An Ensemble Learning Based Intrusion Detection Model for Industrial IoT Security by Mouaad Mohy-Eddine, Azidine Guezzaz, Said Benkirane, Mourade Azrour, Yousef Farhaoui

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A Systematic, Cross-Model Evaluation of Ensemble Light Scattering Sensors by Abhay Vidwans, Pratim Biswas

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Uncertainties in the finite-time Lyapunov exponent in an ocean ensemble prediction model by M. Matuszak, J. Röhrs, P. E. Isachsen, P. E. Isachsen, M. Idžanović

“…We find that averaging over ensemble members can reveal robust FTLE ridges, i.e., FTLE ridges that exist across ensemble realizations. …”
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Development of an Ensemble Intelligent Model for Assessing the Strength of Cemented Paste Backfill by Yuantian Sun, Guichen Li, Junfei Zhang, Junbo Sun, Jiahui Xu

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Excitation Spectrum of the Néel Ensemble of Antiferromagnetic Nanoparticles as Revealed in Mössbauer Spectroscopy by Mikhail A. Chuev

“…The excitation spectrum of the Néel ensemble of antiferromagnetic nanoparticles with uncompensated magnetic moment is deduced in the two-sublattice approximation following the exact solution of equations of motion for magnetizations of sublattices. …”
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Constructing Better Classifier Ensemble Based on Weighted Accuracy and Diversity Measure by Xiaodong Zeng, Derek F. Wong, Lidia S. Chao

“…A weighted accuracy and diversity (WAD) method is presented, a novel measure used to evaluate the quality of the classifier ensemble, assisting in the ensemble selection task. …”
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A Novel Ensemble Classifier Selection Method for Software Defect Prediction by Xin Dong, Jie Wang, Yan Liang

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Identification of Civil Infrastructure Damage Using Ensemble Transfer Learning Model by A. Shamila Ebenezer, S. Deepa Kanmani, V. Sheela, K. Ramalakshmi, V. Chandran, M. G. Sumithra, B. Elakkiya, Bharani Murugesan

“…The proposed ensemble transfer learning model provided a validation accuracy of 87.1%.…”
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Detecting Obfuscated Malware Infections on Windows Using Ensemble Learning Techniques by Yadigar Imamverdiyev, Elshan Baghirov, John Chukwu Ikechukwu

“…The study demonstrated the superiority of ensemble learning techniques in enhancing detection accuracy and robustness. …”
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A Decomposed-Ensemble Prediction Framework for Gate-In Operations at Container Terminals by Yifan Shen, Beng Xuan, Hongtao Hu, Yansong Wu, Ning Zhao, Zhen Yang

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Ensemble learning-based predictor for driver synonymous mutation with sequence representation. by Chuanmei Bi, Yong Shi, Junfeng Xia, Zhen Liang, Zhiqiang Wu, Kai Xu, Na Cheng

“…Subsequently, we propose EPEL, an effect predictor for synonymous mutations employing ensemble learning. EPEL combines five tree-based models and optimizes feature selection to enhance predictive accuracy. …”
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An Application of ANN Ensemble for Estimating of Precipitation Using Regional Climate Models by Dongwoo Jang

“…In this study, the precipitation of five regional climate models and actual observed precipitation provided in Korea are applied to ANN (artificial neural network), which suggests ways to improve prediction accuracy for precipitation. The ANN ensemble of RCMs simulates the actual observed precipitation more accurately than the individual RCM. …”
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Application of ensemble learning techniques to model the atmospheric concentration of SO2 by A. Masih

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Enhancing Telemarketing Success Using Ensemble-Based Online Machine Learning by Shahriar Kaisar, Md Mamunur Rashid, Abdullahi Chowdhury, Sakib Shahriar Shafin, Joarder Kamruzzaman, Abebe Diro

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Ensemble automated approaches for producing high‐quality herbarium digital records by Robert P. Guralnick, Raphael LaFrance, Julie M. Allen, Michael W. Denslow

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Edge Statistics for Lozenge Tilings of Polygons, II: Airy Line Ensemble by Amol Aggarwal, Jiaoyang Huang

“…We show that the edge statistics around any point on the arctic boundary, that is not a cusp or tangency location, converge to the Airy line ensemble. Our proof proceeds by locally comparing these edge statistics with those for a random tiling of a hexagon, which are well understood. …”
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Éditorial - Environnements culturels et naturels : Apprendre pour agir ensemble

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Greedy Algorithm for Deriving Decision Rules from Decision Tree Ensembles by Evans Teiko Tetteh, Beata Zielosko

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Extraction of Fast Changes in the Structure of a Disordered Ensemble of Photoexcited Biomolecules by H.-C. Poon, M. Schmidt, D. K. Saldin

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Research on the Rate of Penetration Prediction Method Based on Stacking Ensemble Learning by Yangfeng Ren, Baoping Lu, Shuangjin Zheng, Kai Bai, Lin Cheng, Hao Yan, Gan Wang

“…In order to accurately predict the ROP of an oilfield in Xinjiang working area, a ROP prediction model based on the historical drilling data of this working area was established based on stacking ensemble learning. This model integrates the K-nearest neighbor algorithm and support vector machine algorithm by stacking ensemble strategy and uses genetic algorithm to optimize model parameters, forming a new method of ROP prediction suitable for this oilfield. …”
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