• International Journal of Internet, Broadcasting and Communication
• /
• 제16권3호
• /
• pp.370-378
• /
• 2024

Recently, Artificial Intelligence (AI) has been emerging as a technology that has transformed and revolutionized various industries around the world. In recent years, businesses in Vietnam have also started to embrace AI applications to enhance their operations and gain a competitive edge in the market. As AI technologies continue to evolve rapidly, their impact on Vietnamese businesses is becoming increasingly profound. As artificial intelligence continues to progress across various fields, the need to democratize AI technology becomes increasingly clear. In a rapidly growing market like Vietnam, leveraging AI offers significant opportunities for businesses to improve operational efficiency, customer engagement, and overall competitiveness. However, significant barriers to AI adoption in Vietnam are the scarcity of skilled developers and the high cost of implementing traditional AI. No-code/low-code platforms offer an innovative solution that can accelerate AI adoption by making these technologies accessible to a wider audience. This article analyzes and understands the benefits of no-code/low-code solutions and proposes a roadmap for implementing no-code/low-code solutions in promoting AI applications in Vietnamese businesses.

• 한국컴퓨터정보학회:학술대회논문집
• /
• 한국컴퓨터정보학회 2023년도 제67차 동계학술대회논문집 31권1호
• /
• pp.447-448
• /
• 2023

본 연구에서는 비전공자들을 위한 디지털 교육을 위하여 노 코드 프로그램을 활용한 정보, 수학, 과학 융합교육 교양 강좌를 개발하였다. 노 코드 프로그램으로는 오렌지3 데이터 마이닝을 선정하였는데, 이는 데이터 분석, 시각화, 머신러닝 모델의 활용이 용이하다는 강점을 가지고 있다. 또한, 산업환경 변화에 대비하는 핵심 교과인 과학, 수학, 정보의 중요성과 데이터 분석과의 밀접성을 고려하여 교육 내용을 융합할 수 있도록 선정하였다. 개발된 교육 프로그램은 8인이 전문가 검토 결과 내용 타당도가 확보되었음을 확인할 수 있었다. 추후 연구에서는 이 강좌를 대학의 학부생에게 적용하여 그 효과성을 확인해 보고자 한다.

• Nuclear Engineering and Technology
• /
• 제44권4호
• /
• pp.393-404
• /
• 2012

After the Fukushima nuclear accident in 2011, there has been increasing concern regarding severe accidents in nuclear facilities. Severe accident scenarios are difficult for operators to monitor and identify. Therefore, accurate prediction of a severe accident is important in order to manage it appropriately in the unfavorable conditions. In this study, artificial intelligence (AI) techniques, such as support vector classification (SVC), probabilistic neural network (PNN), group method of data handling (GMDH), and fuzzy neural network (FNN), were used to monitor the major transient scenarios of a severe accident caused by three different initiating events, the hot-leg loss of coolant accident (LOCA), the cold-leg LOCA, and the steam generator tube rupture in pressurized water reactors (PWRs). The SVC and PNN models were used for the event classification. The GMDH and FNN models were employed to accurately predict the important timing representing severe accident scenarios. In addition, in order to verify the proposed algorithm, data from a number of numerical simulations were required in order to train the AI techniques due to the shortage of real LOCA data. The data was acquired by performing simulations using the MAAP4 code. The prediction accuracy of the three types of initiating events was sufficiently high to predict severe accident scenarios. Therefore, the AI techniques can be applied successfully in the identification and monitoring of severe accident scenarios in real PWRs.

• Journal of information and communication convergence engineering
• /
• 제21권4호
• /
• pp.300-305
• /
• 2023

Audience reactions in response to remote virtual performances must be compressed before being transmitted to the server. The server, which aggregates these data for group insights, requires a distribution code for the transfer. Recently, distributed learning algorithms such as federated learning have gained attention as alternatives that satisfy both the information security and efficiency requirements. In distributed learning, no individual user has access to complete information, and the objective is to achieve a learning effect similar to that achieved with the entire information. It is therefore important to distribute interdependent information among users and subsequently aggregate this information following training. In this paper, we present a new extension technique for minimal code that allows a new minimal code with a different length and Hamming weight to be generated through the product of any vector and a given minimal code. Thus, the proposed technique can generate minimal codes with previously unknown parameters. We also present a scenario wherein these combined methods can be applied.

• 한국방송∙미디어공학회:학술대회논문집
• /
• 한국방송∙미디어공학회 2021년도 추계학술대회
• /
• pp.172-175
• /
• 2021

4차 산업혁명이 시작된 이래로 다양한 산업 분야에서 AI가 활용되고 있고, 그 중에서도 컴퓨터 비전 분야에서 딥러닝 기술이 각광받고 있다. 하지만 딥러닝 기술은 높은 전문 지식이 요구되어 관련 지식이 없는 일반인들은 활용하기 어렵다. 본 논문에서는 AI 관련 배경지식이 없는 사용자들도 UI를 통해 쉽게 이미지 분류 모델을 학습시킬 수 있는 노 코드 플랫폼에 관하여 기술하고, django 프레임워크를 이용해 웹 개발과 딥러닝 모델 학습을 통합 개발을 위한 아키텍처와 방향성을 제시하고자 한다. 사용자가 웹서버에 업로드한 이미지들을 웹 인터페이스를 통해 라벨링 하여 학습 데이터를 생성한 후, 이 데이터를 사용하여 모델을 학습시킨다. CNN 모델에 데이터를 학습시키는 과정과 생성된 모델 기반으로 이미지 예측하는 모듈을 통해 전문지식이 없는 사용자가 딥러닝 기술에 대해 쉽게 이해하고 이용하는 것을 기대할 수 있다.

• International journal of advanced smart convergence
• /
• 제11권4호
• /
• pp.68-80
• /
• 2022

Since the launch of AI, technology development to implement complete and sophisticated AI functions has continued. In efforts to develop technologies for complete automation, Machine Learning techniques and deep learning techniques are mainly used. These techniques deal with supervised learning, unsupervised learning, and reinforcement learning as internal technical elements, and use the Big-data Analysis method again to set the cornerstone for decision-making. In addition, established decision-making is being improved through subsequent repetition and renewal of decision-making standards. In other words, big data analysis, which enables data classification and recognition/recognition, is important enough to be called a key technical element of AI function. Therefore, big data analysis itself is important and requires sophisticated analysis. In this study, among various tools that can analyze big data, we will use a Python program to find out what variables can affect addiction according to smartphone use in a decision tree environment. We the Python program checks whether data classification by decision tree shows the same performance as other tools, and sees if it can give reliability to decision-making about the addictiveness of smartphone use. Through the results of this study, it can be seen that there is no problem in performing big data analysis using any of the various statistical tools such as Python and R when analyzing big data.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권5호
• /
• pp.1466-1488
• /
• 2022

Recently, the importance and necessity of artificial intelligence (AI), especially machine learning, has been emphasized. In fact, studies are actively underway to solve complex and challenging problems through the use of AI systems, such as intelligent CCTVs, intelligent AI security systems, and AI surgical robots. Information security that involves analysis and response to security vulnerabilities of software is no exception to this and is recognized as one of the fields wherein significant results are expected when AI is applied. This is because the frequency of malware incidents is gradually increasing, and the available security technologies are limited with regard to the use of software security experts or source code analysis tools. We conducted a study on MalDC, a technique that converts malware into images using machine learning, MalDC showed good performance and was able to analyze and classify different types of malware. MalDC applies a preprocessing step to minimize the noise generated in the image conversion process and employs an image augmentation technique to reinforce the insufficient dataset, thus improving the accuracy of the malware classification. To verify the feasibility of our method, we tested the malware classification technique used by MalDC on a dataset provided by Microsoft and malware data collected by the Korea Internet & Security Agency (KISA). Consequently, an accuracy of 97% was achieved.

• International Journal of Internet, Broadcasting and Communication
• /
• 제12권4호
• /
• pp.232-237
• /
• 2020

Given the recent pace of development and expansion of Artificial Intelligence (AI) technology, the influence and ripple effects of AI technology on the whole of our lives will be very large and spread rapidly. The National Artificial Intelligence R&D Strategy, published in 2019, emphasizes the importance of artificial intelligence education for K-12 students. It also mentions STEM education, AI convergence curriculum, and budget for supporting the development of teaching materials and tools. However, it is necessary to create a new type of curriculum at a time when artificial intelligence curriculum has never existed before. With many attempts and discussions going very fast in all countries on almost the same starting line. Also, there is no suitable professor for K-12 students, and it is difficult to make K-12 students understand the concept of AI. In particular, it is difficult to teach elementary school students through professional programming in AI education. It is also difficult to learn tools that can teach AI concepts. In this paper, we propose an educational model for elementary school students to improve their understanding of AI through play or experience. This an experiential education model that combineds exploratory learning and discovery learning using multi-intelligence and the PLAY teaching-learning model to undertand the importance of data training or data required for AI education. This educational model is designed to learn how a computer that knows only binary numbers through UA recognizes images. Through code.org, students were trained to learn AI robots and configured to understand data bias like play. In addition, by learning images directly on a computer through TeachableMachine, a tool capable of supervised learning, to understand the concept of dataset, learning process, and accuracy, and proposed the process of AI inference.

• 한국컴퓨터정보학회논문지
• /
• 제29권4호
• /
• pp.31-38
• /
• 2024

본 연구는 온라인 원격교육에서 코딩 교육 활성화를 위해, 생성형 AI 기반의 학습 지원 도구개발에 필요한 하이퍼 파라미터 설정을 제안한다. 연구를 위해 세 가지 다른 학습 맥락에 따라 하이퍼 파라미터를 설정할 수 있는 실험 도구를 구현하고, 실험 도구를 통해 생성형 AI의 응답 품질을 평가하였다. 생성형 AI 자체의 기본 하이퍼 파라미터 설정을 유지한 실험은 대조군으로, 연구에서 설정한 하이퍼 파라미터를 사용한 실험은 실험군으로 하였다. 실험 결과, 첫 번째 학습맥락인 "학습 지원"에서는 실험군과 대조군 사이의 유의한 차이가 관찰되지 않았으나, 두 번째와 세 번째 학습 맥락인 "코드생성"과 "주석생성"에서는 실험군의 평가점수 평균이 대조군보다 각각 11.6% 포인트, 23% 포인트 높은 것으로 나타났다. 또한, system content에 응답이 학습 동기에 미칠 수 있는 영향을 제시하면 학습 정서를 고려한 응답이 생성되는 것이 관찰되었다.

• 한국방사선학회논문지
• /
• 제18권3호
• /
• pp.267-273
• /
• 2024

본 연구에서는 코딩없이 인공지능 학습 모델을 개발할 수 있는 클라우드 기반의 버텍스 AI 플렛폼을 이용하여 비전문가인 일반인들이 손쉽게 인공지능 학습 모델을 개발하였고 임상적 적용가능성을 확인하였다. 학습용 데이터는 캐글 사이트에 공개된 총9개 치과 질환, 2,999장 치근병 X선 영상을 사용하였고, 무작위로 학습, 검증 및 테스트 데이터 이미지를 분류하였다. 버텍스 AI의 기본 학습모델 워크플로우에서 학습 파이프라인을 사용하여 하이퍼 파라미터 조정작업을 통해 영상분류, 멀티레이블 학습을 수행하였다. Auto ML을 수행한 결과 AUC가 0.967, 정밀도는 95.6%, 재현율은 95.2%로 나타났으며, 학습된 인공지능 모델이 임상적 진단에 충분한 의미가 있음을 확인하였다.