Results 241 to 250 of about 6,285,017 (364)

Random Forest Machine Learning Matches Human Expert Accuracy in Trauma Severity Scoring. [PDF]

World J Surg
Laing GL, Bruce JL, Bekker W, Manchev V, Wain H, Clarke DL.   +5 more
europepmc   +1 more source

Random Forest (RF).

Ahood Alazwari (12160802), Laleh Tafakori (12160805), Alice Johnstone (12160808), Mali Abdollahian (5940038)   +3 more
openalex   +1 more source

Modular, Textile‐Based Soft Robotic Grippers for Agricultural Produce Handling

Advanced Robotics Research, EarlyView.
This article introduces textile‐based pneumatic grippers that transform simple textiles into robust bending actuators. Detailed experiments uncover how cut geometry and fabric selection shape performance. Successful handling of fragile agricultural items showcases the potential of textile robotics for safe, scalable automation in food processing and ...
Zeyu Hou, Eran Beeri Bamani, Joao Buzzatto, Bryan Busby, Lucas Ferrari Gerez   +4 more
wiley   +1 more source

Fluoro-forest: a random forest workflow for cell type annotation in high-dimensional immunofluorescence imaging with limited training data. [PDF]

Bioinform Adv
Brand J, Zhang W, Carchman E, Dinh HQ.
europepmc   +1 more source

Calibration plots for random forest applied to accelerometer data under three approaches (NR, PCA, fFPCA) and two cross validation methods (n-fold and farm-fold).

Muhammad Usman Riaz (21615573), Luke O’Grady (3774166), Conor G. McAloon (10712896), Finnian Logan (21615576), Isobel Claire Gormley (11599588)   +4 more
openalex   +1 more source

Smart REASSURED Sensors via Machine‐Augmented Printable On‐Paper Arrays

Advanced Sensor Research, EarlyView.
This perspective highlights the emerging role of pattern‐recognition, printable on‐paper sensor arrays for intelligent PoC diagnostics. It discusses how paper's inherent limitations can be overcome through surface modification and scalable printing, and how machine‐learning analysis of cross‐reactive arrays enables multiplexed, low‐cost, and REASSURED ...
Naimeh Naseri, Saba Ranjbar
wiley   +1 more source

Development and comparison of OCT-based prediction models for diabetic retinopathy using LASSO and random forest. [PDF]

Medicine (Baltimore)
Tuerhongjiang M, Li X, Kalibinuer A, Dong Y, Ailiyaer A, Cheng F, Gao W.   +6 more
europepmc   +1 more source

2314056640-hao/Random-forest-prediction-model-for-Cd: Random forest prediction model.-Cd

2314056640-hao
openalex   +1 more source

Electronic Nose for Indoor Mold Detection and Identification

Advanced Sensor Research, EarlyView.
This study explores the potential of a SnO2 nanowire‐based chemiresistive electronic nose not only to detect but also to identify two common indoor mold species, Stachybotrys chartarum and Chaetomium globosum on two different growth substrates. In a laboratory setup, the electronic nose displays high classification performance using optimized linear ...
Hankun Yang, Martin Sommer, Stephanie Bauer, Uli Lemmer   +3 more
wiley   +1 more source

Modeling student satisfaction in online learning using random forest. [PDF]

Sci Rep
Li J, Chen X.
europepmc   +1 more source