Results 111 to 120 of about 82,468 (189)

Tree-based machine learning methods for predicting vehicle insurance claim size. [PDF]

Front Big Data
Terefe EM, Aga MA.
europepmc   +1 more source

Optimized intrusion detection for IoT networks using Cauchy-Gaussian hybrid evolutionary feature selection. [PDF]

Sci Rep
Saranya T, Indra Priyadharshini S.
europepmc   +1 more source

Active Sampling Techniques for Two-Spot Cotton Leafhopper (<i>Amrasca biguttula</i> Ishida) (Hemiptera: Cicadellidae) in Fields with High and Low Populations. [PDF]

Insects
Zapsas D, Halbert SE, Cong MY, Soto Adames F, Kc S, Seal D, Roda AL.   +6 more
europepmc   +1 more source

Early diagnosis of nasopharyngeal carcinoma based on machine learning modelling and blood plasma metallomics analysis. [PDF]

Sci Rep
Chong VY, Chan YN, Lum JT, Chun KH, Gao H, Lung HL, Leung KS.   +6 more
europepmc   +1 more source

Non-destructive estimation of maize carotenoids using reflectance-based spectral indices. [PDF]

Front Plant Sci
Nagy A, Elbeltagi A, Radócz L, Tamás J, Szabó A.   +4 more
europepmc   +1 more source

Machine Learning Models to Predict Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) With Simple Anthropometric and Biochemical Variables: A Cross-Sectional Study in US Population. [PDF]

Int J Hepatol
Du S, Yu H, Du J, Xu Y, Xu Y.
europepmc   +1 more source

Machine learning models with SHAP for performance prediction of eco-friendly fiber reinforced mortars with glass waste. [PDF]

Sci Rep
Rahmani S, Belkadi AA, Achour Y, Alsuhaibani E, Berkouche A, Douadi A, Chajec A.   +6 more
europepmc   +1 more source

Machine learning estimating paracetamol solubility in supercritical CO₂ by utilization of K-nearest neighbor regression and metaheuristic algorithms. [PDF]

Sci Rep
Thajudeen KY, Alshehri SA, Rahamathulla M, Ahmed MM.   +3 more
europepmc   +1 more source

Optimizing machine learning and bagging-based hybrid models for landslide susceptibility mapping: a case study in Chenggu County, China. [PDF]

Sci Rep
Lei X, Liu J, Du Y, Liu L, Yuan Y, Zhao X, Guo Z, Wu X.   +7 more
europepmc   +1 more source

Comparison of deep LSTM and machine learning models for predicting compressive strength of fly ash/slag-based geopolymer concrete. [PDF]

Sci Rep
Kina C, Tanyildizi H, Al Bakri Abdullah MM, Razak RA, Imjai T.   +4 more
europepmc   +1 more source