Light gradient boosting machine with optimized hyperparameters for identification of malicious access in IoT network

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.

Coal Rock Condition Detection Model Using Acoustic Emission and Light Gradient Boosting Machine

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.

A Hybrid Ensemble Learning Approach Utilizing Light Gradient Boosting Machine and Category Boosting Model for Lifestyle-Based Prediction of Type-II Diabetes Mellitus

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.

Rapid Prediction Model for Urban Floods Based on a Light Gradient Boosting Machine Approach and Hydrological–Hydraulic Model

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.

Explainable machine learning model for predicting molten steel temperature in the LF refining process

Accurate prediction of molten steel temperature in the ladle furnace(LF)refining process has an important influence on the quality of molten steel and the control of steelmaking cost.Extensive research on establishing models to predict molten steel temperature has been conducted.However,most researchers focus solely on improving the accuracy of the model,neglecting its explainability.The present study aims to develop a high-precision and explainable model with improved reliability and transparency.The eXtreme gradient boosting(XGBoost)and light gradient boosting machine(LGBM)were utilized,along with bayesian optimization and grey wolf optimiz-ation(GWO),to establish the prediction model.Different performance evaluation metrics and graphical representations were applied to compare the optimal XGBoost and LGBM models obtained through varying hyperparameter optimization methods with the other models.The findings indicated that the GWO-LGBM model outperformed other methods in predicting molten steel temperature,with a high pre-diction accuracy of 89.35%within the error range of±5°C.The model’s learning/decision process was revealed,and the influence degree of different variables on the molten steel temperature was clarified using the tree structure visualization and SHapley Additive exPlana-tions(SHAP)analysis.Consequently,the explainability of the optimal GWO-LGBM model was enhanced,providing reliable support for prediction results.

An Intrusion Detection System for SDN Using Machine Learning

Software Defined Networking(SDN)has emerged as a promising and exciting option for the future growth of the internet.SDN has increased the flexibility and transparency of the managed,centralized,and controlled network.On the other hand,these advantages create a more vulnerable environment with substantial risks,culminating in network difficulties,system paralysis,online banking frauds,and robberies.These issues have a significant detrimental impact on organizations,enterprises,and even economies.Accuracy,high performance,and real-time systems are necessary to achieve this goal.Using a SDN to extend intelligent machine learning methodologies in an Intrusion Detection System(IDS)has stimulated the interest of numerous research investigators over the last decade.In this paper,a novel HFS-LGBM IDS is proposed for SDN.First,the Hybrid Feature Selection algorithm consisting of two phases is applied to reduce the data dimension and to obtain an optimal feature subset.In thefirst phase,the Correlation based Feature Selection(CFS)algorithm is used to obtain the feature subset.The optimal feature set is obtained by applying the Random Forest Recursive Feature Elimination(RF-RFE)in the second phase.A LightGBM algorithm is then used to detect and classify different types of attacks.The experimental results based on NSL-KDD dataset show that the proposed system produces outstanding results compared to the existing methods in terms of accuracy,precision,recall and f-measure.

考虑环境因素的电动汽车充电站实时负荷预测模型

为了减少电动汽车大规模集成到电网造成的不利影响,提出了一种能够实现充电站充电负荷精准预测的方法。该方法利用LightGBM(light gradient boosting machine)与XGBoost(eXtreme gradient boosting)模型构建线下-线上组合模型。考虑充电负荷、时间、温度、天气等历史数据,利用LightGBM模型初步建立充电负荷线下预测模型;基于XGBoost模型,以线下预测模型输出负荷和实际负荷的误差为优化目标,实时变化的交通流量为协变量,建立线上预测模型,并对初步预测结果进行误差修正。某市实际充电站预测结果表明,相比于随机森林(RF)、LightGBM模型、XGBoost模型、多层感知机(MLP)以及LightGBM-RF组合模型,该组合模型具有更高的预测精度,同时可以准确预测不同充电站的实时充电负荷。

Classification of aviation incident causes using LGBM with improved cross-validation

Aviation accidents are currently one of the leading causes of significant injuries and deaths worldwide. This entices researchers to investigate aircraft safety using data analysis approaches based on an advanced machine learning algorithm.To assess aviation safety and identify the causes of incidents, a classification model with light gradient boosting machine (LGBM)based on the aviation safety reporting system (ASRS) has been developed. It is improved by k-fold cross-validation with hybrid sampling model (HSCV), which may boost classification performance and maintain data balance. The results show that employing the LGBM-HSCV model can significantly improve accuracy while alleviating data imbalance. Vertical comparison with other cross-validation (CV) methods and lateral comparison with different fold times comprise the comparative approach. Aside from the comparison, two further CV approaches based on the improved method in this study are discussed:one with a different sampling and folding order, and the other with more CV. According to the assessment indices with different methods, the LGBMHSCV model proposed here is effective at detecting incident causes. The improved model for imbalanced data categorization proposed may serve as a point of reference for similar data processing, and the model’s accurate identification of civil aviation incident causes can assist to improve civil aviation safety.

基于机器学习的热带气旋灾害等级评估模型构建及其活动特征分析

在全球变暖的背景下,热带气旋(TC)作为影响我国最严重的自然灾害之一,其活动特征及灾害损失评估研究受到了广泛关注。采用组合赋权和k-means等方法,分析了2000年以来登陆我国的TC及灾害损失特征,并构建了基于机器学习的TC灾害等级评估模型。结果表明:从总体趋势来看,登陆我国的TC频数在逐年减少,但登陆风速的最大值却在缓慢增加;广东、浙江、福建、广西受灾较为严重,但整体上全国综合灾情指数呈下降趋势;与传统的随机森林、支持向量机、朴素贝叶斯算法相比,LightGBM(Light Gradient Boosting Machine)在TC灾害评估中效果最佳,准确率值为0.91,其中致灾因子是模型中最关键的因素,其次是防灾减灾能力、暴露度和脆弱性指标。

基于不同算法预测模型在阻塞性睡眠呼吸暂停低通气综合征中的比较

目的 基于不同算法对阻塞性睡眠呼吸暂停低通气综合征(OSAHS)患者进行建模,比较四种模型的受试者工作曲线下面积(AUC)、准确率、灵敏度与特异性,以建立自动筛选和诊断OSAHS的机器学习预测模型。方法 回顾性分析2019年10月~2022年10月于徐州医科大学附属医院行多导睡眠监测的277例患者,以人口学信息、病史、ESS作为预测变量,采用单因素分析筛选出具有明显差异的变量,然后分别建立轻量级梯度提升机(LightGBM)模型、逻辑回归(LR)模型、极限梯度提升(XGBoost)模型及支持向量机(SVM)模型,采用AUC评价模型性能。结果 LR预测的准确率为0.91,AUC为0.97;XGBoost的预测准确率为0.94,AUC为0.97;LightGBM的预测准确率为0.93,AUC为0.98;SVM的准确率为0.89,AUC为0.95。结论 LR、SVM、XGBoost、LightGBM对OSAHS预测效果均较好,LightGBM的效果最佳。

Ground Passive Microwave Remote Sensing of Atmospheric Profiles Using WRF Simulations and Machine Learning Techniques

Microwave radiometer(MWR) demonstrates exceptional efficacy in monitoring the atmospheric temperature and humidity profiles.A typical inversion algorithm for MWR involves the use of radiosonde measurements as the training dataset.However,this is challenging due to limitations in the temporal and spatial resolution of available sounding data,which often results in a lack of coincident data with MWR deployment locations.Our study proposes an alternative approach to overcome these limitations by harnessing the Weather Research and Forecasting(WRF) model's renowned simulation capabilities,which offer high temporal and spatial resolution.By using WRF simulations that collocate with the MWR deployment location as a substitute for radiosonde measurements or reanalysis data,our study effectively mitigates the limitations associated with mismatching of MWR measurements and the sites,which enables reliable MWR retrieval in diverse geographical settings.Different machine learning(ML) algorithms including extreme gradient boosting(XGBoost),random forest(RF),light gradient boosting machine(LightGBM),extra trees(ET),and backpropagation neural network(BPNN) are tested by using WRF simulations,among which BPNN appears as the most superior,achieving an accuracy with a root-mean-square error(RMSE) of 2.05 K for temperature,0.67 g m~(-3) for water vapor density(WVD),and 13.98% for relative humidity(RH).Comparisons of temperature,RH,and WVD retrievals between our algorithm and the sounding-trained(RAD) algorithm indicate that our algorithm remarkably outperforms the latter.This study verifies the feasibility of utilizing WRF simulations for developing MWR inversion algorithms,thus opening up new possibilities for MWR deployment and airborne observations in global locations.

A method for modelling greenhouse temperature using gradient boost decision tree

y consumption efficiency and to increase the crop yield.With the increase of agri-cultural data generated by the Internet of Things(IoT),more feasible models are necessary to get full usage of such information.In this research,a Gradient Boost Decision Tree(GBDT)model based on the newly-developed Light Gradient Boosting Machine algorithm(LightGBM or LGBM)was proposed to model the internal temperature of a greenhouse.Fea-tures including climate variables,control variables and additional temporal information collected within five years were used to construct a suitable dataset to train and validate the LGBM model.An adaptive cross-validation method was developed as a novelty to improve the LGBM model performance and self-adaptive ability.For comparison of the pre-dictive accuracy,a Back-Propagation(BP)Neural Network model and a Recurrent Neural Network(RNN)model were built under the same process.Another two GBDT algorithms,Extreme Gradient Boosting(Xgboost)and Stochastic Gradient Boosting(SGB),were also introduced to compare the predictive accuracy with LGBM model.Results suggest that the LGBM has best fitting ability for the temperature curves with RMSE value at 0.645℃,as well as the fastest training speed among all algorithms with 60 times faster than the other two neural network algorithms.The LGBM has strongly potential application pro-spect on both greenhouse environment prediction and real-time predictive control.

An Ensemble Classification Model Based on Imbalanced Data for Aviation Safety

Nowadays aviation accidents have become one of the major causes of severe injuries and fatalities around the world. This attracts the research community to look into aviation safety by applying data analysis techniques based on an advanced machine learning algorithm. An ensemble classification model based on Aviation Safety Reporting System(ASRS) has been proposed to analyze aviation safety targeting the people injured in the system.The ensemble classification model shall contain two modules: the data-driven module consisting of data cleaning, feature selection,and imbalanced data division and reorganization, and the modeldriven module stacked by Random Forest(RF), XGBoost(XGB),and Light Gradient Boosting Machine(LGBM) separately. The results indicate that the ensemble model could solve the data imbalance while vastly improving accuracy. LGBM illustrates higher accuracy and faster run in the analysis of a single model of the ASRS-based imbalanced data, while the ensemble model has the best performance in classification at the same time. The ensemble model proposed for imbalanced data classification can provide a certain reference for similar data processing while improving the safety of civil aviation.

Data-driven model for predicting production periods in the SAGD process

Many studies have analyzed the cumulative production performance in the SAGD(steam assisted gravity drainage)process by data-driven models but a study based on these models for a dynamic analysis of a SAGD production period is still rare.It is important for engineers to define the production period in a SAGD process as it has a stable and high oil production rate and engineers need to reset operational conditions after the production period starts.In this paper,a series of SAGD models were constructed with selected ranges of reservoir properties and operational conditions.Three SAGD production period parameters,including the start date,end date,and duration,are collected based on the simulated production performances.artificial neural network,extreme gradient boosting,light gradient boosting machine,and catboost were constructed to reveal the hidden relationships between twelve input parameters and three output parameters.The data-driven models were trained,tested,and evaluated.The results showed that compared with the other output parameters,the R^(2) of the end date is the highest and it becomes higher with a larger training data set.The extreme gradient boosting algorithm is a better choice to predict the Start date while the artificial neural network generates better prediction for the other two output parameters.This study shows a significant potential in the use of data-driven models for the SAGD production dynamic analysis.The results also serve to support the utilization of the datadriven models as efficient tools for predicting a SAGD production period.