Results 31 to 40 of about 95,729 (269)

Frugal Optimization for Cost-related Hyperparameters

, 2020
The increasing demand for democratizing machine learning algorithms calls for hyperparameter optimization (HPO) solutions at low cost. Many machine learning algorithms have hyperparameters which can cause a large variation in the training cost.
Huang, Silu, Wang, Chi, Wu, Qingyun
core   +2 more sources

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

Predicting Antibiotic Resistance in ICUs Patients by Applying Machine Learning in Vietnam

Infection and Drug Resistance, 2023
Viet Tran Quoc,1 Dung Nguyen Thi Ngoc,2,3 Trung Nguyen Hoang,4 Hoa Vu Thi,4 Minh Tong Duc,4 Thanh Do Pham Nguyet,2 Thanh Nguyen Van,5 Diep Ho Ngoc,2 Giang Vu Son,6 Thanh Bui Duc7 1Intensive Care Unit, Military Hospital 175, Ho Chi Minh City, Vietnam ...
Tran Quoc V, Nguyen Thi Ngoc D, Nguyen Hoang T, Vu Thi H, Tong Duc M, Do Pham Nguyet T, Nguyen Van T, Ho Ngoc D, Vu Son G, Bui Duc T   +9 more
doaj  

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

Comparative Analysis of Feature Selection Methods with XGBoost for Malware Detection on the Drebin Dataset

Jurnal Sisfokom
Malware, or malicious software, continues to evolve alongside increasing cyberattacks targeting individual devices and critical infrastructure. Traditional detection methods, such as signature-based detection, are often ineffective against new or ...
Ines Aulia Latifah, Fauzi Adi Rafrastara, Jevan Bintoro, Wildanil Ghozi, Waleed Mahgoub Osman   +4 more
doaj   +1 more source

Accurate ADMET Prediction with XGBoost

, 2022
The absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties are important in drug discovery as they define efficacy and safety. In this work, we applied an ensemble of features, including fingerprints and descriptors, and a tree-based machine learning model, extreme gradient boosting, for accurate ADMET prediction.
Tian, Hao, Ketkar, Rajas, Tao, Peng
openaire   +2 more sources

Interpretable Machine Learning with SHAP and XGBoost for Lung Cancer Prediction Insights

Journal of Applied Informatics and Computing
Kanker paru-paru tetap menjadi salah satu penyebab kematian utama di seluruh dunia, dan deteksi dini melalui metode yang akurat dan andal sangat penting untuk meningkatkan prognosis pasien.
Taufik Kurniawan, Laily Hermawanti, Achmad Nuruddin Safriandono   +2 more
doaj   +1 more source

A comparative analysis of Deep Neural Networks and Gradient Boosting Algorithms in long-term wind power forecasting [PDF]

Zbornik Radova: Elektrotehnički Institut "Nikola Tesla"
A vital step toward a sustainable future is the power grid's incorporation of renewable energy sources. Wind energy is significant because of its broad availability and minimal environmental impact.
Ivanović Luka, Milić Saša D., Sokolović Živko, Rakić Aleksandar   +3 more
doaj   +1 more source

Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis

, 2017
An electroencephalography (EEG) based brain activity recognition is a fundamental field of study for a number of significant applications such as intention prediction, appliance control, and neurological disease diagnosis in smart home and smart ...
Gu, Tao, Sheng, Quan Z., Wang, Xianzhi, Yao, Lina, Zhang, Dalin, Zhang, Xiang   +5 more
core   +1 more source

RaMBat: Accurate identification of medulloblastoma subtypes from diverse data sources with severe batch effects

Molecular Oncology, EarlyView.
To integrate multiple transcriptomics data with severe batch effects for identifying MB subtypes, we developed a novel and accurate computational method named RaMBat, which leveraged subtype‐specific gene expression ranking information instead of absolute gene expression levels to address batch effects of diverse data sources.
Mengtao Sun, Jieqiong Wang, Shibiao Wan
wiley   +1 more source