• Advances in nano research
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• v.7 no.1
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• pp.13-24
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• 2019

Well-crystalline $SnO_2$ nanoparticles of 4-5 nm size with different In contents were synthesized by hydrothermal process at relatively low temperature and characterized by transmission electron microscopy (TEM), microRaman spectroscopy and photoluminescence (PL) spectroscopy. Indium incorporation in $SnO_2$ lattice is seen to cause a lattice expansion, increasing the average size of the nanoparticles. The fundamental phonon vibration modes of $SnO_2$ lattice suffer a broadening, and surface modes associated to particle size shift gradually with the increase of In content. Incorporation of In drastically enhances the PL emission of $SnO_2$ nanoparticles associated to deep electronic defect levels. Although In incorporation reduces the band gap energy of $SnO_2$ crystallites only marginally, it affects drastically their dye degradation behaviors under UV illumination. While the UV degradation of methylene blue (MB) by undoped $SnO_2$ nanoparticles occurs through the production of intermediate byproducts such as azure A, azure B, and azure C, direct mineralization of MB takes place for In-doped $SnO_2$ nanoparticles.

• Asia pacific journal of information systems
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• v.29 no.1
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• pp.35-49
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• 2019

The paper attempts to document the application of relevant Machine Learning (ML) models on Yelp (a crowd-sourced local business review and social networking site) dataset to analyze, predict and recommend business. Strategically using two cloud platforms to minimize the effort and time required for this project. Seven machine learning algorithms in Azure ML of which four algorithms are implemented in Databricks Spark ML. The analyzed Yelp business dataset contained 70 business attributes for more than 350,000 registered business. Additionally, review tips and likes from 500,000 users have been processed for the project. A Recommendation Model is built to provide Yelp users with recommendations for business categories based on their previous business ratings, as well as the business ratings of other users. Classification Model is implemented to predict the popularity of the business as defining the popular business to have stars greater than 3 and unpopular business to have stars less than 3. Text Analysis model is developed by comparing two algorithms, uni-gram feature extraction and n-feature extraction in Azure ML studio and logistic regression model in Spark. Comparative conclusions have been made related to efficiency of Spark ML and Azure ML for these models.

• 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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.427-428
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• 2022

본 논문에서는 공연 및 행사 정보를 제공하는 챗봇을 설계하고 구현한다. 이 챗봇은 메타데이터를 이용하여 공연 및 행사 정보를 가져오고 Google map API를 활용하여 위치 정보와 최단거리 경로를 제공한다. 또한 Microsoft Azure Database에 사용자가 전화번호를 입력함으로써 데이터를 등록해서 특정 공연 및 행사에 대해 즐겨찾기를 할 수 있고 즐겨찾기 한 항목들을 조회할 수 있는 기능을 제공한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.93-94
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• 2023

본 논문에서는 Azure 플랫폼 기반의 ChatBot을 활용한 한국어 학습 챗봇 애플리케이션을 설계하고 구현한다. C# ChatBot Server를 통해 챗봇 메뉴 버튼에 대한 네비게이션을 구현하며, Python 기반의 웹 프레임워크 Django를 활용하여 단어 퀴즈에 필요한 대화 처리를 구현한다. 단어 퀴즈를 통해 언어학습에 대한 흥미를 유발하고 학습 효율을 높일 수 있도록 구현한다.

• Korean Journal of Artificial Intelligence
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• v.10 no.1
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• pp.1-7
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• 2022

In this paper, a study was conducted to predict natural disasters in Afghanistan based on machine learning. Natural disasters need to be prepared not only in Korea but also in other vulnerable countries. Every year in Afghanistan, natural disasters(snow, earthquake, drought, flood) cause property and casualties. We decided to conduct research on this phenomenon because we thought that the damage would be small if we were to prepare for it. The Azure Machine Learning Studio used in the study has the advantage of being more visible and easier to use than other Machine Learning tools. Decision Forest is a model for classifying into decision tree types. Decision forest enables intuitive analysis as a model that is easy to analyze results and presents key variables and separation criteria. Also, since it is a nonparametric model, it is free to assume (normality, independence, equal dispersion) required by the statistical model. Finally, linear/non-linear relationships can be searched considering interactions between variables. Therefore, the study used decision forest. The study found that overall accuracy was 89 percent and average accuracy was 97 percent. Although the results of the experiment showed a little high accuracy, items with low natural disaster frequency were less accurate due to lack of learning. By learning and complementing more data, overall accuracy can be improved, and damage can be reduced by predicting natural disasters.

• Korean Journal of Artificial Intelligence
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• v.9 no.1
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• pp.21-27
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• 2021

In this Paper, Since the 1990s, Korea's credit card industry has steadily developed. As a result, various problems have arisen, such as careless customer information management and loans to low-credit customers. This, in turn, had a high delinquency rate across the card industry and a negative impact on the economy. Therefore, in this paper, based on Azure, we analyze and predict the delinquency and delinquency periods of credit loans according to gender, own car, property, number of children, education level, marital status, and employment status through linear regression analysis and enhanced decision tree algorithm. These predictions can consequently reduce the likelihood of reckless credit lending and issuance of credit cards, reducing the number of bad creditors and reducing the risk of banks. In addition, after classifying and dividing the customer base based on the predicted result, it can be used as a basis for reducing the risk of credit loans by developing a credit product suitable for each customer. The predicted result through Azure showed that when predicting with Linear Regression and Boosted Decision Tree algorithm, the Boosted Decision Tree algorithm made more accurate prediction. In addition, we intend to increase the accuracy of the analysis by assigning a number to each data in the future and predicting again.

• 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.

• 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.