Results 161 to 170 of about 35,702 (279)

Highly efficient stacking ensemble learning model for automated keratoconus screening. [PDF]

Eye Vis (Lond)
Muhsin ZJ, Qahwaji R, Ghafir I, AlShawabkeh M, Al Bdour M, AlRyalat SA, Al-Taee M.   +6 more
europepmc   +1 more source

A Performance Improvement Strategy for Concrete Damage Detection Using Stacking Ensemble Learning of Multiple Semantic Segmentation Networks. [PDF]

Sensors (Basel), 2022
Li S, Zhao X.
europepmc   +1 more source

Electricity Price Prediction Using Multikernel Gaussian Process Regression Combined With Kernel‐Based Support Vector Regression

Journal of Forecasting, EarlyView.
ABSTRACT This paper presents a new hybrid model for predicting German electricity prices. The algorithm is based on a combination of Gaussian process regression (GPR) and support vector regression (SVR). Although GPR is a competent model for learning stochastic patterns within data and for interpolation, its performance for out‐of‐sample data is not ...
Abhinav Das, Stephan Schlüter, Lorenz Schneider   +2 more
wiley   +1 more source

Sorghum yield prediction using UAV multispectral imaging and stacking ensemble learning in arid regions. [PDF]

Front Plant Sci
Deng L, Li Y, Liu X, Zhang Z, Mu J, Jia S, Yan Y, Zhang W.   +7 more
europepmc   +1 more source

Automatic Sleep-Arousal Detection with Single-Lead EEG Using Stacking Ensemble Learning. [PDF]

Sensors (Basel), 2021
Chien YR, Wu CH, Tsao HW.
europepmc   +1 more source

Ultrafast in‐memory computing and highly efficient deep neural networks driven by phase‐change memory materials with partially amorphous state transitions

InfoScience, EarlyView.
This work addresses challenges including the nonlinear weight‐conductance update and the trade‐off between increasing melting uniformity and reducing solid‐to‐liquid transition time. It utilizes all four melting states to create an integrated framework for attaining in‐memory computing and deep neural network applications. The framework achieves a near‐
Kian‐Guan Lim, Shao‐Xiang Go, Qiang Wang, Dong‐Gwan Kim, Chan‐Hwi Baek, Qishen Wang, Tae Hoon Lee, Desmond K. Loke   +7 more
wiley   +1 more source

Stacking ensemble learning models diagnose pulmonary infections using host transcriptome data from metatranscriptomics. [PDF]

Sci Rep
Zhang T, Deng Y, Wang W, Zhao Z, Wu Y, Wang H, Xia S, Liao W, Liao W.   +8 more
europepmc   +1 more source

Recent advances in data‐driven and artificial intelligence‐integrated perovskite solar cells: From design to self‐driving laboratories

InfoMat, EarlyView.
To address the limitations of conventional trial‐and‐error approaches, perovskite solar cell research is shifting toward a new paradigm that utilizes datasets and AI. This review examines the fundamental elements of data‐driven and AI‐integrated research: data platforms, AI methodologies, and self‐driving laboratories, demonstrating how their ...
Jaehee Lee, Juhwan Noh, Hyunjun Lee, Yea‐Lee Lee, Jino Im, Jangwon Seo   +5 more
wiley   +1 more source

Improving thyroid disorder diagnosis via innovative stacking ensemble learning model. [PDF]

Digit Health
Hassan A, Ramzan S, Raza A, Munwar Iqbal M, Smerat A, Latif Fitriyani N, Syafrudin M, Won Lee S.   +7 more
europepmc   +1 more source

Decoding temporal miRNA signatures of semen under in vitro exposure for forensic time since deposition estimation using machine learning‐driven modeling

Interdisciplinary Medicine, EarlyView.
This study develops a novel miRNA‐based framework for estimating the time since deposition of semen stains, combining small RNA sequencing with machine learning. Time‐dependent miRNA modules were identified using Mfuzz clustering and WGCNA, followed by a multi‐stage feature selection pipeline that reduced 261 candidate miRNAs to a minimal 7‐miRNA panel.
Meiming Cai, Qiong Lan, Tong Xie, Qinglin Liu, Ming Zhao, Xiaolian Wu, Xin Shi, Ruonan Shen, Yiman Wu, Chen Mao, Bin Cong, Bofeng Zhu   +11 more
wiley   +1 more source