Results 1 to 10 of about 35,527 (280)

Toward an Accurate Liver Disease Prediction Based on Two-Level Ensemble Stacking Model

IEEE Access
The difficulty of detecting liver disease at an early stage goes back to its limited number of symptoms. In this study, single and ensemble machine learning (ML) algorithms are applied to the Indian Liver Patient Dataset (ILPD) dataset, and their results,
Marghany Hassan Mohamed, Botheina Hussein Ali, Ahmed Ibrahim Taloba, Ahmad O. Aseeri, Mohamed Abd Elaziz, Shaker El-Sappagah, Nora Mahmoud El-Rashidy   +6 more
doaj   +1 more source

Deep Stacking Ensemble Learning Applied to Profiling Side-Channel Attacks

Deep Learning is nowadays widely used by security evaluators to conduct side-channel attacks, especially in profiling attacks that allow a supervised learning phase. However, designing an efficient neural network model in a side-channel attack context can be a difficult task that may require a laborious hyperparameterization process.
Llavata, Dorian, Cagli, Eleonora, Eyraud, Rémi, Grosso, Vincent, Bossuet, Lilian   +4 more
openaire   +2 more sources

Lifestyle data-based multiclass obesity prediction with interpretable ensemble models incorporating SHAP and LIME analysis

Scientific Reports
Obesity is a major public health concern. Predicting obesity risk from lifestyle data can guide targeted interventions, but current models remain limited.
Shahid Mohammad Ganie, Pijush Kanti Dutta Pramanik, Zhongming Zhao   +2 more
doaj   +1 more source

Comprehensive Performance Assessment of Multi-Neural Ensemble Model for Mortality Prediction in ICU

IEEE Access
The development of models to estimate the mortality rate of critically ill patients in the intensive care unit(ICU) is significantly enhanced by technologies based on artificial intelligence.
M. Fathima Begum, Subhashini Narayan
doaj   +1 more source

Diverse Models, United Goal: A Comprehensive Survey of Ensemble Learning

CAAI Transactions on Intelligence Technology
Ensemble learning, a pivotal branch of machine learning, amalgamates multiple base models to enhance the overarching performance of predictive models, capitalising on the diversity and collective wisdom of the ensemble to surpass individual models and ...
Ziwei Fan, Zhiwen Yu, Kaixiang Yang, Wuxing Chen, Xiaoqing Liu, Guojie Li, Xianling Yang, C. L. Philip Chen   +7 more
doaj   +1 more source

Requirement Dependency Extraction Based on Improved Stacking Ensemble Machine Learning

Mathematics
To address the cost and efficiency issues of manually analysing requirement dependency in requirements engineering, a requirement dependency extraction method based on part-of-speech features and an improved stacking ensemble learning model (P-Stacking ...
Hui Guan, Hang Xu, Lie Cai
doaj   +1 more source

StackBRAF: A Large-Scale Stacking Ensemble Learning for BRAF Affinity Prediction. [PDF]

ACS Omega, 2023
Syahid NF, Weerapreeyakul N, Srisongkram T.   +2 more
europepmc   +1 more source

Decoding Electroencephalography Signal Response by Stacking Ensemble Learning and Adaptive Differential Evolution. [PDF]

Sensors (Basel), 2023
Ribeiro MHDM, da Silva RG, Larcher JHK, Mendes A, Mariani VC, Coelho LDS.   +5 more
europepmc   +1 more source

StackTTCA: a stacking ensemble learning-based framework for accurate and high-throughput identification of tumor T cell antigens. [PDF]

BMC Bioinformatics, 2023
Charoenkwan P, Schaduangrat N, Shoombuatong W.   +2 more
europepmc   +1 more source

Rumen Fermentation Parameters Prediction Model for Dairy Cows Using a Stacking Ensemble Learning Method. [PDF]

Animals (Basel), 2023
Wang Y, Zhou J, Wang X, Yu Q, Sun Y, Li Y, Zhang Y, Shen W, Wei X.   +8 more
europepmc   +1 more source