• 디지털융복합연구
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• 제17권10호
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• pp.259-264
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• 2019

본 논문에서는 엔터프라이즈 환경에서의 딥 러닝에 대한 이미지 예측 시스템 아키텍처를 제안한다. 엔터프라이즈 환경에 대해 인공지능 플랫폼으로 변환을 쉽게 하고, 인공지능 플랫폼이 파이선에 집중되어서 자바 중심의 엔터프라이즈 개발이 어려운 단점을 개선하기 위해 자바 중심의 아키텍처에서도 충분한 딥 러닝 서비스의 개발과 수정이 가능하도록 한다. 또한, 제안된 환경을 토대로 이미지 예측 실험을 통해 기존에 학습된 딥 러닝 아키텍처 환경에서의 정확도가 높은 예측 시스템을 제안한다. 실험을 통해 딥 러닝이 수행되기 위해 제공된 이미지 예에서 95.23%의 정확도를 보이며, 제안된 모델은 유사한 다른 모델에 비교해 96.54%의 정확도를 보인다. 제시된 아키텍처를 활용하여 활발한 엔터프라이즈급 환경의 딥 러닝 서비스가 개발 및 제공될 것으로 보이며, 기존 엔터프라이즈 환경이 딥 러닝 아키텍처가 탑재된 환경으로 전환이 활발히 이루어질 것이다.

• International Journal of Advanced Culture Technology
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• 제8권4호
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• pp.263-270
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• 2020

The growth of the mobile app markets has made it popular among people who recommend relevant information about restaurants. The recommendation service app based on AI Chatbot is that it can efficiently manage time and finances by making it easy for restaurant consumers to easily access the information they want anytime, anywhere. Eating out consumers use smartphone applications for finding restaurants, making reservations, and getting reviews and how to use them. In addition, social attention has recently been focused on the research of AI chatbot. The Chatbot is combined with the mobile messenger platform and enabling various services due to the text-type interactive service. It also helps users to find the services and data that they need information tersely. Applying this to restaurant recommendation services will increase the reliability of the information in providing personal information. In this paper, an artificial intelligence chatbot-based smartphone restaurant recommendation app using personalization information is proposed. The recommendation service app utilizes personalization information such as gender, age, interests, occupation, search records, visit records, wish lists, reviews, and real-time location information. Users can get recommendations for restaurants that fir their purpose through chatting using AI chatbot. Furthermore, it is possible to check real-time information about restaurants, make reservations, and write reviews. The proposed app uses a collaborative filtering recommendation system, and users receive information on dining out using artificial intelligence chatbots. Through chatbots, users can receive customized services using personal information while minimizing time and space limitations.

• 융합정보논문지
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• 제11권1호
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• pp.28-33
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• 2021

주가는 그 기업의 미래 가치의 척도이기 때문에 주가를 분석할 때 기업의 성장성인 매출과 이익 등을 고려하여 주식을 투자한다. 기관투자자들은 종목 선정 기준을 잡기 위해서 현재 산업의 트렌드와 거시경제 지표를 보고 성장 가능한 관련 분야를 먼저 정하고 관련 기업을 선정한 후 기업에 대한 분석을 하고 목표가를 설정 후에 매수를 하고 목표가에 도달하면 매도하는 방식으로 주식 매매를 실시한다. 하지만, 일반 개인 투자자들은 경제에 대한 지식이 기관이나 외국인 투자자에 비교하여 부족하고, 기업에 대한 재무재표 분석이나 성장성에 대한 분석 없이 전문가나 지인의 추천종목을 따라 투자를 하여 기관투자자나 외국인 투자자들 보다 수익률 면에서 낮은 편이다. 따라서, 본 연구에서는 기업의 성장성인 매출과 이익 등을 고려한 지표인 ROE를 분석하여 저평가된 종목을 선택하고, 선택된 종목의 주가 흐름을 딥러닝 알고리즘을 통하여 예측하는 연구방법을 제안하여 투기가 아닌 건전한 투자에 도움이 되기 위해 본 연구를 진행한다.

• 융합정보논문지
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• 제11권2호
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• pp.16-24
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• 2021

본 논문은 고령화 사회로 진입함에 따라 급속히 늘어나는 고령자를 위하여, IoT와 인공지능 기술을 적극 활용하여 고령자로 하여금 행복한 노년을 영위할 수 있도록 도와주는 smart aging 서비스를 소개한다. 특히 고령화문제를 해결하려는 기존의 복지개념에서 탈피하여 긴급 상황에서 자신을 보호하고 감성을 만족시키어 활기찬 고령사회 구축으로의 패러다임 변화를 이끌어내는, 미래지향의 고령 친화적 wellbeing 지원 시스템을 제안한다. IoT(사물인터넷)와 AI(인공지능)를 도입하여 고령자의 생활정보로부터 생활상황 및 감성상태를 판단하여 긴급 상황 대응, 기분전환과 감성 위로 제공 및 모임을 추천한다. 제안 시스템은 맥박, 위험한 단어사용 및 외부소통 등의 정보를 입력하면 인공지능 기법을 이용하여 우울증의 정도를 판단해줌으로써, 기존 헬스케어 중심의 복지개념에서 탈피하여 고령자에게 감정적인 행복감을 제공하는 새로운 개념의 wellbeing 지원 시스템의 실현가능성을 보여주었다고 생각한다.

• 한국융합학회논문지
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• 제12권3호
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• pp.61-67
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• 2021

본 연구의 목적은 EFL학습자들을 위해 구체적 학습활동 모형을 통해 교실 수업의 한계를 극복하고 의사소통의 기회를 창출해 내고자 하였다. 연구 방법으로는 모형개발, 타당화, 적용으로 전략, 지침 등을 개발하고 도출하고자 2019년 3월부터 6월까지 실시하였다. 사전학습에서는 인공지능을 활용하여 교실 밖 자기 주도적 학습을 유도하고, 본 수업에서는 문제 해결 능력을 향상시키고 학습 내재화를 목표로 협력과 참여를 통해 결과물을 만들어내는 메이커 교육을 적용한 학습자 중심활동으로 구성하였다. 두 번의 타당성 테스트 후 수정 된 모델을 실험 그룹에 적용한 결과 창의성을 제외한 자기 주도, 관심, 문제 해결 및 참여도가 유의미했고 사후 테스트 결과는 모든 분야에서 유의미한 결과를 나타냄으로 연구 기대효과의 유용성을 확인하였다. 다만 영어 학습과 관련된 인공 지능을 수업에 쉽게 적용할 수 있는 소프트웨어의 개발과 방법에 대한 심화연구 그리고 학습활동에서 보다 체계적인 메이커 교육과의 융합활동의 제시 등 지속적인 후속 연구가 필요하다.

• 대한치과교정학회지
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• 제51권2호
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• pp.77-85
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• 2021

Objective: To evaluate the accuracy of a multi-stage convolutional neural network (CNN) model-based automated identification system for posteroanterior (PA) cephalometric landmarks. Methods: The multi-stage CNN model was implemented with a personal computer. A total of 430 PA-cephalograms synthesized from cone-beam computed tomography scans (CBCT-PA) were selected as samples. Twenty-three landmarks used for Tweemac analysis were manually identified on all CBCT-PA images by a single examiner. Intra-examiner reproducibility was confirmed by repeating the identification on 85 randomly selected images, which were subsequently set as test data, with a two-week interval before training. For initial learning stage of the multi-stage CNN model, the data from 345 of 430 CBCT-PA images were used, after which the multi-stage CNN model was tested with previous 85 images. The first manual identification on these 85 images was set as a truth ground. The mean radial error (MRE) and successful detection rate (SDR) were calculated to evaluate the errors in manual identification and artificial intelligence (AI) prediction. Results: The AI showed an average MRE of 2.23 ± 2.02 mm with an SDR of 60.88% for errors of 2 mm or lower. However, in a comparison of the repetitive task, the AI predicted landmarks at the same position, while the MRE for the repeated manual identification was 1.31 ± 0.94 mm. Conclusions: Automated identification for CBCT-synthesized PA cephalometric landmarks did not sufficiently achieve the clinically favorable error range of less than 2 mm. However, AI landmark identification on PA cephalograms showed better consistency than manual identification.

• Tuberculosis and Respiratory Diseases
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• 제84권2호
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• pp.115-124
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• 2021

Background: This study aimed to determine the parameters for worsening oxygenation in non-severe coronavirus disease 2019 (COVID-19) pneumonia. Methods: This retrospective cohort study included cases of confirmed COVID-19 pneumonia in a public hospital in South Korea. The worsening oxygenation group was defined as that with SpO₂ ≤94% or received oxygen or mechanical ventilation (MV) throughout the clinical course versus the non-worsening oxygenation group that did not experience any respiratory event. Parameters were compared, and the extent of viral pneumonia from an initial chest computed tomography (CT) was calculated using artificial intelligence (AI) and measured visually by a radiologist. Results: We included 136 patients, with 32 (23.5%) patients in the worsening oxygenation group; of whom, two needed MV and one died. Initial vital signs and duration of symptoms showed no difference between the two groups; however, univariate logistic regression analysis revealed that a variety of parameters on admission were associated with an increased risk of a desaturation event. A subset of patients was studied to eliminate potential bias, that ferritin ≥280 ㎍/L (p=0.029), lactate dehydrogenase ≥240 U/L (p=0.029), pneumonia volume (p=0.021), and extent (p=0.030) by AI, and visual severity scores (p=0.042) were the predictive parameters for worsening oxygenation in a sex-, age-, and comorbid illness-matched case-control study using propensity score (n=52). Conclusion: Our study suggests that initial CT evaluated by AI or visual severity scoring as well as serum markers of inflammation on admission are significantly associated with worsening oxygenation in this COVID-19 pneumonia cohort.

• 한국의학물리학회지:의학물리
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• 제31권3호
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• pp.111-123
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• 2020

Deep learning (DL) is a subset of machine learning and artificial intelligence that has a deep neural network with a structure similar to the human neural system and has been trained using big data. DL narrows the gap between data acquisition and meaningful interpretation without explicit programming. It has so far outperformed most classification and regression methods and can automatically learn data representations for specific tasks. The application areas of DL in radiation oncology include classification, semantic segmentation, object detection, image translation and generation, and image captioning. This article tries to understand what is the potential role of DL and what can be more achieved by utilizing it in radiation oncology. With the advances in DL, various studies contributing to the development of radiation oncology were investigated comprehensively. In this article, the radiation treatment process was divided into six consecutive stages as follows: patient assessment, simulation, target and organs-at-risk segmentation, treatment planning, quality assurance, and beam delivery in terms of workflow. Studies using DL were classified and organized according to each radiation treatment process. State-of-the-art studies were identified, and the clinical utilities of those researches were examined. The DL model could provide faster and more accurate solutions to problems faced by oncologists. While the effect of a data-driven approach on improving the quality of care for cancer patients is evidently clear, implementing these methods will require cultural changes at both the professional and institutional levels. We believe this paper will serve as a guide for both clinicians and medical physicists on issues that need to be addressed in time.

• 한국축산식품학회지
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• 제41권4호
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• pp.563-588
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• 2021

Increasing meat demand in terms of both quality and quantity in conjunction with feeding a growing population has resulted in regulatory agencies imposing stringent guidelines on meat quality and safety. Objective and accurate rapid non-destructive detection methods and evaluation techniques based on artificial intelligence have become the research hotspot in recent years and have been widely applied in the meat industry. Therefore, this review surveyed the key technologies of non-destructive detection for meat quality, mainly including ultrasonic technology, machine (computer) vision technology, near-infrared spectroscopy technology, hyperspectral technology, Raman spectra technology, and electronic nose/tongue. The technical characteristics and evaluation methods were compared and analyzed; the practical applications of non-destructive detection technologies in meat quality assessment were explored; and the current challenges and future research directions were discussed. The literature presented in this review clearly demonstrate that previous research on non-destructive technologies are of great significance to ensure consumers' urgent demand for high-quality meat by promoting automatic, real-time inspection and quality control in meat production. In the near future, with ever-growing application requirements and research developments, it is a trend to integrate such systems to provide effective solutions for various grain quality evaluation applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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• pp.2049-2068
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• 2021

AI combined with NLP techniques has promoted the use of Virtual Assistants and have made people rely on them for many diverse uses. Conversational Agents are the most promising technique that assists computer users through their operation. An important challenge in developing Conversational Agents globally is transferring the groundbreaking expertise obtained in English to other languages. AI is making it possible to transfer this learning. There is a dire need to develop systems that understand secular languages. One such difficult language is Hindi, which is the fourth most spoken language in the world. Semantic similarity is an important part of Natural Language Processing, which involves applications such as ontology learning and information extraction, for developing conversational agents. Most of the research is concentrated on English and other European languages. This paper presents a Corpus-based word semantic similarity measure for Hindi. An experiment involving the translation of the English benchmark dataset to Hindi is performed, investigating the incorporation of the corpus, with human and machine similarity ratings. A significant correlation to the human intuition and the algorithm ratings has been calculated for analyzing the accuracy of the proposed similarity measures. The method can be adapted in various applications of word semantic similarity or module for any other language.