• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.61-71
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• 2022

When communication such as light, radio wave, or magnetic field is used underwater, the communication distance is very short, so sound waves are mainly used. However, by combining the strengths of each medium and communicating, stable communication may be possible. Underwater multi-media communication requires a protocol stack that supports it, which is very complex. To this end, this paper proposes a standard protocol stack and modeling technique to enable easy protocol stack modeling for the purpose. In fact, in this paper, a random model was created and analyzed through the proposal of modeling elements and similarity measurement methods, and as a result, it was analyzed that it was very helpful in creating a new model based on a standard model.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.186-191
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• 2023

Neural Networks are widely used for huge variety of tasks solution. Machine Learning methods are used also for signal and time series analysis, including electrocardiograms. Contemporary wearable devices, both medical and non-medical type like smart watch, allow to gather the data in real time uninterruptedly. This allows us to transfer these data for analysis or make an analysis on the device, and thus provide preliminary diagnosis, or at least fix some serious deviations. Different methods are being used for this kind of analysis, ranging from medical-oriented using distinctive features of the signal to machine learning and deep learning approaches. Here we will demonstrate a neural network-based approach to this task by building an ensemble of 1D CNN classifiers and a final classifier of selection using logistic regression, random forest or support vector machine, and make the conclusions of the comparison with other approaches.

• Advances in nano research
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• v.14 no.1
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• pp.93-101
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• 2023

This research aims to explore the influence of early entrepreneurial education on cognitive and non-cognitive abilities of male sixth-grade primary school pupils using a randomized pretest-posttest control group design. A total of 45 students were randomly allocated to experimental, active-control, and control groups using a multi-stage random selection procedure. The experimental group was taught entrepreneurship using the Bizworld entrepreneurship education package. The active control group did not get entrepreneurship education but was instructed on a non-entrepreneurship-related issue (hygiene). The Control group received no instruction. The findings revealed that early entrepreneurial education skills impacted noncognitive abilities (such as risk-taking propensity, creativity, self-efficacy, persistence, and need for achievement). Early entrepreneurship education seems to be an effective technique for developing children's non-cognitive abilities in the late years of primary school. As a result, entrepreneurship education may be taught in primary schools, emphasizing the development of non-cognitive abilities, which will affect children's individual, educational, social, and vocational futures and can have long-term advantages for students, families, and society.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.2
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• pp.59-65
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• 2023

Currently, polysomnography is essential to diagnose sleep-related breathing disorders. However, there are several disadvantages to polysomnography, such as the requirement for multiple sensors and a long reading time. In this paper, we propose a system for predicting the severity of sleep-related breathing disorders at home utilizing measurable elements in a wearable device. To predict severity, the variables were refined through a three-step variable selection process, and the refined variables were used as inputs into three machine-learning models. As a result of the study, random forest models showed excellent prediction performance throughout. The best performance of the model in terms of F1 scores for the three threshold criteria of 5, 15, and 30 classified as the AHI index was about 87.3%, 90.7%, and 90.8%, respectively, and the maximum performance of the model for the three threshold criteria classified as the RDI index was approx 79.8%, 90.2%, and 90.1%, respectively.

• Journal of Distribution Science
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• v.22 no.4
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• pp.47-58
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• 2024

Purpose: Achieving organizational effectiveness is the ultimate goal that every business entity or institution targets. To achieve this, organizations need to consider various factors that have an impact on their performance. This article analyzes the distribution influence of six main elements that have a central role in shaping sustainable organizational effectiveness, which are organizational culture, job satisfaction, interpersonal communication, talent management, knowledge management, and information technology. Research Design Data and Methodology: This research uses a quantitative approach, focusing on manufacturing companies located in Surabaya as the main object, involving twenty manufacturing companies as research targets, and 10 employees in each company. The sample selection process was carried out through the application of random sampling techniques. The analysis in this research uses the multiple linear regression method and uses SPSS version 26 software. Results: Distribution of six major factors used in this research are related to each other and contribute significantly to overall organizational effectiveness. Conclusion: Organizations that can combine the distribution of a positive culture, prioritize employee satisfaction, encourage effective communication, manage talent and knowledge efficiently, and utilize information technology wisely will have greater potential to achieve their goals and survive in the intensely competitive business environment.

• The Korean Journal of Applied Statistics
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• v.31 no.3
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• pp.367-382
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• 2018

Prediction models for a corporate bond rating in existing studies have been developed using various models such as linear regression, ordered logit, and random forest. Financial characteristics help build prediction models that are expected to be contained in the assigning model of the bond rating agencies. However, the ranges of bond ratings in existing studies vary from 5 to 20 and the prediction models were developed with samples in which the target companies and the observation periods are different. Thus, a simple comparison of the prediction accuracies in each study cannot determine the best prediction model. In order to conduct a fair comparison, this study has collected corporate bond ratings and financial characteristics from 2013 to 2017 and applied prediction models to them. In addition, we applied the elastic-net penalty for the linear regression, the ordered logit, and the ordered probit. Our comparison shows that data-driven variable selection using the elastic-net improves prediction accuracy in each corresponding model, and that the random forest is the most appropriate model in terms of prediction accuracy, which obtains 69.6% accuracy of the exact rating prediction on average from the 5-fold cross validation.

• Journal of Navigation and Port Research
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• v.42 no.6
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• pp.539-545
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• 2018

In recent years, research on shipping market forecasting with the employment of non-linear AI models has attracted significant interest. In previous studies, input variables were selected with reference to past papers or by relying on the intuitions of the researchers. This paper attempts to address this issue by applying the stepwise regression model and the random forest model to the Cape-size bulk carrier market. The Cape market was selected due to the simplicity of its supply and demand structure. The preliminary selection of the determinants resulted in 16 variables. In the next stage, 8 features from the stepwise regression model and 10 features from the random forest model were screened as important determinants. The chosen variables were used to test both models. Based on the analysis of the models, it was observed that the random forest model outperforms the stepwise regression model. This research is significant because it provides a scientific basis which can be used to find the determinants in shipping market forecasting, and utilize a machine-learning model in the process. The results of this research can be used to enhance the decisions of chartering desks by offering a guideline for market analysis.

• Animal Bioscience
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• v.35 no.5
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• pp.648-658
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• 2022

Objective: The identification of nonlinear mixed models that describe the growth trajectory of New Zealand rabbits was performed based on weight records and carcass measures obtained using ultrasonography. Methods: Phenotypic records of body weight (BW) and loin eye area (LEA) were collected from 66 animals raised in a didactic-productive module of cuniculture located in the southern Piaui state, Brazil. The following nonlinear models were tested considering fixed parameters: Brody, Gompertz, Logistic, Richards, Meloun 1, modified Michaelis-Menten, Santana, and von Bertalanffy. The coefficient of determination (R²), mean squared error, percentage of convergence of each model (%C), mean absolute deviation of residuals, Akaike information criterion (AIC), and Bayesian information criterion (BIC) were used to determine the best model. The model that best described the growth trajectory for each trait was also used under the context of mixed models, considering two parameters that admit biological interpretation (A and k) with random effects. Results: The von Bertalanffy model was the best fitting model for BW according to the highest value of R² (0.98) and lowest values of AIC (6,675.30) and BIC (6,691.90). For LEA, the Logistic model was the most appropriate due to the results of R² (0.52), AIC (783.90), and BIC (798.40) obtained using this model. The absolute growth rates estimated using the von Bertalanffy and Logistic models for BW and LEA were 21.51g/d and 3.16 cm², respectively. The relative growth rates at the inflection point were 0.028 for BW (von Bertalanffy) and 0.014 for LEA (Logistic). Conclusion: The von Bertalanffy and Logistic models with random effect at the asymptotic weight are recommended for analysis of ponderal and carcass growth trajectories in New Zealand rabbits. The inclusion of random effects in the asymptotic weight and maturity rate improves the quality of fit in comparison to fixed models.

• The Mathematical Education
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• v.63 no.2
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• pp.165-186
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• 2024

Despite the growing attention on artificial intelligence-based automated scoring technology as a support method for the introduction of descriptive items in school environments and large-scale assessments, there is a noticeable lack of foundational research in mathematics compared to other subjects. This study developed an automated scoring model for two descriptive items in first-year middle school mathematics using the Random Forest algorithm, evaluated its performance, and explored ways to enhance this performance. The accuracy of the final models for the two items was found to be between 0.95 to 1.00 and 0.73 to 0.89, respectively, which is relatively high compared to automated scoring models in other subjects. We discovered that the strategic selection of the number of evaluation categories, taking into account the amount of data, is crucial for the effective development and performance of automated scoring models. Additionally, text preprocessing by mathematics education experts proved effective in improving both the performance and interpretability of the automated scoring model. Selecting a vectorization method that matches the characteristics of the items and data was identified as one way to enhance model performance. Furthermore, we confirmed that oversampling is a useful method to supplement performance in situations where practical limitations hinder balanced data collection. To enhance educational utility, further research is needed on how to utilize feature importance derived from the Random Forest-based automated scoring model to generate useful information for teaching and learning, such as feedback. This study is significant as foundational research in the field of mathematics descriptive automatic scoring, and there is a need for various subsequent studies through close collaboration between AI experts and math education experts.

• Proceedings of the Korean Society of Developmental Biology Conference
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• 2003.10a
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• pp.86-86
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• 2003

Research has been in progress for more than a decade to production of useful proteins by genetic modification in cattle. However, the levels of protein production in transgenic cattle have been reported very low. To enhance protein production in transgenic animal, we tried homologous recombination to donor cells for production of transgenic clone cattle through nuclear transfer procedure. Thus, we constructed the two targeting vectors of human thrombopoietin (TPO) at bovine $\beta$-casein locus using homologous recombination with 13.6 kb and 9.6 kb homology. In two targeting vectors, positive selection was through the neomycin resistance gene and negative selection was by the diphtheria toxin (DT). Gene targeting was attempted in bovine embryonic fibroblasts (bEF) and bovine ear skin fibroblasts (bESF). To determine the most appropriate concentration of neomycin for bEF and bESF, G4l8 resistance was confirmed by culturing the cells in various concentrations of the drug and both of the cells were optimally selected at $900 \mu g/ml$ of neomycin. The transfected bEF and bESF by the targeting vectors were colonized efficiently at the ratio of DNA to transfection reagent such as $4 \mu g$:2 ${mu}ell$ and $1 \mu g$:$2 \mu l$. Comparing number of healthy clones from passage 4 to passage 8, bESF (17%) persist in culture for much longer than bEF (6%). The two gene-targeted bESF clones of 30 random-integrated clones with 9.6 kb homology length were confirmed, however, nothing was out of 72 random integration clones with 13.6 kb homology length, The DT also worked more efficiently in clones transfected with the vector of 9.6 kb homology length. Our data suggests that the choice of donor cell for long culture period should be considered to obtain targeted cell clone, and the gene-targeting frequency and the DT working efficiency are dependent on the length of target homology.