Results 31 to 40 of about 33,486 (254)

RESEARCH ON GEAR BOX FAULT DIAGNOSIS BASED ON DCNN AND XGBOOST ALGORITHM

Jixie qiangdu, 2020
In order to solve the problem of complex fault diagnosis of gearbox,the DCNN（ Deep Convolution Neural Network） was combined with the XGBoost（ e Xtreme Gradient Boosting） algorithm to establish the fault diagnosis model. Firstly,the DCNN Model was used to
ZHANG RongTao, CHEN ZhiGao, LI BinBin, JIAO Bin   +3 more
doaj  

Applying interpretable machine learning algorithms to predict risk factors for permanent stoma in patients after TME

Frontiers in Surgery, 2023
ObjectiveThe purpose of this study was to develop a machine learning model to identify preoperative and intraoperative high-risk factors and to predict the occurrence of permanent stoma in patients after total mesorectal excision (TME).MethodsA total of ...
Yuan Liu, Songyun Zhao, Wenyi Du, Zhiqiang Tian, Hao Chi, Cheng Chao, Wei Shen   +6 more
doaj   +1 more source

Short-term Price Prediction in Initial Public Offerings Using XGBoost: Bist Technology Sector Example

Ekonomi, Politika & Finans Araştırmaları Dergisi
The aim of the study is to predict the closing price of the next trading day's stocks, of the initial public offering firms in the short term (between 5 and 10 days).
Ayse Merve Acılar, Gamze Şekeroğlu
doaj   +1 more source

An eXtreme Gradient Boosting Algorithm Combining Artificial Bee Colony Parameters Optimized Technique for Single Sand Body Identification

IEEE Access, 2021
Due to the problems of traditional artificial single sand body identification methods such as strong subjectivity, heavy workload and low efficiency, we propose a fast and objective ABC-XGBoost.
Renze Luo, Liang Guo, Xingyu Li, Juanjuan Tuo, Canru Lei, Yang Zhou   +5 more
doaj   +1 more source

Fuel flow prediction of domestic civil aircraft based on LSTM-XGBoost

Hangkong gongcheng jinzhan
The neural network model is widely used in fuel consumption prediction，but single model is less used in the application of civil aircraft because of its low prediction accuracy.
YANG Junli, LI Likun, QIAN Yu, TANG Shengxiang, YANG Peida   +4 more
doaj   +1 more source

Harnessing LSTM and XGBoost algorithms for storm prediction

Scientific Reports
AbstractStorms can cause significant damage, severe social disturbance and loss of human life, but predicting them is challenging due to their infrequent occurrence. To overcome this problem, a novel deep learning and machine learning approach based on long short-term memory (LSTM) and Extreme Gradient Boosting (XGBoost) was applied to predict storm ...
Ayyoub Frifra, Mohamed Maanan, Mehdi Maanan, Hassan Rhinane   +3 more
openaire   +5 more sources

Examination and evaluation of multi-source monthly scale fusion precipitation product in China based on machine learning algorithm

暴雨灾害, 2023
Grid format precipitation products have better spatial monitoring capabilities compared to ground meteorological station observations, but there are significant differences in performance among different products.
Xuan YANG, Yan ZENG, Xinfa QIU, Xiaochen ZHU   +3 more
doaj   +1 more source

Stock Price Prediction of Technology Enterprises Based on XGBoost Algorithm [PDF]

ITM Web of Conferences
Stock price prediction is a popular research field in the financial and big data industries. Over decades, researchers have leveraged various machine learning and deep learning algorithms to build many innovative models to predict stock prices, and ...
Zhai Songbo
doaj   +1 more source

Research on error estimation model of smart electricity meter based on ITSO-XGBoost algorithm

Diance yu yibiao
Traditional methods have limitations such as difficulty in adapting to complex and changeable environments, poor sensitivity and high complexity when dealing with practical problems.
JIN Yao
doaj   +1 more source

A Two‐Stage Questionnaire and Actigraphy Screening for iRBD in a Multicenter Retrospective Cohort

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Isolated rapid‐eye‐movement sleep behavior disorder is a prodromal marker of synucleinopathies. However, most cases remain undiagnosed due to the insufficient predictive value of questionnaires and limited access to confirmatory video‐polysomnography. We assessed a two‐stage screening strategy combining a brief questionnaire on rapid‐
Caleb A. Massimi, Giorgio Ricciardiello Mejia, Andre Metzger, Kang Hyun Ryu, Salonee Marwaha, Eva Grzegorczyk, Li Zhou, Eliana Jacobs, Boris Gilyadov, Claudia Kunney, Liqhwa Ncube, Ankit Parekh, Emmanuel Mignot, Fanny M. Elahi, Joseph Winer, Kathleen Poston, Andreas Brink‐Kjaer, Emmanuel H. During   +17 more
wiley   +1 more source