Deep forest model for diagnosing COVID-19 from routine blood tests
Sign Up to like & getrecommendations! Published in 2021 at "Scientific Reports"
DOI: 10.1038/s41598-021-95957-w
Abstract: The Coronavirus Disease 2019 (COVID-19) global pandemic has threatened the lives of people worldwide and posed considerable challenges. Early and accurate screening of infected people is vital for combating the disease. To help with the… read more here.
Keywords: deep forest; covid routine; forest model; model ... See more keywords
Classification of Bird and Drone Targets Based on Motion Characteristics and Random Forest Model Using Surveillance Radar Data
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Access"
DOI: 10.1109/access.2021.3130231
Abstract: Accurate detection and tracking of birds and drones are of great significance in various low altitude airspace surveillance scenarios. Radar is currently the most proper long range surveillance technology for this problem but also challenged… read more here.
Keywords: surveillance; forest model; motion; classification ... See more keywords
Prediction of the Strength of Rubberized Concrete by an Evolved Random Forest Model
Sign Up to like & getrecommendations! Published in 2019 at "Advances in Civil Engineering"
DOI: 10.1155/2019/5198583
Abstract: Rubberized concrete (RC) has attracted more attention these years as it is an economical and environmental-friendly construction material. Normally, the uniaxial compressive strength (UCS) of RC needs to be evaluated before application. In this study,… read more here.
Keywords: strength; rubberized concrete; forest model; model ... See more keywords
Construction of Pulmonary Nodule CT Radiomics Random Forest Model Based on Artificial Intelligence Software for STAS Evaluation of Stage IA Lung Adenocarcinoma
Sign Up to like & getrecommendations! Published in 2022 at "Computational and Mathematical Methods in Medicine"
DOI: 10.1155/2022/2173412
Abstract: Objective Spread through air space (STAS) is an invasive characterization of lung adenocarcinoma and is regarded as a risk factor for poor prognosis. The aim of this study is to develop a random forest model… read more here.
Keywords: lung adenocarcinoma; lung; random forest; stage lung ... See more keywords
DCE-DForest: A Deep Forest Model for the Prediction of Anticancer Drug Combination Effects
Sign Up to like & getrecommendations! Published in 2022 at "Computational and Mathematical Methods in Medicine"
DOI: 10.1155/2022/8693746
Abstract: Drug combinations have recently been studied intensively due to their critical role in cancer treatment. Computational prediction of drug synergy has become a popular alternative strategy to experimental methods for anticancer drug synergy predictions. In… read more here.
Keywords: dce dforest; anticancer drug; deep forest; model ... See more keywords
BCDForest: a boosting cascade deep forest model towards the classification of cancer subtypes based on gene expression data
Sign Up to like & getrecommendations! Published in 2018 at "BMC Bioinformatics"
DOI: 10.1186/s12859-018-2095-4
Abstract: BackgroundThe classification of cancer subtypes is of great importance to cancer disease diagnosis and therapy. Many supervised learning approaches have been applied to cancer subtype classification in the past few years, especially of deep learning… read more here.
Keywords: classification; deep forest; forest model; model ... See more keywords
Prediction of Clinical Outcome at Discharge After Rupture of Anterior Communicating Artery Aneurysm Using the Random Forest Technique
Sign Up to like & getrecommendations! Published in 2020 at "Frontiers in Neurology"
DOI: 10.3389/fneur.2020.538052
Abstract: Background: Aneurysmal subarachnoid hemorrhage (SAH) is a devastating disease. Anterior communicating artery (ACoA) aneurysm is the most frequent location of intracranial aneurysms. The purpose of this study is to predict the clinical outcome at discharge… read more here.
Keywords: outcome; rupture; forest model; anterior communicating ... See more keywords
Effective Macrosomia Prediction Using Random Forest Algorithm
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Environmental Research and Public Health"
DOI: 10.3390/ijerph19063245
Abstract: (1) Background: Macrosomia is prevalent in China and worldwide. The current method of predicting macrosomia is ultrasonography. We aimed to develop new predictive models for recognizing macrosomia using a random forest model to improve the… read more here.
Keywords: using random; macrosomia; random forest; forest model ... See more keywords
What Constitutes the High-Quality Soundscape in Human Habitats? Utilizing a Random Forest Model to Explore Soundscape and Its Geospatial Factors Behind
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Environmental Research and Public Health"
DOI: 10.3390/ijerph192113913
Abstract: Soundscape is the production of sounds and the acoustic environment, and it emphasizes peoples’ perceiving and experiencing process in the context. To this end, this paper focuses on the Pearl River Delta in China, and… read more here.
Keywords: quality; soundscape; geospatial factors; random forest ... See more keywords
Prediction of Prognosis and Survival of Patients with Gastric Cancer by Weighted Improved Random Forest Model
Sign Up to like & getrecommendations! Published in 2021 at "Archives of Medical Science"
DOI: 10.5114/aoms/135594
Abstract: Introduction It’s very necessary to predict the survival status of patients based on their prognosis. This can assist physicians in evaluating treatment decisions. Random Forest is an excellent machine learning algorithm even without any modification.… read more here.
Keywords: gastric cancer; forest model; random forest; survival ... See more keywords
Gap filling of turbulent heat fluxes over rice–wheat rotation croplands using the random forest model
Sign Up to like & getrecommendations! Published in 2023 at "Atmospheric Measurement Techniques"
DOI: 10.5194/amt-16-2197-2023
Abstract: Abstract. This study investigated the accuracy of the random forest (RF) model in gap filling the sensible (H) and latent heat (LE) fluxes, by using the observation data collected at a site over rice–wheat rotation… read more here.
Keywords: random forest; model; gap filling; forest model ... See more keywords