Employing an Artificial Neural Network in Correlating a Hydrogen-Selective Catalytic Reduction Performance with Crystallite Sizes of a Biomass-Derived Bimetallic Catalyst

Yakub, Ibrahim; Kueh, Ahmad Beng Hong; O, Edwin Andres Pineda De La; Rahman, Md. Rezaur; Barawi, Mohamad Hardyman; Abdullah, Mohammad Omar; Amran, Mugahed; Fediuk, Roman; Vatin, Nikolai

doi:10.3390/catal12070779

Cited by 13 publications

(3 citation statements)

References 31 publications

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“…In the study of Yakub et al [ 28 ], the suggested model was used to discuss the impacts of crystallite size on the performance of copper and iron oxides in reducing NOx, as well as the construction of a prediction model that links crystallite size with H2-SCR efficiency. Monometallic as well as bimetallic catalysts doped over palm kernel shell-activated carbon were explored.…”

Section: Introductionmentioning

confidence: 99%

Molecular Property Prediction of Modified Gedunin Using Machine Learning

Aly

Alotaibi

2023

Molecules

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Images of molecules are often utilized in education and synthetic exploration to predict molecular characteristics. Deep learning (DL) has also had an influence on drug research, such as the interpretation of cellular images as well as the development of innovative methods for the synthesis of organic molecules. Although research in these areas has been significant, a comprehensive review of DL applications in drug development would be beyond the scope of a single Account. In this study, we will concentrate on a single major area where DL has influenced molecular design: the prediction of molecular properties of modified gedunin using machine learning (ML). AI and ML technologies are critical in drug research and development. In these other words, deep learning (DL) algorithms and artificial neural networks (ANN) have changed the field. In short, advances in AI and ML present a good potential for rational drug design and exploration, which will ultimately benefit humanity. In this paper, long short-term memory (LSTM) was used to convert a modified gedunin SMILE into a molecular image. The 2D molecular representations and their immediately visible highlights should then provide adequate data to predict the subordinate characteristics of atom design. We aim to find the properties of modified gedunin using K-means clustering; RNN-like ML tools. To support this postulation, neural network (NN) clustering based on the AI picture is used and evaluated in this study. The novel chemical developed via profound learning has long been predicted on characteristics. As a result, LSTM with RNNs allow us to predict the properties of molecules of modified gedunin. The total accuracy of the suggested model is 98.68%. The accuracy of the molecular property prediction of modified gedunin research is promising enough to evaluate extrapolation and generalization. The model suggested in this research requires just seconds or minutes to calculate, making it faster as well as more effective than existing techniques. In short, ML can be a useful tool for predicting the properties of modified gedunin molecules.

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Section: Introductionmentioning

confidence: 99%

Molecular Property Prediction of Modified Gedunin Using Machine Learning

Aly

Alotaibi

2023

Molecules

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“…Yakub et al. 14 developed two predictive equations via an ANN technique to determine the performance of catalysts based on temperature and crystallite size. Abhyankar et al.…”

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confidence: 99%

“…Ujene and Umoh 13 developed a neural network model for predicting the percentage cost and time overrun using the site characteristics of building projects. Yakub et al 14 developed two predictive equations via an ANN technique to determine the performance of catalysts based on temperature and crystallite size. Abhyankar et al 15 identified flooded areas due to cyclonic storms using Envisat ASAR VV polarized data and an ANN.…”

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confidence: 99%

Characterization of woven fabric drape based on neural networks

Han

Luo

Wang

et al. 2023

Textile Research Journal

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Previous research on fabric drape has not provided an objective and comprehensive characterization of drape characteristics. In light of this, we proposed an approach that utilizes a neural network-based framework for characterizing the umbrella drape of woven fabrics. Fabric drapes with the same macro-level mechanical characteristics can be categorized together, thereby establishing objective classification criteria. Our method involved feature extraction and classification from drape images/point clouds via neural networks, namely ResNet18 and the deep graph convolutional neural network (DGCNN). We assessed the effectiveness of both networks through supervised learning and selected the best candidate to distinguish/retrieve drape styles from unlabeled data. Moreover, a sketch down-sampling (SDS) tailored to accurately represent point clouds of umbrella-shaped drapes was devised. In all, 5160 drape meshes were collected by RGB-D cameras and GeomagicTM. Two neural networks were trained for 30 epochs using stochastic gradient descent with a momentum of 0.9. The learning rate was set to 0.1 for ResNet18 and 0.001 for the DGCNN. Experimental results demonstrated that the DGCNN coupled with the SDS method was the optimal feature extraction solution for woven fabric drapes, given that the accuracy reached 97% with the coefficient of variation of 7%. Therefore, our approach offered an objective and precise quantification of fabric drape, which provided a possible downstream application for searching fabrics based on drape similarity.

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Resonant frequency of coronavirus: The tensegrity approach

Kueh

2023

Alexandria Engineering Journal

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Employing an Artificial Neural Network in Correlating a Hydrogen-Selective Catalytic Reduction Performance with Crystallite Sizes of a Biomass-Derived Bimetallic Catalyst

Cited by 13 publications

References 31 publications

Molecular Property Prediction of Modified Gedunin Using Machine Learning

Molecular Property Prediction of Modified Gedunin Using Machine Learning

Characterization of woven fabric drape based on neural networks

Resonant frequency of coronavirus: The tensegrity approach

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