Critical factors influencing live birth rates in fresh embryo transfer for IVF: insights from cluster ensemble algorithms by Zheng Yu, Xiaoyan Zheng, Jiaqi Sun, Pengfei Zhang, Ying Zhong, Xingyu Lv, Hongwen Yuan, Fanrong Liang, Dexian Wang, Jie Yang

Subjects: “…Cluster ensemble algorithm…”
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An effective electrocardiogram segments denoising method combined with ensemble empirical mode decomposition, empirical mode decomposition, and wavelet packet by Yaru Yue, Chengdong Chen, Xiaoyuan Wu, Xiaoguang Zhou

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Hybrid GNSS time-series prediction method based on ensemble empirical mode decomposition with long short-term memory by Yu Zhou, Xiaoxing He, Shengdao Wang, Shunqiang Hu, Xiwen Sun, Jiahui Huang

“…To address the shortcomings of traditional GNSS time series prediction methods including insufficient feature selection, limited stability, and low predictive accuracy, this paper proposes a prediction model that combines the Ensemble Empirical Mode Decomposition (EEMD) with Long Short-Term Memory (LSTM) algorithm. …”
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A hybrid data assimilation method based on real-time Ensemble Kalman filtering and KNN for COVID-19 prediction by SongTao Zhang, LiHong Yang

“…The approach integrates real-time Ensemble Kalman Filtering (EnKF) with the K-Nearest Neighbors (KNN) algorithm, combining dynamic real-time adjustments with pattern recognition techniques tailored to the specific dynamics of epidemics. …”
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A Novel Federated & Ensembled Learning-Based Battery State-of-Health Estimation for Connected Electric Vehicles by Praveen Abbaraju, Subrata Kumar Kundu

“…This paper introduces a novel federated & ensembled learning (FEL) algorithm for precise estimation of battery State of Health (SoH). …”
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3D Web Application of Wind Prediction Under Typhoon on Coastal Cities Based on Ensemble k-NN and OpenFOAM by Mamad Tamamadin, Seong-Hoon Kee, Jurng-Jae Yee

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Identification of Potential Selective PAK4 Inhibitors Through Shape and Protein Conformation Ensemble Screening and Electrostatic-Surface-Matching Optimization by Xiaoxuan Zhang, Meile Zhang, Yihao Li, Ping Deng

“…We then performed screening using shape and protein conformation ensembles, followed by a re-evaluation of the docking results with deep-learning-driven GNINA to identify the candidate molecule, STOCK7S-56165. …”
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Links between Temperature Biases and Flow Anomalies in an Ensemble of CNRM-CM5.1 Global Climate Model Historical Simulations by O. Lhotka, A. Farda

“…Too low pressure was found over midlatitudes of the Northern Hemisphere, and CNRM-CM5.1 simulated too frequent zonal flow in these latitudes. The usage of three ensemble members with different initial conditions did not improve model’s outputs because the bias is found to be considerably larger compared to the ensemble members’ spread. …”
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Utilizing the subtractive proteomics approach to design ensemble vaccine against Candida lusitaniae for immune response stimulation; a bioinformatics study. by Habiba Naz, Rimsha Timotheous, Muhammad Farhan Sarwar, Tariq Nadeem, Mudassar Fareed Awan, Sajed Ali, Sophia Awais, Irfan Ahmed

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HIDRA3: a deep-learning model for multipoint ensemble sea level forecasting in the presence of tide gauge sensor failures by M. Rus, M. Rus, H. Mihanović, M. Ličer, M. Ličer, M. Kristan

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Ensemble, on va plus loin ? Différences d’appréciation du travail à plusieurs chez des élèves de primaire by Amélia Legavre

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Assessing Nonlinear Dynamics and Trends in Precipitation by Ensemble Empirical Mode Decomposition (EEMD) and Fractal Approach in Benin Republic (West Africa) by Médard Noukpo Agbazo, Gabin Koto N’Gobi, Eric Alamou, Basile Kounouhewa, Abel Afouda

“…Based on daily precipitation data observed in the six synoptic stations of Benin Republic, in the period from 1951 to 2010, we have proposed (i) determining the local trends of precipitations, (ii) investigating precipitation nonlinear dynamics, and (iii) assessing climatic shift in the study period by Ensemble Empirical Mode Decomposition (EEMD) and Multifractal Detrended Fluctuation Analysis (MFDFA) method. …”
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Addressing Label Noise in Colorectal Cancer Classification Using Cross-Entropy Loss and pLOF Methods With Stacking-Ensemble Technique by Ishrat Zahan Tani, Kah Ong Michael Goh, Md Nazmul Islam, Md Tarek Aziz, S. M. Hasan Mahmud, Dip Nandi

“…In this paper, we address label noise in training data and propose a stacking-ensemble model for classifying colorectal cancer along with a trustworthy computer-aided diagnosis (CAD) system. …”
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Removal of Muscle Artifacts from Single-Channel EEG Based on Ensemble Empirical Mode Decomposition and Multiset Canonical Correlation Analysis by Xun Chen, Chen He, Hu Peng

“…In this work, we propose a simple, yet effective method to achieve the muscle artifact removal from single-channel EEG, by combining ensemble empirical mode decomposition (EEMD) with multiset canonical correlation analysis (MCCA). …”
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Prise en compte de la pluralité des mémoires d’habitants dans la « patrimonialisation » des grands ensembles by Rachid Kaddour

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Enhanced thyroid disease prediction using ensemble machine learning: a high-accuracy approach with feature selection and class balancing by Md. Rezaul Islam, Aniruddha Islam Chowdhury, Sharmin Shama, Md. Masudul Hasan Lamyea

“…To achieve optimal prediction outcomes, we evaluated seventeen machine learning models and implemented an Ensemble ML classifier using a hard voting strategy. …”
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Correction: Hybrid GNSS time-series prediction method based on ensemble empirical mode decomposition with long short-term memory by Yu Zhou, Xiaoxing He, Shengdao Wang, Shunqiang Hu, Xiwen Sun, Jiahui Huang

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Environnements culturels et naturels : Apprendre pour agir ensemble – Entretien avec Gérard Vergnaud et Jean-Louis Martinand

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Analyzing the relationship between gene expression and phenotype in space-flown mice using a causal inference machine learning ensemble by James A. Casaletto, Ryan T. Scott, Makenna Myrick, Graham Mackintosh, Hamed Chok, Amanda Saravia-Butler, Adrienne Hoarfrost, Jonathan M. Galazka, Lauren M. Sanders, Sylvain V. Costes

“…In this work, we use a machine learning ensemble of causal inference methods called the Causal Research and Inference Search Platform (CRISP) which was developed to predict causal features of a binary response variable from high-dimensional input. …”
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Soil Moisture Assimilation Using a Modified Ensemble Transform Kalman Filter Based on Station Observations in the Hai River Basin by Guocan Wu, Bo Dan, Xiaogu Zheng

“…A proposed forecast error inflation and water balance constraint are adopted in the Ensemble Transform Kalman Filter to reduce the analysis error and water budget residuals. …”
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