Výsledky vyhledávání - "Cherif Si‐Moussa"

Akademický článek

Machine Learning and Neural Networks for Modelling the Retention of PPhACs by NF/RO

Autor: Yamina Ammi, Cherif Si-Moussa, Salah Hanini

Publikováno v: Kemija u Industriji, Vol 72, Iss 11-12, Pp 617-626 (2023)

The retention of polar pharmaceutical active compounds (PPhACs) by nanofiltration and reverse osmosis (NF/RO) membranes is of paramount importance in membrane separation processes. The retention of 21 PPhACs was correlated using artificial intelligen

Externí odkaz: https://doaj.org/article/992f141517044553bf7399c92d73f408

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Akademický článek

Artificial Neural Network Models for Prediction of Density and Kinematic Viscosity of Different Systems of Biofuels and Their Blends with Diesel Fuel. Comparative Analysis

Autor: Souad Belmadani, Salah Hanini, Maamar Laidi, Cherif Si-Moussa, Mabrouk Hamadache

Publikováno v: Kemija u Industriji, Vol 69, Iss 7-8, Pp 355-364 (2020)

In the present article, two models based on the artificial neural network methodology (ANN) have been optimised to predict the density (ρ) and kinematic viscosity (μ) of different systems of biofuels and their blends with diesel fuel. An experiment

Externí odkaz: https://doaj.org/article/a4e76fde5cdc4bb8b089778d2a803f2e

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A new hybrid quantitative structure property relationships‐support vector regression ( QSPR‐SVR ) approach for predicting the solubility of drug compounds in supercritical carbon dioxide

Autor: Imane Euldji, Aicha Belghait, Cherif Si‐Moussa, Othmane Benkortbi, Abdeltif Amrane

Publikováno v: AIChE Journal
AIChE Journal, 2023, pp.e18115. ⟨10.1002/aic.18115⟩

International audience; The purpose of this work was to compare the performance of 7 meta-heuristics algorithms namely: Dragonfly (DA), Ant Lion (ALO), Grey Wolf (GWO), Artificial Bee Colony (ABC), Particle Swarm (PSO), Whale (WAO), and a hybrid Part

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6ab0f10921eb63602a24b8d7ea181345
https://doi.org/10.1002/aic.18115

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Machine learning approach for the prediction of surface tension of binary mixtures containing ionic liquids using σ‐profile descriptors

Autor: Widad Benmouloud, Cherif Si‐Moussa, Othmane Benkortbi

Publikováno v: International Journal of Quantum Chemistry. 123

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d410f44fdbdaf8ac4fc05f5c2fb058ab
https://doi.org/10.1002/qua.27026

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CMC of diverse Gemini surfactants modelling using a hybrid approach combining SVR-DA

Autor: Cherif Si-Moussa, Othmane Benkortebi, Salah Hanini, Maamar Laidi, el Hadj Abdallah Abdallah, Mohamed Hentabli

Publikováno v: Chemical Industry and Chemical Engineering Quarterly, Vol 27, Iss 3, Pp 299-312 (2021)

Quantitative structure-property relationship (QSPR) technique provides a suitable tool to predict the critical micelle concentration (CMC) of Gemini surfactants from their structure descriptors. In this study, a comparative work was conducted to mode

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::30183e9da89f404167ee9b59e9ab513c
https://doi.org/10.2298/ciceq200907048l

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Artificial Neural Network Models for Prediction of Density and Kinematic Viscosity of Different Systems of Biofuels and Their Blends with Diesel Fuel. Comparative Analysis

Autor: Maamar Laidi, Souad Belmadani, Salah Hanini, Cherif Si-Moussa, Mabrouk Hamadache

Publikováno v: Kemija u Industriji, Vol 69, Iss 7-8, Pp 355-364 (2020)
Kemija u industriji : Časopis kemičara i kemijskih inženjera Hrvatske
Volume 69
Issue 7-8

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2871755ac493802a8a8b29186c707335
http://silverstripe.fkit.hr/kui/assets/Uploads/1-355-364.pdf

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Dragonfly-Support Vector Machine for Regression Modeling of the Activity Coefficient at Infinite Dilution of Solutes in Imidazolium Ionic Liquids Using σ-Profile Descriptors

Autor: Salah Hanini, Hania Benimam, Cherif Si Moussa, Maamar Laidi, Mohamed Hentabli

Publikováno v: Journal of Chemical & Engineering Data. 65:3161-3172

Ionic liquids (ILs) have shown remarkable potential for applications in separation, such as extractive distillation and liquid–liquid extraction. Crucial to these applications is the estimation of ...

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a774e023011c0cd28c87fc8617e3b9f4
https://doi.org/10.1021/acs.jced.0c00168

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Modeling the activity coefficient at infinite dilution of water in ionic liquids using artificial neural networks and support vector machines

Autor: Hania Benimam, Maamar Laidi, Cherif Si-Moussa, Salah Hanini

Publikováno v: Neural Computing and Applications. 32:8635-8653

The activity coefficient at infinite dilution of water in ionic liquids is a thermodynamic property of a paramount importance in separation processes. However, accurate modeling of this parameter remains a challenging task due to the highly nonlinear

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8f8c3fe8374c62074e5915d9ca12d77c
https://doi.org/10.1007/s00521-019-04356-w

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QSPR Modelling of the Solubility of Drug and Drug‐like Compounds in Supercritical Carbon Dioxide

Autor: Imane Euldji, Cherif Si‐Moussa, Mabrouk Hamadache, Othmane Benkortbi

Publikováno v: Molecular Informatics. 41:2200026

Quantitative structure-property relationship (QSPR) modeling was investigated to predict drug and drug-like compounds solubility in supercritical carbon dioxide. A dataset of 148 drug\drug-like compounds, accounting for 3971 experimental data points

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0d21261d8a46fbf3b6bf625781e2291f
https://doi.org/10.1002/minf.202200026

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