• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.31-32
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• 2019

본 논문에서는 Microsoft Azure 기반의 운동 방법 안내 챗봇을 설계하고 구현한다. 이 챗봇은 자동 추천과 부위 선택 기능을 제공한다. 자동 추천은 본 프로그램을 처음 접하거나 편리하게 사용하고 싶은 사용자에게 권장하는 기능을 제공한다. 이 챗봇은 사용자에게 맞춤 운동법을 효과적으로 제시하기 위해 키, 몸무게, 나이, 성별 같은 사용자 정보를 입력시킨다. 그리고 운동 부위 선택 기능은 사용자가 운동하고 싶은 특정 부위를 명확하게 인식하고 있을 때 사용한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.425-426
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• 2022

본 논문에서는 사용자가 원하는 주제를 통해 전시장을 추천, 등록, 조회하는 Microsoft Bot Framework, Microsoft Azure 기반의 챗봇을 설계하고 구현한다. 이 챗봇은 사용자가 원하는 주제를 입력하면, 해당하는 주제의 전시장을 추천하게 된다. 주제는 알고리즘으로 단어를 지정한 것이 아닌, Azure Luis로 단어를 학습시켜서 비슷한 주제의 단어를 도출하는 알고리즘을 선택한다. 등록 부분은 Form 형식이 아닌 대화형으로 사용자 정보를 수집하게 된다. 사용자 정보는 Microsoft SQL Database 서버에 저장이 되고, 구현한 챗봇은 애뮬레이터 형식이 아닌 Channel 연동으로 Line 서비스로 배포한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.01a
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• pp.47-48
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• 2017

본 논문에서는 Microsoft Azure 플랫폼의 Azure PowerShell, Azure CLI(Command Line Interface), REST API를 활용하여 클라우드 기반 서비스 포털과 관리 포털을 개발함으로서 중소건설사에서 건설현장의 공사원가 관리 및 일일 관리를 위한 모듈과 서비스 제공을 위해 필요한 서비스 포털 및 관리 포털과 제품 관리 모듈 등 클라우드 서비스 구축 수행하였다.

• Journal of Engineering Education Research
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• v.22 no.1
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• pp.55-60
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• 2019

This paper deals with the process and requirements of a capstone design project performed by undergraduate students. We discuss about the preliminary study for the capstone design, the derivation of a subject, the level descriptor of the subject, and the system requirements. And then, we summarize the results of the capstone design project entitled as Microsoft Azure based IoT (Internet of Things) system which is performed by 4 undergraduate students during 10 months. This system is composed of a Zigbee sensor network, the TCP/IP Internet, an IoT server, and a smartphone application program, with which we can gather the sensor data and control actuators at the far away area.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.235-236
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• 2022

본 논문에서는 입학 정보 서비스를 제공하는 챗봇을 설계하고 구현한다. 이 챗봇은 Microsoft Azure와 네이버 LINE 채널에서 인하공업전문대학 입학 정보 안내기능을 제공한다. 사용자의 입력을 통한 입학처 챗봇의 대답으로 입학처 정보에 접근 할 수 있다. 사용자가 입력한 데이터는 데이터베이스에서 가공되어 사용자가 접근한 입학 정보를 얻어 낼 수 있어 이를 통한 전형 선호도의 추세와 사용자가 원하는 전형별 정보가 무엇인지 알 수 있으므로 입학처가 추후 나아가야 할 방향을 알 수 있다.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.3
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• pp.12-26
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• 2017

The objective of this study was to develop a runoff analysis system of the Nakdong River watershed using the GRM (Grid-based Rainfall-runoff Model), a physically-based distributed rainfall-runoff model, and to assess the system run time performance according to Microsoft Azure VM (Virtual Machine) settings. Nakdong River watershed was divided into 20 sub-watersheds, and GRM model was constructed for each subwatershed. Runoff analysis of each watershed was calculated in separated CPU process that maintained the upstream and downstream topology. MoLIT (Ministry of Land, Infrastructure and Transport) real-time radar rainfall and dam discharge data were applied to the analysis. Runoff analysis system was run in Azure environment, and simulation results were displayed through web page. Based on this study, the Nakdong River real-time runoff analysis system, which consisted of a real-time data server, calculation node (Azure), and user PC, could be developed. The system performance was more dependent on the CPU than RAM. Disk I/O and calculation bottlenecks could be resolved by distributing disk I/O and calculation processes, respectively, and simulation runtime could thereby be decreased. The study results could be referenced to construct a large watershed runoff analysis system using a distributed model with high resolution spatial and hydrological data.

• Korea Science and Art Forum
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• v.37 no.3
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• pp.125-134
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• 2019

This study was conducted to organize a program to classify and analyze the characteristics of images for the automation of background music selection in the video content production process. The results and contents of the study are as follows: video characteristics are selected as subject category, emotion, pixel motion speed, color, and character material. Subject categories and feelings were extracted using Microsoft's Azure Video Indexer, Pixel Movement Speed was an Optional flow, Color was an Image Histogram for Image, and character materials was CNN(Convolutional Neural Network). The results of this study are significant in that video analysis was conducted to match background music in the recent content production process of 'Internet One-person Broadcasting Creators'.

• Korean Journal of Artificial Intelligence
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• v.10 no.1
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• pp.15-19
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• 2022

This study is to understand the relationship between turnover and various conditions. Turnover refers to workers moving from one company to another, which exists in various ways and forms. Currently, a large number of workers are considering many turnover rates to satisfy their income levels, distance between work and residence, and age. In addition, they consider changing jobs a lot depending on the type of work, the decision-making ability of workers, and the level of education. The company needs to accept the conditions required by workers so that competent workers can work for a long time and predict what measures should be taken to convert them into long-term workers. The study was conducted because it was necessary to predict what conditions workers must meet in order to become long-term workers by comparing various conditions and turnover using regression and decision trees. It used Microsoft Azure machines to produce results, and it found that among the various conditions, it looked for different items for long-term work. Various methods were attempted in conducting the research, and among them, suitable algorithms adopted algorithms that classify various kinds of algorithms and derive results, and among them, two decision tree algorithms were used to derive results.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.89-96
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• 2023

Intrusion detection has been widely studied in both industry and academia, but cybersecurity analysts always want more accuracy and global threat analysis to secure their systems in cyberspace. Big data represent the great challenge of intrusion detection systems, making it hard to monitor and analyze this large volume of data using traditional techniques. Recently, deep learning has been emerged as a new approach which enables the use of Big Data with a low training time and high accuracy rate. In this paper, we propose an approach of an IDS based on cloud computing and the integration of big data and deep learning techniques to detect different attacks as early as possible. To demonstrate the efficacy of this system, we implement the proposed system within Microsoft Azure Cloud, as it provides both processing power and storage capabilities, using a convolutional neural network (CNN-IDS) with the distributed computing environment Apache Spark, integrated with Keras Deep Learning Library. We study the performance of the model in two categories of classification (binary and multiclass) using CSE-CIC-IDS2018 dataset. Our system showed a great performance due to the integration of deep learning technique and Apache Spark engine.

• Korean Journal of Artificial Intelligence
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• v.12 no.1
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• pp.25-29
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• 2024

In this study, research was conducted to predict the probability of cervical cancer occurrence associated with the use of hormonal contraceptives. Cervical cancer is influenced by various environmental factors; however, the human papillomavirus (HPV) is detected in 99% of cases, making it the primary attributed cause. Additionally, although cervical cancer ranks 10th in overall female cancer incidence, it is nearly 100% preventable among known cancers. Early-stage cervical cancer typically presents no symptoms but can be detected early through regular screening. Therefore, routine tests, including cytology, should be conducted annually, as early detection significantly improves the chances of successful treatment. Thus, we employed artificial intelligence technology to forecast the likelihood of developing cervical cancer. We utilized the logistic regression algorithm, a predictive model, through Microsoft Azure. The classification model yielded an accuracy of 80.8%, a precision of 80.2%, a recall rate of 99.0%, and an F1 score of 88.6%. These results indicate that the use of hormonal contraceptives is associated with an increased risk of cervical cancer. Further development of the artificial intelligence program, as studied here, holds promise for reducing mortality rates attributable to cervical cancer.