High-precision prediction of non-resonant high-order harmonics energetic particle modes via stacking ensemble strategies by Sheng Liu, Zhenzhen Ren, Weihua Wang, Kai Zhong, Jinhong Yang, Hongwei Ning

“…The evaluation results indicate that the performance of the proposed model surpasses most supervised learning algorithms. Specifically, in comparison with the SVR and Bagging algorithms, the growth rate predictions of stacking model reduces Root mean squared error (RMSE) by 45% and 33%, mean absolute error (MAE) by 47% and 32%, and increases the R -squared coefficient ( R ^2 ) by 5% and 3%, respectively. …”
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BanglaNewsClassifier: A machine learning approach for news classification in Bangla Newspapers using hybrid stacking classifiers. by Tanzir Hossain, Ar-Rafi Islam, Md Humaion Kabir Mehedi, Annajiat Alim Rasel, M Abdullah-Al-Wadud, Jia Uddin

“…The use of traditional machine learning algorithms, deep learning architectures, and hybrid models, including novel stacking classifiers, was a part of our experiment. …”
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Spatiotemporal evolution of interseismic coupling and stress accumulation near an asperity on a vertical strike-slip fault: Insights from three-dimensional viscoelastic numerical s... by LI Yebo, HUANG Luyuan, YAO Rui, TIAN Yiwei, YANG Shuxin

“…These models incorporate vertical strike-slip faults and use sophisticated contact algorithms to simulate the mechanical locking associated with asperities. …”
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Credit Rating Model Based on Improved TabNet by Shijie Wang, Xueyong Zhang

“…Under the rapid evolution of financial technology, traditional credit risk management paradigms relying on expert experience and singular algorithmic architectures have proven inadequate in addressing complex decision-making demands arising from dynamically correlated multidimensional risk factors and heterogeneous data fusion. …”
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State-Aware Energy Management Strategy for Marine Multi-Stack Hybrid Energy Storage Systems Considering Fuel Cell Health by Pan Geng, Jingxuan Xu

“…Leveraging a fuel cell efficiency decay model and lithium-ion battery cycle life assessment, power distribution is reformulated as an equivalent hydrogen consumption optimization problem with stack degradation constraints. A hybrid Genetic Algorithm–Particle Swarm Optimization (GA-PSO) approach achieves global optimization. …”
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PM2.5 concentration 7-day prediction in the Beijing–Tianjin–Hebei region using a novel stacking framework by Xintong Gao, Xiaohong Wang, Fuping Li, Wenhao Jiang, Meng Zhe, Jiaxing Sun, Ao Zhang, Linlin Jiao

“…The findings of this study demonstrated that the integration of the LSTM-RF model with the fusion-based Stacking algorithm led to a substantial enhancement in the accuracy of PM2.5 predictions. …”
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A Novel Model for Noninvasive Haemoglobin Detection Based on Visibility Network and Clustering Network for Multi-Wavelength PPG Signals by Lei Liu, Ziyi Wang, Xiaohan Zhang, Yan Zhuang, Yongbo Liang

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Ensemble stacking: a powerful tool for landslide susceptibility assessment – a case study in Anhua County, Hunan Province, China by Lei-Lei Liu, Aasim Danish, Xiao-Mi Wang, Wen-Qing Zhu

“…Initially, we employ an ensemble stacking technique that combines the strengths of three machine learning classifiers. …”
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Efficient hybrid heuristic adopted deep learning framework for diagnosing breast cancer using thermography images by Ahmad Y. A. Bani Ahmad, Jafar A. Alzubi, Manimaran Vasanthan, Suresh Babu Kondaveeti, J. Shreyas, Thella Preethi Priyanka

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Machine Learning-Driven Prediction of Vitamin D Deficiency Severity with Hybrid Optimization by Usharani Bhimavarapu, Gopi Battineni, Nalini Chintalapudi

“…To gauge the effectiveness of the proposed IWOA algorithm, evaluation metrics like precision, accuracy, recall, and F1-score were used. …”
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Research on a Fusion Technique of YOLOv8-URE-Based 2D Vision and Point Cloud for Robotic Grasping in Stacked Scenarios by Xuhui Ye, Xiaoyang Qin, Leming Zhan, Jun Wang, Yan Chen

“…In industrial robotic grasping tasks, traditional 3D point cloud registration and pose estimation methods often struggle with low efficiency and limited accuracy in stacked and cluttered environments. To address these challenges, this paper proposes a grasp pose estimation algorithm that integrates 2D object detection based on YOLOv8-URE with 3D point cloud registration. …”
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Predicting Protein Interactions Using a Deep Learning Method-Stacked Sparse Autoencoder Combined with a Probabilistic Classification Vector Machine by Yanbin Wang, Zhuhong You, Liping Li, Li Cheng, Xi Zhou, Libo Zhang, Xiao Li, Tonghai Jiang

“…This method was developed based on a deep learning algorithm-stacked sparse autoencoder (SSAE) combined with a Legendre moment (LM) feature extraction technique. …”
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ASTER GDEM Correction Based on Stacked Ensemble Learning and ICEsat-2/ATL08: A Case Study from the Qilian Mountains by Qi Wei, Yanli Zhang, Yalong Ma, Ruirui Yang, Kairui Lei

“…However, most correction methods rely on a single ML model, which limits the improvement of DEM accuracy. Stacked ensemble learning (SEL) is a newly developed method of improving model performance by combining multiple ML models. …”
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An enhanced machine learning approach with stacking ensemble learner for accurate liver cancer diagnosis using feature selection and gene expression data by Amena Mahmoud, Eiko Takaoka

“…The stacking ensemble achieved an accuracy of (97%), outperforming individual machine learning algorithms and traditional diagnostic methods. …”
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ACD-ML: Advanced CKD detection using machine learning: A tri-phase ensemble and multi-layered stacking and blending approach by Mir Faiyaz Hossain, Shajreen Tabassum Diya, Riasat Khan

“…This work proposes a novel Tri-phase Ensemble technique combining Voting, Bagging, and Stacking approaches and two other ensemble models: Multi-layer Stacking and Multi-layer Blending classifiers. …”
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LogiTriBlend: A Novel Hybrid Stacking Approach for Enhanced Phishing Email Detection Using ML Models and Vectorization Approach by Aqsa Khalid, Maria Hanif, Abdul Hameed, Zeeshan Ashraf, Mrim M. Alnfiai, Salma M. Mohsen Alnefaie

“…These techniques were applied to traditional machine learning algorithms, and their performance was evaluated against a proposed stacking model, LogiTriBlend. …”
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Q8S: Emulation of Heterogeneous Kubernetes Clusters Using QEMU by Jonathan Decker, Vincent Florens Hasse, Julian Kunkel

“…To address this, we introduce Q8S, a tool for emulating heterogeneous Kubernetes clusters including x86_64 and ARM64 architectures on OpenStack using QEMU. Emulations created through Q8S provide a higher level of detail than simulations and can be used to train machine learning scheduling algorithms. …”
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Computational complexity when constructing rational plans for program execution in a given field of parallel computers by V. M. Bakanov

Subjects: “…algorithm graph…”
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Mapping the air temperature in China from time-normalized MODIS land surface temperature data via zone-based stacking ensemble models by Yan Xin, Yongming Xu, Xudong Tong, Yaping Mo, Yonghong Liu, Shanyou Zhu

“…Zone-based modeling exhibited enhanced performance compared to holistic modeling strategy. The stacking-based ensemble model outperformed each of the base models. …”
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MetaStackD A robust meta learning based deep ensemble model for prediction of sensors battery life in IoE environment by D. Gayathri, S. P. Shantharajah

“…This work focuses on proposing a novel framework integrating pre-processing, standardization, encoding scheme, and predictive modeling that includes two algorithms, RFRImpute and MetaStackD, for predicting the RBL of sensors in any IoE device using a meta-learning-based deep ensemble approach blue for analyzing factors such as power consumption, environmental conditions, operational frequency, and workload patterns. …”
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