Results 31 to 40 of about 139,430 (360)

A Semi-Supervised Abbreviation Disambiguation Method Based on ACNN and Bi-LSTM

Journal of Harbin University of Science and Technology, 2022
In order to improve disambiguation accuracy of biomedical abbreviations， a semi-supervised abbreviation disambiguation method based on asymmetric convolutional neural networks and bidirectional long short term memory networks is proposed. Abbreviation is
ZHANG Chun-xiang, PANG Shu-yang, GAO Xue-yao   +2 more
doaj   +1 more source

Privacy-Preserving XGBoost Inference

, 2020
Although machine learning (ML) is widely used for predictive tasks, there are important scenarios in which ML cannot be used or at least cannot achieve its full potential. A major barrier to adoption is the sensitive nature of predictive queries. Individual users may lack sufficiently rich datasets to train accurate models locally but also be unwilling
Meng, Xianrui, Feigenbaum, Joan
openaire   +2 more sources

CLASSIFICATION OF STUDENT GRADUATION STATUS USING XGBOOST ALGORITHM

Barekeng, 2023
College is an optional final stage in formal education. At this time, universities are required to have good quality by utilizing all the resources they have. Therefore, efforts are needed to help the faculty and study program make policies and decisions.
Maria Welita Dwinanda, Neva Satyahadewi, Wirda Andani   +2 more
doaj   +1 more source

A Retrospective Cohort Study: Predicting 90-Day Mortality for ICU Trauma Patients with a Machine Learning Algorithm Using XGBoost Using MIMIC-III Database

Journal of Multidisciplinary Healthcare, 2023
Shan Yang,1 Lirui Cao,2 Yongfang Zhou,3 Chenggong Hu1 1Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, People’s Republic of China; 2West China Hospital of Sichuan University, Chengdu, Sichuan ...
Yang S, Cao L, Zhou Y, Hu C
doaj  

Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization

, 2017
We aimed to evaluate computer-aided diagnosis (CADx) system for lung nodule classification focusing on (i) usefulness of gradient tree boosting (XGBoost) and (ii) effectiveness of parameter optimization using Bayesian optimization (Tree Parzen Estimator,
Kojima, Ryosuke, Kuroda, Tomohiro, Nishio, Mizuho, Nishizawa, Mitsuo, Sugiyama, Osamu, Togashi, Kaori, Yakami, Masahiro   +6 more
core   +2 more sources

Comparison of Random Forest, XGBoost, and LightGBM Methods for the Human Development Index Classification

Jambura Journal of Mathematics
Machine learning classification is an effective tool for categorizing data based on patterns, which is particularly useful in analyzing the Human Development Index (HDI) in Indonesia. HDI serves as a key indicator of regional development progress, making
Yunna Mentari Indah, Rafika Aristawidya, Anwar Fitrianto, Erfiani Erfiani, L.M. Risman Dwi Jumansyah   +4 more
doaj   +1 more source

Comprehensive Analysis of Random Forest and XGBoost Performance with SMOTE, ADASYN, and GNUS Under Varying Imbalance Levels

Technologies
This study examines the efficacy of Random Forest and XGBoost classifiers in conjunction with three upsampling techniques—SMOTE, ADASYN, and Gaussian noise upsampling (GNUS)—across datasets with varying class imbalance levels, ranging from moderate to ...
Mehdi Imani, Ali Beikmohammadi, H. Arabnia   +2 more
semanticscholar   +1 more source

Early Breast Cancer Detection in Coimbra Dataset Using Supervised Machine Learning (XGBoost)

Buana Information Technology and Computer Sciences
Worldwide, breast cancer (BC) represents one of the serious health concerns for adult females. The early detection and accurate prediction of risks are vital for the provision of optimum care and enhancement of patient outcomes.
Ahmed Sami Jaddoa
doaj   +1 more source

Machine learning-guided synthesis of advanced inorganic materials

, 2019
Synthesis of advanced inorganic materials with minimum number of trials is of paramount importance towards the acceleration of inorganic materials development.
Chouhan, Tushar, Golani, Prafful, Guan, Cuntai, Liu, Zheng, Lu, Yuhao, Tang, Bijun, Wang, Han, Xu, Manzhang, Xu, Quan, Zhou, Jiadong   +9 more
core   +1 more source

A novel improved model for building energy consumption prediction based on model integration [PDF]

, 2020
Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems.
Feng, W, Lu, S, Wang, R
core