Results 281 to 290 of about 150,441 (331)

XGBoost-Based Android Malware Detection

2017 13th International Conference on Computational Intelligence and Security (CIS), 2017
Malware remains the most significant security threat to smartphones in spite of the constantly upgrading of the system. In this paper, we introduce an Android malware detection method based on XGBoost model. We subsequently discuss the effect of feature selection on the classification.
Jiong Wang, Boquan Li, Yuwei Zeng
openaire   +1 more source

Predict Students' Dropout and Academic Success with XGBoost

Journal of Education and Computer Applications
The attrition rate of students in higher education is a worldwide issue that profoundly affects both individuals and institutions. Students who fail to complete their studies often encounter economic and social difficulties, while educational ...
A. Ridwan, A. M. Priyatno
semanticscholar   +1 more source

Machine learning-driven optimization of mRNA-lipid nanoparticle vaccine quality with XGBoost/Bayesian method and ensemble model approaches

Journal of Pharmaceutical Analysis
To enhance the efficiency of vaccine manufacturing, this study focuses on optimizing the microfluidic conditions and lipid mix ratios of messenger RNA-lipid nanoparticles (mRNA-LNP).
Ravi Maharjan, Ki Hyun Kim, K. Lee, Hyo-Kyung Han, Seong Hoon Jeong   +4 more
semanticscholar   +1 more source

State-of-the-art XGBoost, RF and DNN based soft-computing models for PGPN piles

Geomechanics and Geoengineering
Machine learning (ML) has made significant advancements in predictive modelling across many engineering sectors. However, predicting the bearing capacity of pre-bored grouted planted nodular (PGPN) piles remains a relatively unexplored area due to the ...
Manish Kumar, Pijush Samui, D. R. Kumar, P. Asteris   +3 more
semanticscholar   +1 more source

XGboost Algoritması

Aşırı Gradyan Artırma Algoritması, kısa adıyla “XGBoost” (Extreme Gradient Boosting Algorithm), Karar Ağaçlarının (KA) özelleştirilmiş bir formu olup sınıflandırma, tahmin ve sıralama yöntemi olarak literatürde ön plana çıkmaktadır. 2015 Bilgi Keşfi ve Veri Madenciliği (Knowledge Discovery and Data Mining-KDD) kupasında seçilen en iyi 10 çözümün ...
openaire   +1 more source

Predict credit risk with XGBoost

Applied and Computational Engineering
The risk of credit loan exists when the bank issues a loan to the borrower, because the borrower has no way to repay the amount or defaults, which exposes the financial institution to the risk of loss. This causes financial institutions to suffer from effects that affect their creditworthiness, loss of capital and increased management and collection of
Wenhao Wang, Xiyi Zuo, Dantong Han
openaire   +1 more source

Multimodal sentiment analysis leveraging the strength of deep neural networks enhanced by the XGBoost classifier

Computer Methods in Biomechanics and Biomedical Engineering
Multimodal sentiment analysis, an increasingly vital task in the realms of natural language processing and machine learning, addresses the nuanced understanding of emotions and sentiments expressed across diverse data sources.
Ganesh Chandrasekaran, S. Dhanasekaran, C. Moorthy, A. Arul Oli   +3 more
semanticscholar   +1 more source

Improving XGBoost with Imagination Sampling

Communications of the Blyth Institute, 2020
Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.
openaire   +1 more source

XGBoost algorithm assisted multi-component quantitative analysis with Raman spectroscopy.

Spectrochimica Acta Part A - Molecular and Biomolecular Spectroscopy
To improve prediction performance and reduce artifacts in Raman spectra, we developed an eXtreme Gradient Boosting (XGBoost) preprocessing method to preprocess the Raman spectra of glucose, glycerol and ethanol mixtures.
Qiaoyun Wang, Xin Zou, Yinji Chen, Ziheng Zhu, Chongyue Yan, Peng Shan, Shuyu Wang, Yongqing Fu   +7 more
semanticscholar   +1 more source

Predictive Modeling for Medical Insurance Malpractice Using Random Forest and XGBoost

2024 International Conference on Communication, Computing and Internet of Things (IC3IoT)
The study promotes a thorough research effort to create a predictive model specifically for medical malpractice litigation, highlighting the intricate relationships between insurance and medical malpractice claims.
M. J. Carmel Mary Belida, Almas Begum, S. Alex David, E. Kannan, K. Senthil, N. Ruth Naveena   +5 more
semanticscholar   +1 more source