark in the accuracy of disease diagnosis through DNA sequencing and promise future studies on improved personalized medicine and healthcare approaches with precise genetic information.展开更多
To improve the performance of the multiple classifier system, a new method of feature-decision level fusion is proposed based on knowledge discovery. In the new method, the base classifiers operate on different featur...To improve the performance of the multiple classifier system, a new method of feature-decision level fusion is proposed based on knowledge discovery. In the new method, the base classifiers operate on different feature spaces and their types depend on different measures of between-class separability. The uncertainty measures corresponding to each output of each base classifier are induced from the established decision tables （DTs） in the form of mass function in the Dempster-Shafer theory （DST）. Furthermore, an effective fusion framework is built at the feature-decision level on the basis of a generalized rough set model and the DST. The experiment for the classification of hyperspectral remote sensing images shows that the performance of the classification can be improved by the proposed method compared with that of plurality voting （PV）.展开更多
To improve the performance of multiple classifier system, a knowledge discovery based dynamic weighted voting （KD-DWV） is proposed based on knowledge discovery. In the method, all base classifiers may be allowed to ...To improve the performance of multiple classifier system, a knowledge discovery based dynamic weighted voting （KD-DWV） is proposed based on knowledge discovery. In the method, all base classifiers may be allowed to operate in different measurement/feature spaces to make the most of diverse classification information. The weights assigned to each output of a base classifier are estimated by the separability of training sample sets in relevant feature space. For this purpose, some decision tables （DTs） are established in terms of the diverse feature sets. And then the uncertainty measures of the separability are induced, in the form of mass functions in Dempster-Shafer theory （DST）, from each DTs based on generalized rough set model. From the mass functions, all the weights are calculated by a modified heuristic fusion function and assigned dynamically to each classifier varying with its output. The comparison experiment is performed on the hyperspectral remote sensing images. And the experimental results show that the performance of the classification can be improved by using the proposed method compared with the plurality voting （PV）.展开更多
Support vector machine (SVM), as a novel approach in pattern recognition, has demonstrated a success in face detection and face recognition. In this paper, a face recognition approach based on the SVM classifier with ...Support vector machine (SVM), as a novel approach in pattern recognition, has demonstrated a success in face detection and face recognition. In this paper, a face recognition approach based on the SVM classifier with the nearest neighbor classifier (NNC) is proposed. The principal component analysis (PCA) is used to reduce the dimension and extract features. Then one-against-all stratedy is used to train the SVM classifiers. At the testing stage, we propose an al-展开更多
Numerous models have been proposed to reduce the classification error of Naive Bayes by weakening its attribute independence assumption and some have demonstrated remarkable error performance. Considering that ensembl...Numerous models have been proposed to reduce the classification error of Naive Bayes by weakening its attribute independence assumption and some have demonstrated remarkable error performance. Considering that ensemble learning is an effective method of reducing the classifmation error of the classifier, this paper proposes a double-layer Bayesian classifier ensembles （DLBCE） algorithm based on frequent itemsets. DLBCE constructs a double-layer Bayesian classifier （DLBC） for each frequent itemset the new instance contained and finally ensembles all the classifiers by assigning different weight to different classifier according to the conditional mutual information. The experimental results show that the proposed algorithm outperforms other outstanding algorithms.展开更多
This paper proposed an algorithm in which the maximum probability and the weighted average strategy were used for the combination of member classifiers. Using parallel computing, we test the algorithm on a China-Brazi...This paper proposed an algorithm in which the maximum probability and the weighted average strategy were used for the combination of member classifiers. Using parallel computing, we test the algorithm on a China-Brazil Earth Resources Satellite （CBERS） image for land cover classification. The results show that using three computers in parallel can reduce the classification time by 30%, as compared with using only one computer with a dual core processor. The accuracy of the final image is 93.34%, and Kappa is 0.92. Multiple classifier combination can enhance the precision of the image classification, and parallel computing can increase the speed of calculation so that it becomes possible to process remote sensing images with high efficiency and accuracy.展开更多
The participation of ordinary devices in networking has created a world of connected devices rapidly.The Internet of Things(IoT)includes heterogeneous devices from every field.There are no definite protocols or standa...The participation of ordinary devices in networking has created a world of connected devices rapidly.The Internet of Things(IoT)includes heterogeneous devices from every field.There are no definite protocols or standards for IoT communication,and most of the IoT devices have limited resources.Enabling a complete security measure for such devices is a challenging task,yet necessary.Many lightweight security solutions have surfaced lately for IoT.The lightweight security protocols are unable to provide an optimum protection against prevailing powerful threats in cyber world.It is also hard to deploy any traditional security protocol on resource-constrained IoT devices.Software-defined networking introduces a centralized control in computer networks.SDN has a programmable approach towards networking that decouples control and data planes.An SDN-based intrusion detection system is proposed which uses deep learning classifier for detection of anomalies in IoT.The proposed intrusion detection system does not burden the IoT devices with security profiles.The proposed work is executed on the simulated environment.The results of the simulation test are evaluated using various matrices and compared with other relevant methods.展开更多
Mapping and monitoring the distribution of croplands and crop types support policymakers and international organizations by reducing the risks to food security,notably from climate change and,for that purpose,remote s...Mapping and monitoring the distribution of croplands and crop types support policymakers and international organizations by reducing the risks to food security,notably from climate change and,for that purpose,remote sensing is routinely used.However,identifying specific crop types,cropland,and cropping patterns using space-based observations is challenging because different crop types and cropping patterns have similarity spectral signatures.This study applied a methodology to identify cropland and specific crop types,including tobacco,wheat,barley,and gram,as well as the following cropping patterns:wheat-tobacco,wheat-gram,wheat-barley,and wheat-maize,which are common in Gujranwala District,Pakistan,the study region.The methodology consists of combining optical remote sensing images from Sentinel-2 and Landsat-8 with Machine Learning(ML)methods,namely a Decision Tree Classifier(DTC)and a Random Forest(RF)algorithm.The best time-periods for differentiating cropland from other land cover types were identified,and then Sentinel-2 and Landsat 8 NDVI-based time-series were linked to phenological parameters to determine the different crop types and cropping patterns over the study region using their temporal indices and ML algorithms.The methodology was subsequently evaluated using Landsat images,crop statistical data for 2020 and 2021,and field data on cropping patterns.The results highlight the high level of accuracy of the methodological approach presented using Sentinel-2 and Landsat-8 images,together with ML techniques,for mapping not only the distribution of cropland,but also crop types and cropping patterns when validated at the county level.These results reveal that this methodology has benefits for monitoring and evaluating food security in Pakistan,adding to the evidence base of other studies on the use of remote sensing to identify crop types and cropping patterns in other countries.展开更多
The turbo air classifier is widely used powder classification equipment in a variety of fields. The flow field characteristics of the turbo air classifier are important basis for the improvement of the turbo air class...The turbo air classifier is widely used powder classification equipment in a variety of fields. The flow field characteristics of the turbo air classifier are important basis for the improvement of the turbo air classifier＇s structural design. The flow field characteristics of the rotor cage in turbo air classifiers were investigated trader different operating conditions by laser Doppler velocimeter（LDV）, and a measure diminishing the axial velocity is proposed. The investigation results show that the tangential velocity of the air flow inside the rotor cage is different from the rotary speed of the rotor cage on the same measurement point due to the influences of both the negative pressure at the exit and the rotation of the rotor cage. The tangential velocity of the air flow likewise decreases as the radius decreases in the case of the rotor cage＇s low rotary speed. In contrast, the tangential velocity of the air flow increases as the radius decreases in the case of the rotor cage＇s high rotary speed. Meanwhile, the vortex inside the rotor cage is found to occur near the pressure side of the blade when the rotor cage＇s rotary speed is less than the tangential velocity of air flow. On the contrary, the vortex is found to occur near the blade suction side once the rotor cage＇s rotary speed is higher than the tangential velocity of air flow. Inside the rotor cage, the axial velocity could not be disregarded and is largely determined by the distances between the measurement point and the exit.展开更多
In this work,the reflux classifier with closely spaced inclined channels is used as the pre-concentration facility to improve the separation efficiency before the shaking table separation.Three operating parameters of...In this work,the reflux classifier with closely spaced inclined channels is used as the pre-concentration facility to improve the separation efficiency before the shaking table separation.Three operating parameters of reflux classifier(RC)to pre-concentrate fine(0.023−0.15 mm)tailings of antimony oxide were optimized by response surface methodology(RSM)using a three-level Box-Behnken design(BBD).The parameters studied for the optimization were feeding speed,underflow,and ascending water speed.Second-order response functions were produced for the Sb grade and recovery rate of the concentrate.Taking advantage of the quadratic programming,when the factors of feeding,underflow and ascending water are respectively 225,30 and 133 cm^3/min,a better result can be achieved for the concentrate grade of 2.31% and recovery rate of 83.17%.At the same time,70.48% of the tailings with the grade of 0.20% were discarded out of the feeding.The results indicated that the reflux classifier has a good performance in dealing with fine tailings of antimony oxide.Moreover,second-order polynomial equations,ANOVA,and three-dimensional surface plots were developed to evaluate the effects of each parameter on Sb grade and recovery rate of the concentrate.展开更多
Predicting stock price movements is a challenging task for academicians and practitioners. In particular, forecasting price movements in emerging markets seems to be more elusive because they are usually more volatile...Predicting stock price movements is a challenging task for academicians and practitioners. In particular, forecasting price movements in emerging markets seems to be more elusive because they are usually more volatile often accompa-nied by thin trading-volumes and they are susceptible to more manipulation compared to mature markets. Technical analysis of stocks and commodities has become a science on its own;quantitative methods and techniques have been applied by many practitioners to forecast price movements. Lagging and sometimes leading technical indicators pro-vide rich quantitative tools for traders and investors in their attempt to gain advantage when making investment or trading decisions. Artificial Neural Networks (ANN) have been used widely in predicting stock prices because of their capability in capturing the non-linearity that often exists in price movements. Recently, Polynomial Classifiers (PC) have been applied to various recognition and classification application and showed favorable results in terms of recog-nition rates and computational complexity as compared to ANN. In this paper, we present two prediction models for predicting securities’ prices. The first model was developed using back propagation feed forward neural networks. The second model was developed using polynomial classifiers (PC), as a first time application for PC to be used in stock prices prediction. The inputs to both models were identical, and both models were trained and tested on the same data. The study was conducted on Dubai Financial Market as an emerging market and applied to two of the market’s leading stocks. In general, both models achieved very good results in terms of mean absolute error percentage. Both models show an average error around 1.5% predicting the next day price, an average error of 2.5% when predicting second day price, and an average error of 4% when predicted the third day price.展开更多
The classification performance of model coal mill classifiers with different bottom incoming flow inlets was experimentally and numerically studied.The flow field adjacent to two neighboring impeller blades was measur...The classification performance of model coal mill classifiers with different bottom incoming flow inlets was experimentally and numerically studied.The flow field adjacent to two neighboring impeller blades was measured using the particle image velocimetry technique.The results showed that the flow field adjacent to two neighboring blades with the swirling inlet was significantly different from that with the non-swirling inlet.With the swirling inlet,there was a vortex located between two neighboring blades,while with the nonswirling inlet,the vortex was attached to the blade tip.The vorticity of the vortex with the non-swirling inlet was much lower than that with the swirling inlet.The classifier with the non-swirling inlet demonstrated a larger cut size than that with the swirling inlet when the impeller was stationary(~0 r·min-1).As the impeller rotational speed increased,the cut size of the cases with non-swirling and swirling inlets both decreased,and the one with the non-swirling inlet decreased more dramatically.The values of the cut size of the two classifiers were close to each other at a high impeller rotational speed(≥120 r·min-1).The overall separation efficiency of the classifier with the non-swirling inlet was lower than that with the swirling inlet,and monotonically increased as the impeller rotational speed increased.With the swirling inlet,the overall separation efficiency first increased with the impeller rotational speed and then decreased when the rotational speed was above 120 r·min-1,and the variation trend of the separation efficiency was more moderate.As the initial particle concentration increased,the cut sizes of both swirling and non-swirling inlet cases decreased first and then barely changed.At a low initial particle concentration(b 0.04 kg·m-3),the classifier with the swirling inlet had a larger cut size than that with the non-swirling inlet.展开更多
Support vector classifier （SVC） has the superior advantages for small sample learning problems with high dimensions, with especially better generalization ability. However there is some redundancy among the high dim...Support vector classifier （SVC） has the superior advantages for small sample learning problems with high dimensions, with especially better generalization ability. However there is some redundancy among the high dimensions of the original samples and the main features of the samples may be picked up first to improve the performance of SVC. A principal component analysis （PCA） is employed to reduce the feature dimensions of the original samples and the pre-selected main features efficiently, and an SVC is constructed in the selected feature space to improve the learning speed and identification rate of SVC. Furthermore, a heuristic genetic algorithm-based automatic model selection is proposed to determine the hyperparameters of SVC to evaluate the performance of the learning machines. Experiments performed on the Heart and Adult benchmark data sets demonstrate that the proposed PCA-based SVC not only reduces the test time drastically, but also improves the identify rates effectively.展开更多
The suitable process parameters for a two-stage turbo air classifier are important for obtaining the ultrafine powder that has a narrow particle-size distribution, however little has been published internationally on ...The suitable process parameters for a two-stage turbo air classifier are important for obtaining the ultrafine powder that has a narrow particle-size distribution, however little has been published internationally on the classification process for the two-stage turbo air classifier in series. The influence of the process parameters of a two-stage turbo air classifier in series on classification performance is empirically studied by using aluminum oxide powders as the experimental material. The experimental results show the following: 1) When the rotor cage rotary speed of the first-stage classifier is increased from 2 300 r/min to 2 500 r/min with a constant rotor cage rotary speed of the second-stage classifier, classification precision is increased from 0.64 to 0.67. However, in this case, the final ultrafine powder yield is decreased from 79% to 74%, which means the classification precision and the final ultrafine powder yield can be regulated through adjusting the rotor cage rotary speed of the first-stage classifier. 2) When the rotor cage rotary speed of the second-stage classifier is increased from 2 500 r/min to 3 100 r/min with a constant rotor cage rotary speed of the first-stage classifier, the cut size is decreased from 13.16 μm to 8.76 μm, which means the cut size of the ultrafine powder can be regulated through adjusting the rotor cage rotary speed of the second-stage classifier. 3) When the feeding speed is increased from 35 kg/h to 50 kg/h, the 'fish-hook' effect is strengthened, which makes the ultrafine powder yield decrease. 4) To weaken the 'fish-hook' effect, the equalization of the two-stage wind speeds or the combination of a high first-stage wind speed with a low second-stage wind speed should be selected. This empirical study provides a criterion of process parameter configurations for a two-stage or multi-stage classifier in series, which offers a theoretical basis for practical production.展开更多
This study investigated the efficiency of learning the Chinese numeral classifiers by L2 Chinese learners by means of an alignment-oriented task. Participants were a total of 96 intermediate learners of L2 Chinese, wh...This study investigated the efficiency of learning the Chinese numeral classifiers by L2 Chinese learners by means of an alignment-oriented task. Participants were a total of 96 intermediate learners of L2 Chinese, who were randomly assigned to two experimental groups and one control group, with each group consisting of 32 participants. The continuation task used in this study consisted of a picture-based Chinese text depicting a room with an array of objects, which necessitates the use of classifiers. The two experimental groups were both required to first read the text and then write to describe their own rooms in comparison with the one in the text. One group was instructed to use the classifiers from the text as much as possible in their writing, whereas the other was not required to do so. Participants in the control group were first given the picture to look at in the absence of the text and then asked to describe their own rooms. The results showed that the continuation task significantly enhanced participants’ retention of the Chinese numeral classifiers, suggesting that the alignment-based approach is an effective way to learn difficult linguistic categories such as the Chinese classifiers.展开更多
This paper addresses the high dimension sample problem in discriminate analysis under nonparametric and supervised assumptions. Since there is a kind of equivalence between the probabilistic dependence measure and the...This paper addresses the high dimension sample problem in discriminate analysis under nonparametric and supervised assumptions. Since there is a kind of equivalence between the probabilistic dependence measure and the Bayes classification error probability, we propose to use an iterative algorithm to optimize the dimension reduction for classification with a probabilistic approach to achieve the Bayes classifier. The estimated probabilities of different errors encountered along the different phases of the system are realized by the Kernel estimate which is adjusted in a means of the smoothing parameter. Experiment results suggest that the proposed approach performs well.展开更多
基金funded by the Open Access Initiative of the University of Bremen and the DFG via SuUB Bremen.Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2024R348),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot interaction.However,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera angles.This research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)videos.The first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale images.The YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further processing.The joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are included.These features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity classification.Real-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing methods.In particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos.
文摘Background:In the field of genetic diagnostics,DNA sequencing is an important tool because the depth and complexity of this field have major implications in light of the genetic architectures of diseases and the identification of risk factors associated with genetic disorders.Methods:Our study introduces a novel two-tiered analytical framework to raise the precision and reliability of genetic data interpretation.It is initiated by extracting and analyzing salient features from DNA sequences through a CNN-based feature analysis,taking advantage of the power inherent in Convolutional neural networks(CNNs)to attain complex patterns and minute mutations in genetic data.This study embraces an elite collection of machine learning classifiers interweaved through a stern voting mechanism,which synergistically joins the predictions made from multiple classifiers to generate comprehensive and well-balanced interpretations of the genetic data.Results:This state-of-the-art method was further tested by carrying out an empirical analysis on a variants'dataset of DNA sequences taken from patients affected by breast cancer,juxtaposed with a control group composed of healthy people.Thus,the integration of CNNs with a voting-based ensemble of classifiers returned outstanding outcomes,with performance metrics accuracy,precision,recall,and F1-scorereaching the outstanding rate of 0.88,outperforming previous models.Conclusions:This dual accomplishment underlines the transformative potential that integrating deep learning techniques with ensemble machine learning might provide in real added value for further genetic diagnostics and prognostics.These results from this study set a new benchmark in the accuracy of disease diagnosis through DNA sequencing and promise future studies on improved personalized medicine and healthcare approaches with precise genetic information.
文摘To improve the performance of the multiple classifier system, a new method of feature-decision level fusion is proposed based on knowledge discovery. In the new method, the base classifiers operate on different feature spaces and their types depend on different measures of between-class separability. The uncertainty measures corresponding to each output of each base classifier are induced from the established decision tables （DTs） in the form of mass function in the Dempster-Shafer theory （DST）. Furthermore, an effective fusion framework is built at the feature-decision level on the basis of a generalized rough set model and the DST. The experiment for the classification of hyperspectral remote sensing images shows that the performance of the classification can be improved by the proposed method compared with that of plurality voting （PV）.
基金This project was supported by the National Basic Research Programof China (2001CB309403)
文摘To improve the performance of multiple classifier system, a knowledge discovery based dynamic weighted voting （KD-DWV） is proposed based on knowledge discovery. In the method, all base classifiers may be allowed to operate in different measurement/feature spaces to make the most of diverse classification information. The weights assigned to each output of a base classifier are estimated by the separability of training sample sets in relevant feature space. For this purpose, some decision tables （DTs） are established in terms of the diverse feature sets. And then the uncertainty measures of the separability are induced, in the form of mass functions in Dempster-Shafer theory （DST）, from each DTs based on generalized rough set model. From the mass functions, all the weights are calculated by a modified heuristic fusion function and assigned dynamically to each classifier varying with its output. The comparison experiment is performed on the hyperspectral remote sensing images. And the experimental results show that the performance of the classification can be improved by using the proposed method compared with the plurality voting （PV）.
基金This project was supported by Shanghai Shu Guang Project.
文摘Support vector machine (SVM), as a novel approach in pattern recognition, has demonstrated a success in face detection and face recognition. In this paper, a face recognition approach based on the SVM classifier with the nearest neighbor classifier (NNC) is proposed. The principal component analysis (PCA) is used to reduce the dimension and extract features. Then one-against-all stratedy is used to train the SVM classifiers. At the testing stage, we propose an al-
基金supported by National Natural Science Foundation of China (Nos. 61073133, 60973067, and 61175053)Fundamental Research Funds for the Central Universities of China(No. 2011ZD010)
文摘Numerous models have been proposed to reduce the classification error of Naive Bayes by weakening its attribute independence assumption and some have demonstrated remarkable error performance. Considering that ensemble learning is an effective method of reducing the classifmation error of the classifier, this paper proposes a double-layer Bayesian classifier ensembles （DLBCE） algorithm based on frequent itemsets. DLBCE constructs a double-layer Bayesian classifier （DLBC） for each frequent itemset the new instance contained and finally ensembles all the classifiers by assigning different weight to different classifier according to the conditional mutual information. The experimental results show that the proposed algorithm outperforms other outstanding algorithms.
基金Supported by the National Natural Science Foundation of China (70873117)
文摘This paper proposed an algorithm in which the maximum probability and the weighted average strategy were used for the combination of member classifiers. Using parallel computing, we test the algorithm on a China-Brazil Earth Resources Satellite （CBERS） image for land cover classification. The results show that using three computers in parallel can reduce the classification time by 30%, as compared with using only one computer with a dual core processor. The accuracy of the final image is 93.34%, and Kappa is 0.92. Multiple classifier combination can enhance the precision of the image classification, and parallel computing can increase the speed of calculation so that it becomes possible to process remote sensing images with high efficiency and accuracy.
基金The authors are grateful to MANF UGC,Government of India,for providing financial support under MANF-UGC(MANF-2015-17-JAM-60,506)programme to carry out this work.
文摘The participation of ordinary devices in networking has created a world of connected devices rapidly.The Internet of Things(IoT)includes heterogeneous devices from every field.There are no definite protocols or standards for IoT communication,and most of the IoT devices have limited resources.Enabling a complete security measure for such devices is a challenging task,yet necessary.Many lightweight security solutions have surfaced lately for IoT.The lightweight security protocols are unable to provide an optimum protection against prevailing powerful threats in cyber world.It is also hard to deploy any traditional security protocol on resource-constrained IoT devices.Software-defined networking introduces a centralized control in computer networks.SDN has a programmable approach towards networking that decouples control and data planes.An SDN-based intrusion detection system is proposed which uses deep learning classifier for detection of anomalies in IoT.The proposed intrusion detection system does not burden the IoT devices with security profiles.The proposed work is executed on the simulated environment.The results of the simulation test are evaluated using various matrices and compared with other relevant methods.
文摘Mapping and monitoring the distribution of croplands and crop types support policymakers and international organizations by reducing the risks to food security,notably from climate change and,for that purpose,remote sensing is routinely used.However,identifying specific crop types,cropland,and cropping patterns using space-based observations is challenging because different crop types and cropping patterns have similarity spectral signatures.This study applied a methodology to identify cropland and specific crop types,including tobacco,wheat,barley,and gram,as well as the following cropping patterns:wheat-tobacco,wheat-gram,wheat-barley,and wheat-maize,which are common in Gujranwala District,Pakistan,the study region.The methodology consists of combining optical remote sensing images from Sentinel-2 and Landsat-8 with Machine Learning(ML)methods,namely a Decision Tree Classifier(DTC)and a Random Forest(RF)algorithm.The best time-periods for differentiating cropland from other land cover types were identified,and then Sentinel-2 and Landsat 8 NDVI-based time-series were linked to phenological parameters to determine the different crop types and cropping patterns over the study region using their temporal indices and ML algorithms.The methodology was subsequently evaluated using Landsat images,crop statistical data for 2020 and 2021,and field data on cropping patterns.The results highlight the high level of accuracy of the methodological approach presented using Sentinel-2 and Landsat-8 images,together with ML techniques,for mapping not only the distribution of cropland,but also crop types and cropping patterns when validated at the county level.These results reveal that this methodology has benefits for monitoring and evaluating food security in Pakistan,adding to the evidence base of other studies on the use of remote sensing to identify crop types and cropping patterns in other countries.
基金supported by National Natural Science Foundation of China (Grant No. 50474035)
文摘The turbo air classifier is widely used powder classification equipment in a variety of fields. The flow field characteristics of the turbo air classifier are important basis for the improvement of the turbo air classifier＇s structural design. The flow field characteristics of the rotor cage in turbo air classifiers were investigated trader different operating conditions by laser Doppler velocimeter（LDV）, and a measure diminishing the axial velocity is proposed. The investigation results show that the tangential velocity of the air flow inside the rotor cage is different from the rotary speed of the rotor cage on the same measurement point due to the influences of both the negative pressure at the exit and the rotation of the rotor cage. The tangential velocity of the air flow likewise decreases as the radius decreases in the case of the rotor cage＇s low rotary speed. In contrast, the tangential velocity of the air flow increases as the radius decreases in the case of the rotor cage＇s high rotary speed. Meanwhile, the vortex inside the rotor cage is found to occur near the pressure side of the blade when the rotor cage＇s rotary speed is less than the tangential velocity of air flow. On the contrary, the vortex is found to occur near the blade suction side once the rotor cage＇s rotary speed is higher than the tangential velocity of air flow. Inside the rotor cage, the axial velocity could not be disregarded and is largely determined by the distances between the measurement point and the exit.
基金Project(2015SK20792)supported by Key Province Key Technology Research and Development Program of the Ministry of Science and Technology of Hunan,ChinaProjects(2019zzts703,2020zzts740,2020zzts202)supported by the Fundamental Research Funds for the Central Universities of ChinaProject(2020P4FZG03A)supported by State Key Laboratory of Vanadium and Titanium Resources Comprehensive Utilization,China。
文摘In this work,the reflux classifier with closely spaced inclined channels is used as the pre-concentration facility to improve the separation efficiency before the shaking table separation.Three operating parameters of reflux classifier(RC)to pre-concentrate fine(0.023−0.15 mm)tailings of antimony oxide were optimized by response surface methodology(RSM)using a three-level Box-Behnken design(BBD).The parameters studied for the optimization were feeding speed,underflow,and ascending water speed.Second-order response functions were produced for the Sb grade and recovery rate of the concentrate.Taking advantage of the quadratic programming,when the factors of feeding,underflow and ascending water are respectively 225,30 and 133 cm^3/min,a better result can be achieved for the concentrate grade of 2.31% and recovery rate of 83.17%.At the same time,70.48% of the tailings with the grade of 0.20% were discarded out of the feeding.The results indicated that the reflux classifier has a good performance in dealing with fine tailings of antimony oxide.Moreover,second-order polynomial equations,ANOVA,and three-dimensional surface plots were developed to evaluate the effects of each parameter on Sb grade and recovery rate of the concentrate.
文摘Predicting stock price movements is a challenging task for academicians and practitioners. In particular, forecasting price movements in emerging markets seems to be more elusive because they are usually more volatile often accompa-nied by thin trading-volumes and they are susceptible to more manipulation compared to mature markets. Technical analysis of stocks and commodities has become a science on its own;quantitative methods and techniques have been applied by many practitioners to forecast price movements. Lagging and sometimes leading technical indicators pro-vide rich quantitative tools for traders and investors in their attempt to gain advantage when making investment or trading decisions. Artificial Neural Networks (ANN) have been used widely in predicting stock prices because of their capability in capturing the non-linearity that often exists in price movements. Recently, Polynomial Classifiers (PC) have been applied to various recognition and classification application and showed favorable results in terms of recog-nition rates and computational complexity as compared to ANN. In this paper, we present two prediction models for predicting securities’ prices. The first model was developed using back propagation feed forward neural networks. The second model was developed using polynomial classifiers (PC), as a first time application for PC to be used in stock prices prediction. The inputs to both models were identical, and both models were trained and tested on the same data. The study was conducted on Dubai Financial Market as an emerging market and applied to two of the market’s leading stocks. In general, both models achieved very good results in terms of mean absolute error percentage. Both models show an average error around 1.5% predicting the next day price, an average error of 2.5% when predicting second day price, and an average error of 4% when predicted the third day price.
基金financial support from the National Key Technologies R&D Program of China(2018YFF0216002)。
文摘The classification performance of model coal mill classifiers with different bottom incoming flow inlets was experimentally and numerically studied.The flow field adjacent to two neighboring impeller blades was measured using the particle image velocimetry technique.The results showed that the flow field adjacent to two neighboring blades with the swirling inlet was significantly different from that with the non-swirling inlet.With the swirling inlet,there was a vortex located between two neighboring blades,while with the nonswirling inlet,the vortex was attached to the blade tip.The vorticity of the vortex with the non-swirling inlet was much lower than that with the swirling inlet.The classifier with the non-swirling inlet demonstrated a larger cut size than that with the swirling inlet when the impeller was stationary(~0 r·min-1).As the impeller rotational speed increased,the cut size of the cases with non-swirling and swirling inlets both decreased,and the one with the non-swirling inlet decreased more dramatically.The values of the cut size of the two classifiers were close to each other at a high impeller rotational speed(≥120 r·min-1).The overall separation efficiency of the classifier with the non-swirling inlet was lower than that with the swirling inlet,and monotonically increased as the impeller rotational speed increased.With the swirling inlet,the overall separation efficiency first increased with the impeller rotational speed and then decreased when the rotational speed was above 120 r·min-1,and the variation trend of the separation efficiency was more moderate.As the initial particle concentration increased,the cut sizes of both swirling and non-swirling inlet cases decreased first and then barely changed.At a low initial particle concentration(b 0.04 kg·m-3),the classifier with the swirling inlet had a larger cut size than that with the non-swirling inlet.
基金the National Natural Science of China (50675167)a Foundation for the Author of National Excellent Doctoral Dissertation of China(200535)
文摘Support vector classifier （SVC） has the superior advantages for small sample learning problems with high dimensions, with especially better generalization ability. However there is some redundancy among the high dimensions of the original samples and the main features of the samples may be picked up first to improve the performance of SVC. A principal component analysis （PCA） is employed to reduce the feature dimensions of the original samples and the pre-selected main features efficiently, and an SVC is constructed in the selected feature space to improve the learning speed and identification rate of SVC. Furthermore, a heuristic genetic algorithm-based automatic model selection is proposed to determine the hyperparameters of SVC to evaluate the performance of the learning machines. Experiments performed on the Heart and Adult benchmark data sets demonstrate that the proposed PCA-based SVC not only reduces the test time drastically, but also improves the identify rates effectively.
基金supported by National Natural Science Foundation of China (Grant Nos. 51074012, 51204009)
文摘The suitable process parameters for a two-stage turbo air classifier are important for obtaining the ultrafine powder that has a narrow particle-size distribution, however little has been published internationally on the classification process for the two-stage turbo air classifier in series. The influence of the process parameters of a two-stage turbo air classifier in series on classification performance is empirically studied by using aluminum oxide powders as the experimental material. The experimental results show the following: 1) When the rotor cage rotary speed of the first-stage classifier is increased from 2 300 r/min to 2 500 r/min with a constant rotor cage rotary speed of the second-stage classifier, classification precision is increased from 0.64 to 0.67. However, in this case, the final ultrafine powder yield is decreased from 79% to 74%, which means the classification precision and the final ultrafine powder yield can be regulated through adjusting the rotor cage rotary speed of the first-stage classifier. 2) When the rotor cage rotary speed of the second-stage classifier is increased from 2 500 r/min to 3 100 r/min with a constant rotor cage rotary speed of the first-stage classifier, the cut size is decreased from 13.16 μm to 8.76 μm, which means the cut size of the ultrafine powder can be regulated through adjusting the rotor cage rotary speed of the second-stage classifier. 3) When the feeding speed is increased from 35 kg/h to 50 kg/h, the 'fish-hook' effect is strengthened, which makes the ultrafine powder yield decrease. 4) To weaken the 'fish-hook' effect, the equalization of the two-stage wind speeds or the combination of a high first-stage wind speed with a low second-stage wind speed should be selected. This empirical study provides a criterion of process parameter configurations for a two-stage or multi-stage classifier in series, which offers a theoretical basis for practical production.
文摘This study investigated the efficiency of learning the Chinese numeral classifiers by L2 Chinese learners by means of an alignment-oriented task. Participants were a total of 96 intermediate learners of L2 Chinese, who were randomly assigned to two experimental groups and one control group, with each group consisting of 32 participants. The continuation task used in this study consisted of a picture-based Chinese text depicting a room with an array of objects, which necessitates the use of classifiers. The two experimental groups were both required to first read the text and then write to describe their own rooms in comparison with the one in the text. One group was instructed to use the classifiers from the text as much as possible in their writing, whereas the other was not required to do so. Participants in the control group were first given the picture to look at in the absence of the text and then asked to describe their own rooms. The results showed that the continuation task significantly enhanced participants’ retention of the Chinese numeral classifiers, suggesting that the alignment-based approach is an effective way to learn difficult linguistic categories such as the Chinese classifiers.
文摘This paper addresses the high dimension sample problem in discriminate analysis under nonparametric and supervised assumptions. Since there is a kind of equivalence between the probabilistic dependence measure and the Bayes classification error probability, we propose to use an iterative algorithm to optimize the dimension reduction for classification with a probabilistic approach to achieve the Bayes classifier. The estimated probabilities of different errors encountered along the different phases of the system are realized by the Kernel estimate which is adjusted in a means of the smoothing parameter. Experiment results suggest that the proposed approach performs well.
