Results 31 to 40 of about 43,844 (258)

Extreme Gradient Boosting Combined with Conformal Predictors for Informative Solubility Estimation. [PDF]

Molecules, 2023
We used the extreme gradient boosting (XGB) algorithm to predict the experimental solubility of chemical compounds in water and organic solvents and to select significant molecular descriptors. The accuracy of prediction of our forward stepwise top-importance XGB (FSTI-XGB) on curated solubility data sets in terms of RMSE was found to be 0.59–0.76 Log ...
Jovic O, Mouras R.
europepmc   +6 more sources

Time-Series Prediction of Intense Wind Shear Using Machine Learning Algorithms: A Case Study of Hong Kong International Airport

Atmosphere, 2023
Machine learning algorithms are applied to predict intense wind shear from the Doppler LiDAR data located at the Hong Kong International Airport. Forecasting intense wind shear in the vicinity of airport runways is vital in order to make intelligent ...
Afaq Khattak, Pak-Wai Chan, Feng Chen, Haorong Peng   +3 more
doaj   +1 more source

Intelligent Fault Diagnosis of Diesel Engines via Extreme Gradient Boosting and High-Accuracy Time–Frequency Information of Vibration Signals

Sensors, 2019
Accurate and timely misfire fault diagnosis is of vital significance for diesel engines. However, existing algorithms are prone to fall into model over-fitting and adopt low energy-concentrated features.
Jianfeng Tao, Chengjin Qin, Weixing Li, Chengliang Liu   +3 more
doaj   +1 more source

Residual geochemical gold grade prediction using extreme gradient boosting

Environmental Challenges, 2022
The increasing cost of exploration and decreasing recovery in mineral exploration necessitates the use of reliable and robust models for spatial gold grade prediction. In comparison, ordinary kriging (OK) is widely applied in geospatial data analysis for
Bemah Ibrahim, Fareed Majeed, Anthony Ewusi, Isaac Ahenkorah   +3 more
doaj   +1 more source

Credit Risk Analysis With Extreme Gradient Boosting and Adaptive Boosting Algorithm

Journal of Information System,Graphics, Hospitality and Technology, 2023
Credit Risk Analysis digunakan untuk mengenali resiko terhadap pinjaman untuk mencegah penunggakan pembayaran utang. Pemberian uji kelayakan pinjaman dapat di analisis menggunakan model klasifikasi. Untuk menghasilkan model credit risk analysis yang sesuai, penulis mengajukan Algoritma Extreme Gradient Boosting (XGBoost) dan Adaptive Boosting (AdaBoost)
Rosa Delima Mendrofa, Maria Hosianna Siallagan, Junita Amalia, Diana Pebrianty Pakpahan   +3 more
openaire   +1 more source

Fusion and Classification of SAR and Optical Data Using Multi-Image Color Components with Differential Gradients

Remote Sensing, 2023
This paper proposes a gradient-based data fusion and classification approach for Synthetic Aperture Radar (SAR) and optical image. This method is used to intuitively reflect the boundaries and edges of land cover classes present in the dataset.
Achala Shakya, Mantosh Biswas, Mahesh Pal   +2 more
doaj   +1 more source

Rainfall Prediction using Extreme Gradient Boosting

International Journal for Research in Applied Science and Engineering Technology, 2023
Abstract: Rainfall greatly affects human life in various sectors including agriculture, transportation, etc. and also can affect natural disasters such as drought, floods, and landslides. This situation prompts us to build an accurate rainfall prediction model so that prescriptive measures can be made.
Abhinav Kulkarni, Shaikh Adil, Pingalkar Venkatesh, Rajas Uchagaonkar, Prof. S.S. Kolte   +4 more
openaire   +1 more source

Gradient boosting with extreme-value theory for wildfire prediction

Extremes, 2023
AbstractThis paper details the approach of the teamKohrrelationin the 2021 Extreme Value Analysis data challenge, dealing with the prediction of wildfire counts and sizes over the contiguous US. Our approach uses ideas from extreme-value theory in a machine learning context with theoretically justified loss functions for gradient boosting.
openaire   +3 more sources

Investigation on eXtreme Gradient Boosting for cutting force prediction in milling

Journal of Intelligent Manufacturing, 2023
Accurate prediction of cutting forces is critical in milling operations, with implications for cost reduction and improved manufacturing efficiency. While traditional mechanistic models provide high accuracy, their reliance on extensive milling data for force coefficient fitting poses challenges.
Thomas Heitz, Ning He, Addi Ait-Mlouk, Daniel Bachrathy, Ni Chen, Guolong Zhao, Liang Li   +6 more
openaire   +3 more sources

Evaluation of Nonparametric Decision Tree Models for Predicting Scour Depth of Bridges [PDF]

منابع آب و تغییر اقلیم
Objective: The complex nature of bridge pier scour and the influence of various parameters on its estimation highlight the necessity of using a comprehensive nonlinear model.
Rezvan Dalvand, Mehdi Komasi
doaj   +1 more source