• Journal of Convergence for Information Technology
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• v.10 no.8
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• pp.15-22
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• 2020

The 4th industrial revolution and the rapid change in the data environment revealed technical limitations in the existing relational database(RDB). As a new analysis method for unstructured data in all fields such as IDC/finance/insurance, interest in graph database(GDB) technology is increasing. The graph database is an efficient technique for expressing interlocked data and analyzing associations in a wide range of networks. This study extended the existing RDB to the GDB model and applied machine learning algorithms (pattern recognition, clustering, path distance, core extraction) to detect new abnormal signs. As a result of the performance analysis, it was confirmed that the performance of abnormal behavior(about 180 times or more) was greatly improved, and that it was possible to extract an abnormal symptom pattern after 5 steps that could not be analyzed by RDB.

• The Journal of Bigdata
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• v.3 no.1
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• pp.55-62
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• 2018

In this study, we have developed a model for predicting the number of patients (flu, pneumonia, and outbreak) using Big Data, which has been mainly performed overseas. Existing patient number system by government adopt procedures that collects the actual number and percentage of patients from several big hospital. However, prediction model in this study was developed combing a real-time collection of disease-related words and various other climate data provided in real time. Also, prediction number of patients were counted by machine learning algorithm method. The advantage of this model is that if the epidemic spreads rapidly, the propagation rate can be grasped in real time. Also, we used a variety types of data to complement the failures in Google Flu Trends.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.4
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• pp.717-724
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• 2020

Due to the important role played by emotion in human interaction, affective computing is dedicated in trying to understand and regulate emotion through human-aware artificial intelligence. By understanding, emotion mental diseases such as depression, autism, attention deficit hyperactivity disorder, and game addiction will be better managed as they are all associated with emotion. Various studies for emotion recognition have been conducted to solve these problems. In applying machine learning for the emotion recognition, the efforts to reduce the complexity of the algorithm and improve the accuracy are required. In this paper, we investigate emotion Electroencephalogram (EEG) feature reduction and classification using Stack AutoEncoder (SAE) and Long-Short-Term-Memory/Recurrent Neural Networks (LSTM/RNN) classification respectively. The proposed method reduced the complexity of the model and significantly enhance the performance of the classifiers.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.10
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• pp.503-510
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• 2016

FsGr model, which has been proposed recently, is an approach of accelerometer-based gesture recognition by applying DTW algorithm in two steps, which improved recognition success rate. In FsGr model, sets of similar gestures will be produced through training phase, in order to define the notion of a set of similar gestures. At the 1st attempt of gesture recognition, if the result turns out to belong to a set of similar gestures, it makes the 2nd recognition attempt to feature-strengthened parts extracted from the set of similar gestures. However, since a same gesture show drastically different characteristics according to physical traits such as body size, age, and sex, FsGr model may not be good enough to apply to multi-user environments. In this paper, we propose FsGrM model that extends FsGr model for multi-user environment and present a program which controls channel and volume of smart TV using FsGrM model.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10a
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• pp.28-32
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• 2006

최근 연구결과에 의하면 심장질환을 포함한 심혈관질환은 성별에 관계없이 미국 및 전 세계적으로 질병사망의 주요 원인으로 조사되었다. 본 연구에서는 보다 효율적으로 진단하기 위해 진단의사 결정 보조시스템에 대해서 다룬다. 개발된 시스템은 혈청 내의 특정 단백질의 상대적 양을 측정할 수 있는 바이오칩인 압타머칩을 이용해 생성한 환자들의 칩 데이터를 Support Vector Machine, Neural Network, Decision Tree, Bayesian Network 등의 총 4가지 기계학습 알고리즘으로 분석하여 질환단계를 예측하고 진단을 위한 보조정보를 제공한다. 논문에서는 총 135개 샘플로 구성된 3K 압타머칩 데이터에 대해 측정된 초기 시스템의 질환단계 분류성능을 제시하고 보다 유용한 진단의사결정 보조 시스템을 구성하기 위한 요소들에 대해서 논의한다.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.859-866
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• 2021

Agricultural Photovoltaic power generation is a new model that installs solar power generation facilities on top of farmland. Through this, it is possible to increase farm household income by producing crops and electricity at the same time. Recently, various attempts have been made to utilize agricultural solar power generation. Agricultural photovoltaic power generation has a disadvantage in that maintenance is relatively difficult because it is installed on a relatively high structure unlike conventional photovoltaic power generation. To solve these problems, intelligent and efficient operation and diagnostic functions are required. In this paper, we discuss the design and implementation of a prediction and diagnosis system to collect and store the power output of agricultural solar power generation facilities and implement an intelligent prediction model. The proposed system predicts the amount of power generation based on the amount of solar power generation and environmental sensor data, determines whether there is an abnormality in the facility, calculates the aging degree of the facility and provides it to the user.

• The Journal of Bigdata
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• v.6 no.1
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• pp.133-144
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• 2021

This paper derives the fire risk of buildings in Seoul through the prediction of property damage and the occurrence of fires. This study differs from prior research in that it utilizes variables that include not only a building's characteristics but also its affiliated administrative area as well as the accessibility of nearby fire-fighting facilities. We use Ensemble Voting techniques to merge different machine learning algorithms to predict property damage and fire occurrence, and to extract feature importance to produce fire risk. Fire risk prediction was made on 300 buildings in Seoul utilizing the established model, and it has been derived that with buildings at Level 1 for fire risks, there were a high number of households occupying the building, and the buildings had many factors that could contribute to increasing the size of the fire, including the lack of nearby fire-fighting facilities as well as the far location of the 119 Safety Center. On the other hand, in the case of Level 5 buildings, the number of buildings and businesses is large, but the 119 Safety Center in charge are located closest to the building, which can properly respond to fire.

• Journal of Broadcast Engineering
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• v.23 no.6
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• pp.886-895
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• 2018

In this paper, we introduce a technique to detect the video forgery by using the regularity that occurs in the video compression process. The proposed method uses the hierarchical regularity lost by the video double compression and the frame deletion. In order to extract such irregularities, the depth information of CU and TU, which are basic units of HEVC, is used. For improving performance, we make a depth map of CU and TU using local information, and then create input data by grouping them in GoP units. We made a decision whether or not the video is double-compressed and forged by using a general three-dimensional convolutional neural network. Experimental results show that it is more effective to detect whether or not the video is forged compared with the results using the existing machine learning algorithm.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.265-276
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• 2019

The document-term frequency matrix is a general data of objects in text mining. In this study, we introduce a traditional term weighting scheme TF-IDF (term frequency-inverse document frequency) which is applied in the document-term frequency matrix and used for text classifications. In addition, we introduce and compare TF-IDF-ICSDF and TF-IGM schemes which are well known recently. This study also provides a method to extract keyword enhancing the quality of text classifications. Based on the keywords extracted, we applied support vector machine for the text classification. In this study, to compare the performance term weighting schemes, we used some performance metrics such as precision, recall, and F1-score. Therefore, we know that TF-IGM scheme provided high performance metrics and was optimal for text classification.

• The Journal of Bigdata
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• v.3 no.2
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• pp.35-49
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• 2018

It seems unrealistic to say that fried chicken, often known as the American soul food, has one of the biggest markets in South Korea. Yet, South Korea owns more numbers of fried chicken restaurants than those of McDonald's franchise globally[4]. Needless to say not all these fast-food commerce survive in such small country. In this study, we propose a predictive model that could potentially help one's decision whilst deciding to open a store. We've extracted all fried chicken restaurants registered at the Korean Ministry of the Interior and Safety, then collected a number of features that seem relevant to a store's closure. After comparing the results of different algorithms, we conclude that in order to best predict a store's survival is FDA(Flexible Discriminant Analysis). While Neural Network showed the highest prediction rate, FDA showed better balanced performance considering sensitivity and specificity.