A New QSPR Model for Predicting the Densities of Ionic Liquids

El‐Harbawi, Mohanad; Belhaouari, Samir Brahim; Babaa, Moulay-Rachid; Mutalib, M.I. Abdul

doi:10.1007/s13369-014-1223-3

Cited by 16 publications

(12 citation statements)

References 63 publications

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“…Different research has been made on the COVID-19 vaccination since the vaccination was introduced. For future prediction, different techniques may be helpful with different approaches to predict the results of this vaccination [38,39]. The goal of this study is to predict the trend of the people towards the COVID-19 vaccinations, besides different myths and side effects of vaccination on health.…”

Section: Discussionmentioning

confidence: 99%

Future Prediction of COVID-19 Vaccine Trends Using a Voting Classifier

Zaidi

Tariq

Belhaouari

2021

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Machine learning (ML)-based prediction is considered an important technique for improving decision making during the planning process. Modern ML models are used for prediction, prioritization, and decision making. Multiple ML algorithms are used to improve decision-making at different aspects after forecasting. This study focuses on the future prediction of the effectiveness of the COVID-19 vaccine effectiveness which has been presented as a light in the dark. People bear several reservations, including concerns about the efficacy of the COVID-19 vaccine. Under these presumptions, the COVID-19 vaccine would either lower the risk of developing the malady after injection, or the vaccine would impose side effects, affecting their existing health condition. In this regard, people have publicly expressed their concerns regarding the vaccine. This study intends to estimate what perception the masses will establish about the role of the COVID-19 vaccine in the future. Specifically, this study exhibits people’s predilection toward the COVID-19 vaccine and its results based on the reviews. Five models, e.g., random forest (RF), a support vector machine (SVM), decision tree (DT), K-nearest neighbor (KNN), and an artificial neural network (ANN), were used for forecasting the overall predilection toward the COVID-19 vaccine. A voting classifier was used at the end of this study to determine the accuracy of all the classifiers. The results prove that the SVM produces the best forecasting results and that artificial neural networks (ANNs) produce the worst prediction toward the individual aptitude to be vaccinated by the COVID-19 vaccine. When using the voting classifier, the proposed system provided an overall accuracy of 89.9% for the random dataset and 45.7% for the date-wise dataset. Thus, the results show that the studied prediction technique is a promising and encouraging procedure for studying the future trends of the COVID-19 vaccine.

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Section: Discussionmentioning

confidence: 99%

Future Prediction of COVID-19 Vaccine Trends Using a Voting Classifier

Zaidi

Tariq

Belhaouari

2021

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“…Quantitative structure property relationship (QSPR) is a long-utilized cheminformatic techniques that has often been applied to predict the physicochemical properties of a large range of chemicals [ 16 , 17 , 18 ]. Despite the significant number of QSPR modelling studies targeting predictions of the density of ILs (predecessors of DES) [ 19 ], to the best of our knowledge, only two QSPR studies, both based on the COSMO-RS approach, have been reported so far (by Lemaoui et. al.)…”

Section: Introductionmentioning

confidence: 99%

Density of Deep Eutectic Solvents: The Path Forward Cheminformatics-Driven Reliable Predictions for Mixtures

et al. 2021

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Deep eutectic solvents (DES) are often regarded as greener sustainable alternative solvents and are currently employed in many industrial applications on a large scale. Bearing in mind the industrial importance of DES—and because the vast majority of DES has yet to be synthesized—the development of cheminformatic models and tools efficiently profiling their density becomes essential. In this work, after rigorous validation, quantitative structure-property relationship (QSPR) models were proposed for use in estimating the density of a wide variety of DES. These models were based on a modelling dataset previously employed for constructing thermodynamic models for the same endpoint. The best QSPR models were robust and sound, performing well on an external validation set (set up with recently reported experimental density data of DES). Furthermore, the results revealed structural features that could play crucial roles in ruling DES density. Then, intelligent consensus prediction was employed to develop a consensus model with improved predictive accuracy. All models were derived using publicly available tools to facilitate easy reproducibility of the proposed methodology. Future work may involve setting up reliable, interpretable cheminformatic models for other thermodynamic properties of DES and guiding the design of these solvents for applications.

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“…[13,14] Their applications include dye abatement in water, [13] organic synthesis, [14,15] water gas shift, [16] polymerization, [17] and photocatalysis. [18] Heavy metals in industrial wastewater represent another issue for the ecosystem, [19] in particular for aquatic ecosystems, due to their highly carcinogenic nature and their impossibility to be biodegraded. [15,16] Cu is one of the most widespread heavy metals in water.…”

Section: Introductionmentioning

confidence: 99%

“…It causes severe consequences over 60 mg/kg. [18] Moreover, excessive Fe in the blood damages the gastrointestinal tract. [14] In the last few decades, reverse osmosis, flocculation, [23] bacterial action, ion exchange, [19] and adsorption processes [24,25] have been adopted to reduce heavy metals from waters and wastewaters.…”

Section: Introductionmentioning

confidence: 99%

Eco‐friendly synthesis from industrial wastewater of Fe and Cu nanoparticles over NaX zeolite and activity in 4‐nitrophenol reduction

et al. 2018

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We deposited Fe and Cu over zeolite NaX (Fe/NaX and Cu/NaX) by adsorption from effluent industrial wastewater. We synthesized the zeolite NaX by the hydrothermal method. 5 g of NaX completely adsorbed 350 and 380 mg of Fe and Cu from the industrial wastewater, respectively, in 6 h. The distribution of Fe and Cu over the NaX was uniform and amounted at 14 and 18 mass%, respectively. Fe and Cu modify the morphology of the NaX zeolite: the particle size increased from 9 μm to 10 μm for the former and decreased to 3 μm for the latter. Fe/NaX and Cu/NaX are less crystalline than NaX. BET analysis showed that the specific surface area decreased by 30 % and 50 % compared to NaX for Fe/NaX and Cu/NaX, but the ratio between meso‐ and micropores increased by 7 and 13 times, respectively. Fe/NaX and Cu/NaX synthesized by adsorption from industrial wastewater reduced +99 % of 4‐p‐nitrophenol to 4‐aminophenol in less than 100 s, which is comparable to noble metal.

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A New QSPR Model for Predicting the Densities of Ionic Liquids

Cited by 16 publications

References 63 publications

Future Prediction of COVID-19 Vaccine Trends Using a Voting Classifier

Future Prediction of COVID-19 Vaccine Trends Using a Voting Classifier

Density of Deep Eutectic Solvents: The Path Forward Cheminformatics-Driven Reliable Predictions for Mixtures

Eco‐friendly synthesis from industrial wastewater of Fe and Cu nanoparticles over NaX zeolite and activity in 4‐nitrophenol reduction

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