• 자동차안전학회지
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• 제14권1호
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• pp.26-31
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• 2022

Changwon City has the second highest accident rate with 79.6 according to the city bus accident rate. In fact, 250,000 people use the city bus a day in Changwon, The number of accidents is increasing gradually. In addition, a recent fire accident occurred in the engine room of a city bus (CNG) in Changwon, which has gradually expanded the public's anxiety. In the case of business vehicles, the government conducts inspections with a short inspection cycle for the purpose of periodic safety inspections, etc., but it is not in the monitoring stage. In the case of city buses, the operation records are monitored using Digital Tacho Graph (DTG). As such, driving records, methods, etc. are continuously monitored, but inspections are conducted every six months to ascertain the safety and performance of automobiles. It is difficult to identify real-time information on automobile safety. Therefore, in this study, individual automobile management solutions are presented through machine learning techniques of inspection results based on driving records or habits by linking DTG data and Vehicle Inspection Management System (VIMS) data for city buses in Changwon from 2019 to 2020.

• 여성건강간호학회지
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• 제26권1호
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• pp.5-9
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• 2020

Artificial intelligence (AI), which includes machine learning and deep learning has been introduced to nursing care in recent years. The present study reviews the following topics: the concepts of AI, machine learning, and deep learning; examples of AI-based nursing research; the necessity of education on AI in nursing schools; and the areas of nursing care where AI is useful. AI refers to an intelligent system consisting not of a human, but a machine. Machine learning refers to computers' ability to learn without being explicitly programmed. Deep learning is a subset of machine learning that uses artificial neural networks consisting of multiple hidden layers. It is suggested that the educational curriculum should include big data, the concept of AI, algorithms and models of machine learning, the model of deep learning, and coding practice. The standard curriculum should be organized by the nursing society. An example of an area of nursing care where AI is useful is prenatal nursing interventions based on pregnant women's nursing records and AI-based prediction of the risk of delivery according to pregnant women's age. Nurses should be able to cope with the rapidly developing environment of nursing care influenced by AI and should understand how to apply AI in their field. It is time for Korean nurses to take steps to become familiar with AI in their research, education, and practice.

• 공학교육연구
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• 제23권2호
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• pp.24-30
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• 2020

This paper proposes the approaches to the evaluation of learning using concepts of artificial intelligence. Among various techniques, deep learning algorithm is employed to achieve quantitative results of evaluation. In particular, this paper focuses on the process-based evaluation instead of the result-based one using face expression. The expression is simply acquired by digital camera that records face expression when students solve sample test problems. Face expressions are trained using convolutional neural network (CNN) model followed by classification of expression data into three categories, i.e., easy, neutral, difficult. To substantiate the proposed approach, the simulation results show promising results, and this work is expected to open opportunities for intelligent evaluation system in the future.

• 대한한의학회지
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• 제15권2호
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• pp.306-320
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• 1994

An account book of medical treatment is a form of collection materials for diagnostic standardization, and it is a basis of standardization, standardization of medical records is a preconsideration of each standardization. But an account book of medical treatment is only a kind of form for recording medical treatment, therefore standardization of medical treatment eventually holds the key to the standardization of recording charts. However until now we have gradually reformed medical records in accordance with individual characters of medical treatment, and didn't have even standard sheme of medical records, also medical terms for medical records had an inconsistency of redescription and reiterative representation for an identical terms in all parts of the East learning, medical terms for medical records didn't unity. To make better this realities, standardization study used orginated system in the process of existing study, it can get ready the basis of discussion between O.M.D and O.M.D. it can make analysis of diagnostic course and can clearly understand usable information by diagnostic course. for that reason we hope that the basis of standardization is accomplished. And in advance of study for this standardization we have to analysis the course of medical treatment with demonstration of roof, first of all we have to study term definition by diagnostic course and prepare basis by diagnostic course. because this study have limits of indivisual study, it needs to long and synthetic investigation in Association levels. Although we cann't completely alternate with methods of measurement which relyed on individual mastery, if we exclude erroes of individual measurement through mechanization and verify results of diagnosis through keynotes, we can realize standardization of medical treatment with demonstration of proof and in this process we can use medical records as a tool collecting exact data, also we can realize standardization of drawing up medical records.

• 대한간호학회지
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• 제7권1호
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• pp.1-9
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• 1977

Primary function of health record is that as tool of communication between the health processionals with the mutual goal, the promotion of health care standard. Studies have been carried out world over oil tile subject, among those, Weed's Problem-Oriented Health Record is considered a paramount achievement. This study was designed to assess tile possibility of implementing tile problem-oriented health record system through ail experiment in order to provide data for nurse administrators infiltrating reformation of recording system and format. Record of 29 patients admitted at Korea University Hospital, Seoul, from March through June, 1976 for 4 to 14 days were sampled. Nursing notes were recorded by research assistants; senior nursing student trailed extensively by the researcher oil Problem-Oriented Records, oil Problem Oriented Nursing Record format (experimental group) and analysis were carried out comparative, with that of traditional nursing records noted by other nursing personnel (control group) on the same patient. Attitude towards Problem Oriented Nursing Record system and format were attained through questionaries responded by the 51 research assistants. Results are as fellows: Comparative analysis revealed that: 1. Assessment of patients' health problems recorded significantly more in traditional records. 2. Focus of health Problem differed; traditional records slowed significantly higher frequency in medical and procedure as focus while problem oriented records on nursing focus problems. 3. Problem- Oriented records were better organized, Mean value scores of attitude towards Problem- Oriented Records revealed that: Positive value scores on all 4 categories: 1) Assessment of nursing needs, 2) Nursing care planning 3) Patient progress assessment and 4) Tool of teaching and learning revealed that the Problem-Oriented Nursing Record is positively accepted by tile respondents. Recommendation Further experiments on implementation of Problem- Oriented Health Record are recommended: experiment involving all health professionals, in larger scope and longitudinal.

• 컴퓨터교육학회논문지
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• 제9권1호
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• pp.11-18
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• 2006

최근 웹 기반 학습의 장점을 극대화하고 단점을 극복하려는 연구가 많이 진행되고 있다. 하지만 웹 기반 학습의 문제점은 학습자의 중도 탈락률이 높다는 것이다. 교실 환경 수업에서 학습자의 만족도와 학습 유지 간에는 높은 상관관계가 있음이 알려져 있다. 과거 웹 기반 자기조절학습 시스템이 많이 연구되어 있으나 학습자 만족도와의 관계를 깊이 분석한 연구는 미비하다. 본 연구에서는 웹 기반 학습 환경에서 학습자 만족도에 영향을 미치는 자기조절학습 요인들을 도출하고 분석하였다. 웹 기반 자기조절학습 시스템을 학생들에게 적용한 결과 자기평가, 목표설정 및 계획, 정보탐색, 사회적도움, 자료검토이 직접적으로 학습자 만족도에 영향을 주는 것으로 나타났다. 본 연구에서 제시한 만족도 요인을 고려하여 학습 시스템을 개발한다면 학습 유지에 도움을 줄 것으로 기대된다.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.190-198
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• 2023

To enhance customer satisfaction for higher profits, an e-commerce sector can establish a continuous relationship and acquire new customers. Utilize machine-learning models to analyse their customer's behavioural evidence to produce their competitive advantage to the e-commerce platform by helping to improve overall satisfaction. These models will forecast customers who will churn and churn causes. Forecasts are used to build unique business strategies and services offers. This work is intended to develop a machine-learning model that can accurately forecast retainable customers of the entire e-commerce customer data. Developing predictive models classifying different imbalanced data effectively is a major challenge in collected data and machine learning algorithms. Build a machine learning model for solving class imbalance and forecast customers. The satisfaction accuracy is used for this research as evaluation metrics. This paper aims to enable to evaluate the use of different machine learning models utilized to forecast satisfaction. For this research paper are selected three analytical methods come from various classifications of learning. Classifier Selection, the efficiency of various classifiers like Random Forest, Logistic Regression, SVM, and Gradient Boosting Algorithm. Models have been used for a dataset of 8000 records of e-commerce websites and apps. Results indicate the best accuracy in determining satisfaction class with both gradient-boosting algorithm classifications. The results showed maximum accuracy compared to other algorithms, including Gradient Boosting Algorithm, Support Vector Machine Algorithm, Random Forest Algorithm, and logistic regression Algorithm. The best model developed for this paper to forecast satisfaction customers and accuracy achieve 88 %.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권4호
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• pp.270-275
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• 2016

We present an approach for acceleration actor-critic algorithm for reinforcement learning with continuous action space. Actor-critic algorithm has already proved its robustness to the infinitely large action spaces in various high dimensional environments. Despite that success, the main problem of the actor-critic algorithm remains the same-speed of convergence to the optimal policy. In high dimensional state and action space, a searching for the correct action in each state takes enormously long time. Therefore, in this paper we suggest a search accelerating function that allows to leverage speed of algorithm convergence and reach optimal policy faster. In our method, we assume that actions may have their own distribution of preference, that independent on the state. Since in the beginning of learning agent act randomly in the environment, it would be more efficient if actions were taken according to the some heuristic function. We demonstrate that heuristically-accelerated actor-critic algorithm learns optimal policy faster, using Educational Process Mining dataset with records of students' course learning process and their grades.

• 문화기술의 융합
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• 제9권3호
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• pp.731-736
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• 2023

최근 의료 현장은 전자의무기록, 전자건강기록 등의 의료 기록을 전산화하여 저장하고 관리하는 시스템이 의무적으로 적용되거나 전체 의료 현장에 보급되어 환자 개개인의 과거 의료 기록을 추가적인 의료 행위에 활용하고 있다. 그러나 일반적인 의료 문진 및 상담 간 발생하는 의료진과 환자 간의 대화는 별도로 기록되거나 저장되지 않고 있어 추가적인 환자의 주요 정보는 효율적으로 활용되지 못하고 있다. 이에 따라, 의료 문진 현장에서 발생하는 의료진과 환자와의 대화를 저장하고 이를 텍스트 데이터로 변환하여 주요한 문진 내용만 자동으로 추출, 요약하여 정보화하는 음성인식과 자연어 처리 딥러닝을 통한 의료상담 요약문을 자동으로 생성하는 전자의무기록 시스템을 제안한다. 본 시스템은 의료 종사자와 환자의 의료 상담 내용의 인식과정을 거쳐서 텍스트 정보를 획득한다. 이렇게 획득된 텍스트를 복수의 문장으로 구분하고, 생성된 문장에 포함된 복수 키워드의 중요도를 산출한다. 산출된 중요도를 기반으로 복수의 문장에 순위를 매기고, 순위를 기반으로 문장들을 요약하여 최종 전자의무기록 데이터를 생성한다. 제안하는 시스템 성능은 정량적 분석을 통하여 우수함을 확인한다.

• 한국응급구조학회지
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• 제17권2호
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• pp.77-87
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• 2013

Purpose : The purpose of this study was to develop a scenario and to evaluate the students by simulation-based learning of acute abdominal pain case in an emergency unit. The expert group of simulation developed the scenario based on actual abdominal pain by medical treatment records. Methods : Scenario was developed to evaluate the simMan3G simulation-based learning. The scenario was used in 2013 with ten groups of fourth grade 50 nursing students who voluntarily participated in the simulation class. Results : The nursing students were able to express nursing knowledge, learning attitude and self-efficacy. The simulation-based scenario proved to be very effective to students' skill training. The performance of nursing practice through simulation class made the nursing students more confident with patient care. Conclusion : Simulation-based learning was found to be the most effective curriculum to the nursing students and made the students satisfied and confident. So the simulation-based learning would be helpful to other students including paramedic students and medical school students.