Multi-Domain Controversial Text Detection Based on a Machine Learning and Deep Learning Stacked Ensemble by Jiadi Liu, Zhuodong Liu, Qiaoqi Li, Weihao Kong, Xiangyu Li

“…Secondly, we design a two-tier stacked ensemble architecture, which not only combines the strengths of multiple machine learning algorithms, e.g., gradient-boosted decision tree (GBDT), random forest (RF), and extreme gradient boosting (XGBoost), with deep learning models, e.g., gated recurrent unit (GRU) and long short-term memory (LSTM), but also implements the support vector machine (SVM) for efficient meta-learning. …”
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Predicting the permeability and compressive strength of pervious concrete using a stacking ensemble machine learning approach by Fan Yu, Wei Chu, Rui Zhang, Zhang Gao, Yunan Yang

“…Firstly, six independent models, the multiple linear regression and the Stacking algorithm were applied to construct the ensemble model. …”
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Dual Passive-Aggressive Stacking k-Nearest Neighbors for Class-Incremental Multi-Label Stream Classification by Hann Hsen Tan, Chu Kiong Loo, Chaw Seng Woo

“…Most learning algorithms in the literature can fulfill only a subset but not all of these desiderata. …”
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Optimizing blood glucose predictions in type 1 diabetes patients using a stacking ensemble approach by Vincent B. Liu, Laura Y. Sue, Oscar Madrid Padilla, Yingnian Wu

“…Grey Wolf optimization was used to tune and evaluate three machine learning algorithms – Random Forest, LSTM, GRU – for blood glucose predictions, whose predictions were then combined into an XGBoost stacking ensemble meta-learner. …”
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Interpretable artificial intelligence (AI) for cervical cancer risk analysis leveraging stacking ensemble and expert knowledge by Priyanka Roy, Mahmudul Hasan, Md Rashedul Islam, Md Palash Uddin

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A stacked ensemble machine learning model for the prediction of pentavalent 3 vaccination dropout in East Africa by Meron Asmamaw Alemayehu, Shimels Derso Kebede, Agmasie Damtew Walle, Daniel Niguse Mamo, Ermias Bekele Enyew, Jibril Bashir Adem

“…The H2O package facilitated the development of base learners and the stacking of super learners. Feature selection (FS) and comparisons were performed using the LASSO and Boruta algorithms. …”
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WEST GCN-LSTM: Weighted stacked spatio-temporal graph neural networks for regional traffic forecasting by Theodoros Theodoropoulos, Angelos-Christos Maroudis, Uwe Zdun, Antonios Makris, Konstantinos Tserpes

“…The end-product of this scientific endeavor is a novel spatio-temporal graph neural network architecture for regional traffic forecasting referred to as WEST (WEighted STacked) GCN-LSTM. Furthermore, the aforementioned information is included via two novel dedicated algorithms, the Shared Borders Policy and the Adjustable Hops Policy. …”
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ECG-based heart arrhythmia classification using feature engineering and a hybrid stacked machine learning by Raiyan Jahangir, Muhammad Nazrul Islam, Md. Shofiqul Islam, Md. Motaharul Islam

“…Other conventional machine-learning, bagging, and boosting ensemble algorithms were also explored along with the proposed stack classifiers. …”
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Facial Beauty Prediction Combining Dual-Branch Feature Fusion With a Stacked Broad Learning System by Junying Gan, Hantian Chen, Wenchao Xu, Huicong Li, Zhenxin Zhuang, Zhen Chen

“…Building upon DB-BLS, this paper introduces a stacked BLS module to replace the BLS module. Stacked BLS enhancing the network’s ability to perceive features at different levels. …”
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Stacking ensemble learning models diagnose pulmonary infections using host transcriptome data from metatranscriptomics by Tian Zhang, Ying Deng, Wentao Wang, Zhe Zhao, Yiling Wu, Haoqian Wang, Shutao Xia, Weifang Liao, Weijie Liao

“…Leveraging these characteristic genes, we constructed classification sub-models employing 13 types of machine learning algorithms, and we further integrated these sub-models into stacking-based ensemble models with Lasso regression, resulting in diagnostic models that required only a small set of gene expression inputs. …”
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A hybrid attack detection strategy for cybersecurity using moth elephant herding optimisation‐based stacked autoencoder by Abdullah Shawan Alotaibi

“…Finally, the attack detection is performed using the stacked autoencoder classifier, which is trained using the proposed MEHO algorithm. …”
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Determining the Range of Applicability of Analytical Methods for Belleville Springs and Novel Approach of Calculating Quasi-Progressive Spring Stacks by Jędrzej Koralewski, Michał Wodtke

“…The findings identified areas of consistency between analytical methods and FEM models, leading to the development of an algorithm for selecting the appropriate computational method for different types of Belleville springs. …”
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Scene Target 3D Point Cloud Reconstruction Technology Combining Monocular Focus Stack and Deep Learning by Yanzhu Hu, Yingjian Wang, Song Wang

“…The method first collects multiple frames of continuous images at different focal lengths of the scene, using a divide and conquer algorithm strategy, uplink uses YOLO neural network to identify the target in 3D space and track the position information; the downlink reconstructs the four-dimensional (4D) light field data based on the focus stack image frequency domain back projection, and then uses light field imaging technology to invert the scene parallax; subsequently, achieve scene depth estimation and reconstruction of all focus image; finally, the uplink and downlink are merged to realize the reconstruction of the 3D point cloud of the space target. …”
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An Approach to Data Reduction for Learning from Big Datasets: Integrating Stacking, Rotation, and Agent Population Learning Techniques by Ireneusz Czarnowski, Piotr Jędrzejowicz

“…Stacking is seen as the technique allowing to take advantage of the multiple classification models. …”
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Adaptive Feedforward Vibration Control of Helicopter Cabin Floor Driven by Piezoelectric Stack Actuators: Modeling, Simulation and Experiments by Laishou Song, Yingquan Wang, Xiaoyu Shen

“…The model of PSA is integrated into the attached beam element based on the conditions of force equilibrium and displacement compatibility, and adaptive feedforward control is implemented by the filtered-x least mean square (Fx-LMS) algorithm. Simulations and experimental studies under diverse excitations have been carried out. …”
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Speech emotion recognition based on a stacked autoencoders optimized by PSO based grass fibrous root optimization by Chi Zeng, Jialing Li, Abbas Habibi

“…This study introduces an innovative approach that merges deep learning with metaheuristic algorithms to boost the efficiency of SER systems. Specifically, a stacked autoencoder (SAE) serves as the primary model, and its performance is fine-tuned using a nature-inspired hybrid algorithm that combines particle swarm optimization (PSO) with Grass Fibrous Root Optimization (GFRO). …”
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Single-Sheet Separation from Paper Stack Based on Friction Uncertainty Using High-Speed Robot Hand by Taku Senoo, Atsushi Konno, Yuuki Yamana, Idaku Ishii

“…Under the condition of uncertain friction coefficients, a stochastic algorithm introducing randomness is formulated, which converges a paper stack to a state of single-sheet separation through the repetition of simple robot operations. …”
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Subpixel stacking and detection of blurred star images observed by an astroinertial attitude sensor against the background of the daytime sky by N.N. Vasilyuk

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Predicting the Remaining Useful Life of an Aircraft Engine Using a Stacked Sparse Autoencoder with Multilayer Self-Learning by Jian Ma, Hua Su, Wan-lin Zhao, Bin Liu

“…The choice of such features is highly significant, but there are some weaknesses in the current algorithm for RUL prediction, notably, the inability to obtain tendencies from the data. …”
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An efficient enhanced stacked auto encoder assisted optimized deep neural network for forecasting Dry Eye Disease by Steffi Rajan, Suresh Ponnan

“…The current study introduces the ESAE-ODNN, an improved stacked autoencoder-aided optimised deep neural network, as a new way to predict DED using feature selection (FS), feature extraction (FE), and classification. …”
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