• The Korean Journal of Nuclear Medicine
• /
• v.35 no.2
• /
• pp.89-99
• /
• 2001

Purpose: In this study, we developed a new software tool for the analysis of renal scintigraphy which can be modified more easily by a user who needs to study new clinical applications, and the appropriateness of the results from our program was studied. Materials and Methods: The analysis tool was programmed with IDL5.2 and designed for use on a personal computer running Windows. For testing the developed tool and studying the appropriateness of the calculated glomerular filtration rate (GFR), $^{99m}Tc$-DTPA was administered to 10 adults in normal condition. In order to study the appropriateness of the calculated mean transit time (MTT), $^{99m}Tc-DTPA\;and\;^{99m}Tc-MAG3$ were administered to 11 adults in normal condition and 22 kidneys were analyzed. All the images were acquired with ORBITOR. the Siemens gamma camera. Results: With the developed tool, we could show dynamic renal images and time activity curve (TAC) in each ROI and calculate clinical parameters of renal function. The results calculated by the developed tool were not different statistically from the results obtained by the Siemens application program (Tmax: p=0.68, Relative Renal Function: p:1.0, GFR: p=0.25) and the developed program proved reasonable. The MTT calculation tool proved to be reasonable by the evaluation of the influence of hydration status on MTT. Conclusion: We have obtained reasonable clinical parameters for the evaluation of renal function with the software tool developed in this study. The developed tool could prove more practical than conventional, commercial programs.

• Journal of Intelligence and Information Systems
• /
• v.23 no.3
• /
• pp.119-138
• /
• 2017

Recently, SNS has become an important channel for marketing as well as personal communication. However, cybercrime has also evolved with the development of information and communication technology, and illegal advertising is distributed to SNS in large quantity. As a result, personal information is lost and even monetary damages occur more frequently. In this study, we propose a method to analyze which sentences and documents, which have been sent to the SNS, are related to financial fraud. First of all, as a conceptual framework, we developed a matrix of conceptual characteristics of cybercriminality on SNS and emergency management. We also suggested emergency management process which consists of Pre-Cybercriminality (e.g. risk identification) and Post-Cybercriminality steps. Among those we focused on risk identification in this paper. The main process consists of data collection, preprocessing and analysis. First, we selected two words 'daechul(loan)' and 'sachae(private loan)' as seed words and collected data with this word from SNS such as twitter. The collected data are given to the two researchers to decide whether they are related to the cybercriminality, particularly financial fraud, or not. Then we selected some of them as keywords if the vocabularies are related to the nominals and symbols. With the selected keywords, we searched and collected data from web materials such as twitter, news, blog, and more than 820,000 articles collected. The collected articles were refined through preprocessing and made into learning data. The preprocessing process is divided into performing morphological analysis step, removing stop words step, and selecting valid part-of-speech step. In the morphological analysis step, a complex sentence is transformed into some morpheme units to enable mechanical analysis. In the removing stop words step, non-lexical elements such as numbers, punctuation marks, and double spaces are removed from the text. In the step of selecting valid part-of-speech, only two kinds of nouns and symbols are considered. Since nouns could refer to things, the intent of message is expressed better than the other part-of-speech. Moreover, the more illegal the text is, the more frequently symbols are used. The selected data is given 'legal' or 'illegal'. To make the selected data as learning data through the preprocessing process, it is necessary to classify whether each data is legitimate or not. The processed data is then converted into Corpus type and Document-Term Matrix. Finally, the two types of 'legal' and 'illegal' files were mixed and randomly divided into learning data set and test data set. In this study, we set the learning data as 70% and the test data as 30%. SVM was used as the discrimination algorithm. Since SVM requires gamma and cost values as the main parameters, we set gamma as 0.5 and cost as 10, based on the optimal value function. The cost is set higher than general cases. To show the feasibility of the idea proposed in this paper, we compared the proposed method with MLE (Maximum Likelihood Estimation), Term Frequency, and Collective Intelligence method. Overall accuracy and was used as the metric. As a result, the overall accuracy of the proposed method was 92.41% of illegal loan advertisement and 77.75% of illegal visit sales, which is apparently superior to that of the Term Frequency, MLE, etc. Hence, the result suggests that the proposed method is valid and usable practically. In this paper, we propose a framework for crisis management caused by abnormalities of unstructured data sources such as SNS. We hope this study will contribute to the academia by identifying what to consider when applying the SVM-like discrimination algorithm to text analysis. Moreover, the study will also contribute to the practitioners in the field of brand management and opinion mining.

• Journal of the Korean Institute of Traditional Landscape Architecture
• /
• v.36 no.3
• /
• pp.100-114
• /
• 2018

Nationally designated Cultural Properties 'Scenic site No.12 Maisan Mountain, Jinan' designated areas and some protected areas, and taking into account the dynamics of naturalized plants causing problems, we will restore the original vegetation scenery of Mt. Maisan. The results of this study are as follows. A total of 76 families, 192 genera, 286 taxa, and inland and inhabited areas, 76 and 138 genera and 163 taxa were identified in the areas of Ammaibong. The total number of naturalized plants identified in this study area is 28 taxa total, which corresponds to 7.1% naturalization rate(NR) among the vascular plants of all 395 taxa, and the urbanization index(UI) corresponds to 8.4% of the 333 taxa of Korean naturalized plants. Ecosystem disturbance plants identified in the survey area were Ambrosia artemisiifolia 1 taxa. The naturalized plants controlled and managed by separate anthropogenic vegetation management within the designation and protection area of Maisan scenic place are three species of herbaceous Rumex acetosella, A. artemisiifolia and Festuca arundinacea. It was identified as a breed. Indigofera bungeana and F. arundinacea communities around the stairway and Amorpha fruticosa, I. bungeana, A. artemisiifolia and Amaranthus patulus of the top of Am-Maibong were selected as the first priority sites for exclusion of exotic species in Maisan area and target naturalized plants species to the Ammaibong peak. In addition, R. acetosella community around the temple was suggested to be removed first to preserve endemic species. For the restoration of vegetation, we suggest that Stephanandra incisa, Spiraea blumei, Weigela subsessilis, etc. should be planted after removal of I. bungeana, and F. arundinacea, C. lanceolata, Carex callitrichos var. nana.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.55 no.2
• /
• pp.177-186
• /
• 2010

This study was carried out to compare the physicochemical characteristics and preference as a sensory quality of Sogokju (Korean traditional rice wine) from waxy rice varieties. The protein and moisture contents of milled waxy rice varieties were ranged 6.9~7.2% and 12.1~ 12.6%, respectively. Nunbora had the largest grain size. In pasting properties, Hangangchalbyeo had the highest peak, trough and final viscosities, and Dongjinchalbyeo had the lowest viscosity curve. These differences suppose to be caused by the amylopectin(AP) structure: Dongjinchalbyeo has the largest short AP chains (degree of polymerization (DP) 6-12) and the smallest middle AP chains (DP 13-24) in 9 waxy rice varieties, while Hangangchalbyeo has the smallest short AP chains and the largest middle AP chains. The alcohol contents of Sogokju brewed from 9 waxy rice varieties were 17.6～19.9%. The brix degree were ranged $20.5{\sim}23.9^{\circ}Bx$. The organic acid of Sogokju consisted mainly of succinic acid, and the free sugar of it consisted mostly of glucose. The sensory evaluation showed the highest palatability at the Sogokju from Baegseolchalbyeo. The palatability was positively correlated with the brix degree, the glucose content, and the turbidity, and negatively correlated with the production yield of Sogokju.

• Journal of Intelligence and Information Systems
• /
• v.21 no.3
• /
• pp.79-99
• /
• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Journal of Intelligence and Information Systems
• /
• v.25 no.4
• /
• pp.105-122
• /
• 2019

Dimensionality reduction is one of the methods to handle big data in text mining. For dimensionality reduction, we should consider the density of data, which has a significant influence on the performance of sentence classification. It requires lots of computations for data of higher dimensions. Eventually, it can cause lots of computational cost and overfitting in the model. Thus, the dimension reduction process is necessary to improve the performance of the model. Diverse methods have been proposed from only lessening the noise of data like misspelling or informal text to including semantic and syntactic information. On top of it, the expression and selection of the text features have impacts on the performance of the classifier for sentence classification, which is one of the fields of Natural Language Processing. The common goal of dimension reduction is to find latent space that is representative of raw data from observation space. Existing methods utilize various algorithms for dimensionality reduction, such as feature extraction and feature selection. In addition to these algorithms, word embeddings, learning low-dimensional vector space representations of words, that can capture semantic and syntactic information from data are also utilized. For improving performance, recent studies have suggested methods that the word dictionary is modified according to the positive and negative score of pre-defined words. The basic idea of this study is that similar words have similar vector representations. Once the feature selection algorithm selects the words that are not important, we thought the words that are similar to the selected words also have no impacts on sentence classification. This study proposes two ways to achieve more accurate classification that conduct selective word elimination under specific regulations and construct word embedding based on Word2Vec embedding. To select words having low importance from the text, we use information gain algorithm to measure the importance and cosine similarity to search for similar words. First, we eliminate words that have comparatively low information gain values from the raw text and form word embedding. Second, we select words additionally that are similar to the words that have a low level of information gain values and make word embedding. In the end, these filtered text and word embedding apply to the deep learning models; Convolutional Neural Network and Attention-Based Bidirectional LSTM. This study uses customer reviews on Kindle in Amazon.com, IMDB, and Yelp as datasets, and classify each data using the deep learning models. The reviews got more than five helpful votes, and the ratio of helpful votes was over 70% classified as helpful reviews. Also, Yelp only shows the number of helpful votes. We extracted 100,000 reviews which got more than five helpful votes using a random sampling method among 750,000 reviews. The minimal preprocessing was executed to each dataset, such as removing numbers and special characters from text data. To evaluate the proposed methods, we compared the performances of Word2Vec and GloVe word embeddings, which used all the words. We showed that one of the proposed methods is better than the embeddings with all the words. By removing unimportant words, we can get better performance. However, if we removed too many words, it showed that the performance was lowered. For future research, it is required to consider diverse ways of preprocessing and the in-depth analysis for the co-occurrence of words to measure similarity values among words. Also, we only applied the proposed method with Word2Vec. Other embedding methods such as GloVe, fastText, ELMo can be applied with the proposed methods, and it is possible to identify the possible combinations between word embedding methods and elimination methods.

• Korean Journal of Food Science and Technology
• /
• v.45 no.1
• /
• pp.20-24
• /
• 2013

Food irradiation has recently become one of the most successful techniques to preserve food with increased shelf life. This study aims to analyze hydrocarbons in almonds (Prunus amygosalus L.) and peanuts (Arachis hypogaea) induced by electron beam irradiation. The samples were irradiated at 0, 1, 3, 5 and 10 kGy by e-beam and using florisil column chromatography fat, and content was extracted. The induced hydrocarbons were identified using gas chromatography-mass spectrometry (GC/MS). The major hydrocarbons in both irradiated samples were 1,7-hexadecadiene ($C_{16:2}$) and 8-heptadecene ($C_{17:1}$) from oleic acid, 1,7,10-hexadecatriene ($C_{16:3}$) and 6,9-heptadecadiene ($C_{17:2}$) from linoleic acid and 1-tetradecene ($C_{14:1}$) and pentadecane ($C_{15:0}$) from palmitic acid. Concentrations of the hydrocarbons produced by e-beam were found to be depended upon the composition of fatty acid in both almonds and peanuts. The $C_{n-2}$ compound was found to be higher than $C_{n-1}$ compound in oleic acid and palmitic acid, while in case of linoleic acid, $C_{n-1}$compound was higher than $C_{n-2}$ compound. The radiation induced hydrocarbons were detected only in irradiated samples, with 1 kGy or above, and not in the non-irradiated ones. The production of 1,7-hexadecadiene ($C_{16:2}$), 8-heptadecene ($C_{17:1}$), 1,7,10-hexadecatriene ($C_{16:3}$) and 6,9-heptadecadiene ($C_{17:2}$), in high concentration gave enough information to suggest that these may be the possible marker compounds of electron beam irradiation in almonds and peanuts.

• Horticultural Science & Technology
• /
• v.33 no.1
• /
• pp.70-82
• /
• 2015

This study was conducted to establish an efficient screening system to identify melon resistant to Fusarium oxysporum f. sp. melonis. F. oyxsporum f. sp. melonis GR was isolated from infected melon plants collected at Goryeong and identified as F. oxysporum f. sp. melonis based on morphological characteristics, molecular analyses, and host-specificity tests on cucurbits including melon, oriental melon, cucumber, and watermelon. In addition, the GR isolate was determined as race 1 based on resistance responses of melon differentials to the fungus. To select optimized medium for mass production of inoculum of F. oxysporum f. sp. melonis GR, six media were tested. The fungus produced the most spores (microconidia) in V8-juice broth. Resistance degrees to the GR isolate of 22 commercial melon cultivars and 6 rootstocks for melon plants were investigated. All tested rootstocks showed no symptoms of Fusarium wilt. Among the tested melon cultivars, only three cultivars were susceptible and the other cultivars displayed moderate to high resistance to the GR isolate. For further study, six melon cultivars (Redqueen, Summercool, Superseji, Asiapapaya, Eolukpapaya, and Asiahwanggeum) showing different degrees of resistance to the fungus were selected. The development of Fusarium wilt on the cultivars was tested according to several conditions such as plant growth stage, root wounding, dipping period of roots in spore suspension, inoculum concentration, and incubation temperature to develop the disease. On the basis of the test results, we suggest that an efficient screening method for melon plants resistant to F. oxysporum f. sp. melonis is to remove soil from roots of seven-day-old melon seedlings, to dip the seedlings without cutting in s pore s uspension of $3{\times}10^5conidia/mL$ for 30 min, to transplant the inoculated seedlings to plastic pots with horticulture nursery media, and then to cultivate the plants in a growth room at 25 to $28^{\circ}C$ for about 3 weeks with 12-hour light per day.

• Journal of Intelligence and Information Systems
• /
• v.22 no.2
• /
• pp.97-113
• /
• 2016

Due to the growth of internet data and the rapid development of internet technology, "big data" analysis is actively conducted to analyze enormous data for various purposes. Especially in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of existing structured data analysis. Various studies on sentiment analysis, the part of text mining techniques, are actively studied to score opinions based on the distribution of polarity of words in documents. Usually, the sentiment analysis uses sentiment dictionary contains positivity and negativity of vocabularies. As a part of such studies, this study tries to construct sentiment dictionary which is customized to specific data domain. Using a common sentiment dictionary for sentiment analysis without considering data domain characteristic cannot reflect contextual expression only used in the specific data domain. So, we can expect using a modified sentiment dictionary customized to data domain can lead the improvement of sentiment analysis efficiency. Therefore, this study aims to suggest a way to construct customized dictionary to reflect characteristics of data domain. Especially, in this study, movie review data are divided by genre and construct genre-customized dictionaries. The performance of customized dictionary in sentiment analysis is compared with a common sentiment dictionary. In this study, IMDb data are chosen as the subject of analysis, and movie reviews are categorized by genre. Six genres in IMDb, 'action', 'animation', 'comedy', 'drama', 'horror', and 'sci-fi' are selected. Five highest ranking movies and five lowest ranking movies per genre are selected as training data set and two years' movie data from 2012 September 2012 to June 2014 are collected as test data set. Using SO-PMI (Semantic Orientation from Point-wise Mutual Information) technique, we build customized sentiment dictionary per genre and compare prediction accuracy on review rating. As a result of the analysis, the prediction using customized dictionaries improves prediction accuracy. The performance improvement is 2.82% in overall and is statistical significant. Especially, the customized dictionary on 'sci-fi' leads the highest accuracy improvement among six genres. Even though this study shows the usefulness of customized dictionaries in sentiment analysis, further studies are required to generalize the results. In this study, we only consider adjectives as additional terms in customized sentiment dictionary. Other part of text such as verb and adverb can be considered to improve sentiment analysis performance. Also, we need to apply customized sentiment dictionary to other domain such as product reviews.

• Annals of Clinical Microbiology
• /
• v.21 no.4
• /
• pp.86-91
• /
• 2018

Background: The PANA RealTyper HPV kit (PANAGENE, Korea; PANA RealTyper) was developed to genotype human papillomavirus (HPV) and was based on multiplex real-time PCR amplification and melting curve analysis. In this study, we compared PANA RealTyper to the AdvanSure HPV GenoBlot assay (LG Life Sciences, Korea; AdvanSure assay) and attempted to evaluate the performance of PANA RealTyper. Methods: A total of 60 cervical specimens were collected from women undergoing routine cervical cancer screening. The AdvanSure assay and PANA RealTyper kit identified the same 20 high-risk genotypes. However, the AdvanSure assay identified 15 low-risk genotypes, while the PANA RealTyper kit identified only 2 but detected 18 low-risk genotypes. Results: Among the total 60 specimens, 54 high-risk genotypes (40 specimens) and 20 low-risk genotypes (18 specimens) were detected. The agreement rates of the assays ranged from 94.4 to 100% for high-risk genotypes. Among 9 genotypes that were positive in the PANA RealTyper kit but negative in the AdvanSure assay, 7 were confirmed as true positive (HPV genotypes 16 (n=1), 39 (n=1), 52 (n=1), 58 (n=2), 68 (n=2)). Among 4 genotypes that were negative in the PANA RealTyper kit but positive in the AdvanSure assay, 3 were confirmed as HPV genotype 59. Among the 19 low-risk genotypes positive in the AdvanSure assay, there were 2 cases of HPV 6 and 1 case of HPV 11. In comparison, only 1 positive case of HPV 6 was determined by the PANA RealTyper kit. Conclusion: The PANA RealTyper kit was comparable with the AdvanSure assay. The PANA RealTyper kit would be useful and suitable for HPV genotyping in the clinical laboratory.