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
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
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
局地微地形产生的微气象环境是造成气象预报误差的重要因素之一,也是导致覆冰预报准确性不高的主要原因。该研究利用高精度MODIS系统15 s(约500 m)地形数据驱动中尺度天气研究和预报(weather research and forecasting,WRF)模式,并使用...局地微地形产生的微气象环境是造成气象预报误差的重要因素之一,也是导致覆冰预报准确性不高的主要原因。该研究利用高精度MODIS系统15 s(约500 m)地形数据驱动中尺度天气研究和预报(weather research and forecasting,WRF)模式,并使用基于决策树的梯度提升框架(light gradient boosting machine,LightGBM)对WRF预报进行订正,通过局地个例评估订正后的覆冰预测效果。结果表明:在假设条件下,过冷液滴覆冰速率随温度降低先快速增加,后增长速率保持不变,且液滴粒径越大,完全冻结所需温度越低;WRF-LightGBM订正算法在山区微地形下有效提升了温度预报准确度,典型冬季寒潮条件下预测温度与实际温度的误差在2℃以内,预报准确率为76%;以典型区域杆塔覆冰为例,输入订正后的温度和相对湿度数据后,覆冰融化时段被消除,覆冰厚度曲线与实际基本一致,增长速率接近一致。展开更多
文摘In this paper,an advanced and optimized Light Gradient Boosting Machine(LGBM)technique is proposed to identify the intrusive activities in the Internet of Things(IoT)network.The followings are the major contributions:i)An optimized LGBM model has been developed for the identification of malicious IoT activities in the IoT network;ii)An efficient evolutionary optimization approach has been adopted for finding the optimal set of hyper-parameters of LGBM for the projected problem.Here,a Genetic Algorithm(GA)with k-way tournament selection and uniform crossover operation is used for efficient exploration of hyper-parameter search space;iii)Finally,the performance of the proposed model is evaluated using state-of-the-art ensemble learning and machine learning-based model to achieve overall generalized performance and efficiency.Simulation outcomes reveal that the proposed approach is superior to other considered methods and proves to be a robust approach to intrusion detection in an IoT environment.
基金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.
文摘局地微地形产生的微气象环境是造成气象预报误差的重要因素之一,也是导致覆冰预报准确性不高的主要原因。该研究利用高精度MODIS系统15 s(约500 m)地形数据驱动中尺度天气研究和预报(weather research and forecasting,WRF)模式,并使用基于决策树的梯度提升框架(light gradient boosting machine,LightGBM)对WRF预报进行订正,通过局地个例评估订正后的覆冰预测效果。结果表明:在假设条件下,过冷液滴覆冰速率随温度降低先快速增加,后增长速率保持不变,且液滴粒径越大,完全冻结所需温度越低;WRF-LightGBM订正算法在山区微地形下有效提升了温度预报准确度,典型冬季寒潮条件下预测温度与实际温度的误差在2℃以内,预报准确率为76%;以典型区域杆塔覆冰为例,输入订正后的温度和相对湿度数据后,覆冰融化时段被消除,覆冰厚度曲线与实际基本一致,增长速率接近一致。
