• Environmental Engineering Research
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• 제14권2호
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• pp.102-110
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• 2009

Application of artificial intelligence (AI) approaches in eco-environmental modeling has gradually increased for the last decade. Comprehensive understanding and evaluation on the applicability of this approach to eco-environmental modeling are needed. In this study, we reviewed the previous studies that used AI-techniques in eco-environmental modeling. Decision Tree (DT) and Artificial Neural Network (ANN) were found to be major AI algorithms preferred by researchers in ecological and environmental modeling areas. When the effect of the size of training data on model prediction accuracy was explored using the data from the previous studies, the prediction accuracy and the size of training data showed nonlinear correlation, which was best-described by hyperbolic saturation function among the tested nonlinear functions including power and logarithmic functions. The hyperbolic saturation equations were proposed to be used as a guideline for optimizing the size of training data set, which is critically important in designing the field experiments required for training AI-based eco-environmental modeling.

• Journal of information and communication convergence engineering
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• 제21권4호
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• pp.294-299
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• 2023

Micro ribonucleic acids (miRNAs) can regulate the protein expression levels of genes in the human body and have recently been reported to be closely related to the cause of disease. Determining the genes related to miRNAs will aid in understanding the mechanisms underlying complex miRNAs. However, the identification of miRNA-related genes through wet experiments (in vivo, traditional methods are time- and cost-consuming). To overcome these problems, recent studies have investigated the prediction of miRNA relevance using deep learning models. This study presents a method for predicting the relationships between miRNAs and genes. First, we reconstruct a negative dataset using the proposed method. We then extracted the feature using an autoencoder, after which the feature vector was concatenated with the original data. Thereafter, the concatenated data were used to train a long short-term memory model. Our model exhibited an area under the curve of 0.9609, outperforming previously reported models trained using the same dataset.

• 한국가시화정보학회:학술대회논문집
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• 한국가시화정보학회 2002년도 추계학술대회 논문집
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• pp.77-80
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• 2002

The recent 3D visualization such as volume rendering, iso-surface rendering or stream line visualization gives more understanding about structures or distribution of data in a space and, moreover, the real-time rendering of a scene enables the animation of time-series data. Because the meteorological data is frequently formed as multi-variables, 3-dimensional and time-series data, the spatial analysis, time-series analysis, vector display, and animation techniques can do important roles to get more understanding about data. In this research, our aim is to develop the 3-dimensional visualization techniques for meteorological data in the PC environment by using IDL. The visualization technology from :his research will be used as basic technology not only for the deeper understanding and the more exact prediction about meteorological environments but also for the scientific and spatial data visualization research in any field from which three-dimensional data comes out such as oceanography, earth science, or aeronautical engineering.

• 한국소음진동공학회논문집
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• 제24권8호
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• pp.600-605
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• 2014

Two mufflers for a large-size sedan are suggested aiming (1) sporty-sound and (2) quiet-sound as well as both satisfying low back-pressure and low manufacturing cost. Transmission loss prediction considering heat and flow may increase the accuracy and reduce the development cost in muffler design; thus, GT-power prediction considering heat, flow, and acoustics is utilized. By understanding the fundamentals of flow-acoustic theory in small orifice(hole), an effective muffler design concept is proposed. Vehicle tests show the consistence with predictions for sound; also a back-pressure test bench confirms the advantage in pressure drop for both suggested mufflers. Those suggested mufflers also have advantages in manufacturing cost due to simplicity of the design.

• 한국지형학회지
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• 제23권3호
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• pp.67-78
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• 2016

Soil carbon(C) is an essential property for characterizing soil quality. Understanding spatial patterns of soil C is particularly limited for mountain areas. This study aims to predict the spatial pattern of soil C using terrain analysis in a steep mountainous area. Specifically, model performances and prediction uncertainties were investigated based on the number of resampling repetitions. Further, important predictors for soil C were also identified. Finally, the spatial distribution of uncertainty was analyzed. A total of 91 soil samples were collected via conditioned latin hypercube sampling and a digital soil C map was developed using support vector regression which is one of the powerful machine learning methods. Results showed that there were no distinct differences of model performances depending on the number of repetitions except for 10-fold cross validation. For soil C, elevation and surface curvature were selected as important predictors by recursive feature elimination. Soil C showed higher values in higher elevation and concave slopes. The spatial pattern of soil C might possibly reflect lateral movement of water and materials along the surface configuration of the study area. The higher values of uncertainty in higher elevation and concave slopes might be related to geomorphological characteristics of the research area and the sampling design. This study is believed to provide a better understanding of the relationship between geomorphology and soil C in the mountainous ecosystem.

• 한국정보시스템학회지:정보시스템연구
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• 제21권3호
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• pp.137-161
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• 2012

The increasing interest in big data analysis using various data mining techniques indicates that many commercial data mining tools now need to be equipped with fundamental text analysis modules. The most essential prerequisite for accurate analysis of text documents is an understanding of the exact semantics of each term in a document. The main difficulties in understanding the exact semantics of terms are mainly attributable to homonym and synonym problems, which is a traditional problem in the natural language processing field. Some major text mining tools provide a thesaurus to solve these problems, but a thesaurus cannot be used to resolve complex synonym problems. Furthermore, the use of a thesaurus is irrelevant to the issue of homonym problems and hence cannot solve them. In this paper, we propose a semantic text mining methodology that uses ontologies to improve the quality of text mining results by resolving the semantic ambiguity caused by homonym and synonym problems. We evaluate the practical applicability of the proposed methodology by performing a classification analysis to predict customer churn using real transactional data and Q&A articles from the "S" online shopping mall in Korea. The experiments revealed that the prediction model produced by our proposed semantic text mining method outperformed the model produced by traditional text mining in terms of prediction accuracy such as the response, captured response, and lift.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2007년도 가을 학술발표논문집 Vol.34 No.2 (B)
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• pp.22-27
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• 2007

Phosphorylation is one of the most important post translational modifications which regulate the activity of proteins. The problem of predicting phosphorylation sites is the first step of understanding various biological processes that initiate the actual function of proteins in each signaling pathway. Although many prediction methods using single or multiple features extracted from protein sequences have been proposed, systematic data integration approach has not been applied in order to improve the accuracy of predicting general phosphorylation sites. In this paper, we propose an optimal way of integrating multiple features in the framework of multiple kernel learning. We optimally combine seven kernels extracted from sequence, physico-chemical properties, pairwise alignment, and structural information. Using the data set of Phospho. ELM, the accuracy evaluated by 5-fold cross-validation reaches 85% for serine, 85% for threonine, and 81% for tyrosine. Our computational experiments show significant improvement in the performance of prediction relative to a single feature, or to the combined feature with equal weights. Moreover, our systematic integration method significantly improves the prediction preformance compared with the previous well-known methods.

• International Journal of Advanced Culture Technology
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• 제10권1호
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• pp.274-283
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• 2022

Korea is facing a number difficulties arising from rising housing prices. As 'housing' takes the lion's share in personal assets, many difficulties are expected to arise from fluctuating housing prices. The purpose of this study is creating housing price prediction model to prevent such risks and induce reasonable real estate purchases. This study made many attempts for understanding real estate instability and creating appropriate housing price prediction model. This study predicted and validated housing prices by using the LSTM technique - a type of Artificial Intelligence deep learning technology. LSTM is a network in which cell state and hidden state are recursively calculated in a structure which added cell state, which is conveyor belt role, to the existing RNN's hidden state. The real sale prices of apartments in autonomous districts ranging from January 2006 to December 2019 were collected through the Ministry of Land, Infrastructure, and Transport's real sale price open system and basic apartment and commercial district information were collected through the Public Data Portal and the Seoul Metropolitan City Data. The collected real sale price data were scaled based on monthly average sale price and a total of 168 data were organized by preprocessing respective data based on address. In order to predict prices, the LSTM implementation process was conducted by setting training period as 29 months (April 2015 to August 2017), validation period as 13 months (September 2017 to September 2018), and test period as 13 months (December 2018 to December 2019) according to time series data set. As a result of this study for predicting 'prices', there have been the following results. Firstly, this study obtained 76 percent of prediction similarity. We tried to design a prediction model of real estate transaction price with the LSTM Model based on AI and Bigdata. The final prediction model was created by collecting time series data, which identified the fact that 76 percent model can be made. This validated that predicting rate of return through the LSTM method can gain reliability.

• 한국콘텐츠학회논문지
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• 제9권5호
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• pp.332-336
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• 2009

유전체 분석에서 중요한 부분 중 하나는 기능이 알려지지 않은 미지 단백질에 대한 기능 예측이다. 단백질-단백질 상호작용 네트워크를 분석하는 것은 미지 단백질에 대한 기능을 보다 쉽게 예측할 수 있게 한다. 단백질-단백질 상호작용 네트워크로부터 미지 단백질의 기능을 예측하기 위한 다양한 연구들이 시도되어 왔다. 카이-제곱(Chi-square) 방식은 단백질-단백질 상호작용 네트워크를 통해 기능을 예측하고자 하는 연구 중 대표적인 방식이다. 하지만 카이-제곱 방식은 네트워크의 토폴로지를 반영하지 않아 네트워크 크기에 따라 예측의 정확성이 떨어지는 문제점이 있다. 따라서 본 논문에서는 카이-제곱 방식을 보완하여 정확성을 높인 새로운 기능 예측 방법을 제안한다 이를 위해 MIPS, DIP 그리고 SGD와 같은 공개된 단백질 상호작용 데이터베이스들로부터 데이터를 수집하여 분석하였다. 그리고 제안된 방식의 우수성을 입증하기 위해 각 데이터베이스들에 대해 카이-제곱방식과 제안하는 보완된 카이-제곱(Modified Chi-square)방식으로 예측해보고 이들의 정확성을 평가하였다.

• Journal of Information Processing Systems
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• 제16권5호
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• pp.1196-1213
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• 2020

There is a growing interest in the development of smart environments through predicting the behaviors of inhabitants of smart spaces in the recent past. Various smart services are deployed in modern smart cities to facilitate residents and city administration. Prediction algorithms are broadly used in the smart fields in order to well equip the smart services for the future demands. Hence, an accurate prediction technology plays a vital role in the smart services. In this paper, we take out an extensive survey of smart spaces such as smart homes, smart farms and smart cars and smart applications such as smart health and smart energy. Our extensive survey is based on more than 400 articles and the final list of research studies included in this survey consist of 134 research papers selected using Google Scholar database for period of 2008 to 2018. In this survey, we highlight the role of prediction algorithms in each sub-domain of smart Internet of Things (IoT) environments. We also discuss the main algorithms which play pivotal role in a particular IoT subfield and effectiveness of these algorithms. The conducted survey provides an efficient way to analyze and have a quick understanding of state of the art work in the targeted domain. To the best of our knowledge, this is the very first survey paper on main categories of prediction algorithms covering statistical, heuristic and hybrid approaches for smart environments.