A Neural Network Nonlinear Multimodel Ensemble to Improve Precipitation Forecasts over Continental US by Vladimir M. Krasnopolsky, Ying Lin

“…The developed nonlinear approach allowed us to account for nonlinear correlation between ensemble members and to produce “optimal” forecast represented by a nonlinear NN ensemble mean. …”
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The performance of the weather research & forecasting model (WRF) using ensemble method to predict weather parameters by Pratama Alvin, Oktaviana Ade A., Kombara Prawira Y., Isnaenda Ikhsan Muhammad

“…There are 12 members, and the ensemble mean method is used to evaluate the temperature, humidity, wind direction, and wind speed. …”
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Short-Term Prediction of Traffic State for a Rural Road Applying Ensemble Learning Process by Arash Rasaizadi, Seyedehsan Seyedabrishami, Mohammad Saniee Abadeh

“…To find the most precise prediction for each time interval for segments, several ensemble methods, including voting methods and ordinal logit (OL) model, are utilized to ensemble predictions of four machine learning algorithms. …”
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A Heterogeneous Ensemble Learning Method Combining Spectral, Terrain, and Texture Features for Landslide Mapping by Yi He, Hesheng Chen, Qing Zhu, Qing Zhang, Lifeng Zhang, Tao Liu, Wende Li, Huaiyuan Chen

Subjects: “…Ensemble learning…”
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Climate Change Hotspots Identification in China through the CMIP5 Global Climate Model Ensemble by Huanghe Gu, Zhongbo Yu, Jigan Wang, Qin Ju, Chuanguo Yang, Chuanhao Fan

“…The potential climate change hotspots in China throughout the 21st century are identified in this study by using a multimodel, multiscenario climate model ensemble that includes Phase Five of the Coupled Model Intercomparison Project (CMIP5) atmosphere-ocean general circulation models. …”
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An Ensemble Feature Selection Approach-Based Machine Learning Classifiers for Prediction of COVID-19 Disease by Md. Jakir Hossen, Thirumalaimuthu Thirumalaiappan Ramanathan, Abdullah Al Mamun

“…The performance of the machine learning classifiers based on the ensemble feature selection methods is analyzed.…”
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Real-World Parkinson’s Hand Tremor Detection Using Ensemble Learning Techniques by Sungwook Hur, Jieming Zhang, Moon-Hyun Kim, Tai-Myoung Chung

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Applying YOLOv6 as an ensemble federated learning framework to classify breast cancer pathology images by Chhaya Gupta, Nasib Singh Gill, Preeti Gulia, Noha Alduaiji, J. Shreyas, Piyush Kumar Shukla

“…Although FedL has been famous as a safeguarding privacy algorithm, its similarities to ensemble learning methods, such as federated averaging (FEDAvrg), still need to be thoroughly investigated. …”
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Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features by Emmanuel Adetiba, Oludayo O. Olugbara

“…This paper reports an experimental comparison of artificial neural network (ANN) and support vector machine (SVM) ensembles and their “nonensemble” variants for lung cancer prediction. …”
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Ensembles of spectral-spatial convolutional neural network models for classifying soil types in hyperspectral images by N.A. Firsov, V.V. Podlipnov, N.A. Ivliev, D.D. Ryskova, A.V. Pirogov, A.A. Muzyka, A.R. Makarov, V.E. Lobanov, V.I. Platonov, A.N. Babichev, V.A. Monastyrskiy, V.I. Olgarenko, D.P. Nikolaev, R.V. Skidanov, A.V. Nikonorov, N.L. Kazanskiy, V.A. Soyfer

“…As a result of the work, an approach to the classification of high-resolution hyper-spectral images based on the refinement of a multiclass convolutional neural network using an ensemble of binary classifiers is proposed. It is shown that the classification of soil samples by carbon and calcium content is carried out with an accuracy of 0.96.…”
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Adaptive Ensemble Learning Model-Based Binary White Shark Optimizer for Software Defect Classification by Jameel Saraireh, Mary Agoyi, Sofian Kassaymeh

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Robust fault detection and classification in power transmission lines via ensemble machine learning models by Tahir Anwar, Chaoxu Mu, Muhammad Zain Yousaf, Wajid Khan, Saqib Khalid, Ahmad O. Hourani, Ievgen Zaitsev

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Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3DCNN by Ali Erbey, Necaattin Barışçı

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Development and interpretation of a pathomics-driven ensemble model for predicting the response to immunotherapy in gastric cancer by Jing Wang, Zhe Li, Wei Wang, Md Tauhidul Islam, Xiaoyan Wang, Zhen Han, Zihan Li, Guoxin Li, Yuming Jiang, Taojun Zhang, Wenjun Xiong, Zepang Sun, Lequan Yu, Zhicheng Zhang, Xianqi Yang, Shengtian Sang, Alyssa A Guo

“…Pathogenomics analysis suggested that the ensemble model is driven by molecular-level immune, cancer, metabolism-related pathways, and was correlated with the immune-related characteristics, including immune score, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data score, and tumor purity.Conclusions Our pathomics-driven ensemble model exhibited high accuracy and robustness in predicting the response to ICIs using WSIs. …”
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A Study on the Application of Explainable AI on Ensemble Models for Predictive Analysis of Chronic Kidney Disease by K. M. Tawsik Jawad, Anusha Verma, Fathi Amsaad, Lamia Ashraf

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Volatile Organic Compounds for the Prediction of Lung Cancer by Using Ensembled Machine Learning Model and Feature Selection by Divya Khanna, Arun Kumar, Shahid Ahmad Bhat

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Integrating fast iterative filtering and ensemble neural network structure with attention mechanism for carbon price forecasting by Wang Zhong, Wang Yue, Wang Haoran, Tang Nan, Wang Shuyue

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Enhancing prediction of wildfire occurrence and behavior in Alaska using spatio-temporal clustering and ensemble machine learning by A. Ahajjam, M. Allgaier, R. Chance, E. Chukwuemeka, J. Putkonen, T. Pasch

“…Histogram Gradient Boosting (HistGB) is then used for predictive modeling of wildfire occurrence, burnt area, and wildfire duration. This ensemble model’s performance is benchmarked across four prediction horizons (same-day, +7 days, +30 days, +90 days) and against various conventional ML and deep learning techniques. …”
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Robust Automated Harmonization of Heterogeneous Data Through Ensemble Machine Learning: Algorithm Development and Validation Study by Doris Yang, Doudou Zhou, Steven Cai, Ziming Gan, Michael Pencina, Paul Avillach, Tianxi Cai, Chuan Hong

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Employing the concept of stacking ensemble learning to generate deep dream images using multiple CNN variants by Lafta Alkhazraji, Ayad R. Abbas, Abeer S. Jamil, Zahraa Saddi Kadhim, Wissam Alkhazraji, Sabah Abdulazeez Jebur, Bassam Noori Shaker, Mohammed Abdallazez Mohammed, Mohanad A. Mohammed, Basim Mohammed Al-Araji, Abdulkareem Z. Mohmmed, Wasiq Khan, Bilal Khan, Abir Jaafar Hussain

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