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
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
提出一种基于旋转不变信号参数估计技术ESPRIT(Estimation of signal parameters via rotational invariance technique)、模式搜索算法PSA(Pattern search algorithm)与轻型梯度提升机LightGBM(Light gradient boosting machine)结合...提出一种基于旋转不变信号参数估计技术ESPRIT(Estimation of signal parameters via rotational invariance technique)、模式搜索算法PSA(Pattern search algorithm)与轻型梯度提升机LightGBM(Light gradient boosting machine)结合的感应电动机转子断条数目诊断新方法。模拟了转子断条故障下的瞬时无功功率信号并用其衡量ESPRIT-PSA的性能。结果表明:ESPRIT-PSA只需短时数据就能准确测量瞬时无功功率信号中的转子断条故障特征分量。随后,为解决现有的电机瞬时无功功率信号分析MIRPSA(Motor instantaneous reactive power signal analysis)类方法无法准确诊断转子断条数目的问题,引入LightGBM对转子断条故障进行多分类以准确诊断转子断条数目。最后针对一台异步电动机进行转子断条诊断实验,结果表明:该方法是有效的,并且因将瞬时无功功率作为分析信号而适用于电机低转差率的情况。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
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.
文摘提出一种基于旋转不变信号参数估计技术ESPRIT(Estimation of signal parameters via rotational invariance technique)、模式搜索算法PSA(Pattern search algorithm)与轻型梯度提升机LightGBM(Light gradient boosting machine)结合的感应电动机转子断条数目诊断新方法。模拟了转子断条故障下的瞬时无功功率信号并用其衡量ESPRIT-PSA的性能。结果表明:ESPRIT-PSA只需短时数据就能准确测量瞬时无功功率信号中的转子断条故障特征分量。随后,为解决现有的电机瞬时无功功率信号分析MIRPSA(Motor instantaneous reactive power signal analysis)类方法无法准确诊断转子断条数目的问题,引入LightGBM对转子断条故障进行多分类以准确诊断转子断条数目。最后针对一台异步电动机进行转子断条诊断实验,结果表明:该方法是有效的,并且因将瞬时无功功率作为分析信号而适用于电机低转差率的情况。
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.
