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
Accurate prediction ofmonthly oil and gas production is essential for oil enterprises tomake reasonable production plans,avoid blind investment and realize sustainable development.Traditional oil well production trend...Accurate prediction ofmonthly oil and gas production is essential for oil enterprises tomake reasonable production plans,avoid blind investment and realize sustainable development.Traditional oil well production trend prediction methods are based on years of oil field production experience and expertise,and the application conditions are very demanding.With the rapid development of artificial intelligence technology,big data analysis methods are gradually applied in various sub-fields of the oil and gas reservoir development.Based on the data-driven artificial intelligence algorithmGradient BoostingDecision Tree(GBDT),this paper predicts the initial single-layer production by considering geological data,fluid PVT data and well data.The results show that the GBDT algorithm prediction model has great accuracy,significantly improving efficiency and strong universal applicability.The GBDTmethod trained in this paper can predict production,which is helpful for well site optimization,perforation layer optimization and engineering parameter optimization and has guiding significance for oilfield development.展开更多
The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. This paper ...The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. This paper proposes an automated methodology for mapping burn scars using pairs of Sentinel-2 imagery, exploiting the state-of-the-art eXtreme Gradient Boosting (XGB) machine learning framework. A large database of 64 reference wildfire perimeters in Greece from 2016 to 2019 is used to train the classifier. An empirical methodology for appropriately sampling the training patterns from this database is formulated, which guarantees the effectiveness of the approach and its computational efficiency. A difference (pre-fire minus post-fire) spectral index is used for this purpose, upon which we appropriately identify the clear and fuzzy value ranges. To reduce the data volume, a super-pixel segmentation of the images is also employed, implemented via the QuickShift algorithm. The cross-validation results showcase the effectiveness of the proposed algorithm, with the average commission and omission errors being 9% and 2%, respectively, and the average Matthews correlation coefficient (MCC) equal to 0.93.展开更多
Addressing classification and prediction challenges, tree ensemble models have gained significant importance. Boosting ensemble techniques are commonly employed for forecasting Type-II diabetes mellitus. Light Gradien...Addressing classification and prediction challenges, tree ensemble models have gained significant importance. Boosting ensemble techniques are commonly employed for forecasting Type-II diabetes mellitus. Light Gradient Boosting Machine (LightGBM) is a widely used algorithm known for its leaf growth strategy, loss reduction, and enhanced training precision. However, LightGBM is prone to overfitting. In contrast, CatBoost utilizes balanced base predictors known as decision tables, which mitigate overfitting risks and significantly improve testing time efficiency. CatBoost’s algorithm structure counteracts gradient boosting biases and incorporates an overfitting detector to stop training early. This study focuses on developing a hybrid model that combines LightGBM and CatBoost to minimize overfitting and improve accuracy by reducing variance. For the purpose of finding the best hyperparameters to use with the underlying learners, the Bayesian hyperparameter optimization method is used. By fine-tuning the regularization parameter values, the hybrid model effectively reduces variance (overfitting). Comparative evaluation against LightGBM, CatBoost, XGBoost, Decision Tree, Random Forest, AdaBoost, and GBM algorithms demonstrates that the hybrid model has the best F1-score (99.37%), recall (99.25%), and accuracy (99.37%). Consequently, the proposed framework holds promise for early diabetes prediction in the healthcare industry and exhibits potential applicability to other datasets sharing similarities with diabetes.展开更多
Accurate assessment of undrained shear strength(USS)for soft sensitive clays is a great concern in geotechnical engineering practice.This study applies novel data-driven extreme gradient boosting(XGBoost)and random fo...Accurate assessment of undrained shear strength(USS)for soft sensitive clays is a great concern in geotechnical engineering practice.This study applies novel data-driven extreme gradient boosting(XGBoost)and random forest(RF)ensemble learning methods for capturing the relationships between the USS and various basic soil parameters.Based on the soil data sets from TC304 database,a general approach is developed to predict the USS of soft clays using the two machine learning methods above,where five feature variables including the preconsolidation stress(PS),vertical effective stress(VES),liquid limit(LL),plastic limit(PL)and natural water content(W)are adopted.To reduce the dependence on the rule of thumb and inefficient brute-force search,the Bayesian optimization method is applied to determine the appropriate model hyper-parameters of both XGBoost and RF.The developed models are comprehensively compared with three comparison machine learning methods and two transformation models with respect to predictive accuracy and robustness under 5-fold cross-validation(CV).It is shown that XGBoost-based and RF-based methods outperform these approaches.Besides,the XGBoostbased model provides feature importance ranks,which makes it a promising tool in the prediction of geotechnical parameters and enhances the interpretability of model.展开更多
It is important for regional water resources management to know the agricultural water consumption information several months in advance.Forecasting reference evapotranspiration(ET_(0))in the next few months is import...It is important for regional water resources management to know the agricultural water consumption information several months in advance.Forecasting reference evapotranspiration(ET_(0))in the next few months is important for irrigation and reservoir management.Studies on forecasting of multiple-month ahead ET_(0) using machine learning models have not been reported yet.Besides,machine learning models such as the XGBoost model has multiple parameters that need to be tuned,and traditional methods can get stuck in a regional optimal solution and fail to obtain a global optimal solution.This study investigated the performance of the hybrid extreme gradient boosting(XGBoost)model coupled with the Grey Wolf Optimizer(GWO)algorithm for forecasting multi-step ahead ET_(0)(1-3 months ahead),compared with three conventional machine learning models,i.e.,standalone XGBoost,multi-layer perceptron(MLP)and M5 model tree(M5)models in the subtropical zone of China.The results showed that theGWO-XGB model generally performed better than the other three machine learning models in forecasting 1-3 months ahead ET_(0),followed by the XGB,M5 and MLP models with very small differences among the three models.The GWO-XGB model performed best in autumn,while the MLP model performed slightly better than the other three models in summer.It is thus suggested to apply the MLP model for ET_(0) forecasting in summer but use the GWO-XGB model in other seasons.展开更多
In the process of oil recovery,experiments are usually carried out on core samples to evaluate the recovery of oil,so the numerical data are fitted into a non-dimensional equation called scaling-law.This will be essen...In the process of oil recovery,experiments are usually carried out on core samples to evaluate the recovery of oil,so the numerical data are fitted into a non-dimensional equation called scaling-law.This will be essential for determining the behavior of actual reservoirs.The global non-dimensional time-scale is a parameter for predicting a realistic behavior in the oil field from laboratory data.This non-dimensional universal time parameter depends on a set of primary parameters that inherit the properties of the reservoir fluids and rocks and the injection velocity,which dynamics of the process.One of the practical machine learning(ML)techniques for regression/classification problems is gradient boosting(GB)regression.The GB produces a prediction model as an ensemble of weak prediction models that can be done at each iteration by matching a least-squares base-learner with the current pseudoresiduals.Using a randomization process increases the execution speed and accuracy of GB.Hence in this study,we developed a stochastic regression model of gradient boosting(SGB)to forecast oil recovery.Different nondimensional time-scales have been used to generate data to be used with machine learning techniques.The SGB method has been found to be the best machine learning technique for predicting the non-dimensional time-scale,which depends on oil/rock properties.展开更多
The fast spread of coronavirus disease(COVID-19)caused by SARSCoV-2 has become a pandemic and a serious threat to the world.As of May 30,2020,this disease had infected more than 6 million people globally,with hundreds...The fast spread of coronavirus disease(COVID-19)caused by SARSCoV-2 has become a pandemic and a serious threat to the world.As of May 30,2020,this disease had infected more than 6 million people globally,with hundreds of thousands of deaths.Therefore,there is an urgent need to predict confirmed cases so as to analyze the impact of COVID-19 and practice readiness in healthcare systems.This study uses gradient boosting regression(GBR)to build a trained model to predict the daily total confirmed cases of COVID-19.The GBR method can minimize the loss function of the training process and create a single strong learner from weak learners.Experiments are conducted on a dataset of daily confirmed COVID-19 cases from January 22,2020,to May 30,2020.The results are evaluated on a set of evaluation performance measures using 10-fold cross-validation to demonstrate the effectiveness of the GBR method.The results reveal that the GBR model achieves 0.00686 root mean square error,the lowest among several comparative models.展开更多
This paper aims to design an optimizer followed by a Kawahara filter for optimal classification and prediction of employees’performance.The algorithm starts by processing data by a modified K-means technique as a hie...This paper aims to design an optimizer followed by a Kawahara filter for optimal classification and prediction of employees’performance.The algorithm starts by processing data by a modified K-means technique as a hierarchical clustering method to quickly obtain the best features of employees to reach their best performance.The work of this paper consists of two parts.The first part is based on collecting data of employees to calculate and illustrate the performance of each employee.The second part is based on the classification and prediction techniques of the employee performance.This model is designed to help companies in their decisions about the employees’performance.The classification and prediction algorithms use the Gradient Boosting Tree classifier to classify and predict the features.Results of the paper give the percentage of employees which are expected to leave the company after predicting their performance for the coming years.Results also show that the Grasshopper Optimization,followed by“KF”with the Gradient Boosting Tree as classifier and predictor,is characterized by a high accuracy.The proposed algorithm is compared with other known techniques where our results are fund to be superior.展开更多
Coal rock mass instability fracture may result in serious hazards to underground coal mining.Acoustic emissions(AE)stimulated by internal structure fracture should carry lots of favorable information about health cond...Coal rock mass instability fracture may result in serious hazards to underground coal mining.Acoustic emissions(AE)stimulated by internal structure fracture should carry lots of favorable information about health condition of rock mass.AE as a sensitive non-destructive test method is gradually utilized to detect anomaly conditions of coal rock.This paper proposes an improved multi-resolution feature to extract AE waveform at different frequency resolutions using Coilflet Wavelet Transform method(CWT).It is further adopt an efficient Light Gradient Boosting Machine(LightGBM)by several cascaded sub weak classifier models to merge AE features at different views of frequency for coal rock anomaly damage recognition.The results denote that the proposed method achieves excellent recognition performance on anomaly damage levels of coal rock.It is an effective method to detect the critical stability further to predict the rock mass bursting in time.展开更多
In today’s world of connectivity there is a huge amount of data than we could imagine.The number of network users are increasing day by day and there are large number of social networks which keeps the users connecte...In today’s world of connectivity there is a huge amount of data than we could imagine.The number of network users are increasing day by day and there are large number of social networks which keeps the users connected all the time.These social networks give the complete independence to the user to post the data either political,commercial or entertainment value.Some data may be sensitive and have a greater impact on the society as a result.The trustworthiness of data is important when it comes to public social networking sites like facebook and twitter.Due to the large user base and its openness there is a huge possibility to spread spam messages in this network.Spam detection is a technique to identify and mark data as a false data value.There are lot of machine learning approaches proposed to detect spam in social networks.The efficiency of any spam detection algorithm is determined by its cost factor and accuracy.Aiming to improve the detection of spam in the social networks this study proposes using statistical based features that are modelled through the supervised boosting approach called Stochastic gradient boosting to evaluate the twitter data sets in the English language.The performance of the proposed model is evaluated using simulation results.展开更多
With the increasing of civil aviation business,flight delay has become a key problem in civil aviation field in recent years,which has brought a considerable economic impact to airlines and related industries.The dela...With the increasing of civil aviation business,flight delay has become a key problem in civil aviation field in recent years,which has brought a considerable economic impact to airlines and related industries.The delay prediction of specific flights is very important for airlines’plan,airport resource allocation,insurance company strategy and personal arrangement.The influence factors of flight delay have high complexity and non-linear relationship.The different situations of various regions and airports,and even the deviation of airport or airline arrangement all have certain influence on flight delay,which makes the prediction more difficult.In view of the limitations of the existing delay prediction models,this paper proposes a flight delay prediction model with more generalization ability and corresponding machine learning classification algorithm.This model fully exploits temporal and spatial characteristics of higher dimensions,such as the influence of preceding flights,the situation of departure and landing airports,and the overall situation of flights on the same route.In the process of machine learning,the model is trained with historical data and tested with the latest actual data.The test result shows that the model and this machine learning algorithm can provide an effective method for the prediction of flight delay.展开更多
This work was to generate landslide susceptibility maps for the Three Gorges Reservoir(TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting de...This work was to generate landslide susceptibility maps for the Three Gorges Reservoir(TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting decision tree(GBDT), random forest(RF) and information value(InV) models, were used, and the performances were assessed and compared. In total, 202 landslides were mapped by using a series of field surveys, aerial photographs, and reviews of historical and bibliographical data. Nine causative factors were then considered in landslide susceptibility map generation by using the GBDT, RF and InV models. All of the maps of the causative factors were resampled to a resolution of 28.5 m. Of the 486289 pixels in the area,28526 pixels were landslide pixels, and 457763 pixels were non-landslide pixels. Finally, landslide susceptibility maps were generated by using the three machine learning models, and their performances were assessed through receiver operating characteristic(ROC) curves, the sensitivity, specificity,overall accuracy(OA), and kappa coefficient(KAPPA). The results showed that the GBDT, RF and In V models in overall produced reasonable accurate landslide susceptibility maps. Among these three methods, the GBDT method outperforms the other two machine learning methods, which can provide strong technical support for producing landslide susceptibility maps in TGR.展开更多
The detection of phishing and legitimate websites is considered a great challenge for web service providers because the users of such websites are indistinguishable.Phishing websites also create traffic in the entire ...The detection of phishing and legitimate websites is considered a great challenge for web service providers because the users of such websites are indistinguishable.Phishing websites also create traffic in the entire network.Another phishing issue is the broadening malware of the entire network,thus highlighting the demand for their detection while massive datasets(i.e.,big data)are processed.Despite the application of boosting mechanisms in phishing detection,these methods are prone to significant errors in their output,specifically due to the combination of all website features in the training state.The upcoming big data system requires MapReduce,a popular parallel programming,to process massive datasets.To address these issues,a probabilistic latent semantic and greedy levy gradient boosting(PLS-GLGB)algorithm for website phishing detection using MapReduce is proposed.A feature selection-based model is provided using a probabilistic intersective latent semantic preprocessing model to minimize errors in website phishing detection.Here,the missing data in each URL are identified and discarded for further processing to ensure data quality.Subsequently,with the preprocessed features(URLs),feature vectors are updated by the greedy levy divergence gradient(model)that selects the optimal features in the URL and accurately detects the websites.Thus,greedy levy efficiently differentiates between phishing websites and legitimate websites.Experiments are conducted using one of the largest public corpora of a website phish tank dataset.Results show that the PLS-GLGB algorithm for website phishing detection outperforms stateof-the-art phishing detection methods.Significant amounts of phishing detection time and errors are also saved during the detection of website phishing.展开更多
Global climate change and sea level rise have led to increased losses from flooding.Accurate prediction of floods is essential to mitigating flood losses in coastal cities.Physically based models cannot satisfy the de...Global climate change and sea level rise have led to increased losses from flooding.Accurate prediction of floods is essential to mitigating flood losses in coastal cities.Physically based models cannot satisfy the demand for real-time prediction for urban flooding due to their computational complexity.In this study,we proposed a hybrid modeling approach for rapid prediction of urban floods,coupling the physically based model with the light gradient boosting machine(LightGBM)model.A hydrological–hydraulic model was used to provide sufficient data for the LightGBM model based on the personal computer storm water management model(PCSWMM).The variables related to rainfall,tide level,and the location of flood points were used as the input for the LightGBM model.To improve the prediction accuracy,the hyperparameters of the LightGBM model are optimized by grid search algorithm and K-fold cross-validation.Taking Haidian Island,Hainan Province,China as a case study,the optimum values of the learning rate,number of estimators,and number of leaves of the LightGBM model are 0.11,450,and 12,respectively.The Nash-Sutcliffe efficiency coefficient(NSE)of the LightGBM model on the test set is 0.9896,indicating that the LightGBM model has reliable predictions and outperforms random forest(RF),extreme gradient boosting(XGBoost),and k-nearest neighbor(KNN).From the LightGBM model,the variables related to tide level were analyzed as the dominant variables for predicting the inundation depth based on the Gini index in the study area.The proposed LightGBM model provides a scientific reference for flood control in coastal cities considering its superior performance and computational efficiency.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.
文摘Accurate prediction ofmonthly oil and gas production is essential for oil enterprises tomake reasonable production plans,avoid blind investment and realize sustainable development.Traditional oil well production trend prediction methods are based on years of oil field production experience and expertise,and the application conditions are very demanding.With the rapid development of artificial intelligence technology,big data analysis methods are gradually applied in various sub-fields of the oil and gas reservoir development.Based on the data-driven artificial intelligence algorithmGradient BoostingDecision Tree(GBDT),this paper predicts the initial single-layer production by considering geological data,fluid PVT data and well data.The results show that the GBDT algorithm prediction model has great accuracy,significantly improving efficiency and strong universal applicability.The GBDTmethod trained in this paper can predict production,which is helpful for well site optimization,perforation layer optimization and engineering parameter optimization and has guiding significance for oilfield development.
文摘The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. This paper proposes an automated methodology for mapping burn scars using pairs of Sentinel-2 imagery, exploiting the state-of-the-art eXtreme Gradient Boosting (XGB) machine learning framework. A large database of 64 reference wildfire perimeters in Greece from 2016 to 2019 is used to train the classifier. An empirical methodology for appropriately sampling the training patterns from this database is formulated, which guarantees the effectiveness of the approach and its computational efficiency. A difference (pre-fire minus post-fire) spectral index is used for this purpose, upon which we appropriately identify the clear and fuzzy value ranges. To reduce the data volume, a super-pixel segmentation of the images is also employed, implemented via the QuickShift algorithm. The cross-validation results showcase the effectiveness of the proposed algorithm, with the average commission and omission errors being 9% and 2%, respectively, and the average Matthews correlation coefficient (MCC) equal to 0.93.
文摘Addressing classification and prediction challenges, tree ensemble models have gained significant importance. Boosting ensemble techniques are commonly employed for forecasting Type-II diabetes mellitus. Light Gradient Boosting Machine (LightGBM) is a widely used algorithm known for its leaf growth strategy, loss reduction, and enhanced training precision. However, LightGBM is prone to overfitting. In contrast, CatBoost utilizes balanced base predictors known as decision tables, which mitigate overfitting risks and significantly improve testing time efficiency. CatBoost’s algorithm structure counteracts gradient boosting biases and incorporates an overfitting detector to stop training early. This study focuses on developing a hybrid model that combines LightGBM and CatBoost to minimize overfitting and improve accuracy by reducing variance. For the purpose of finding the best hyperparameters to use with the underlying learners, the Bayesian hyperparameter optimization method is used. By fine-tuning the regularization parameter values, the hybrid model effectively reduces variance (overfitting). Comparative evaluation against LightGBM, CatBoost, XGBoost, Decision Tree, Random Forest, AdaBoost, and GBM algorithms demonstrates that the hybrid model has the best F1-score (99.37%), recall (99.25%), and accuracy (99.37%). Consequently, the proposed framework holds promise for early diabetes prediction in the healthcare industry and exhibits potential applicability to other datasets sharing similarities with diabetes.
基金financial support from High-end Foreign Expert Introduction program(No.G20190022002)Chongqing Construction Science and Technology Plan Project(2019-0045)as well as Chongqing Engineering Research Center of Disaster Prevention&Control for Banks and Structures in Three Gorges Reservoir Area(Nos.SXAPGC18ZD01 and SXAPGC18YB03)。
文摘Accurate assessment of undrained shear strength(USS)for soft sensitive clays is a great concern in geotechnical engineering practice.This study applies novel data-driven extreme gradient boosting(XGBoost)and random forest(RF)ensemble learning methods for capturing the relationships between the USS and various basic soil parameters.Based on the soil data sets from TC304 database,a general approach is developed to predict the USS of soft clays using the two machine learning methods above,where five feature variables including the preconsolidation stress(PS),vertical effective stress(VES),liquid limit(LL),plastic limit(PL)and natural water content(W)are adopted.To reduce the dependence on the rule of thumb and inefficient brute-force search,the Bayesian optimization method is applied to determine the appropriate model hyper-parameters of both XGBoost and RF.The developed models are comprehensively compared with three comparison machine learning methods and two transformation models with respect to predictive accuracy and robustness under 5-fold cross-validation(CV).It is shown that XGBoost-based and RF-based methods outperform these approaches.Besides,the XGBoostbased model provides feature importance ranks,which makes it a promising tool in the prediction of geotechnical parameters and enhances the interpretability of model.
基金This study was jointly supported by the National Natural Science Foundation of China(Nos.51879196,51790533,51709143)Jiangxi Natural Science Foundation of China(No.20181BAB206045).
文摘It is important for regional water resources management to know the agricultural water consumption information several months in advance.Forecasting reference evapotranspiration(ET_(0))in the next few months is important for irrigation and reservoir management.Studies on forecasting of multiple-month ahead ET_(0) using machine learning models have not been reported yet.Besides,machine learning models such as the XGBoost model has multiple parameters that need to be tuned,and traditional methods can get stuck in a regional optimal solution and fail to obtain a global optimal solution.This study investigated the performance of the hybrid extreme gradient boosting(XGBoost)model coupled with the Grey Wolf Optimizer(GWO)algorithm for forecasting multi-step ahead ET_(0)(1-3 months ahead),compared with three conventional machine learning models,i.e.,standalone XGBoost,multi-layer perceptron(MLP)and M5 model tree(M5)models in the subtropical zone of China.The results showed that theGWO-XGB model generally performed better than the other three machine learning models in forecasting 1-3 months ahead ET_(0),followed by the XGB,M5 and MLP models with very small differences among the three models.The GWO-XGB model performed best in autumn,while the MLP model performed slightly better than the other three models in summer.It is thus suggested to apply the MLP model for ET_(0) forecasting in summer but use the GWO-XGB model in other seasons.
文摘In the process of oil recovery,experiments are usually carried out on core samples to evaluate the recovery of oil,so the numerical data are fitted into a non-dimensional equation called scaling-law.This will be essential for determining the behavior of actual reservoirs.The global non-dimensional time-scale is a parameter for predicting a realistic behavior in the oil field from laboratory data.This non-dimensional universal time parameter depends on a set of primary parameters that inherit the properties of the reservoir fluids and rocks and the injection velocity,which dynamics of the process.One of the practical machine learning(ML)techniques for regression/classification problems is gradient boosting(GB)regression.The GB produces a prediction model as an ensemble of weak prediction models that can be done at each iteration by matching a least-squares base-learner with the current pseudoresiduals.Using a randomization process increases the execution speed and accuracy of GB.Hence in this study,we developed a stochastic regression model of gradient boosting(SGB)to forecast oil recovery.Different nondimensional time-scales have been used to generate data to be used with machine learning techniques.The SGB method has been found to be the best machine learning technique for predicting the non-dimensional time-scale,which depends on oil/rock properties.
基金The financial support provided from the Deanship of Scientific Research at King SaudUniversity,Research group No.RG-1441-502.
文摘The fast spread of coronavirus disease(COVID-19)caused by SARSCoV-2 has become a pandemic and a serious threat to the world.As of May 30,2020,this disease had infected more than 6 million people globally,with hundreds of thousands of deaths.Therefore,there is an urgent need to predict confirmed cases so as to analyze the impact of COVID-19 and practice readiness in healthcare systems.This study uses gradient boosting regression(GBR)to build a trained model to predict the daily total confirmed cases of COVID-19.The GBR method can minimize the loss function of the training process and create a single strong learner from weak learners.Experiments are conducted on a dataset of daily confirmed COVID-19 cases from January 22,2020,to May 30,2020.The results are evaluated on a set of evaluation performance measures using 10-fold cross-validation to demonstrate the effectiveness of the GBR method.The results reveal that the GBR model achieves 0.00686 root mean square error,the lowest among several comparative models.
文摘This paper aims to design an optimizer followed by a Kawahara filter for optimal classification and prediction of employees’performance.The algorithm starts by processing data by a modified K-means technique as a hierarchical clustering method to quickly obtain the best features of employees to reach their best performance.The work of this paper consists of two parts.The first part is based on collecting data of employees to calculate and illustrate the performance of each employee.The second part is based on the classification and prediction techniques of the employee performance.This model is designed to help companies in their decisions about the employees’performance.The classification and prediction algorithms use the Gradient Boosting Tree classifier to classify and predict the features.Results of the paper give the percentage of employees which are expected to leave the company after predicting their performance for the coming years.Results also show that the Grasshopper Optimization,followed by“KF”with the Gradient Boosting Tree as classifier and predictor,is characterized by a high accuracy.The proposed algorithm is compared with other known techniques where our results are fund to be superior.
基金This work is supported by the National Nature Science Foundation of China(No.51875100,No.61673108,No.61674133)The authors would like to thank anonymous reviewers and the associate editor,whose constructive comments help improve the presentation of this work.
文摘Coal rock mass instability fracture may result in serious hazards to underground coal mining.Acoustic emissions(AE)stimulated by internal structure fracture should carry lots of favorable information about health condition of rock mass.AE as a sensitive non-destructive test method is gradually utilized to detect anomaly conditions of coal rock.This paper proposes an improved multi-resolution feature to extract AE waveform at different frequency resolutions using Coilflet Wavelet Transform method(CWT).It is further adopt an efficient Light Gradient Boosting Machine(LightGBM)by several cascaded sub weak classifier models to merge AE features at different views of frequency for coal rock anomaly damage recognition.The results denote that the proposed method achieves excellent recognition performance on anomaly damage levels of coal rock.It is an effective method to detect the critical stability further to predict the rock mass bursting in time.
文摘In today’s world of connectivity there is a huge amount of data than we could imagine.The number of network users are increasing day by day and there are large number of social networks which keeps the users connected all the time.These social networks give the complete independence to the user to post the data either political,commercial or entertainment value.Some data may be sensitive and have a greater impact on the society as a result.The trustworthiness of data is important when it comes to public social networking sites like facebook and twitter.Due to the large user base and its openness there is a huge possibility to spread spam messages in this network.Spam detection is a technique to identify and mark data as a false data value.There are lot of machine learning approaches proposed to detect spam in social networks.The efficiency of any spam detection algorithm is determined by its cost factor and accuracy.Aiming to improve the detection of spam in the social networks this study proposes using statistical based features that are modelled through the supervised boosting approach called Stochastic gradient boosting to evaluate the twitter data sets in the English language.The performance of the proposed model is evaluated using simulation results.
基金supported by the National Natural Science Foundation of China(61871046).
文摘With the increasing of civil aviation business,flight delay has become a key problem in civil aviation field in recent years,which has brought a considerable economic impact to airlines and related industries.The delay prediction of specific flights is very important for airlines’plan,airport resource allocation,insurance company strategy and personal arrangement.The influence factors of flight delay have high complexity and non-linear relationship.The different situations of various regions and airports,and even the deviation of airport or airline arrangement all have certain influence on flight delay,which makes the prediction more difficult.In view of the limitations of the existing delay prediction models,this paper proposes a flight delay prediction model with more generalization ability and corresponding machine learning classification algorithm.This model fully exploits temporal and spatial characteristics of higher dimensions,such as the influence of preceding flights,the situation of departure and landing airports,and the overall situation of flights on the same route.In the process of machine learning,the model is trained with historical data and tested with the latest actual data.The test result shows that the model and this machine learning algorithm can provide an effective method for the prediction of flight delay.
基金This work was supported in part by the National Natural Science Foundation of China(61601418,41602362,61871259)in part by the Opening Foundation of Hunan Engineering and Research Center of Natural Resource Investigation and Monitoring(2020-5)+1 种基金in part by the Qilian Mountain National Park Research Center(Qinghai)(grant number:GKQ2019-01)in part by the Geomatics Technology and Application Key Laboratory of Qinghai Province,Grant No.QHDX-2019-01.
文摘This work was to generate landslide susceptibility maps for the Three Gorges Reservoir(TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting decision tree(GBDT), random forest(RF) and information value(InV) models, were used, and the performances were assessed and compared. In total, 202 landslides were mapped by using a series of field surveys, aerial photographs, and reviews of historical and bibliographical data. Nine causative factors were then considered in landslide susceptibility map generation by using the GBDT, RF and InV models. All of the maps of the causative factors were resampled to a resolution of 28.5 m. Of the 486289 pixels in the area,28526 pixels were landslide pixels, and 457763 pixels were non-landslide pixels. Finally, landslide susceptibility maps were generated by using the three machine learning models, and their performances were assessed through receiver operating characteristic(ROC) curves, the sensitivity, specificity,overall accuracy(OA), and kappa coefficient(KAPPA). The results showed that the GBDT, RF and In V models in overall produced reasonable accurate landslide susceptibility maps. Among these three methods, the GBDT method outperforms the other two machine learning methods, which can provide strong technical support for producing landslide susceptibility maps in TGR.
文摘The detection of phishing and legitimate websites is considered a great challenge for web service providers because the users of such websites are indistinguishable.Phishing websites also create traffic in the entire network.Another phishing issue is the broadening malware of the entire network,thus highlighting the demand for their detection while massive datasets(i.e.,big data)are processed.Despite the application of boosting mechanisms in phishing detection,these methods are prone to significant errors in their output,specifically due to the combination of all website features in the training state.The upcoming big data system requires MapReduce,a popular parallel programming,to process massive datasets.To address these issues,a probabilistic latent semantic and greedy levy gradient boosting(PLS-GLGB)algorithm for website phishing detection using MapReduce is proposed.A feature selection-based model is provided using a probabilistic intersective latent semantic preprocessing model to minimize errors in website phishing detection.Here,the missing data in each URL are identified and discarded for further processing to ensure data quality.Subsequently,with the preprocessed features(URLs),feature vectors are updated by the greedy levy divergence gradient(model)that selects the optimal features in the URL and accurately detects the websites.Thus,greedy levy efficiently differentiates between phishing websites and legitimate websites.Experiments are conducted using one of the largest public corpora of a website phish tank dataset.Results show that the PLS-GLGB algorithm for website phishing detection outperforms stateof-the-art phishing detection methods.Significant amounts of phishing detection time and errors are also saved during the detection of website phishing.
基金supported by the State Key Laboratory of Hydraulic Engineering Simulation and Safety(Tianjin University)(Grant Number HESS-2106),Scientific and Technological Projects of Henan Province(Grant Number 222102320025)Key Scientific Research Project in Colleges and Universities of Henan Province of China(Grant Number 22B570003)+2 种基金National Natural Science Foundation of China(Grant Number 52109040,51739009)Excellent Youth Fund of Henan Province of China(212300410088)Science and Technology Innovation Talents Project of Henan Education Department of China(21HASTIT011).
文摘Global climate change and sea level rise have led to increased losses from flooding.Accurate prediction of floods is essential to mitigating flood losses in coastal cities.Physically based models cannot satisfy the demand for real-time prediction for urban flooding due to their computational complexity.In this study,we proposed a hybrid modeling approach for rapid prediction of urban floods,coupling the physically based model with the light gradient boosting machine(LightGBM)model.A hydrological–hydraulic model was used to provide sufficient data for the LightGBM model based on the personal computer storm water management model(PCSWMM).The variables related to rainfall,tide level,and the location of flood points were used as the input for the LightGBM model.To improve the prediction accuracy,the hyperparameters of the LightGBM model are optimized by grid search algorithm and K-fold cross-validation.Taking Haidian Island,Hainan Province,China as a case study,the optimum values of the learning rate,number of estimators,and number of leaves of the LightGBM model are 0.11,450,and 12,respectively.The Nash-Sutcliffe efficiency coefficient(NSE)of the LightGBM model on the test set is 0.9896,indicating that the LightGBM model has reliable predictions and outperforms random forest(RF),extreme gradient boosting(XGBoost),and k-nearest neighbor(KNN).From the LightGBM model,the variables related to tide level were analyzed as the dominant variables for predicting the inundation depth based on the Gini index in the study area.The proposed LightGBM model provides a scientific reference for flood control in coastal cities considering its superior performance and computational efficiency.
