• Journal of Gastric Cancer
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• 제23권3호
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• pp.375-387
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• 2023

Stomach cancer has a high annual mortality rate worldwide necessitating early detection and accurate treatment. Even experienced specialists can make erroneous judgments based on several factors. Artificial intelligence (AI) technologies are being developed rapidly to assist in this field. Here, we aimed to determine how AI technology is used in gastric cancer diagnosis and analyze how it helps patients and surgeons. Early detection and correct treatment of early gastric cancer (EGC) can greatly increase survival rates. To determine this, it is important to accurately determine the diagnosis and depth of the lesion and the presence or absence of metastasis to the lymph nodes, and suggest an appropriate treatment method. The deep learning algorithm, which has learned gastric lesion endoscopyimages, morphological characteristics, and patient clinical information, detects gastric lesions with high accuracy, sensitivity, and specificity, and predicts morphological characteristics. Through this, AI assists the judgment of specialists to help select the correct treatment method among endoscopic procedures and radical resections and helps to predict the resection margins of lesions. Additionally, AI technology has increased the diagnostic rate of both relatively inexperienced and skilled endoscopic diagnosticians. However, there were limitations in the data used for learning, such as the amount of quantitatively insufficient data, retrospective study design, single-center design, and cases of non-various lesions. Nevertheless, this assisted endoscopic diagnosis technology that incorporates deep learning technology is sufficiently practical and future-oriented and can play an important role in suggesting accurate treatment plans to surgeons for resection of lesions in the treatment of EGC.

• 융합보안논문지
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• 제13권2호
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• pp.101-109
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• 2013

이 연구는 전쟁사 과목에 대하여 2012년 2학기 15주 중 중간고사와 기말고사 주를 제외한 13주간 이루어졌으며 자료의 수집과 동시에 자료의 분석이 이루어졌다. 1개 학기 동안 경험적 연구를 통하여 전쟁사 강좌 문제중심학습법이 효율적이었는지에 대하여는 좀더 연구가 필요하다. 한 학기 동안의 분석해서 나온 개념에 대해서는 강의 시간을 이용하여 학생들과 대화를 가지는 시간을 통해 확인 및 타당화하는 과정을 가졌다. 학생은 1학년 1개반 45명을 대상으로 5명씩 조를 편성하여 9개조로 운영하였다. 연구결과를 요약하면, 초기 교육진행시에는 조별로 진행되는 집단학습의 적응과 학습과정의 불확실성, 제시된 상황에 대한 상황조치 방법 구체화 어려움, 군인이 되고자 하는 의지와 실제 지식의 부족에서 오는 자신감 결여, 조별 토의에서 자신의 역할 미흡에 대한 반성, 자가학습의 중요성과 필요성 인식, 실제 이론과 상황조치간의 괴리 등을 경험하였다. 수업의 중간에는 수업준비의 부담감, 지금 공부하는 것이 과연 임관 후 활용할 수 있을 것인가에 대한 회의감과 갈등, 그러면서도 교수의 지도에 따라 상황조치 완료후 만족감, 학습에 대한 확신감 등을 체험하였다. 수업의 말기에는 자긍심과 자신감 회복, 문제해결의 잠재력 향상, 임관 후 병사들 지도에 대한 자신감 등을 경험하는 것으로 나타났다. 최근 교수법에 대하여 많은 대학에서 관심을 가지고 있다. 교수는 어떻게 학생들의 능력을 이끌어내고 향상시키는 것인가에 초점을 둔 교육을 지향해야 한다. 사회에서 요구하는 인재 양성을 위해 현 상황에서 적용할 수 있는 가장 이상적인 교수법이 무엇인가에 대한 과제는 계속 연구해야한다.

• 한국융합학회논문지
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• 제6권1호
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• pp.23-28
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• 2015

프로그래밍 교육은 컴퓨터 관련학과 뿐만 아니라 공학과 자연과학분야에 걸쳐서 모두 요구되고 있다. 더욱이 최근에는 초등학교와 중등학교에서도 소프트웨어 교육의 중요한 부분으로 프로그래밍 교육이 강조되고 있다. 프로그래밍 능력은 국가의 경쟁력을 이루는 필수적인 요소로 이해되고 있어, 이를 위한 학습시스템이 요구되고 있다. 본 논문에서는 구글에서 개발한 Blockly graphic 툴을 이용해서 웹상에서 실행하는 webc 프로그래밍 융합 학습시스템을 구현하였다. 또 학습시스템 안에는 문제 중심의 학습에 이용할 수 있는 초보자용 문제세트가 내장되어 있다. 이 문제세트는 20여년 동안 현장에서 검증받은 문제들로 학습자들이 최단 시간 내에 초보를 탈출할 수 있게 해주는 문제세트 들이다. Blockly webc 프로그래밍 융합 학습시스템은 이미 개발된 Simple Visual Language2 프로그래밍 학습시스템과 함께 초보자를 위한 프로그래밍 학습시스템으로 중요한 역할을 할 것으로 기대된다.

• 한국생활과학회지
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• 제5권2호
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• pp.17-35
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• 1996

The purpose of this study was to develope Premarial Sexual Education Program. This program was based on survey(466 data used) for the need of PSEP. The result were as followings: 1. The need of sex education was higher(92.9%) and the participation of this PSEP was 80.4%, so this program was systemic structure. 2. Their premarital sexual values were more permissive(52.2%) than the past. But women thought tha female would be vergin(27.1%) their inter course experience rate was 33.4% and Age of experience was under 23 year olds. 3. The unmarried persns wanted that PSEP was consisted of 10 sub themes : (1) pregnancy and child-birth(mean=4.4/5) (2) contraception and family planning(m=4.3) (3) sexual morality and sexual value(m=4.2) (4) sexual healthy family (m=4.1) (5) sexual open communication(m=4.1) (6) venereal disease and coping stratiges(m=4.0) (7) sex role learning(m=3.9) (8) sexual physiology(m=3.8) (9) premarital sex and unwed mother(m=3.7) (10) adultery and society(m=3.6) 4. They want that group meeting would be every Wensday or Friday evening and the required time is two hours. The instruction methods are expected lecture, discussion or seminar and viewing video tapes. 5. So PSEP was consist of 10 sub-themes: (1) orientation and self-disclosure(test, lecture, game) (2) sexual physiology(video tape, lecture) (3) pregnancy and child birth(lecture, video tape) (4) contraceptive methods and family planning(lecture, video tape, test, discussion) (5) sex role learning(test, lecture, role-play) (6) venereal disease and coping stratiges(lecture, video tape) (7) premarital sex and incest(cause study, lecture) (8) sex morality and sex value(seminars, lecture) (9) sexual open communication(seminars) (10) sexual healthy family(lecture, seminars)

• Journal of Electrical Engineering and Technology
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• 제11권4호
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• pp.993-1002
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• 2016

Hepatitis is a major public health problem all around the world. This paper proposes an automatic disease diagnosis system for hepatitis based on Genetic Algorithm (GA) Wavelet Kernel (WK) Extreme Learning Machines (ELM). The classifier used in this paper is single layer neural network (SLNN) and it is trained by ELM learning method. The hepatitis disease datasets are obtained from UCI machine learning database. In Wavelet Kernel Extreme Learning Machine (WK-ELM) structure, there are three adjustable parameters of wavelet kernel. These parameters and the numbers of hidden neurons play a major role in the performance of ELM. Therefore, values of these parameters and numbers of hidden neurons should be tuned carefully based on the solved problem. In this study, the optimum values of these parameters and the numbers of hidden neurons of ELM were obtained by using Genetic Algorithm (GA). The performance of proposed GA-WK-ELM method is evaluated using statical methods such as classification accuracy, sensitivity and specivity analysis and ROC curves. The results of the proposed GA-WK-ELM method are compared with the results of the previous hepatitis disease studies using same database as well as different database. When previous studies are investigated, it is clearly seen that the high classification accuracies have been obtained in case of reducing the feature vector to low dimension. However, proposed GA-WK-ELM method gives satisfactory results without reducing the feature vector. The calculated highest classification accuracy of proposed GA-WK-ELM method is found as 96.642 %.

• 산업경영시스템학회지
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• 제42권4호
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• pp.135-144
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• 2019

In this study, BSC model of center for teaching and learning was developed using balanced scorecard suitable for non-profit organization. Firstly, relevant literature surveys and evaluation indicators of various CTL and institution with similar characteristics were examined. Next, a draft BSC model was designed through interviews of specialists. Lastly, the BSC model was proposed by verifying the content validity of the evaluation model by conducting two Delphi surveys. The BSC model of CTL has 4 perspectives: resource, customer, internal process, learning and growth, 9 critical success factors: 2 factors in resource, customer and learning and growth perspectives, 3 factors in internal process perspective, and 23 key performance Indicators: 4 indicators in resource and learning and growth, 7 indicators in customer perspective, 8 indicators in internal process perspective. The implications of this study through the results were as follows: firstly, the proposed BSC model showed an evaluation model suitable for a non-profit organization. Second, the BSC model was linked to the organization's mission and vision. Third, it could contribute to the long-term development of CTL. Lastly, if it could be applied to management, and evaluated, it is expected to play a role of providing basic data for the budget support and spread of the university.

• International Journal of Computer Science & Network Security
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• 제24권3호
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• pp.125-134
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• 2024

The novel coronavirus 2019 is called COVID-19 has outspread swiftly worldwide. An early diagnosis is more important to control its quick spread. Medical imaging mechanics, chest calculated tomography or chest X-ray, are playing a vital character in the identification and testing of COVID-19 in this present epidemic. Chest X-ray is cost effective method for Covid-19 detection however the manual process of x-ray analysis is time consuming given that the number of infected individuals keep growing rapidly. For this reason, it is very important to develop an automated COVID-19 detection process to control this pandemic. In this study, we address the task of automatic detection of Covid-19 by using a popular deep learning model namely the VGG19 model. We used 1300 healthy and 1300 confirmed COVID-19 chest X-ray images in this experiment. We performed three experiments by freezing different blocks and layers of VGG19 and finally, we used a machine learning classifier SVM for detecting COVID-19. In every experiment, we used a five-fold cross-validation method to train and validated the model and finally achieved 98.1% overall classification accuracy. Experimental results show that our proposed method using the deep learning-based VGG19 model can be used as a tool to aid radiologists and play a crucial role in the timely diagnosis of Covid-19.

• Imaging Science in Dentistry
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• 제50권2호
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• pp.81-92
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• 2020

Intelligent systems(i.e., artificial intelligence), particularly deep learning, are machines able to mimic the cognitive functions of humans to perform tasks of problem-solving and learning. This field deals with computational models that can think and act intelligently, like the human brain, and construct algorithms that can learn from data to make predictions. Artificial intelligence is becoming important in radiology due to its ability to detect abnormalities in radiographic images that are unnoticed by the naked human eye. These systems have reduced radiologists' workload by rapidly recording and presenting data, and thereby monitoring the treatment response with a reduced risk of cognitive bias. Intelligent systems have an important role to play and could be used by dentists as an adjunct to other imaging modalities in making appropriate diagnoses and treatment plans. In the field of maxillofacial radiology, these systems have shown promise for the interpretation of complex images, accurate localization of landmarks, characterization of bone architecture, estimation of oral cancer risk, and the assessment of metastatic lymph nodes, periapical pathologies, and maxillary sinus pathologies. This review discusses the clinical applications and scope of intelligent systems such as machine learning, artificial intelligence, and deep learning programs in maxillofacial imaging.

• Imaging Science in Dentistry
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• 제51권3호
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• pp.237-242
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• 2021

Purpose: The aim of this study was to analyse and review deep learning convolutional neural networks for detecting and diagnosing early-stage dental caries on periapical radiographs. Materials and Methods: In order to conduct this review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA) guidelines were followed. Studies published from 2015 to 2021 under the keywords(deep convolutional neural network) AND (caries), (deep learning caries) AND (convolutional neural network) AND (caries) were systematically reviewed. Results: When dental caries is improperly diagnosed, the lesion may eventually invade the enamel, dentin, and pulp tissue, leading to loss of tooth function. Rapid and precise detection and diagnosis are vital for implementing appropriate prevention and treatment of dental caries. Radiography and intraoral images are considered to play a vital role in detecting dental caries; nevertheless, studies have shown that 20% of suspicious areas are mistakenly diagnosed as dental caries using this technique; hence, diagnosis via radiography alone without an objective assessment is inaccurate. Identifying caries with a deep convolutional neural network-based detector enables the operator to distinguish changes in the location and morphological features of dental caries lesions. Deep learning algorithms have broader and more profound layers and are continually being developed, remarkably enhancing their precision in detecting and segmenting objects. Conclusion: Clinical applications of deep learning convolutional neural networks in the dental field have shown significant accuracy in detecting and diagnosing dental caries, and these models hold promise in supporting dental practitioners to improve patient outcomes.

• 한국인터넷방송통신학회논문지
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• 제21권3호
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• pp.7-14
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• 2021

자동화된 스마트 그리드의 등장은 이러한 문제에 대응을 위한 필수적인 장치가 되고 있으며 스마트 그리드 기반 사회로의 진전을 가져오고 있다. 스마트 그리드는 전기 공급 업체와 소비자 간의 양방향 통신을 가능하게 하는 새로운 패러다임이다. 스마트 그리드는 전력 그리드를 보다 안정적이고 신뢰할 수 있으며 효율적이고 안전하게 만들기 위한 엔지니어의 이니셔티브로 인해 등장했다. 스마트 그리드는 전력 소비자가 전력 사용에서 더 큰 역할을 할 수 있는 기회를 창출하고 전력을 현명하고 효율적으로 사용하도록 동기를 부여한다. 이에 본 연구에서는 기계 학습을 통한 전력 수요 관리에 중점을 둔다. 기계 학습을 사용한 수요 예측과 관련하여 현재 다양한 기계 학습 모델이 소개되어 적용되고 있는 데 이에 관한 체계적인 접근이 요구되고 있다. 특히 GP 학습 모델의 경우에 일반 소비 예측 및 데이터의 가시화와 관련해서 다른 학습 모델보다 장점이 있지만, 스마트 미터 데이터의 예측과 관련해서는 데이터 독립성에 강한 영향을 받는다.