Random forest regression for optimizing variable planting rates for corn and soybean using topographical and soil data
Sign Up to like & getrecommendations! Published in 2020 at "Agronomy Journal"
DOI: 10.1002/agj2.20442
Abstract: Correspondence MichaelA.Gore, PlantBreeding and Genetics Section, School of Integrative Plant Science,CornellUniversity, Ithaca, NY, 14853,USA Email:[email protected] Abstract In recent years, planting machinery that enables precise control of the planting rates has become available for corn (Zea… read more here.
Keywords: corn soybean; rate; soil; random forest ... See more keywords
Glioma brain tumor detection and segmentation using weighting random forest classifier with optimized ant colony features
Sign Up to like & getrecommendations! Published in 2019 at "International Journal of Imaging Systems and Technology"
DOI: 10.1002/ima.22331
Abstract: The uncontrolled growth of cells in brain regions leads to the tumor regions and these abnormal tumor regions are scanned by magnetic resonance imaging (MRI) technique as an image. This paper proposes random forest classifier… read more here.
Keywords: methodology; glioma brain; brain; random forest ... See more keywords
New horizon of contrast‐enhanced sonography in the differential diagnosis of periampullary mass: A random forest nomogram
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Clinical Ultrasound"
DOI: 10.1002/jcu.23259
Abstract: Artificial intelligence (AI) in medicine has been widely explored, Specifically, deep learning has been reported to gain excellent achievement on computed tomography (CT) and magnetic resonance (MR) images. Deep learning models can learn the most… read more here.
Keywords: diagnosis; disease; area; random forest ... See more keywords
Pooling random forest and functional data analysis for biomedical signals supervised classification: Theory and application to electrocardiogram data.
Sign Up to like & getrecommendations! Published in 2022 at "Statistics in medicine"
DOI: 10.1002/sim.9353
Abstract: Scientific progress has contributed to creating many devices to gather vast amounts of biomedical data over time. The goal of these devices is generally to monitor people's health conditions, diagnose, and prevent patients' diseases, for… read more here.
Keywords: supervised classification; biomedical signals; classification; functional data ... See more keywords
Culex pipiens distribution in Tunisia: Identification of suitable areas through Random Forest and MaxEnt approaches
Sign Up to like & getrecommendations! Published in 2022 at "Veterinary Medicine and Science"
DOI: 10.1002/vms3.897
Abstract: Abstract Background Tunisia has experienced several West Nile virus (WNV) outbreaks since 1997. Yet, there is limited information on the spatial distribution of the main WNV mosquito vector Culex pipiens suitability at the national level.… read more here.
Keywords: culex pipiens; pipiens distribution; random forest; tunisia ... See more keywords
A Novel Fuzzy Random Forest Model for Meteorological Drought Classification and Prediction in Ungauged Catchments
Sign Up to like & getrecommendations! Published in 2020 at "Pure and Applied Geophysics"
DOI: 10.1007/s00024-020-02609-7
Abstract: This paper presents a new tree-based model, namely Fuzzy Random Forest (FRF), for one month ahead Standardized Precipitation Evapotranspiration Index (SPEI) classification and prediction with a noteworthy application in ungauged catchments. The proposed FRF model… read more here.
Keywords: random forest; model; classification; classification prediction ... See more keywords
A random forest-based approach for fault location detection in distribution systems
Sign Up to like & getrecommendations! Published in 2020 at "Electrical Engineering"
DOI: 10.1007/s00202-020-01074-8
Abstract: Finding the fault location in distribution network is difficult in comparison to transmission network based upon high branching and high impedances composed of the contact with environmental factors when a fault occurs. For this matter,… read more here.
Keywords: fault; fault location; random forest; forest based ... See more keywords
A protocol for automated timber species identification using metabolome profiling
Sign Up to like & getrecommendations! Published in 2019 at "Wood Science and Technology"
DOI: 10.1007/s00226-019-01111-1
Abstract: Using chemical fingerprints for timber species identification is a relatively new, but promising technique. However, little is known about the effect of pre-processing spectral data parameter settings on the timber species classification accuracy. Therefore, this… read more here.
Keywords: timber species; species identification; classification accuracy; parameter ... See more keywords
Multi-scale and multi-parametric radiomics of gadoxetate disodium–enhanced MRI predicts microvascular invasion and outcome in patients with solitary hepatocellular carcinoma ≤ 5 cm
Sign Up to like & getrecommendations! Published in 2021 at "European Radiology"
DOI: 10.1007/s00330-020-07601-2
Abstract: To develop radiomics-based nomograms for preoperative microvascular invasion (MVI) and recurrence-free survival (RFS) prediction in patients with solitary hepatocellular carcinoma (HCC) ≤ 5 cm. Between March 2012 and September 2019, 356 patients with pathologically confirmed… read more here.
Keywords: hcc; multi; mvi; microvascular invasion ... See more keywords
A random forest algorithm to improve the Lee–Carter mortality forecasting: impact on q-forward
Sign Up to like & getrecommendations! Published in 2020 at "Soft Computing"
DOI: 10.1007/s00500-019-04427-z
Abstract: Increased life expectancy in developed countries has led researchers to pay more attention to mortality projection to anticipate changes in mortality rates. Following the scheme proposed in Deprez et al. (Eur Actuar J 7(2):337–352, 2017)… read more here.
Keywords: carter mortality; mortality forecasting; mortality; random forest ... See more keywords
HEMD: a highly efficient random forest-based malware detection framework for Android
Sign Up to like & getrecommendations! Published in 2017 at "Neural Computing and Applications"
DOI: 10.1007/s00521-017-2914-y
Abstract: Mobile phones are rapidly becoming the most widespread and popular form of communication; thus, they are also the most important attack target of malware. The amount of malware in mobile phones is increasing exponentially and… read more here.
Keywords: malware detection; detection; proposed method; hemd highly ... See more keywords