Prediction method of sugarcane important phenotype data based on multi-model and multi-task. by Jihong Sun, Chen Sun, Zhaowen Li, Ye Qian, Tong Li

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Synergistic Multi-Model Approach for GPR Data Interpretation: Forward Modeling and Robust Object Detection by Hang Zhang, Zhijie Ma, Xinyu Fan, Feifei Hou

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Multi-model learning for vessel ETA prediction in inland waterways using multi-attribute data by Abdullah Al Noman, Anton Zitnikov, Aaron Heuermann, Klaus-Dieter Thoben

“…Existing ETA prediction models largely rely on Automatic Identification System (AIS) data but often overlook additional factors. This study introduces a deep learning-based Multi-Model learning approach that fuses multi-attribute data from multiple sources to enhance ETA prediction accuracy. …”
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Analysis of Happiness in EU Countries Using the Multi-Model Classification Based on Models of Symbolic Data by Marcin Pełka

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Multi‐Model Assessment of Future Hydrogen Soil Deposition and Lifetime Using CMIP6 Data by M. A. J. Brown, N. J. Warwick, A. T. Archibald

“…This work uses an offline hydrogen deposition scheme to perform the first multi‐model assessment of deposition velocities driven using data from five models from the Coupled Model Intercomparison Phase 6 project. …”
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Diagnosis of epileptic seizure neurological condition using EEG signal: a multi-model algorithm by Mosleh Hmoud Al-Adhaileh, Mosleh Hmoud Al-Adhaileh, Sultan Ahmad, Alhasan A. Alharbi, Mohammed Alarfaj, Mohammed Alarfaj, Mukta Dhopeshwarkar, Theyazn H. H. Aldhyani

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Multi-model ensemble mapping of irrigated areas using remote sensing, machine learning, and ground truth data by Muhammad Umar Akbar, Ali Mirchi, Arfan Arshad, Sara Alian, Mukesh Mehata, Saleh Taghvaeian, Kasra Khodkar, Jacob Kettner, Sumon Datta, Kevin Wagner

“…To this end, we introduce a multi-model ensemble mapping (MEM) approach to develop high-fidelity, high-resolution (30 m) annual maps of irrigated areas from 2007 to 2022 in the Upper Red River Basin (URRB), U.S., using remote sensing, machine learning (ML), and ground truth data. …”
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Multi-model deep learning approach for the classification of kidney diseases using medical images by Waleed Obaid, Abir Hussain, Tamer Rabie, Dhafar Hamed Abd, Wathiq Mansoor

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Personalized Course Recommendation System: A Multi-Model Machine Learning Framework for Academic Success by Md Sajid Islam, A. S. M. Sanwar Hosen

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Fast Islanding Detection for Distribution System including PV using Multi-Model Decision Tree Algorithm by Rasool Ebrahimi, Ghazanfar Shahgholian, Bahador Fani

“…In this paper, a new multi-model classification-based method is proposed, in order to detect islanding condition for photovoltaic units. …”
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Dynamic Multi-Model Container Framework for Cloud-Based Distributed Digital Twins (dDTws) by Nidhal Al-Sadoon, Raimar J. Scherer, Christoph F. Strnadl

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Research on load frequency control system attack detection method based on multi-model fusion by Feng Zheng, Weixun Li, Huifeng Li, Libo Yang, Zengjie Sun

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Fusion of Multi-Task Neurophysiological Data to Enhance the Detection of Attention- Deficit/Hyperactivity Disorder by Kai-Feng Zhang, Shih-Ching Yeh, Eric Hsiao-Kuang Wu, Xiu Xu, Ho-Jung Tsai, Chun-Chuan Chen

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Multi-model ensemble machine learning-based downscaling and projection of GRACE data reveals groundwater decline in Saudi Arabia throughout the 21st century by Arfan Arshad, Muhammad Shafeeque, Thanh Nhan Duc Tran, Ali Mirchi, Zaichen Xiang, Cenlin He, Amir AghaKouchak, Jessica Besnier, Md Masudur Rahman

“…Additionally, we used the downscaled GWS and CMIP6 climate data with the Generalized Additive Model (GAM) to project the future GWS changes under climate change. …”
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Multi-model assessment and thermodynamic prediction for oxalate-tungstate complexes by Yong Liang, Ting Pu, Zanhong Chen, Yinliang Liu, Congyu Zhang

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Multi-Model Demand Forecasting in Water Distribution Network Districts by Enrico Creaco, Carlo Giudicianni, Manuel Herrera

“…A multi-model including three modelling elements is developed to solve the Battle of Water Demand Forecasting problem. …”
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Engineering a multi model fallback system for edge devices by Gaurav Kadve, Abishi Chowdhury, Vishal Krishna Singh, Amrit Pal

“…This paper presents a novel multi-model fallback system designed for deployment on resource-constrained edge devices, leveraging the advancements of TinyML. …”
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Pseudo-Labeling Domain Adaptation Using Multi-Model Learning by Victor Akihito Kamada Tomita, Ricardo Marcondes Marcacini

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Heavy Precipitation Forecasts Based on Multi-model Ensemble Members by Zhi Xiefei, Zhao Chen

“…Based on the daily 24-168 h ensemble precipitation forecasts over China from 1 May to 31 August in 2016 from the global ensemble models of ECMWF, JMA, NCEP, CMA and UKMO extracted from the TIGGE archives, the frequency matching method is tested to calibrate the precipitation frequency of each ensemble member. Then results of multi-model ensemble forecasts before and after calibration, including Kalman filter(KF), multi-model super-ensemble (SUP) and bias-removed ensemble mean(BREM), are analyzed in order to improve the prediction of precipitation based on numerical weather forecast data. …”
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Regional Ecological Environment Quality Prediction Based on Multi-Model Fusion by Yiquan Song, Zhengwei Li, Baoquan Wei

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