• Journal of the Korean Statistical Society
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• v.24 no.2
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• pp.519-536
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• 1995

In the design of experiments for response surface analysis, sometimes it is of interest to estimate the difference of responses at two points. If differences at points close together are involved, the design that reliably estimates the slope of response surface is important. This idea was conceptualized by slope rotatability by Hader & Park (1978) and Park (1987). Until now, second order polynomial models were only studied for slope ratatability. In this paper, we will propose the necessary and sufficient conditions for slope rotatability over all directions for the thired order polynomial models in two, three and four independent variables. Also practical slope rotatable designs over all directions for two independent variables are suggested.

• Proceedings of the Korea Society for Simulation Conference
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• 1999.10a
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• pp.210-216
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• 1999

Todays computer communication technology let people to do many unrealistic things possible and the use of those technologies is becoming increasingly prevalent throughout the military operation. Both DIS and ADS are welled defined computer aided military simulations. This study discusses a simulation of stochastic combat network modeling through Internet. We have developed two separate simulation models, one for clients and another for server, and validated for conducting studies with these two models. The object-oriented design was necessary to define the system entities and their relationship, to partition functionality into system entities, and to transform functional metrics into realizations derived from system component behaviors. Heterogeneous forces for each side are assumed at any battle node. The time trajectories for mean number of survivors at each node, some important combat measures, and relative difference computations between models were made. We observe and may conclude that the differences exist and some fo these are significant based on a limited number of experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.2
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• pp.167-177
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• 2002

In Strapdown Inertial Navigation System(SDINS), error models based on previously proposed conversion equations between the attitude errors, are only valid in case the attitude errors are small. The SDINS error models have been independently studied according to the definition of the reference frame and of the attitude error. The conversion equations between the attitude errors applicable to SDINS with large attitude errors are newly derived. Lyapunov transformation matrices are also derived from the obtained results. Furthermore the general method, which is independent of the attitude error and the reference frame to derive SDINS error model, is proposed using the Lyapunov transformation.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.29-34
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• 2021

The article formulates the key characteristics and features of country models of corporate governance. It was revealed that all countries are characterized by a fairly high concentration of ownership, insider control; Key gaps in the implementation of corporate governance principles were found: transparency and disclosure of information, protection of shareholders' rights, gender diversity of boards of directors, implementation of recommendations on the share of independent directors; The criterion of countries' efficiency (total investments) was identified and recommendations for their improvement were developed.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.630-644
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• 2021

Educational data analysis is attracting increasing attention with the rise of the big data industry. The amounts and types of learning data available are increasing steadily, and the information technology required to analyze these data continues to develop. The early identification of potential dropout students is very important; education is important in terms of social movement and social achievement. Here, we analyze educational data and generate predictive models for student dropout using logistic regression, a decision tree, a naïve Bayes method, and a multilayer perceptron. The multilayer perceptron model using independent variables selected via the variance analysis showed better performance than the other models. In addition, we experimentally found that not only grades but also extracurricular activities were important in terms of preventing student dropout.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.228-234
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• 2021

The article defines the problems of forming the models of government regulation of clustering, marketing management and labor market in the context of globalization, business bankruptcy risk and services market development. The clustering models based on the optimal partner network cooperation were proposed in order to ensure the strategic development of territories, to attract budget leading enterprises and to support small businesses. A descriptive model of government regulation of clustering, marketing management and labor market in the context of globalization, business bankruptcy risk and Covid-19 was determined.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.23-30
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• 2021

Sentiment Analysis has become very important field of research because posting of reviews is becoming a trend. Supervised, unsupervised and semi supervised machine learning methods done lot of work to mine this data. Feature engineering is complex and technical part of machine learning. Deep learning is a new trend, where this laborious work can be done automatically. Many researchers have done many works on Deep learning Convolutional Neural Network (CNN) and Long Shor Term Memory (LSTM) Neural Network. These requires high processing speed and memory. Here author suggested two models simple & bidirectional deep leaning, which can work on text data with normal processing speed. At end both models are compared and found bidirectional model is best, because simple model achieve 50% accuracy and bidirectional deep learning model achieve 99% accuracy on trained data while 78% accuracy on test data. But this is based on 10-epochs and 40-batch size. This accuracy can also be increased by making different attempts on epochs and batch size.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.624-626
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• 2023

Credit scoring is a technique used by financial institutions to assess the creditworthiness of potential borrowers. This involves evaluating a borrower's credit history to predict the likelihood of defaulting on a loan. This paper presents an ensemble of two Transformer based models within a framework for discriminating the default risk of loan applications in the field of credit scoring. The first model is FinBERT, a pretrained NLP model to analyze sentiment of financial text. The second model is FT-Transformer, a simple adaptation of the Transformer architecture for the tabular domain. Both models are trained on the same underlying data set, with the only difference being the representation of the data. This multi-modal approach allows us to leverage the unique capabilities of each model and potentially uncover insights that may not be apparent when using a single model alone. We compare our model with two famous ensemble-based models, Random Forest and Extreme Gradient Boosting.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.83-92
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• 2023

The majority of product users rely on the reviews that are posted on the appropriate website. Both users and the product's manufacturer could benefit from these reviews. Daily, thousands of reviews are submitted; how is it possible to read them all? Sentiment analysis has become a critical field of research as posting reviews become more and more common. Machine learning techniques that are supervised, unsupervised, and semi-supervised have worked very hard to harvest this data. The complicated and technological area of feature engineering falls within machine learning. Using deep learning, this tedious process may be completed automatically. Numerous studies have been conducted on deep learning models like LSTM, CNN, RNN, and GRU. Each model has employed a certain type of data, such as CNN for pictures and LSTM for language translation, etc. According to experimental results utilizing a publicly accessible dataset with reviews for all of the models, both positive and negative, and CNN, the best model for the dataset was identified in comparison to the other models, with an accuracy rate of 81%.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.44-50
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• 2024

This paper offers an overview of matrix formation and calculation techniques within the framework of General Linear Models (GLMs). It takes a sequential approach, beginning with a detailed exploration of matrix formation and calculation methods in regression analysis and univariate analysis of variance (ANOVA). Subsequently, it extends the discussion to cover multivariate analysis of variance (MANOVA). The primary objective of this study was to provide a clear and accessible explanation of the underlying matrices that play a crucial role in GLMs. Through linking, essentially different statistical methods, by fundamental principles and algebraic foundations that underpin the GLM estimation. Insights presented here aim to assist researchers, statisticians, and data analysts in enhancing their understanding of GLMs and their practical implementation in diverse research domains. This paper contributes to a better comprehension of the matrix-based techniques that can be extended to GLMs.