• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.30-45
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• 2024

This study introduces an effective method for predicting individual local tax delinquencies using prevalent machine learning and deep learning algorithms. The evaluation of credit risk holds great significance in the financial realm, impacting both companies and individuals. While credit risk prediction has been explored using statistical and machine learning techniques, their application to tax arrears prediction remains underexplored. We forecast individual local tax defaults in Republic of Korea using machine and deep learning algorithms, including convolutional neural networks (CNN), long short-term memory (LSTM), and sequence-to-sequence (seq2seq). Our model incorporates diverse credit and public information like loan history, delinquency records, credit card usage, and public taxation data, offering richer insights than prior studies. The results highlight the superior predictive accuracy of the CNN model. Anticipating local tax arrears more effectively could lead to efficient allocation of administrative resources. By leveraging advanced machine learning, this research offers a promising avenue for refining tax collection strategies and resource management.

• 한국지능시스템학회논문지
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• 제15권2호
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• pp.245-250
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• 2005

인터넷의 급격한 발전으로 교육환경$\cdot$방법에 대한 새로운 패러다임 창출요구가 증가하고 있으며 전통적인 교육산업도 교육의 전 분야에서 이론 활용한 e-teaming이 많은 분야에서 도입되었고, 빠른 속도로 그 영역이 확장되고 있다. 이러한 e-learning 확산 노력에 힘입어 그동안 e-learning의 학습자 만족도에 대한 연구도 많이 진행되어 왔지만 기업체를 대상으로 한 연구가 거의 대부분이었고 고등학교를 대상으로 한 연구는 거의 없는 실정이다. 따라서, 본 연구에서는 이러한 배경을 바탕으로 고등학생을 대상으로 한 e-learning 교육만족도 평가를 위한 모델을 제안하고, 제안한 모델을 대상으로 퍼지구조 모델링법을 이용하여 고등학생의 e-learning 교육 만족도에 관한 의식구조를 분석하였다. 또한, 의식구조분석의 결과가 고려된 평가모델을 구축하여 e-learning 교육 만족도를 평가하고, 민감도분석을 통하여 e-learning 교육만족도 향상 방안을 제시 하였다.

• 한국문헌정보학회지
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• 제58권1호
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• pp.31-51
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• 2024

본 연구의 목적은 에듀테크 기반 학교도서관활용교육 설계 모형을 제안하는 것이다. 에듀테크 기반 교육은 학습 경계를 확장하고, 학습자 중심의 디퍼러닝을 위한 새로운 수업 환경과 학습 경험을 필요로 한다. 이에 본 연구에서는 체제이론에 기반을 둔 ADDIE 모형을 수정하여 '분석 단계, 사전 학습 및 개발 단계, 수업 운영 단계, 협동수업 평가 단계'로 구성된 4단계 교수 설계 모형(안)을 제시하였다. 이 모형은 플립러닝과 백워드 교수 설계 요소 및 탐구기반학습 요소를 반영하여 학생 맞춤형 자료 개발과 탐구활동이 이루어지도록 하였다. 또한, 학습의 범위를 사전 학습, 대면 학습, 추가 학습으로 확대하여, 협동수업의 다양성을 도모하였다. 그리고 에듀테크 환경에서 학교도서관활용교육 활성화 방안을 사서교사의 전문성, 학교도서관 공간, 예산, 표준 교육과정 개발 및 독서교육종합지원시스템 측면에서 제안하였다.

• 인터넷정보학회논문지
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• 제19권4호
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• pp.35-44
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• 2018

Monitoring through Synthesis Aperture Radar (SAR) is responsible for marine safety from floating icebergs. However, there are limits to distinguishing between icebergs and ships in SAR images. Convolutional Neural Network (CNN) is used to distinguish the iceberg from the ship. The goal of this paper is to increase the accuracy of identifying icebergs from SAR images. The metrics for performance evaluation uses the log loss. The two-layer CNN model proposed in research of C.Bentes et al.[1] is used as a benchmark model and compared with the four-layer CNN model using data augmentation. Finally, the performance of the final CNN model using the VGG-16 pre-trained model is compared with the previous model. This paper shows how to improve the benchmark model and propose the final CNN model.

• 한국실내디자인학회논문집
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• 제27호
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• pp.12-19
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• 2001

The purpose of this study is to develope instructional strategies and assessment model for distance learning in interior design education in Korea. Literature from various sources were examined to provide guidelines for instructional and technical strategies, communication system, and administrative process. Strategies and assessment model for transition to distance learning from goal setting to program evaluation were introduced. The results of this study indicated that distance learning in interior design education is optimized when applied in studio critique, portfolio production, and professional practice and with other traditional programs depending on the characteristics of the instructional materials. It is suggested for further studies that various distance learning programs based on instructional theories to be conducted and evaluated in different areas of interior design education for their maximum application as instructional tool.

• International Journal of Advanced Culture Technology
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• 제9권1호
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• pp.190-195
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• 2021

Currently, one of the fields leading the 4th industrial revolution is the image recognition field of artificial intelligence, which is showing good results in many fields. In this paper, using is a YOLO V2 model, which is one of the image recognition models, we intend to classify and select into three types according to the characteristics of fruits. To this end, it was designed to proceed the number of iterations of learning 9000 counts based on 640 mandarin image data of 3 classes. For model evaluation, normal, rotten, and unripe mandarin oranges were used based on images. We as a result of the experiment, the accuracy of the learning model was different depending on the number of learning. Normal mandarin oranges showed the highest at 60.5% in 9000 repetition learning, and unripe mandarin oranges also showed the highest at 61.8% in 9000 repetition learning. Lastly, rotten tangerines showed the highest accuracy at 86.0% in 7000 iterations. It will be very helpful if the results of this study are used for fruit farms in rural areas where labor is scarce.

• 디지털산업정보학회논문지
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• 제19권3호
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• pp.177-192
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• 2023

The employment rate of university graduates has been decreasing significantly recently. With the advent of the Fourth Industrial Revolution, the demand for software employment has increased. It is necessary to analyze the factors for software employment of university graduates. This paper proposes explainable software employment model of university graduates using machine learning and explainable AI. The Graduates Occupational Mobility Survey(GOMS) provided by the Korea Employment Information Service is used. The employment model uses boosting machine learning. Then, performance evaluation is performed with four algorithms of boosting model. Moreover, it explains the factors affecting the employment using SHAP. The results indicates that the top 3 factors are major, employment goal setting semester, and vocational education and training.

• International Journal of Advanced Culture Technology
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• 제9권4호
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• pp.274-281
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• 2021

In the field of artificial intelligence education, discussions on the direction of artificial intelligence education are actively underway, and it is necessary to establish a foundation for future information education. It is necessary to design a creative convergence teaching-learning and evaluation method. Although AI experience coding education has been applied, the evaluation stage is insufficient. In this paper, we propose an evaluation system that can verify the validity of the proposed education model to find a way to supplement the existing learning module. The core components of this proposed system are Assessment-Factor, Self-Diagnosis, Item Bank, and Evaluation Result modules, which are designed to enable system access according to the roles of administrator, instructors and learners. This system enables individualized learning through online and offline connection.

• 4차산업연구
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• 제3권1호
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• pp.13-20
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• 2023

Purpose - By designing a PEF(Personalized Education Feedback) system for real-time prediction of learning achievement and motivation through real-time EEG analysis of learners, this system provides some modules of a personalized adaptive learning system. By applying these modules to e-learning and offline learning, they motivate learners and improve the quality of learning progress and effective learning outcomes can be achieved for immersive self-directed learning Research design, data, and methodology - EEG data were collected simultaneously as the English test was given to the experimenters, and the correlation between the correct answer result and the EEG data was learned with a machine learning algorithm and the predictive model was evaluated.. Result - In model performance evaluation, both artificial neural networks(ANNs) and support vector machines(SVMs) showed high accuracy of more than 91%. Conclusion - This research provides some modules of personalized adaptive learning systems that can more efficiently complete by designing a PEF system for real-time learning achievement prediction and learning motivation through an adaptive learning system based on real-time EEG analysis of learners. The implication of this initial research is to verify hypothetical situations for the development of an adaptive learning system through EEG analysis-based learning achievement prediction.

• 의학교육논단
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• 제10권2호
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• pp.19-24
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• 2008

Purpose: The 'learning cycle' proposed by Guilbert in 1981 has been accredited as an effective and useful model for curriculum design. Three components of learning cycle, learning objective, instructional method, and assessment are connected organically and form basic structure of curriculum. In this study, we intend to analyze how the learning cycle and its three components are applied to present medical curriculum and examine the points at issue of the learning cycle in medical education. Also, we try to identify the educational significance of the leaning cycle in medical education. Results: First, concerning the learning objective, it was identified that impractical and abstract expressions are major controversial points. Also, there is a need to make learning objectives covering entire medical curriculum. Second, because of various structural problems, it is hard to practice new and various instructional methods. Third, even though there is a growing need for medical curriculum to develop and utilize more various and detailed assessment and evaluation, it was revealed that only are standardized and traditional assessments mainly used. Conclusion: Synthetically, we have some suggestions as follows. First, it is necessary to specify and actualize the learning objectives. Also, instructional methods and assessments should be diversified. And finally, there is a need to build organic and delicate medical curriculum by applying the learning cycle to medical education more actively.