QSPR estimation models of normal boiling point and relative liquid density of pure hydrocarbons using MLR and MLP-ANN methods

Fissa, Mohamed Roubehie; Lahiouel, Yasmina; Khaouane, Latifa; Hanini, Salah

doi:10.1016/j.jmgm.2018.11.013

Cited by 52 publications

(16 citation statements)

References 41 publications

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“…In the feature selection process, for each set of features ( i.e. , feature set-A, feature set-B, and feature set-C), the stepwise multiple linear regression method 41 was used with an F value of 4.00 for inclusion and F value of 3.90 for exclusion to correlate and extract the final set of features for the training set molecules. 39,40…”

Section: Methodsmentioning

confidence: 99%

“…The primary function of executing the feature selection process is to eliminate redundant features while retaining the most relevant and important ones. Here, the S-MLR-based feature selection method was used 41 to identify the important molecular descriptors for the dataset molecules (ESI, † Tables S3-S5). Additionally, the evaluation of the reliability and robustness of the selected features was performed by constructing MLR models (ESI, † Table S6).…”

Section: Feature Selectionmentioning

confidence: 99%

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Quantitative structural assessments of potential meprin β inhibitors by non-linear QSAR approaches and validation by binding mode of interaction analysis

et al. 2023

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Section: Methodsmentioning

confidence: 99%

Section: Feature Selectionmentioning

confidence: 99%

Quantitative structural assessments of potential meprin β inhibitors by non-linear QSAR approaches and validation by binding mode of interaction analysis

et al. 2023

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“…As a result, where a solution is being sought for a given problem using multilayer perceptrons, there is no need to build networks with a large number of hidden layers. Numerous works indicate that a single hidden layer is often sufficient, in accordance with the principle that the simpler the network, the better [31,32,33,34,35,36,37,38,39,40,41].…”

Section: Methodsmentioning

confidence: 99%

Predicting Methane Concentration in Longwall Regions Using Artificial Neural Networks

Tutak

Brodny

2019

IJERPH

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Methane, which is released during mining exploitation, represents a serious threat to this process. This is because the gas may ignite or cause an explosion. Both of these phenomena are extremely dangerous. High levels of methane concentration in mine headings disrupt mining operations and cause the risk of fire or explosion. Therefore, it is necessary to monitor and predict its concentration in the areas of ongoing mining exploitation. The paper presents the results of tests performed to improve work safety. The article presents the methodology of using artificial neural networks for predicting methane concentration values in one mining area. The objective of the paper is to develop an effective method for forecasting methane concentration in the mining industry. The application of neural networks for this purpose represents one of the first attempts in this respect. The method developed makes use of direct methane concentration values measured by a system of sensors located in the exploitation area. The forecasting model was built on the basis of a Multilayer Perceptron (MLP) network. The corresponding calculations were performed using a three-layered network with non-linear activation functions. The results obtained in the form of methane concentration prediction demonstrated minor errors in relation to the recorded values of this concentration. This offers an opportunity for a broader application of intelligent systems for effective prediction of mining hazards.

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“…The QSPR modeling on a set of structure‐related chemicals aims to build a mathematical correlation between molecular structures and quantitative chemical attributes. Many QSPR models are built with various mathematical algorithms, such as multiple linear regression, 3–7 artificial neural networks (ANN), 5,8–12 and support vector machine 13–15 . In the fields of biology and chemistry, ANN demonstrates excellent capabilities in nonlinear function approximation, especially when facing multidimensional regression problems.…”

Section: Introductionmentioning

confidence: 99%

“…In the fields of biology and chemistry, ANN demonstrates excellent capabilities in nonlinear function approximation, especially when facing multidimensional regression problems. To date, ANN is widely used in QSPRs 5,8,11,12,16–18 thanks to its excellent ability in adaptive learning, nonlinear fitting and processing multidimensional inputs. Pan et al 9,10 developed several models to estimate FPT based on the back‐propagation neural networks (BPNN) for 44 alkanes, 40 fatty alcohols and 92 alkanes.…”

Section: Introductionmentioning

confidence: 99%

A systematic modeling methodology of deep neural network‐based structure‐property relationship for rapid and reliable prediction on flashpoints

et al. 2021

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Deep neural networks (DNNs) based quantitative structure–property relationship (QSPR) studies are receiving increasing attention due to their excellent performances. A systematic methodology coupling multiple machine learning technologies is proposed to systematically solve vital problems including applicability domain and prediction uncertainty in DNN‐based QSPR modeling. Key features are rapidly extracted from plentiful but chaotic descriptors by principal component analysis (PCA) and kernel PCA. Then, a detailed applicability domain (AD) is defined by K‐means algorithm to avoid unreliable predictions and discover its potential impact on prediction uncertainty. Moreover, prediction uncertainty is analyzed with dropout‐embedded DNN by thousands of independent tests to assess the reliability of predictions. The prediction of flashpoint temperature is employed as a case study, demonstrating that the model accuracy is remarkably improved comparing with the referenced model. Furthermore, the proposed methodology breaks through difficulties in analyzing the uncertainty of DNN‐based QSPRs and presents an AD correlated with the uncertainty.

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QSPR estimation models of normal boiling point and relative liquid density of pure hydrocarbons using MLR and MLP-ANN methods

Cited by 52 publications

References 41 publications

Quantitative structural assessments of potential meprin β inhibitors by non-linear QSAR approaches and validation by binding mode of interaction analysis

Quantitative structural assessments of potential meprin β inhibitors by non-linear QSAR approaches and validation by binding mode of interaction analysis

Predicting Methane Concentration in Longwall Regions Using Artificial Neural Networks

A systematic modeling methodology of deep neural network‐based structure‐property relationship for rapid and reliable prediction on flashpoints

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