Results 61 to 70 of about 58,557 (260)
Algorithms based on support vector regression to predict future glycemia in type 1 diabetes therapy
, 2022 openIl diabete mellito è una patologia epidemica mondiale e, secondo i più recenti dati dell’Organizzazione Mondiale della Sanità, si classifica come la nona causa di morte nel mondo. Si distinguono due diverse tipologie di diabete mellito: il diabete
BUSINARO, GIADA
core
Advanced Intelligent Discovery, EarlyView.In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley +1 more source
, 2014 The actual Shanghai stock market index and its predicted values from ICA-CCA-SVR, MICA-SVR, and A ICA-SVR.
Huaiqing Wang (589863) +3 more
core +1 more source
Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion
Advanced Intelligent Discovery, EarlyView.This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia +1 more
wiley +1 more source
Artificial Intelligence Models for Financial Time Series [PDF]
Ovidius University Annals: Economic Sciences Series, 2021 Modeling and predicting the evolution of financial series has become an essential research domain for scientists and practitioners in the field of economics or finance.
Alina Barbulescu +1 more
doaj
The actual Dow Jones index and its predicted values from ICA-CCA-SVR, MICA-SVR, and AICA-SVR.
, 2014 The actual Dow Jones index and its predicted values from ICA-CCA-SVR, MICA-SVR, and AICA-SVR.
Huaiqing Wang (589863) +3 more
core +1 more source
Advanced Intelligent Discovery, EarlyView.A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley +1 more source
Harnessing Machine Learning to Understand and Design Disordered Solids
Advanced Intelligent Discovery, EarlyView.This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
Overcoming the Nyquist Limit in Molecular Hyperspectral Imaging by Reinforcement Learning
Advanced Intelligent Discovery, EarlyView.Explorative spectral acquisition guide automatically selects informative spectral bands to optimize downstream tasks, outperforming full‐spectrum acquisition. The selected hyperspectral data are used for tasks such as unmixing and segmentation. BandOptiNet encodes selection states and outputs optimal bands to guide spectral acquisition. Recent advances
Xiaobin Tang +4 more
wiley +1 more source