• The Korean Journal of Applied Statistics
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• v.32 no.3
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• pp.391-404
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• 2019

Customers' fixed characteristics have often been used to predict customer behavior. It has recently become possible to track customer web logs as customer activities move from offline to online. It has become possible to collect large amounts of web log data; however, the researchers only focused on organizing the log data or describing the technical characteristics. In this study, we predict the decision-making time until each customer makes the first reservation, using Airbnb customer data provided by the Kaggle website. This data set includes basic customer information such as gender, age, and web logs. We use various methodologies to find the optimal model and compare prediction errors for cases with web log data and without it. We consider six models such as Lasso, SVM, Random Forest, and XGBoost to explore the effectiveness of the web log data. As a result, we choose Random Forest as our optimal model with a misclassification rate of about 20%. In addition, we confirm that using web log data in our study doubles the prediction accuracy in predicting customer behavior compared to not using it.

• Journal of IKEEE
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• v.23 no.1
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• pp.127-133
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• 2019

Electric power demand forecasting is one of the important research areas for future smart grid introduction. However, It is difficult to predict because it is affected by many external factors. Traditional methods of forecasting power demand have been limited in making accurate prediction because they use raw power data. In this paper, a probability-based CRBM is proposed to solve the problem of electric power demand prediction using raw power data. The stochastic model is suitable to capture the probabilistic characteristics of electric power data. In order to compare the mid-term power demand forecasting performance of the proposed model, we compared the performance with Recurrent Neural Network(RNN). Performance comparison using electric power data provided by the University of Massachusetts showed that the proposed algorithm results in better performance in mid-term energy demand forecasting.

• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.67-76
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• 2019

In this paper, we propose an efficient object classification method based on semantic segmentation for multi-labeled image data. In addition to various pixel unit information and processing techniques such as color information, contour, contrast, and saturation included in image data, a detailed region in which each object is located is extracted as a meaningful unit and the experiment is conducted to reflect the result in the inference. We use a neural network that has been proven to perform well in image classification to understand which object is located where image data containing various class objects are located. Based on these researches, we aim to provide artificial intelligence services that can classify real-time detailed areas of complex images containing various objects in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.8
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• pp.993-998
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• 2020

The technology of interiors is currently developing around the world. According to various studies, the use of plants to create an environment in the home interior is increasing. However, households using furniture are designed as environment-friendly environment interiors, and in Korea and abroad, plants are used for home interiors. Unexpected accidents are occurring. As a result, there were books and broadcasts about the dangers of specific plants, but until now, accidents continue to occur because they do not properly recognize the dangers of specific plants. Therefore, in this paper, we propose a toxic plant identification system based on a multiplicative neural network model that identifies common toxic plants commonly found in Korea. We propose a high efficiency model. Through this, toxic plants can be identified with higher accuracy and safety accidents caused by toxic plants.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1574-1580
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• 2020

Traditional pose estimation methods include using special devices or images through image processing. The disadvantage of using a device is that the environment in which the device can be used is limited and costly. The use of cameras and image processing has the advantage of reducing environmental constraints and costs, but the performance is lower. CNN(Convolutional Neural Networks) were studied for pose estimation just using only camera without these disadvantage. Various techniques were proposed to increase cognitive performance. In this paper, the effect of the skip connection on the network was experimented by using various skip connections on the joint recognition of the hand. Experiments have confirmed that the presence of additional skip connections other than the basic skip connections has a better effect on performance, but the network with downward skip connections is the best performance.

• Annual Conference of KIPS
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• 2013.05a
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• pp.59-60
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• 2013

Infobutton is an important concept from long time in use and much has been done with respect to its standardization and context supplementation. The concept is to create contextual links to information resources from within the information systems usually health information systems. The need which has been realized by the authors of this paper is the augmentation of Infobuttons from the level of only information links to the level of knowledge links. The authors proposed the concept of knowledge links named as "Knowledgebuttons" which complements the concept Infobuttons. It adds further capabilities of getting knowledge to the users instead of just connectivity to information resources. The better representation of the information retrieved with Infobuttons is the first and foundation step to achieve the goal of getting knowledge. This paper discusses about the concept and applicability of Knowledgebuttons in health information systems. It is envisioned that this concept will add to the overall quality of patient care. Both physicians and patients can benefit from this technique as per their needs. Physicians can help in patient diagnosis and treatment critical decisions while patients can educate them to know more about their health conditions by studying the right knowledge at right time. Knowledgebuttons are able to create a true learning environment for the users while using health information systems.

• The Journal of Korean Institute of Information Technology
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• v.17 no.12
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• pp.21-28
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• 2019

Side effects of drugs mean harmful and unintended effects resulting from drugs used to prevent, diagnose, or treat diseases. These side effects can lead to patients' death and are the main causes of drug developmental failures. Thus, various methods have been tried to identify side effects. These can be divided into biological and systems biology approaches. In this study, we use systems biology approach and focus on using various phenotypic information in addition to the chemical structure and target proteins. First, we collect datasets that are used in this study, and calculate similarities individually. Second, we generate a set of features using the similarities for each drug-side effect pair. Finally, we confirm the results by AUC(Area Under the ROC Curve), and showed the significance of this study through a comparison experiment.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1053-1065
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• 2020

With the recent development of hardware performance and artificial intelligence technology, sophisticated fake videos that are difficult to distinguish with the human's eye are increasing. Face synthesis technology using artificial intelligence is called Deepfake, and anyone with a little programming skill and deep learning knowledge can produce sophisticated fake videos using Deepfake. A number of indiscriminate fake videos has been increased significantly, which may lead to problems such as privacy violations, fake news and fraud. Therefore, it is necessary to detect fake video clips that cannot be discriminated by a human eyes. Thus, in this paper, we propose a deep-fake detection model applied with Bidirectional Convolution LSTM and Attention Module. Unlike LSTM, which considers only the forward sequential procedure, the model proposed in this paper uses the reverse order procedure. The Attention Module is used with a Convolutional neural network model to use the characteristics of each frame for extraction. Experiments have shown that the model proposed has 93.5% accuracy and AUC is up to 50% higher than the results of pre-existing studies.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1485-1492
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• 2020

It has been more than 20 years since the launch of KOMPSAT-1, and so far, a total of 5 satellites have been successfully launched. Until now, KOMPSAT has been used in various fields, including the production of various thematic maps, land change, environmental analysis, and marine monitoring. Many researchers have conducted research to process, analyze, and utilize KOMPSAT images. According to the national space development plan, the KOMPSAT series will be continuously developed to meet the demand for satellite images at the national level. If the ultimate purpose of satellite development is to utilize acquired images, systematic research to effectively utilize the developed satellites should be followed. This special issue introduces the recently conducted research on the use of KOMPSAT images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.24-29
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• 2022

Semantic role labeling task used in various natural language processing fields, such as information extraction and question answering systems, is the task of identifying the arugments for a given sentence and predicate. Predicate used as semantic role labeling input are extracted using lexical analysis results such as POS-tagging, but the problem is that predicate can't extract all linguistic patterns because predicate in korean language has various patterns, depending on the meaning of sentence. In this paper, we propose a korean predicate recognition method using neural network model with pre-trained embedding models and lexical features. The experiments compare the performance on the hyper parameters of models and with or without the use of embedding models and lexical features. As a result, we confirm that the performance of the proposed neural network model was 92.63%.