• Steel and Composite Structures
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• v.32 no.4
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• pp.455-466
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• 2019

Despite the rapid advancement in computing resources, many real-life design and optimization problems in structural engineering involve huge computation costs. To counter such challenges, approximate models are often used as surrogates for the highly accurate but time intensive finite element models. In this paper, surrogates for first-order shear deformation based finite element models are built using a polynomial regression approach. Using statistical techniques like Box-Cox transformation and ANOVA, the effectiveness of the surrogates is enhanced. The accuracy of the surrogate models is evaluated using statistical metrics like $R^2$, $R^2{_{adj}}$, $R^2{_{pred}}$ and $Q^2{_{F3}}$. By combining these surrogates with nature-inspired multi-criteria decision-making algorithms, namely multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO), the optimal combination of various design variables to simultaneously maximize fundamental frequency and frequency separation is predicted. It is seen that the proposed approach is simple, effective and good at inexpensively producing a host of optimal solutions.

• Geosystem Engineering
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• v.21 no.5
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• pp.273-281
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• 2018

In this study, the activated red mud was used as a new and appropriate adsorbent for the removal of ferrocyanide and ferricyanide from aqueous solution. Predicting the removal percentage and adsorption capacity of ferro-ferricyanide by activated red mud during the adsorption process is necessary which has been done by modeling and simulation. The artificial neural network (ANN) was used to develop new models for the predictions. A back propagation algorithm model was trained to develop a predictive model. The effective variables including pH, absorbent amount, absorbent type, ionic strength, stirring rate, time, adsorbate type, and adsorbate dosage were considered as inputs of the models. The correlation coefficient value ($R^2$) and root mean square error (RMSE) values of the testing data for the removal percentage and adsorption capacity using ANN models were 0.8560, 12.5667, 0.9329, and 10.8117, respectively. The results showed that the proposed ANN models can be used to predict the removal percentage and adsorption capacity of activated red mud for the removal of ferrocyanide and ferricyanide with reasonable error.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.526-538
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• 2021

Machine and deep learning-based models are emerging techniques that are being used to address prediction problems in biomedical data analysis. DNA sequence prediction is a critical problem that has attracted a great deal of attention in the biomedical domain. Machine and deep learning-based models have been shown to provide more accurate results when compared to conventional regression-based models. The prediction of the gene sequence that leads to cancerous diseases, such as prostate cancer, is crucial. Identifying the most important features in a gene sequence is a challenging task. Extracting the components of the gene sequence that can provide an insight into the types of mutation in the gene is of great importance as it will lead to effective drug design and the promotion of the new concept of personalised medicine. In this work, we extracted the exons in the prostate gene sequences that were used in the experiment. We built a Deep Neural Network (DNN) and Bi-directional Long-Short Term Memory (Bi-LSTM) model using a k-mer encoding for the DNA sequence and one-hot encoding for the class label. The models were evaluated using different classification metrics. Our experimental results show that DNN model prediction offers a training accuracy of 99 percent and validation accuracy of 96 percent. The bi-LSTM model also has a training accuracy of 95 percent and validation accuracy of 91 percent.

• Journal of Korean Society for Quality Management
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• v.50 no.3
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• pp.305-332
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• 2022

Purpose: As the public interest in entrepreneurship has been highlighted and entrepreneurship policies have been generated, this study is to construct Entrepreneurship Ecosystem (EE) models which have a significant relationship to national entrepreneurship with quantitative analysis. It aims to provide implications to EE policymakers that which national components are effective in cultivating innovative entrepreneurship and validate its EE quality based on quantitative performance goals. Methods: This study utilizes secondary data, categorized under the PESTLE factor from credible international organizations (WB, UNDP, GEM, GEDI, and OECD) to determine significant factors in the quality of the entrepreneurial ecosystem. This paper uses the Multiple Linear Regression (MLR) analysis to select the significant variables contributing to entrepreneurship performance. Using the AUC-ROC performance evaluation method for machine learning MLR results, this paper evaluates the performance of EE models so that it can allow approving EE quality by predicting potential performance. Results: Among nine hypothesis models, MLR analysis examines that the number of the Unicorn company, Unicorn companies' economic value, and entrepreneurship measured as GEI can be reasonable dependent variables to indicate the performance derived from EE quality. Rather than government policies and regulations, the social, finance, technology, and economic variables are significant factors of EE quality determining its performance. By having high Area Under Curve values under AUC-ROC analysis, accepted MLR models are regarded as having high prediction accuracy. Conclusion: Superior EE contributes to the outstanding Unicorn companies, and improvement in macro-environmental components can enhance EE quality.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.4_2
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• pp.657-668
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• 2023

It's important to note that air compressors in the industrial sector are major energy consumers, accounting for a significant portion of total energy costs in manufacturing plants, ranging from 12% to 40%. To address this issue, researchers have compared forecasting models that can predict the power consumption of air compressors. The forecasting models were designed to incorporate variables such as flow rate, pressure, temperature, humidity, and dew point, utilizing statistical methods, machine learning, and deep learning techniques. The model performance was compared using measures such as RMSE, MAE and SMAPE. Out of the 21 models tested, the Elastic Net, a statistical method, proved to be the most effective in power comsumption forecasting.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.77-84
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• 2023

Given the growing volume of biomedical papers, the ability to efficiently extract keywords has become crucial for accessing and responding to important information in the literature. In this study, we conduct a comprehensive evaluation of different unsupervised learning-based models and BERT-based models for keyword extraction in the biomedical field. Our experimental findings reveal that the BioBERT model, trained on biomedical-specific data, achieves the highest performance. This study offers precise and dependable insights to guide forthcoming research in biomedical keyword extraction. By establishing a well-suited experimental framework and conducting thorough comparisons and analyses of diverse models, we have furnished essential information. Furthermore, we anticipate extending our contributions to other domains by providing comparative experiments and practical guidelines for effective keyword extraction.

• Geomechanics and Engineering
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• v.33 no.2
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• pp.183-194
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• 2023

This study presents the prediction of the California bearing ratio (CBR) of coarse- and fine-grained soils using artificial intelligence technology. The group method of data handling (GMDH) algorithm, an artificial neural network-based model, was used in the prediction of the CBR values. In the design of the prediction models, various combinations of independent input variables for both coarse- and fine-grained soils have been used. The results obtained from the designed GMDH-type neural networks (GMDH-type NN) were compared with other regression models, such as linear, support vector, and multilayer perception regression methods. The performance of models was evaluated with a regression coefficient (R²), root-mean-square error (RMSE), and mean absolute error (MAE). The results showed that GMDH-type NN algorithm had higher performance than other regression methods in the prediction of CBR value for coarse- and fine-grained soils. The GMDH model had an R² of 0.938, RMSE of 1.87, and MAE of 1.48 for the input variables {G, S, and MDD} in coarse-grained soils. For fine-grained soils, it had an R² of 0.829, RMSE of 3.02, and MAE of 2.40, when using the input variables {LL, PI, MDD, and OMC}. The performance evaluations revealed that the GMDH-type NN models were effective in predicting CBR values of both coarse- and fine-grained soils.

• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.1105-1117
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• 2023

In this study, the pressure drop behavior of single- and two-phase flows of air and water through the porous beds filled with uniform and non-uniform sized spherical particles was examined. The pressure drop data in the single-phase flow experiments for the uniform particle beds agreed well with the original Ergun correlation. The results from the two-phase flow experiments were analyzed using numerical results based on three types of previous models. In the experiments for the uniform particle beds, the data on the two-phase pressure drop clearly showed the effect of the flow regime transition with a variation in the gas flow rate under stagnant liquid condition. The numerical analyses indicated that the predictability of the previous models for the experimental data relied mainly on the sub-models of the flow regime transitions and interfacial drag. In the experiments for the non-uniform particle beds, the two-phase pressure loss could be predicted well with numerical calculations based on the effective particle diameter. However, the previous models failed to accurately predict the counter-current flooding limit observed in the experiments. Finally, we propose a relation of falling liquid velocity into the particle bed by gravity to appropriately simulate the CCFL phenomenon.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.42.3-42.3
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• 2020

We investigate the observational properties of Wolf-Rayet stars, suggest the constraint of their mass-loss rate and apply our results to the observed progenitor candidates of Type Ib/Ic supernovae (iPTF13bvn and SN 2017ein). For this purpose, we adopt the WR star models with various mass-loss rates and wind terminal velocities. We obtain the high resolution spectra of those models at the pre-supernova phase using the radiative transfer code CMFGEN. We verify the optically faint property of SN Ic progenitors and show that the optical faintness is mainly originated by the high effective temperature at the photosphere. We also show that a simple analytic model for WR winds using a constant opacity can roughly predict the photospheric parameters. We show that the change of the mass-loss rate and the terminal wind velocity critically affects the optical luminosity. We find the optical luminosities of SN Ic progenitor models with our fiducial mass-loss rate prescription are fainter than the detection limits. We also suggest the mass-loss rate of WR stars may not exceed 2 times of our fiducial value by comparing our predictions with the detection limit of SN Ib/Ic progenitors. The directly observed progenitor candidate of iPTF13bvn can be explained by our SN Ib progenitor models. We find that the SN 2017ein progenitor candidate is too bright and too blue to be a SN Ic progenitor.

• Clinical and Experimental Vaccine Research
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• v.12 no.4
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• pp.291-297
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• 2023

Enterovirus infections induce infectious diseases in young children, such as hand, foot, and mouth disease which is characterized by highly contagious rashes or blisters around the hands, feet, buttocks, and mouth. This predominantly arises from enterovirus A71 or coxsackievirus A16 infections and in severe cases, they can lead to encephalitis, paralysis, pulmonary edema, or even fatality, representing a global health threat. Due to the absence of effective therapeutic strategies for these infections, various experimental animal models are being investigated for the development of vaccines. During the early stages of research on enterovirus infections, non-human primate infections exhibited symptoms like those in humans, leading to their utilization as model animals. However, due to economic and ethical considerations, their current usage is limited. While enterovirus infections do not readily occur in mice, an infection model with mouse-adapted strain in neonatal mice has been employed. Cellular receptors have been identified in human cells, and genetically modified mice expressing these receptors have been used. Most recently, the utilization of Mongolian gerbil model is actively being considered and should be pursued for further animal model development. So, herein, we provide a summarized overview of the current portfolio of available enterovirus infection models, emphasizing their respective advantages and limitations.