Results 51 to 60 of about 185,151 (349)

Ensemble neural networks using interval neutrosophic sets and bagging

, 2007
This paper presents an approach to the problem of binary classification using ensemble neural networks based on interval neutrosophic sets and bagging technique.
Wong, K.W., Pawalai Kraipeerapun, Fung, C.C., Chun Che Fung, Kraipeerapun, P., Kok Wai Wong   +5 more
core   +1 more source

Ensemble Learning of Multiple Models Using Deep Learning for Multiclass Classification of Ultrasound Images of Hepatic Masses

Bioengineering, 2023
Ultrasound (US) is often used to diagnose liver masses. Ensemble learning has recently been commonly used for image classification, but its detailed methods are not fully optimized.
Norio Nakata, Tsuyoshi Siina
doaj   +1 more source

Directed evolution of enzymes at the crossroads of tradition and innovation

FEBS Open Bio, EarlyView.
An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova, Andrej Mirossay, Erik Sedlak   +2 more
wiley   +1 more source

Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications

Journal of Cheminformatics, 2020
Ensemble learning helps improve machine learning results by combining several models and allows the production of better predictive performance compared to a single model. It also benefits and accelerates the researches in quantitative structure–activity
Chia-Hsiu Chen, Kenichi Tanaka, Masaaki Kotera, Kimito Funatsu   +3 more
doaj   +1 more source

Functional and Structural Evidence of Neurofluid Circuit Aberrations in Huntington Disease

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Disrupted neurofluid regulation may contribute to neurodegeneration in Huntington disease (HD). Because neurofluid pathways influence waste clearance, inflammation, and the distribution of central nervous system (CNS)–delivered therapeutics, understanding their dysfunction is increasingly important as targeted treatments emerge.
Kilian Hett, Abigail Dubois, Melanie Leguizamon, Alexander Song, Paula Trujillo, Colin D. McKnight, Ciaran M. Considine, Manus J. Donahue, Daniel O. Claassen   +8 more
wiley   +1 more source

Ensemble machine learning on gene expression data for cancer classification [PDF]

, 2003
Whole genome RNA expression studies permit systematic approaches to understanding the correlation between gene expression profiles to disease states or different developmental stages of a cell.
Tan, A C, Gilbert, D
core  

The Performance Comparison Of Ensemble Machine Learning Classifiers On Medical Datasets

, 2023
: The machine learning methodology consists of two stages: the training stage, during which an algorithmic classification program is taught to assign labels to data, and the testing stage, during which the algorithmic classification program is put ...
Erhan Turan, Mohammed Lawan, Prof. Dr. Beşir Dandil, Prof. Engin Avci   +3 more
core   +1 more source

A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions

Advanced Engineering Materials, EarlyView.
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice, Krzysztof S. Stopka, Michael D. Sangid   +2 more
wiley   +1 more source

Enhancing Credit Card Fraud Detection: An Ensemble Machine Learning Approach [PDF]

In the era of digital advancements, the escalation of credit card fraud necessitates the development of robust and efficient fraud detection systems. This paper delves into the application of machine learning models, specifically focusing on ensemble ...
Jude Osamor, Moses Ashawa, John Adejoh, Nsikak Owoh, Abdul Rehman Khalid, Omair Uthmani   +5 more
core   +1 more source

Foundations and Innovations in Data Fusion and Ensemble Learning for Effective Consensus

Mathematics
Ensemble learning and data fusion techniques play a crucial role in modern machine learning, enhancing predictive performance, robustness, and generalization.
Ke-Lin Du, Rengong Zhang, Bingchun Jiang, Jie Zeng, Jiabin Lu   +4 more
doaj   +1 more source