StackDPPred: Multiclass prediction of defensin peptides using stacked ensemble learning with optimized features by Muhammad Arif (769250)

“…In this paper, we aim to propose a novel multi-class ensemble-based prediction model called StackDPPred for identifying the properties of DPs. …”

Stacking-based ensemble learning for remaining useful life estimation by Begum Ay Ture (17773170)

“…The experimental studies demonstrated that the stacking ensemble learning and the convolutional neural network (CNN) methods are superior to the other investigated methods. …”

Enhanced Deep Belief Network Based on Ensemble Learning and Tree-Structured of Parzen Estimators: An Optimal Photovoltaic Power Forecasting Method by Mohamed Massaoudi (16888710)

Stacked Ensemble Deep Random Vector Functional Link Network With Residual Learning for Medium-Scale Time-Series Forecasting by Ruobin Gao (16003195)

“…Finally, we present an ensemble deep stacking network, SResdRVFL, based on ResdRVFL. …”

Online dynamic ensemble deep random vector functional link neural network for forecasting by Ruobin Gao (16003195)

“…Finally, an online dynamic ensemble method, which can measure the change in the distribution, is proposed for aggregating all layers’ outputs. …”

Heteroscedastic ensemble deep random vector functional link neural network with multiple output layers for High Frequency Volatility Forecasting and Risk Assessment by Aryan Bhambu (18767731)

“…The forecast is generated by combining the outputs of each layer through an ensemble method. A comparative analysis was conducted against several existing forecasting methods, utilizing error metrics and statistical tests on sixteen high frequency cryptocurrency time-series datasets, demonstrating that the proposed model outperforms others in terms of forecasting accuracy and risk assessment.…”

A Novel Multiagent Collaborative Learning Architecture for Automatic Recognition of Mudstone Rock Facies by Saurabh Tewari (22282594)

“…Also, resampling techniques are combined with nine different ML classifiers, including Decision tree, ExtraTree, Random Forest, Logistic regression, Support vector machine, K-nearest Neighbour, Naïve Bayes and Ensemble methods. Stacking and voting ensembles combine the outcomes of diverse classifiers working as team members in MCLA. …”