Ensemble Methods for Heart Disease Prediction
Sign Up to like & getrecommendations! Published in 2021 at "New Generation Computing"
DOI: 10.1007/s00354-021-00124-4
Abstract: Heart disease prediction is a critical task regarding human health. It is based on deriving an Machine Learning model from medical parameters to predict risk levels. In this work, we propose and test novel ensemble… read more here.
Keywords: disease prediction; heart disease; ensemble methods;
Risk upper bounds for general ensemble methods with an application to multiclass classification
Sign Up to like & getrecommendations! Published in 2017 at "Neurocomputing"
DOI: 10.1016/j.neucom.2016.09.016
Abstract: This paper generalizes a pivotal result from the PAC-Bayesian literature -the C - bound - primarily designed for binary classification to the general case of ensemble methods of voters with arbitrary outputs. We provide a… read more here.
Keywords: risk; classification; multiclass classification; bound ... See more keywords
Trajectory Ensemble Methods Provide Single-Molecule Statistics for Quantum Dynamical Systems.
Sign Up to like & getrecommendations! Published in 2022 at "Journal of chemical theory and computation"
DOI: 10.1021/acs.jctc.1c00477
Abstract: The emergence of experiments capable of probing quantum dynamics at the single-molecule level requires the development of new theoretical tools capable of simulating and analyzing these dynamics beyond an ensemble-averaged description. In this article, we… read more here.
Keywords: single molecule; system; methods provide; ensemble methods ... See more keywords
Machine learning classification of schizophrenia patients and healthy controls using diverse neuroanatomical markers and Ensemble methods
Sign Up to like & getrecommendations! Published in 2022 at "Scientific Reports"
DOI: 10.1038/s41598-022-06651-4
Abstract: Schizophrenia is a major psychiatric disorder that imposes enormous clinical burden on patients and their caregivers. Determining classification biomarkers can complement clinical measures and improve understanding of the neural basis underlying schizophrenia. Using neuroanatomical features,… read more here.
Keywords: neuroanatomical measures; ensemble methods; schizophrenia; machine learning ... See more keywords
Ensemble Methods for Identifying RNA Operons and Regulons in the Clock Network of Neurospora Crassa
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3160481
Abstract: With both light entrainment data (L/D) and free running data in the dark (D/D), Variable Topology Ensemble Methods (VTENS) were used to reconstruct the entire genome-scale clock network involving 3380 genes and their products under… read more here.
Keywords: genes italic; ensemble methods; rna operons; clock network ... See more keywords
Adaptive Ensemble Methods for Tampering Detection in Automotive Aftertreatment Systems
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3211387
Abstract: Control and diagnostic processes in modern vehicles incorporate nowadays a wide set of functionalities to preserve the vehicle’s health. Automotive vehicles contain embedded systems that must perform a diverse palette of tasks, ranging from less… read more here.
Keywords: system; tampering detection; adaptive ensemble; detection ... See more keywords
Machine Learning and Deep Learning for Loan Prediction in Banking: Exploring Ensemble Methods and Data Balancing
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Access"
DOI: 10.1109/access.2024.3509774
Abstract: The prediction of loan defaults is crucial for banks and financial institutions due to its impact on earnings, and it also plays a significant role in shaping credit scores. This task is a challenging one,… read more here.
Keywords: deep learning; machine learning; ensemble methods; learning deep ... See more keywords
Ensemble Methods for Peristaltic Pump Accuracy Enhancement
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Access"
DOI: 10.1109/access.2025.3589947
Abstract: This study investigates how ensemble learning techniques can be employed for enhancing peristaltic pump accuracy in pharmaceutical manufacturing, and demonstrates significant accuracy improvements through the novel E-AR implementation, with gains of up to 53.93% at… read more here.
Keywords: pump accuracy; pump; ensemble methods; accuracy ... See more keywords
A Predictive Model for Guillain–Barré Syndrome Based on Ensemble Methods
Sign Up to like & getrecommendations! Published in 2018 at "Computational Intelligence and Neuroscience"
DOI: 10.1155/2018/1576927
Abstract: Nowadays, Machine Learning methods have proven to be highly effective on the identification of various types of diseases, in the form of predictive models. Guillain–Barré syndrome (GBS) is a potentially fatal autoimmune neurological disorder that… read more here.
Keywords: classification; predictive model; guillain barr; model ... See more keywords
Diabetes Early Prediction Using Machine Learning and Ensemble Methods
Sign Up to like & getrecommendations! Published in 2025 at "International Journal on Advanced Science, Engineering and Information Technology"
DOI: 10.18517/ijaseit.15.2.20947
Abstract: This study aims to develop and validate an enhanced early prediction model for diabetes utilizing machine learning and ensemble techniques, aimed at addressing the rapid increase in diabetes prevalence and the associated healthcare burden. Leveraging… read more here.
Keywords: learning ensemble; early prediction; machine learning; ensemble methods ... See more keywords
An Empirical Comparison of Resampling Ensemble Methods of Deep Learning Neural Networks for Cross-Project Software Defect Prediction
Sign Up to like & getrecommendations! Published in 2021 at "International Journal of Intelligent Engineering and Systems"
DOI: 10.22266/ijies2021.0630.18
Abstract: Software defect prediction is one of the most important quality assurance activities during software development. This paper contributes empirical insights into the effectiveness of three resampling ensemble methods (bagging, boosting, and dagging) of Deep Learning… read more here.
Keywords: software defect; software; ensemble methods; defect prediction ... See more keywords