Artificial Neural Network Modeling to Predict Neonatal Metabolic Bone Disease in the Prenatal and Postnatal Periods
Sign Up to like & getrecommendations! Published in 2023 at "JAMA Network Open"
DOI: 10.1001/jamanetworkopen.2022.51849
Abstract: Key Points Question Can the artificial neural network (ANN) predict risk for neonatal metabolic bone disease (MBD) in the prenatal and postnatal periods? Findings In this diagnostic study of 10 801 participants, among 5 ANN models… read more here.
Keywords: neural network; network; artificial neural; neonatal metabolic ... See more keywords
Respiratory motion prediction based on deep artificial neural networks in CyberKnife system: A comparative study
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Applied Clinical Medical Physics"
DOI: 10.1002/acm2.13854
Abstract: Abstract Background In external beam radiotherapy, a prediction model is required to compensate for the temporal system latency that affects the accuracy of radiation dose delivery. This study focused on a thorough comparison of seven… read more here.
Keywords: system; neural networks; deep artificial; prediction ... See more keywords
A robust soft sensor based on artificial neural network for monitoring microbial lipid fermentation processes using Yarrowia lipolytica
Sign Up to like & getrecommendations! Published in 2022 at "Biotechnology and Bioengineering"
DOI: 10.1002/bit.28310
Abstract: Microbial oils produced by Yarrowia lipolytica offer an environmentally friendly and sustainable alternative to petroleum as well as traditional lipids from animals and plants. The accurate measurement of fermentation parameters, including the substrate concentration, dry… read more here.
Keywords: neural network; concentration; soft sensor; fermentation ... See more keywords
Prediction of Cu(II) biosorption performances on wild mushrooms Lactarius piperatus using Artificial Neural Networks (ANN) model
Sign Up to like & getrecommendations! Published in 2017 at "Canadian Journal of Chemical Engineering"
DOI: 10.1002/cjce.22703
Abstract: This work investigates the possible usage of edible mushrooms as support for metabolic quantities of copper. Biosorption potential of natural and biodegradable matrix formed from wild Lactarius piperatus mushrooms, in suspension (LP) and alginate immobilized… read more here.
Keywords: lactarius piperatus; biosorption; artificial neural; ann model ... See more keywords
Image analysis and multi‐layer perceptron artificial neural networks for the discrimination between benign and malignant endometrial lesions
Sign Up to like & getrecommendations! Published in 2017 at "Diagnostic Cytopathology"
DOI: 10.1002/dc.23649
Abstract: This study aims to investigate the efficacy of an Artificial Neural Network based on Multi‐Layer Perceptron (ANN–MPL) to discriminate between benign and malignant endometrial nuclei and lesions in cytological specimens. read more here.
Keywords: benign malignant; layer perceptron; multi layer; artificial neural ... See more keywords
Artificial neural network model of molten carbonate fuel cells: Validation on experimental data
Sign Up to like & getrecommendations! Published in 2019 at "International Journal of Energy Research"
DOI: 10.1002/er.4608
Abstract: This article shows the teaching processes of artificial neural networks that are used to model the molten carbonate fuel cell (MCFC). Researchers model MCFCs to address a variety of issues across a range of complexities,… read more here.
Keywords: layer; artificial neural; fuel; model ... See more keywords
Comparison of the different artificial neural networks in prediction of biomass gasification products
Sign Up to like & getrecommendations! Published in 2019 at "International Journal of Energy Research"
DOI: 10.1002/er.4682
Abstract: In this study, artificial neural networks (ANNs) and a nonlinear autoregressive exogenous (NARX) neural network model were employed in order to model a fixed bed downdraft gasification. The relation between the feature group and the… read more here.
Keywords: gasification; neural networks; comparison different; artificial neural ... See more keywords
An experimental study of single unconventional biomass pellets: Ignition characteristics, combustion processes, and artificial neural network modeling
Sign Up to like & getrecommendations! Published in 2020 at "International Journal of Energy Research"
DOI: 10.1002/er.5117
Abstract: This study applied the artificial neural networks (ANNs) model to the thermal data obtained by suspension ignition and combustion experiment of single peanut shells (PS, millimeter scale) pellet under O2/CO2 atmosphere. ANN11 was the best… read more here.
Keywords: oxygen concentration; combustion; artificial neural; biomass ... See more keywords
Experimental study for thermal conductivity of water‐based zirconium oxide nanofluid: Developing optimal artificial neural network and proposing new correlation
Sign Up to like & getrecommendations! Published in 2020 at "International Journal of Energy Research"
DOI: 10.1002/er.5988
Abstract: In this study, five different water based ZrO2 nanofluids were prepared at volumetric concentrations of 0.0125%, 0.025%, 0.05%, 0.1%, and 0.2%. In the preparation of nanofluids, two‐step method was preferred, magnetic stirrer and ultrasonic homogenizer… read more here.
Keywords: conductivity; thermal conductivity; artificial neural; water ... See more keywords
Diagnosis of diabetes mellitus using artificial neural network and classification and regression tree optimized with genetic algorithm
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Forecasting"
DOI: 10.1002/for.2652
Abstract: Diabetes mellitus is one of the most important public health problems affecting millions of people worldwide. An early and accurate diagnosis of diabetes mellitus has critical importance for the medical treatments of patients. In this… read more here.
Keywords: classification; neural network; diagnosis diabetes; diabetes mellitus ... See more keywords
Prediction of Consumptive Use Under Different Soil Moisture Content and Soil Salinity Conditions Using Artificial Neural Network Models: crop water consumption simulation
Sign Up to like & getrecommendations! Published in 2018 at "Irrigation and Drainage"
DOI: 10.1002/ird.2270
Abstract: Response of water use of crop to soil moisture and salinity is complex to quantify using traditional field experiments. Based on field experimental data for two years, artificial neural networks models with five inputs including… read more here.
Keywords: water; artificial neural; soil; crop ... See more keywords