Results 321 to 330 of about 5,579,561 (370)

Interpretable machine learning method to predict the risk of pre-diabetes using a national-wide cross-sectional data: evidence from CHNS. [PDF]

BMC Public Health
Li X, Ding F, Zhang L, Zhao S, Hu Z, Ma Z, Li F, Zhang Y, Zhao Y, Zhao Y.   +9 more
europepmc   +1 more source

Multi‐Risk‐Level Sarcopenia‐Prone Screening via Machine Learning Classification of Sit‐to‐Stand Motion Metrics from Wearable Sensors

Advanced Intelligent Systems, EarlyView.
A machine learning approach utilizes micro inertial measurement units (μIMUs) for noninvasive sarcopenia screening through a sit‐to‐stand test. With 53 older participants wearing IMUs, 510 features are extracted across four motion phases. Five algorithms (support vector machine, K‐nearest neighbors, decision tree, linear discriminant analysis, and ...
Keer Wang, Hongyu Zhang, Clio Yuen Man Cheng, Meng Chen, King Wai Chiu Lai, Calvin Kalun Or, Yong Hu, Arul Lenus Roy Vellaisamy, Cindy Lo Kuen Lam, Ning Xi, Vivian Weiqun Lou, Wen Jung Li   +11 more
wiley   +1 more source

Assessing delimiting strategies to identify the infested zones of quarantine plant pests and diseases. [PDF]

Sci Rep
Koh JMJ, Cunniffe NJ, Parnell S.
europepmc   +1 more source

A Novel Anomaly Forecasting in Time‐Series Data: Feedback Connection between Forecasting and Detecting Algorithms with Applications to Power Systems

Advanced Intelligent Systems, EarlyView.
This article develops a novel feedback structure for machine learning‐based anomaly forecasting, by which both forecasting the future states and detecting the anomalies in these states can be achieved at the same time. The algorithm is shown to be applicable to power systems, verifying its effectiveness in detecting potential faults before they occur ...
Hyung Tae Choi, Hae Yeon Park, Taewan Kim, Jung Hoon Kim   +3 more
wiley   +1 more source

Assessing regional competitiveness in Peru: An approach using nonlinear machine learning models. [PDF]

PLoS One
Garcia-Lopez YJ, Del Carpio Castro LA.
europepmc   +1 more source

Reprogrammable, In‐Materia Matrix‐Vector Multiplication with Floppy Modes

Advanced Intelligent Systems, EarlyView.
This article describes a metamaterial that mechanically computes matrix‐vector multiplications, one of the fundamental operations in artificial intelligence models. The matrix multiplication is encoded in floppy modes, near‐zero force deformations of soft matter systems.
Theophile Louvet, Parisa Omidvar, Marc Serra‐Garcia   +2 more
wiley   +1 more source

Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper. [PDF]

PLoS Comput Biol
Orcales F, Moctezuma Tan L, Johnson-Hagler M, Suntay JM, Ali J, Recto K, Glenn P, Pennings P.   +7 more
europepmc   +1 more source

Determinants and risk prediction models for frailty among community-living older adults in eastern China. [PDF]

Front Public Health
Qi L, Liu J, Song X, Wang X, Yang M, Cao X, He Y.   +6 more
europepmc   +1 more source

Birth and death processes in phylogenetics and population genetics. [PDF]

Philos Trans R Soc Lond B Biol Sci
Tavaré S.
europepmc   +1 more source

Expression of Concern: Exploring Regional Variation in Roost Selection by Bats: Evidence from a Meta-Analysis. [PDF]

PLoS One
PLOS ONE Editors.
europepmc   +1 more source