检索结果-南通市图书馆

Digital Communications and Networks 2023年第1期9卷 125-137页

作者： Debasmita Mishra Bighnaraj Naik Janmenjoy Nayak Alireza Souri Pandit Byomakesha Dash S.Vimal Department of Computer Application Veer Surendra Sai University of TechnologyBurlaSambalpur768018OdishaIndia Department of Computer Science Maharaja Sriram Chandra Bhanja Deo(MSCB)UniversityBaripada757003OdishaIndia Department of Software Engineering HaliçUniversity34394IstanbulTurkey Department of Information Technology Aditya Institute of Technology and Management(AITAM)Tekkali532201Andhra PradeshIndia Data Analytics Lab Department of Artificial Intelligence and Data ScienceRamco Institute of TechnologyNorth Venganallur VillageRajapalayam 626117 Virudhunagar DistrictTamilnaduIndia

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)*** 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 ***,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 *** 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.

关键词： IoT security Ensemble method light gradient boosting machine machine learning Intrusion detection

Rapid Prediction Model for Urban Floods Based on a light gradient boosting machine Approach and Hydrological–Hydraulic Model

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

International Journal of Disaster Risk Science 2023年第1期14卷 79-97页

作者： Kui Xu Zhentao Han Hongshi Xu Lingling Bin State Key Laboratory of Hydraulic Engineering Simulation and Safety Tianjin UniversityTianjin300350China Yellow River Laboratory Zhengzhou UniversityZhengzhou450001China School of Geographic and Environmental Sciences Tianjin Normal UniversityTianjin300387China

Global climate change and sea level rise have led to increased losses from *** prediction of floods is essential to mitigating flood losses in coastal *** based models cannot satisfy the demand for real-time prediction for urban flooding due to their computational *** 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 *** improve the prediction accuracy,the hyperparameters of the lightGBM model are optimized by grid search algorithm and K-fold *** 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,*** 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 *** proposed lightGBM model provides a scientific reference for flood control in coastal cities considering its superior performance and computational efficiency.

关键词： China Flood prediction Hainan Hydrological-hydraulic model light gradient boosting machine Urban floods

Coal Rock Condition Detection Model Using Acoustic Emission and light gradient boosting machine

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2020年第4期63卷 151-162页

作者： Jing Li Yong Yang Hongmei Ge Li Zhao Ruxue Guo School of Information Engineering Nanjing Audit UniversityNanjing211815China School of Information and Electrical Engineering Xuzhou College of Industrial TechnologyXuzhou221140China School of Information Engineering Southeast UniversitySi Pailou CampusNanjing210096China Department of Psychiatry Vagelos College of Physicians and SurgeonsNew York10027USA

Coal rock mass instability fracture may result in serious hazards to underground coal *** emissions(AE)stimulated by internal structure fracture should carry lots of favorable information about health condition of rock *** as a sensitive non-destructive test method is gradually utilized to detect anomaly conditions of coal *** 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 *** results denote that the proposed method achieves excellent recognition performance on anomaly damage levels of coal *** is an effective method to detect the critical stability further to predict the rock mass bursting in time.

关键词： Acoustic emission light gradient boosting machine coal rock stability

A Hybrid Ensemble Learning Approach Utilizing light gradient boosting machine and Category boosting Model for Lifestyle-Based Prediction of Type-II Diabetes Mellitus

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Data Analysis and Information Processing 2023年第4期11卷 480-511页

作者： Mahadi Nagassou Ronald Waweru Mwangi Euna Nyarige Institute for Basic Sciences Technology and Innovation Department of Mathematics Pan African University Nairobi Kenya School of Computing and Information Technology Department of Computing Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya Department of Mathematics Statistics and Actuarial Sciences Machakos University Nairobi Kenya

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.

关键词： boosting Ensemble Learning Category boosting light gradient boosting machine

HEN:a novel hybrid explainable neural network based framework for robust network intrusion detection

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Science China(Information Sciences) 2024年第7期67卷 72-90页

作者： Wei WEI Sijin CHEN Cen CHEN Heshi WANG Jing LIU Zhongyao CHENG Xiaofeng ZOU School of Computer Science and Engineering Xi'an University of TechnologyShaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Technology Wuhan University of Science and Technology School of Future Technology South China University of Technology Shenzhen Research Institute of Hunan University School of Computer Science Hunan University of Technology and Business Institute for Infocomm Research (I2R) Agency for Science Technology and Reseach (A*STAR)

With the rapid development of network technology and the automation process for 5G, cyberattacks have become increasingly complex and threatening. In response to these threats, researchers have developed various network intrusion detection systems(NIDS) to monitor network traffic. However, the incessant emergence of new attack techniques and the lack of system interpretability pose challenges to improving the detection performance of NIDS. To address these issues, this paper proposes a hybrid explainable neural network-based framework that improves both the interpretability of our model and the performance in detecting new attacks through the innovative application of the explainable artificial intelligence(XAI)method. We effectively introduce the Shapley additive explanations(SHAP) method to explain a light gradient boosting machine(light GBM) model. Additionally, we propose an autoencoder long-term short-term memory(AE-LSTM) network to reconstruct SHAP values previously generated. Furthermore, we define a threshold based on reconstruction errors observed during the training phase. Any network flow that surpasses the specified threshold is classified as an attack flow. This approach enhances the framework's ability to accurately identify attacks. We achieve an accuracy of 92.65%, a recall of 95.26%, a precision of 92.57%,and an F1-score of 93.90% on the dataset NSL-KDD. Experimental results demonstrate that our approach generates detection performance on par with state-of-the-art methods.

关键词： explainable artificial intelligence light gradient boosting machine machine learning network intrusion detection Shapley additive explanation hybrid explainable neural network (HEN)

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

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Petroleum 2022年第3期8卷 363-374页

作者： Ziteng Huang Min Yang Bo Yang Wei Liu Zhangxin Chen Department of Chemical and Petroleum Engineering University of Calgary2500 University Drive NWCalgaryT2N 1N4Canada

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 *** 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 *** this paper,a series of SAGD models were constructed with selected ranges of reservoir properties and operational *** SAGD production period parameters,including the start date,end date,and duration,are collected based on the simulated production *** 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 *** data-driven models were trained,tested,and *** 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 *** 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 *** study shows a significant potential in the use of data-driven models for the SAGD production dynamic *** results also serve to support the utilization of the datadriven models as efficient tools for predicting a SAGD production period.

关键词： Steam assisted gravity drainage Data-driven model Artificial neural network Extreme gradient boosting light gradient boosting machine CatBoost

A method for modelling greenhouse temperature using gradient boost decision tree

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Information Processing in Agriculture 2022年第3期9卷 343-354页

作者： Wentao Cai Ruihua Wei Lihong Xu Xiaotao Ding College of Electronics and Information Engineering TongJi UniversityShanghai 201804PR China Shanghai Dushi Green Engineering Co. LtdHorticultural Research InstituteShanghai Academy of Agricultural SciencesShanghai 201106PR China

y consumption efficiency and to increase the crop *** the increase of agri-cultural data generated by the Internet of Things(IoT),more feasible models are necessary to get full usage of such *** 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 ***-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 *** adaptive cross-validation method was developed as a novelty to improve the LGBM model performance and self-adaptive *** 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 *** two GBDT algorithms,Extreme gradient boosting(Xgboost)and Stochastic gradient boosting(SGB),were also introduced to compare the predictive accuracy with LGBM *** 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 *** LGBM has strongly potential application pro-spect on both greenhouse environment prediction and real-time predictive control.

关键词： gradient boost decision tree light gradient boosting machine Temperature prediction model Neural network

An Intrusion Detection System for SDN Using machine Learning

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Intelligent Automation & Soft Computing 2023年第1期35卷 867-880页

作者： G.Logeswari S.Bose T.Anitha Department of Computer Science and Engineering College of EngineeringGuindyAnna UniversityChennaiTamilnaduIndia

Software Defined Networking(SDN)has emerged as a promising and exciting option for the future growth of the *** has increased the flexibility and transparency of the managed,centralized,and controlled *** the other hand,these advantages create a more vulnerable environment with substantial risks,culminating in network difficulties,system paralysis,online banking frauds,and *** issues have a significant detrimental impact on organizations,enterprises,and even ***,high performance,and real-time systems are necessary to achieve this *** 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 *** this paper,a novel HFS-LGBM IDS is proposed for ***,the Hybrid Feature Selection algorithm consisting of two phases is applied to reduce the data dimension and to obtain an optimal feature *** thefirst phase,the Correlation based Feature Selection(CFS)algorithm is used to obtain the feature *** 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 *** 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.

关键词： Intrusion detection system light gradient boosting machine correlation based feature selection random forest recursive feature elimination software defined networks

Research of Diagnosing Causes of Turbine Faults Based on PSO-lightGBM Algorithm

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Research of Diagnosing Causes of Turbine Faults Based on PSO...