• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.581-584
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• 2019

Food consumption is growing worldwide every year owing to a growing population. Hence, the increasing population needs the production of sufficient and good quality food products. Strawberry is one of the world's most famous fruit. To obtain the highest strawberry output, we worked with three strawberry varieties supplied with three kinds of nutrient water in a greenhouse and with the outcome of the strawberry production, the highest yielding strawberry variety is detected. This Study uses the nutrient water consumed every day by the highest yielding strawberry variety. The atmospheric temperature, humidity and CO2 levels within the greenhouse are identified and used for the prediction, since the water consumption by any plant depends primarily on weather conditions. Machine learning techniques show successful outcomes in a multitude of issues including time series and regression issues. In this study, daily nutrient water consumption of strawberry plants is predicted using machine learning algorithms is proposed. Four Machine learning algorithms are used such as Linear Regression (LR), K nearest neighbour (KNN), Support Vector Machine with Radial Kernel (SVM) and Gradient Boosting Machine (GBM). Gradient Boosting System produces the best results.

• Korean Journal of Childcare and Education
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• v.17 no.5
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• pp.83-103
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• 2021

Objective: This study aimed to develop a longitudinal predictive model that identifies first-grade children who are at risk for ADHD and to investigate the factors that predict the probability of belonging to the at-risk group for ADHD by using machine learning. Methods: The data of 1,445 first-grade children from the 1st, 3rd, 6th, 7th, and 8th waves of the Korean Children's Panel were analyzed. The output factors were the at-risk and non-risk group for ADHD divided by the CBCL DSM-ADHD scale. Prenatal as well as developmental factors during infancy and early childhood were used as input factors. Results: The model that best classifies the at-risk and the non-risk group for ADHD was the LASSO model. The input factors which increased the probability of being in the at-risk group for ADHD were temperament of negative emotionality, communication abilities, gross motor skills, social competences, and academic readiness. Conclusion/Implications: The outcomes indicate that children who showed specific risk indicators during infancy and early childhood are likely to be classified as being at risk for ADHD when entering elementary schools. The results may enable parents and clinicians to identify children with ADHD early by observing early signs and thus provide interventions as early as possible.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.597-603
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• 2021

The current Sudanese preschool system depends on limited methods of education, children's education needs to be equipped to keep pace with technological development, also, the large gap that exists between the families and the Kindergartens, where many parents have no idea on how their child progresses in the KG context. The aim of this research is to integrate ICT in the preschool education to enhance and improve the preschool education, by building an Integrated Educational Application (Computerized Application for Preschool Education CAPE) which will help to improve the learning outcomes. The researchers used the Experimental Research Methodology, the characteristic of CAPE application is; suitable for children's age, the application style is more attractive to the children and contains a different way to help children get learning. Alawaeel and the Smart Child Kindergartens in Republic of Sudan were selected as a sample of the study, with sample size specifically, 50 children's. Also, the Central Bank of Sudan Kindergarten was selected as one of the institutional Kindergartens for easy communication with parents of children with a sample size 21 children. The study found that; using CAPE application in KG enables children to increase general learning effects and developing child's cognitive skills. Also, the children who were allowed to use CAPE by their parents are performed better in the overall evaluation of KG lessons. Also, using the CAPE in the Pre-School education helps the parents following their children's progress better and more reliable. The researcher recommends that to apply the computerized application and includes the second level. Also, converting the computerized program into an application to be used by children by their self, without the intervention of parents.

• Korean journal of medical education
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• v.30 no.4
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• pp.309-315
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• 2018

Purpose: The introduction of community-based medical education would help improve the quality of primary care. This paper suggests learning objectives and an educational program for community-based medical education. Methods: The educational program was developed in a 1-day consensus workshop. Twenty experts, including faculty members from family medicine department of a college of medicine in Seoul and community-based preceptors, participated in the program. A needs-assessment survey was conducted among community-based preceptors before the workshop. Through this workshop, we derived learning objectives and a standardized curriculum for community-based medical education. Results: In the questionnaire before the workshop, community-based preceptors voiced concerns over the program's potential costs and the time required for teaching. The learning objectives and educational programs derived from the workshop's consensus were consistent with the characteristics of the primary care. Based on the results of this workshop, the joint expert team developed a standard educational program on two core topics: clinical teaching and mentoring. Conclusion: From this curriculum development process, participants could construct a more standardized curriculum for community-based medical education. Future studies are needed to evaluate the long-term outcomes of these educational programs, such as the learners' satisfaction and achievement.

• Journal of the Korea Society for Simulation
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• v.29 no.4
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• pp.21-31
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• 2020

NCTR(Non-Cooperative Target Recognition) refers to the function of radar to identify target on its own without support from other systems such as ELINT(ELectronic INTelligence). ISAR(Inverse Synthetic Aperture Radar) image is one of the representative methods of NCTR, but it is difficult to automatically classify the target without an identification database due to the significant changes in the image depending on the target's maneuver and location. In this study, we discuss how to build an identification database using simulation and deep-learning technique even when actual images are insufficient. To simulate ISAR images changing with various radar operating environment, A model that generates and learns images through the process named 'Perfect scattering image,' 'Lost scattering image' and 'JEM noise added image' is proposed. And the learning outcomes of this model show that not only simulation images of similar shapes but also actual ISAR images that were first entered can be classified.

• Journal of Veterinary Science
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• v.24 no.3
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• pp.41.1-41.9
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• 2023

Competencies are defined as an observable and assessable set of knowledge, skills, and attitudes. Graduation competencies, which are more comprehensive, refer to the required abilities of students to perform on-site work immediately after graduation. As graduation competencies set the goal of education, various countries and institutions have introduced them for new veterinary graduates. The Korean Association of Veterinary Medical Colleges has recently established such competencies to standardize veterinary education and enhance quality levels thereof. The purpose of this study is to describe the process of establishing graduation competencies as well as their implication for veterinary education in Korea. Graduation competencies for veterinary education in Korea comprise 5 domains (animal health care and disease management, one health expertise, communication and collaboration, research and learning, and veterinary professionalism). These are further divided into 11 core competencies, and 33 achievement standards, which were carefully chosen from previous case analyses and nation-wide surveys. Currently, graduation competencies are used as a standard for setting clear educational purposes for both instructors and students. Establishing these competencies further initiated the development of detailed learning outcomes, and of a list of basic veterinary clinical performances and skills, which is useful for assessing knowledge and skills. The establishment of graduation competencies is expected to contribute to the continuous development of Korean veterinary education in many ways. These include curriculum standardization and licensing examination reform, which will eventually improve the competencies of new veterinary graduates.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.135-147
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• 2023

Internet of things has revolutionaries every field of life due to the use of artificial intelligence within Machine Learning. It is successfully being used for the study of Radiation monitoring, prediction of Ultraviolet and Electromagnetic rays. However, there is no particular system available that can monitor and detect waves. Therefore, the present study designed in which IOT enables intelligence system based on machine learning was developed for the prediction of the radiation and their effects of human beings. Moreover, a sensor based system was installed in order to detect harmful radiation present in the environment and this system has the ability to alert the humans within the range of danger zone with a buzz, so that humans can move to a safer place. Along with this automatic sensor system; a self-created dataset was also created in which sensor values were recorded. Furthermore, in order to study the outcomes of the effect of these rays researchers used Support Vector Machine, Gaussian Naïve Bayes, Decision Trees, Extra Trees, Bagging Classifier, Random Forests, Logistic Regression and Adaptive Boosting Classifier were used. To sum up the whole discussion it is stated the results give high accuracy and prove that the proposed system is reliable and accurate for the detection and monitoring of waves. Furthermore, for the prediction of outcome, Adaptive Boosting Classifier has shown the best accuracy of 81.77% as compared with other classifiers.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.681-684
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• 2021

In this paper, we study image clustering without labeling using machine learning techniques. We proposed an unsupervised machine learning technique to design an image clustering model that automatically categorizes images into groups. Our experiment focused on inception convolutional neural networks (inception V3) with k-mean methods to cluster images. For this, we collect the public datasets containing Food-K5, Flowers, Handwritten Digit, Cats-dogs, and our dataset Rice Germination, and the owner dataset Palm print. Our experiment can expand into three-part; First, format all the images to un-label and move to whole datasets. Second, load dataset into the inception V3 extraction image features and transferred to the k-mean cluster group hold on six classes. Lastly, evaluate modeling accuracy using the confusion matrix base on precision, recall, F1 to analyze. In this our methods, we can get the results as 1) Handwritten Digit (precision = 1.000, recall = 1.000, F1 = 1.00), 2) Food-K5 (precision = 0.975, recall = 0.945, F1 = 0.96), 3) Palm print (precision = 1.000, recall = 0.999, F1 = 1.00), 4) Cats-dogs (precision = 0.997, recall = 0.475, F1 = 0.64), 5) Flowers (precision = 0.610, recall = 0.982, F1 = 0.75), and our dataset 6) Rice Germination (precision = 0.997, recall = 0.943, F1 = 0.97). Our experiment showed that modeling could get an accuracy rate of 0.8908; the outcomes state that the proposed model is strongest enough to differentiate the different images and classify them into clusters.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.23-32
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• 2023

E-sports, a burgeoning facet of modern sports culture, has achieved global prominence. Particularly, Aeon of Strife (AOS) games, emblematic of E-sports, blend individual player prowess with team dynamics to significantly influence outcomes. This study aggregates and analyzes real user gameplay data using statistical techniques. Furthermore, it develops and tests win-loss prediction models through machine learning, leveraging a substantial dataset of 1,149,950 individual data points and 230,234 team data points. These models, employing five machine learning algorithms, demonstrate an average accuracy of 80% for individual and 95% for team predictions. The findings not only provide insights beneficial to game developers for enhancing game operations but also offer strategic guidance to general users. Notably, the team-based model outperformed the individual-based model, suggesting its superior predictive capability.

• Geomechanics and Engineering
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• v.37 no.1
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• pp.65-72
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• 2024

Water ingress poses a common and intricate geological hazard with profound implications for tunnel construction's speed and safety. The project's success hinges significantly on the precision of estimating water inflow during excavation, a critical factor in early-stage decision-making during conception and design. This article introduces an optimized model employing the gene expression programming (GEP) approach to forecast tunnel water inflow. The GEP model was refined by developing an equation that best aligns with predictive outcomes. The equation's outputs were compared with measured data and assessed against practical scenarios to validate its potential applicability in calculating tunnel water input. The optimized GEP model excelled in forecasting tunnel water inflow, outperforming alternative machine learning algorithms like SVR, GPR, DT, and KNN. This positions the GEP model as a leading choice for accurate and superior predictions. A state-of-the-art machine learning-based graphical user interface (GUI) was innovatively crafted for predicting and visualizing tunnel water inflow. This cutting-edge tool leverages ML algorithms, marking a substantial advancement in tunneling prediction technologies, providing accuracy and accessibility in water inflow projections.