Results 181 to 190 of about 35,702 (279)

Enhancing spatial resolution of satellite soil moisture data through stacking ensemble learning techniques. [PDF]

Sci Rep
Tahmouresi MS, Niksokhan MH, Ehsani AH.
europepmc   +1 more source

A Dynamic‐Weighted Deep Transfer Learning Framework for Thermal Conductivity Prediction and Analysis

Materials Genome Engineering Advances, EarlyView.
Leveraging the visual perception of pretrained models, a deep transfer learning framework with dynamic weighting is proposed to bridge natural vision and material microstructures. This strategy achieves a prediction accuracy (R2) of 0.89 and the model demonstrates superior generalization capabilities across multiple material systems, effectively ...
Zhenzhao Zhang, Yunpeng Guo, Chunran Wu, Tingbo Wang, Ming Huang, Wei Li, Xingyu Chen, Haijun Mao, Weijun Zhang, Wenjian Guo, Fenglin Wang, Zhuofeng Liu   +11 more
wiley   +1 more source

Using machine learning to develop a stacking ensemble learning model for the CT radiomics classification of brain metastases. [PDF]

Sci Rep
Zhang HW, Wang YR, Hu B, Song B, Wen ZJ, Su L, Chen XM, Wang X, Zhou P, Zhong XM, Pang HW, Wang YH.   +11 more
europepmc   +1 more source

Automated Coregistered Segmentation for Volumetric Analysis of Multiparametric Renal MRI

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose This study aims to develop and evaluate a fully automated deep learning‐driven postprocessing pipeline for multiparametric renal MRI, enabling accurate kidney alignment, segmentation, and quantitative feature extraction within a single efficient workflow. Methods Our method has three main stages.
Aya Ghoul, Cecilia Liang, Isabelle Loster, Lavanya Umapathy, Bernd Kühn, Petros Martirosian, Ferdinand Seith, Sergios Gatidis, Thomas Küstner   +8 more
wiley   +1 more source

Enhancing Intelligent HVAC optimization with graph attention networks and stacking ensemble learning, a recommender system approach in Shenzhen Qianhai Smart Community. [PDF]

Sci Rep
He Y, Ali ABM, Aminian SA, Sharma K, Dixit S, Sobti S, Ali R, Ahemedei M, Rajab H, Ziaei Mazinan MA.   +9 more
europepmc   +1 more source

RPI-SE: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information. [PDF]

BMC Bioinformatics, 2020
Yi HC, You ZH, Wang MN, Guo ZH, Wang YB, Zhou JR.   +5 more
europepmc   +1 more source

Lessons Learned From Using Simple Supervised Learning Tools on Small‐Ensemble Data—Applicability to Tunnel Design and Monitoring

International Journal for Numerical and Analytical Methods in Geomechanics, EarlyView.
ABSTRACT Integrating interdisciplinary strategies with artificial intelligence (AI), particularly machine learning (ML), is an effective way of addressing urgent engineering challenges. Therefore, a thorough evaluation of existing methodologies is essential, taking into account their respective strengths, limitations and opportunities.
Lina‐María Guayacán‐Carrillo, Jean‐Michel Pereira, Jean Sulem   +2 more
wiley   +1 more source

Risky lane-changing behavior recognition based on stacking ensemble learning on snowy and icy surfaces. [PDF]

Sci Rep
Du X, Zhao W.
europepmc   +1 more source

From Data to Decisions: How Machine Learning and Generative Artificial Intelligence Are Redefining Precision Medicine in Kidney Transplantation

Organ Medicine, EarlyView.
This review evaluates how machine learning, multimodal integration, and generative AI optimize kidney transplant outcomes. These tools enable superior prediction and personalized therapy but face hurdles in data volume, generalizability, and ethics. Future clinical adoption depends on continued innovation and multidisciplinary collaboration to overcome
Maoxin Liao, Cheng Yang
wiley   +1 more source