A Study on Gradient Boosting Algorithms for Development of AI Monitoring and Prediction Systems
暂无分享,去创建一个
Izzatdin Abdul Aziz | Jafreezal Jaafar | Norshakirah Aziz | Mohd Hilmi Hasan | Emelia Akashah Patah Akhir | Ahmad Naufal Che Abas | M. H. Hasan | E. A. P. Akhir | J. Jaafar | I. Aziz | Norshakirah Aziz
[1] Basilio Sierra,et al. Predictive Maintenance on the Machining Process and Machine Tool , 2019 .
[2] Xiaoyu Yang,et al. A novel diagnostic and prognostic framework for incipient fault detection and remaining service life prediction with application to industrial rotating machines , 2019, Appl. Soft Comput..
[3] Essam Al Daoud,et al. Comparison between XGBoost, LightGBM and CatBoost Using a Home Credit Dataset , 2019 .
[4] Zhigang Tian,et al. A review on pipeline integrity management utilizing in-line inspection data , 2018, Engineering Failure Analysis.
[5] Biliang Zhong,et al. Product marketing prediction based on XGboost and LightGBM algorithm , 2019, AIPR '19.
[6] Svante Gunnarsson,et al. Failure detection in robotic arms using statistical modeling, machine learning and hybrid gradient boosting , 2019, Measurement.
[7] P. E. Komers,et al. Statistical analysis of environmental consequences of hazardous liquid pipeline accidents , 2018, Heliyon.
[8] Fabio Babiloni,et al. A LightGBM-Based EEG Analysis Method for Driver Mental States Classification , 2019, Comput. Intell. Neurosci..
[9] Izzatdin Abdul Aziz,et al. Parameter Calculation in Time Analysis for the Approach of Filtering to Select IMFs of EMD in AE Sensors for Leakage Signature , 2019, CSOC.
[10] Pawel Góra,et al. Investigating performance of neural networks and gradient boosting models approximating microscopic traffic simulations in traffic optimization tasks , 2018, ArXiv.
[11] Osmar R. Zaïane,et al. Detecting the Onset of Machine Failure Using Anomaly Detection Methods , 2019, DaWaK.
[12] Seong-Hoon Hwang,et al. Data-driven machine-learning-based seismic failure mode identification of reinforced concrete shear walls , 2020 .
[13] Saifur Rahman,et al. Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living , 2020, International journal of environmental research and public health.
[14] Didrik Nielsen,et al. Tree Boosting With XGBoost - Why Does XGBoost Win "Every" Machine Learning Competition? , 2016 .
[15] Mir Jafar Sadegh Safari,et al. Pipe failure rate prediction in water distribution networks using multivariate adaptive regression splines and random forest techniques , 2019, Urban Water Journal.
[16] Clay C. Pendarvis. Pipeline and Hazardous Materials Safety Administration , 2009 .
[17] José-Raúl Ruiz-Sarmiento,et al. A predictive model for the maintenance of industrial machinery in the context of industry 4.0 , 2020, Eng. Appl. Artif. Intell..
[18] B. Dindoruk,et al. A comparative analysis of bubble point pressure prediction using advanced machine learning algorithms and classical correlations , 2020 .
[19] Steven X. Ding,et al. An Improved LightGBM Algorithm for Online Fault Detection of Wind Turbine Gearboxes , 2020, Energies.
[20] Mario R. Eden,et al. Formation lithology classification using scalable gradient boosted decision trees , 2019, Comput. Chem. Eng..
[21] Peng Xu,et al. Predicting pipeline leakage in petrochemical system through GAN and LSTM , 2019, Knowl. Based Syst..
[22] Pritika Bahad,et al. Study of AdaBoost and Gradient Boosting Algorithms for Predictive Analytics , 2019 .
[23] Abdulhamit Subasi,et al. Comparison of Bagging and Boosting Ensemble Machine Learning Methods for Automated EMG Signal Classification , 2019, BioMed research international.
[24] Yufei Xia,et al. A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring , 2017, Expert Syst. Appl..
[25] Izzatdin Abdul Aziz,et al. Machine Failure Prediction Technique Using Recurrent Neural Network Long Short-Term Memory-Particle Swarm Optimization Algorithm , 2019, CSOC.
[26] Xinrui Liu,et al. Prediction of Double-High Biochemical Indicators Based on LightGBM and XGBoost , 2019, Proceedings of the 2019 International Conference on Artificial Intelligence and Computer Science.
[27] F. Nasiri,et al. A Review of Failure Prediction Models for Oil and Gas Pipelines , 2020 .
[28] Said Nawar,et al. Comparison between Random Forests, Artificial Neural Networks and Gradient Boosted Machines Methods of On-Line Vis-NIR Spectroscopy Measurements of Soil Total Nitrogen and Total Carbon , 2017, Sensors.
[29] Kheng Cher Yeo,et al. Critical review of machine learning approaches to apply big data analytics in DDoS forensics , 2018, 2018 International Conference on Computer Communication and Informatics (ICCCI).
[30] Tie-Yan Liu,et al. LightGBM: A Highly Efficient Gradient Boosting Decision Tree , 2017, NIPS.
[31] Haruna Chiroma,et al. Machine learning for email spam filtering: review, approaches and open research problems , 2019, Heliyon.
[32] Duc-Kien Thai,et al. Gradient tree boosting machine learning on predicting the failure modes of the RC panels under impact loads , 2019, Engineering with Computers.
[33] Nabil Zemiti,et al. Optimal Feature Selection for EMG-Based Finger Force Estimation Using LightGBM Model , 2019, 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN).
[34] Xinhan Tong,et al. Breast Cancer Prediction from Genome Segments with Machine Learning , 2018 .