Reviewing the Novel Machine Learning Tools for Materials Design

Mosavi, Amir; Rabczuk, Timon; Várkonyi-Kóczy, Annamária R.

doi:10.1007/978-3-319-67459-9_7

Cited by 60 publications

(35 citation statements)

References 28 publications

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“…The application of ML and DL methods in various scientific and engineering domains have been previously investigated [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40]. Generally, the ML methods are reported to be further advancing to through ensemble and hybrid techniques .…”

Section: And DL Methods In Biofuels Researchmentioning

confidence: 99%

Systematic Review of Deep Learning and Machine Learning Models in Biofuels Research<strong> </strong>

Ardabili¹,

Mosavi

Várkonyi-Kóczy

2019

Preprint

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Biofuels construct an essential pillar of energy systems. Biofuels are considered as a popular resource for electricity production, heating, household, and industrial usage, liquid fuels, and mobility around the world. Thus, the need for handling, modeling, decision-making, demand, and forecasting for biofuels are of utmost importance. Recently, machine learning (ML) and deep learning (DL) techniques have been accessible in modeling, optimizing, and handling biofuels production, consumption, and environmental impacts. The main aim of this study is to review and evaluate ML and DL techniques and their applications in handling biofuels production, consumption, and environmental impacts, both for modeling and optimization purposes. Hybrid and ensemble ML methods, as well as DL methods, have found to provide higher performance and accuracy in modeling the biofuels.

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Section: And DL Methods In Biofuels Researchmentioning

confidence: 99%

Systematic Review of Deep Learning and Machine Learning Models in Biofuels Research<strong> </strong>

Ardabili¹,

Mosavi

Várkonyi-Kóczy

2019

Preprint

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“…For obtaining c0, c1, and c2, a simple least square was applied to the training data. A partial least squares method could also employ for this objective (18,22). The important GEP parameters that need to select carefully are the tree depth and the quantity of genes.…”

Section: Gene Expression Programming (Gep)mentioning

confidence: 99%

“…Soft computing techniques such as artificial neural networks (ANN) are widely accepted and popular along the conventional statistical methods (e.g., regression) [11][12][13][14][15][16][17][18][19][20][21] . These techniques were successfully applied to different geotechnical problems such as Cc prediction [7,[22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Prediction Model for Fine-Grained Soils Compression Index Using Gene-Expression Programming

Mohammadzadeh¹,

Kazemi²,

Mosavi³

2019

Preprint

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Appropriate estimation of soil settlement is of significant importance since it directly influences the performance of building and infrastructures that are built on soil. In particular, the settlement of fine-grained soils is critical because of low permeability and continuous settlement with time.Coefficient of consolidation (Cc) is a key parameter to estimate settlement of fine-grained soil layers. However, estimation of this parameter is time consuming, needs skilled technicians, and specific equipment. In this study, Cc was estimated using several soil parameters such as liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Estimating such parameters in laboratory is straight forward and needs substantially less time and cost compared to conventional tests to estimate Cc such as oedometer test. This study presents a novel prediction model for Cc of finegrained soils using gene-expression programming (GEP). GEP is a biologically inspired technique capable of offering closed-form solution for the optimal solution. A database consisted of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of developed GEP-based model was evaluated through coefficient of determination (R 2 ), root mean squared error (RMSE), and mean average error (MAE). High R 2 and low error values indicated the descent performance of the model. Furthermore, the model was evaluated using the additional performance measures and met all the suggested criteria. Furthermore, the model had a better performance in terms of R 2 , RMSE, and MAE compared to most of existing models. It is expected that the developed model will decrease the time and cost associate with determining Cc of fine-grained soils.

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“…A miscellaneous predicting prototypical conjoining neural networks with fuzzy pattern-recognition was developed in view of the fuzziness in the perception of analogous basins, in which initiation tasks in the ANN model are improved with categorization (Ameli, Hemmati-Sarapardeh, Schaffie, Husein, & Shamshirband, 2018;Chau, 2017;Mosavi & Rabczuk, 2017b;Mosavi, Rabczuk, & Varkonyi-Koczy, 2017). Such a fuzzy pattern appreciation initiation task was presented addicted to a hybrid neural network (HNN) model to represent uncertainties of the river flow problematic (Chen, Chau, & Busari, 2015).…”

Section: Extreme Learning Machinementioning

confidence: 99%

Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network

Ghazvinei

Darvishi

Mosavi

et al. 2018

Engineering Applications of Computational Fluid Mechanics

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Management strategies for sustainable sugarcane production need to deal with the increasing complexity and variability of the whole sugar system. Moreover, they need to accommodate the multiple goals of different industry sectors and the wider community. Traditional disciplinary approaches are unable to provide integrated management solutions, and an approach based on whole systems analysis is essential to bring about beneficial change to industry and the community. The application of this approach to water management, environmental management and cane supply management is outlined, where the literature indicates that the application of extreme learning machine (ELM) has never been explored in this realm. Consequently, the leading objective of the current research was set to filling this gap by applying ELM to launch swift and accurate model for crop production datadriven. The key learning has been the need for innovation both in the technical aspects of system function underpinned by modelling of sugarcane growth. Therefore, the current study is an attempt to establish an integrate model using ELM to predict the concluding growth amount of sugarcane. Prediction results were evaluated and further compared with artificial neural network (ANN) and genetic programming models. Accuracy of the ELM model is calculated using the statistics indicators of Root Means Square Error (RMSE), Pearson Coefficient (r), and Coefficient of Determination (R 2 ) with promising results of 0.8, 0.47, and 0.89, respectively. The results also show better generalization ability in addition to faster learning curve. Thus, proficiency of the ELM for supplementary work on advancement of prediction model for sugarcane growth was approved with promising results. ARTICLE HISTORY

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Reviewing the Novel Machine Learning Tools for Materials Design

Cited by 60 publications

References 28 publications

Systematic Review of Deep Learning and Machine Learning Models in Biofuels Research<strong> </strong>

Systematic Review of Deep Learning and Machine Learning Models in Biofuels Research<strong> </strong>

Evolutionary Prediction Model for Fine-Grained Soils Compression Index Using Gene-Expression Programming

Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network

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