Reference health
Prediction of aqueous solubility of compounds based on neural network
33/33 checkable references clean · checked 2026-07-23
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
7 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 33 checked references that resolve
resolves10.1016/j.fuel.2015.01.044
Connectionist model for predicting minimum gas miscibility pressure: Application to gas injection process
resolves10.1002/ente.201402104
Evolving Smart Model to Predict the Combustion Front Velocity for In Situ Combustion
resolves10.1016/j.fuel.2014.07.072
A computational intelligence scheme for prediction equilibrium water dew point of natural gas in TEG dehydration systems
resolves10.1002/ente.201402043
Evolving Connectionist Model to Monitor the Efficiency of an In Situ Combustion Process: Application to Heavy Oil Recovery
resolves10.1016/j.petrol.2014.05.023
Robust intelligent tool for estimating dew point pressure in retrograded condensate gas reservoirs: Application of particle swarm optimization
resolves10.1016/j.fuel.2014.01.073
Evolving predictive model to determine condensate-to-gas ratio in retrograded condensate gas reservoirs
resolves10.1016/j.fuel.2013.10.010
Evolving smart approach for determination dew point pressure through condensate gas reservoirs
resolves10.1016/j.fuel.2012.05.050
New approach for prediction of asphaltene precipitation due to natural depletion by using evolutionary algorithm concept
resolves10.1016/j.fuel.2014.09.058
Connectionist approach estimates gas–oil relative permeability in petroleum reservoirs: Application to reservoir simulation
resolves10.1016/j.petrol.2014.08.026
Connectionist model predicts the porosity and permeability of petroleum reservoirs by means of petro-physical logs: Application of artificial intelligence
resolves10.1016/j.fuel.2014.08.044
A rigorous model to predict the amount of Dissolved Calcium Carbonate Concentration throughout oil field brines: Side effect of pressure and temperature
resolves10.1016/j.supflu.2014.08.011
Estimating hydrogen sulfide solubility in ionic liquids using a machine learning approach
resolves10.1002/ceat.201300155
Gas Analysis by In Situ Combustion in Heavy‐Oil Recovery Process: Experimental and Modeling Studies
resolves10.1080/01932691.2014.904792
Phase Equilibrium Modeling of Clathrate Hydrates of Carbon Dioxide + 1,4-Dioxine Using Intelligent Approaches
resolves10.1016/j.molliq.2015.04.015
Simultaneous prediction of the thermodynamic properties of aqueous solution of ethylene glycol monoethyl ether using artificial neural network
resolves10.1016/j.molliq.2015.07.009
Prediction of solubility of sulfur dioxide in ionic liquids using artificial neural network
resolves10.1016/j.molliq.2018.01.043
Generally trained models to predict drug solubility in N-methyl-2-pyrrolidone + water mixtures at various temperatures
resolves10.1021/ci9901338
Estimation of Aqueous Solubility for a Diverse Set of Organic Compounds Based on Molecular Topology
resolves10.1021/ci400187y
Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like Molecules
resolves10.1016/j.jmgm.2011.12.006
Nonlinear dimensionality reduction and mapping of compound libraries for drug discovery
resolves10.1016/j.petlm.2015.06.004
Determination of oil well production performance using artificial neural network (ANN) linked to the particle swarm optimization (PSO) tool
resolves10.1016/j.fluid.2011.10.016
Neural network based unified particle swarm optimization for prediction of asphaltene precipitation
resolves10.1016/j.petrol.2012.08.011
Neural network based swarm concept for prediction asphaltene precipitation due to natural depletion
resolves10.1007/s13202-011-0013-7
Prediction of asphaltene precipitation using artificial neural network optimized by imperialist competitive algorithm
The 7 references without a DOI — listed, not checked
no DOI — not checkedE. Gawehn, J.A. Hiss, G. Schneider. Mol. Inform.
no DOI — not checkedCIT0016
no DOI — not checkedCIT0030
no DOI — not checkedLearning with Kernels
no DOI — not checkedCIT0033
no DOI — not checkedBioinformatics: The Machine Learning Approach
no DOI — not checkedA.S. Razavian, H. Azizpour and J. Sullivan, et al. 512–519 (2014).
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