A Multilayer Perceptron (MLP) Regressor Network for Monitoring the Depth of Anesthesia

Dutt, Muhammad Ibrahim; Saadeh, Wala

doi:10.1109/newcas52662.2022.9842242

Cited by 11 publications

(7 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MLP Regressor (MLPR): The Multilayer Perceptron (MLP) is a class of feed-forward Artificial Neural Networks (ANN). An MLP architecture connects the input, hidden, and output layers in a feed-forward way . As a type of supervised learning, MLP uses backpropagation (a type of gradient-descent algorithm in which predetermined error-function values are calculated) to train the network.…”

Section: Methodsmentioning

confidence: 99%

“…An MLP architecture connects the input, hidden, and output layers in a feedforward way. 82 As a type of supervised learning, MLP uses backpropagation 83 (a type of gradient-descent algorithm in which predetermined error-function values are calculated) to train the network. The values, which were reintroduced into the network following the computation, are used to adjust the weights of each layer's neurons.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

“…The values, which were reintroduced into the network following the computation, are used to adjust the weights of each layer's neurons. 82 The metrics for all calculations were based on the coefficient of determination (R 2 ) and the Root Mean Square Error (RMSE), and the former is given by…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning Determination of New Hammett’s Constants for meta- and para-Substituted Benzoic Acid Derivatives Employing Quantum Chemical Atomic Charge Methods

2023

View full text Add to dashboard Cite

Hammett's constants σ quantify the electron donor or electron acceptor power of a chemical group bonded to an aromatic ring. Their experimental values have been successfully used in many applications, but some are inconsistent or not measured. Therefore, developing an accurate and consistent set of Hammett's values is paramount. In this work,we employed different types of machine learning (ML) algorithms combined with quantum chemical calculations of atomic charges to predict theoretically new Hammett's constants σ m , σ p , σ m 0 , σ p 0 , σ p + , σ p − , σ R , and σ I for 90 chemical donor or acceptor groups. New σ values (219), including previously unknown 92, are proposed. The substituent groups were bonded to benzene and meta-and para-substituted benzoic acid derivatives. Among the charge methods (Mulliken, Loẅdin, Hirshfeld, and ChelpG), Hirshfeld showed the best agreement for most kinds of σ values. For each type of Hammett constant, linear expressions depending on carbon charges were obtained. The ML approach overall showed very close predictions to the original experimental values, with meta-and para-substituted benzoic acid derivative values showing the most accurate values. A new consistent set of Hammett's constants is presented, as well as simple equations for predicting new values for groups not included in the original set of 90.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Machine Learning Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Determination of New Hammett’s Constants for meta- and para-Substituted Benzoic Acid Derivatives Employing Quantum Chemical Atomic Charge Methods

2023

View full text Add to dashboard Cite

show abstract

“…MLP architecture connects the input, hidden, and output layers in a feed-forward way. 75 As a type of supervised learning, MLP uses backpropagation 76 (a type of gradient-descent algorithm in which predetermined errorfunction values are calculated) to train the network. The values, which were reintroduced into the network following the computation, are used to adjust the weights of each layer's neurons.…”

Section: Machine Learning (Ml) Algorithmsmentioning

confidence: 99%

“…The values, which were reintroduced into the network following the computation, are used to adjust the weights of each layer's neurons. 75 The metrics for all calculations were based on the coefficient of determination (𝑅 + ) and the Root Mean Square Error (RMSE), the former is given by…”

Section: Machine Learning (Ml) Algorithmsmentioning

confidence: 99%

Machine learning determination of new Hammett's constants for meta- and para-substituted benzoic acid derivatives employing quantum chemical atomic charge methods

Monteiro-de-Castro

Duarte

Borges

2023

Preprint

View full text Add to dashboard Cite

Hammett's constants quantify the electron or electron acceptor power of a chemical group bonded to an aromatic ring. Their experimental values have been successfully used in a large variety of applications, but some of them may have inconsistent values or were not measured. For this reason, developing an accurate and consistent set of Hammett's values is paramount. In this work, we employed the machine learning (ML) regression algorithms Decision Tree Regressor, the neural network Multilayer Perceptron Regressor, and Lasso Lars IC in a cross-validation (CV) approach combined with quantum chemical calculations of atomic charges to estimate theoretically the new Hammett's constants for 90 chemical donor or acceptor groups by employing different types of quantum chemical atomic charges of the groups as input properties. New 219 sigma values, including previously unknown ones, are proposed for 90 chemical donor or acceptor groups by employing different types of quantum chemical atomic charges of the groups as input properties. The different substituent groups were bonded to benzene and meta- and para-substituted benzoic acid derivatives. Among the investigated atomic charge methods (Mulliken, Lwdin, Hirshfeld, and ChelpG), Hirshfeld's method showed the best regressions for most of the different kinds of sigma values. For each type of Hammett constant, linear expressions depending only on the atomic charges of the group were obtained. Correlation coefficients as high as 0.945, mean squared errors (MSE) as low as 0.004, and root mean square errors (RMSE) as low 0.062, were found. The ML approach, in most cases, showed very close predictions to the original experimental values, with the values from meta- and para-substituted benzoic acid derivatives showing the most accurate values. A new consistent set of Hammetts constants is presented, as well as simple equations for predicting new values for groups not included in the original set of 90.

show abstract

Machine learning methods for predicting the key metabolic parameters of Halomonas elongata DSM 2581 T

Lai

Wang

et al. 2023

Appl Microbiol Biotechnol

View full text Add to dashboard Cite

A Multilayer Perceptron (MLP) Regressor Network for Monitoring the Depth of Anesthesia

Cited by 11 publications

References 27 publications

Machine Learning Determination of New Hammett’s Constants for meta- and para-Substituted Benzoic Acid Derivatives Employing Quantum Chemical Atomic Charge Methods

Machine Learning Determination of New Hammett’s Constants for meta- and para-Substituted Benzoic Acid Derivatives Employing Quantum Chemical Atomic Charge Methods

Machine learning determination of new Hammett's constants for meta- and para-substituted benzoic acid derivatives employing quantum chemical atomic charge methods

Machine learning methods for predicting the key metabolic parameters of Halomonas elongata DSM 2581 T

Contact Info

Product

Resources

About