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3. Prediction of National Innovation Using Data Scientific Approach.

Author

Chung, Doohee, Lee, Chan Gyu, and Seo, Shinseong

Subjects
MACHINE learning, STANDARD deviations, SUPPORT vector machines, BANKING industry, REGRESSION analysis
Abstract

Countries have traditionally relied on various innovation measures to plan national-wise strategies and analysis, which hinders their ability to take proactive actions beforehand. This study introduces an advanced model for predicting national innovativeness using machine learning techniques. Utilizing a comprehensive dataset that includes 1,410 observation points and 20 variables from 141 countries spanning from 2011 to 2020, the data were sourced from the World Bank Open Data, Global Entrepreneurship Monitor (GEM) and Global Innovation Index (GII). The proposed model employs machine learning algorithms such as regression tree, random forest, support vector machine and extreme gradient boosting (XGBoost), and benchmarks their performance against the traditional linear regression analysis method. The findings reveal that all five machine learning models significantly outperform the traditional linear regression model in terms of correlation, root mean square error (RMSE) and mean absolute error (MAE), with XGBoost demonstrating superior performance. The XGBoost analysis highlights university and industry research collaboration, knowledge workers and logistics performance as the most critical variables influencing national innovativeness. This research not only presents a robust predictive model leveraging machine learning but also contributes to theoretical advancements by uncovering previously overlooked variables, offering new insights and practical implications for enhancing national innovation strategies. [ABSTRACT FROM AUTHOR]

Published
2024
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5. Structural and magnetic study of a dilute magnetic semiconductor: Fe doped CeO2 nanoparticles

Author

Kumar, Shalendra, Kim, Geun Woo, Koo, B. H., Sharma, S. K., Knobel, M., and Lee, Chan Gyu

Subjects
Condensed Matter - Materials Science, Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

This paper reports the effect of Fe doping on the structure and room temperature ferromagnetism of CeO2 nanoparticles. X-ray diffraction and selective area electron diffraction measurement reflects that Ce1-xFexO2 (x = 0.0 - 0.07) nanoparticles exhibit single phase nature with cubic structure and none of the sample showed the presence of any secondary phase. The mean particle size calculated by using a transmission electron microscopy measurement was found to increase with increase in Fe content. DC magnetization measurements performed at room temperature indicates that all the samples exhibit ferromagnetism. The saturation magnetic moment has been found to increase with an increase in the Fe content., Comment: 16 Pages, 5 figure, 1 Table, Accepted in JNN

Published
2009
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6. Electronic structure and magnetic properties of Ni0.2Cd0.3Fe2.5-xAlxO4 (x = 0.0 - 0.4) ferrite nanoparticles

Author

Kumar, Shalendra, Batoo, Khalid Mujasam, Gautam, S., Koo, B. H., Alimuddin, Chae, K. H., and Lee, Chan Gyu

Subjects
Condensed Matter - Materials Science
Abstract

Structural, magnetic and electronic structural properties of Ni0.2Cd0.3Fe2.5-xAlxO4 ferrites nanoparticles have been studied x-ray diffraction (XRD), transmission electron microscopy (TEM), dc magnetization, and near edge x-ray absorption fine structure spectroscopy (NEXAFS) measurements. Nanoparticles of Ni0.2Cd0.3Fe2.5-xAlxO4 (x = 0.0 - 0.4) ferrite were synthesized using sol-gel method. The XRD and TEM measurements show that all samples have single phase nature with cubic structure and have nanocrystalline behavior. From the XRD and TEM analysis, it is observed that particle size increases with Al doping. DC magnetization measurements infer that magnetic moment decreases whereas blocking temperature increases with increase in Al doping. It is observed that the magnetic moment decreases with Al doping which may be due to the dilution of the sublattice by the doping of Al ions. The NEXAFS measurements performed at room temperature indicates that Fe exist in mixed valence state., Comment: 16 pages, 5 figures Accepted for publication in JNN

Published
2009
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10. Abstract 4430: Sacituzumab-Interferon beta mutein fusion protein, ABN202 (anti Trop2-interferon beta mutein), is a potent therapeutics for Trop-2-positive urothelial cancer

Author

Lee, Jiyang, primary, Seo, Myeung-Ryun, additional, Park, Heegeon, additional, Kim, YeongMun, additional, Lee, Saehyung, additional, Kim, Na Young, additional, Lee, Chan Gyu, additional, Jeong, Hae Min, additional, Choi, Jun Young, additional, and Shin, Young kee, additional

Published
2023
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11. A Hybrid Machine Learning Model for Demand Forecasting: Combination of K-means, Elastic-Net, and Gaussian Process Regression

Author

Chung, Doohee, Lee, Chan Gyu, and Yang, Sungmin

Subjects
Hybrid Machine Learning, Demand Forecast, Gaussian Process Regression, K-means, ElasticNet
Abstract

Accurate prediction of demand assists companies in responding flexibly to uncertain market conditions. However, companies face difficulties due to the wide variety of product types and the uncertainty of their scale. To solve this problem, this study proposes a hybrid model that combines K-means, ElasticNet, and Gaussian process regression (GPR). GPR effectively addresses the issue of nonlinear prediction, and its performance is excellent when the model is trained with clusters of similar data instead of training the entire dataset at once. Moreover, the model enhances its training process by extracting crucial and contributing variables for each cluster. To implement these techniques, this study utilizes K-means and ElasticNet in combination with GPR. The model is applied to a case study of a U.S. manufacturing company and is compared with other benchmarking models, including single GPR, K-means+GPR, and ElasticNet+GPR, to evaluate its performance. The hybrid model achieved the best prediction accuracy, recording a mean absolute error (MAE) of 5.57, demonstrating its potential as foundational research for constructing an efficient demand forecasting model under uncertain conditions.

Published
2023

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