• Korean Journal of Radiology
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• v.23 no.5
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• pp.505-516
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• 2022

Objective: To evaluate whether artificial intelligence (AI) for detecting breast cancer on mammography can improve the performance and time efficiency of radiologists reading mammograms. Materials and Methods: A commercial deep learning-based software for mammography was validated using external data collected from 200 patients, 100 each with and without breast cancer (40 with benign lesions and 60 without lesions) from one hospital. Ten readers, including five breast specialist radiologists (BSRs) and five general radiologists (GRs), assessed all mammography images using a seven-point scale to rate the likelihood of malignancy in two sessions, with and without the aid of the AI-based software, and the reading time was automatically recorded using a web-based reporting system. Two reading sessions were conducted with a two-month washout period in between. Differences in the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and reading time between reading with and without AI were analyzed, accounting for data clustering by readers when indicated. Results: The AUROC of the AI alone, BSR (average across five readers), and GR (average across five readers) groups was 0.915 (95% confidence interval, 0.876-0.954), 0.813 (0.756-0.870), and 0.684 (0.616-0.752), respectively. With AI assistance, the AUROC significantly increased to 0.884 (0.840-0.928) and 0.833 (0.779-0.887) in the BSR and GR groups, respectively (p = 0.007 and p < 0.001, respectively). Sensitivity was improved by AI assistance in both groups (74.6% vs. 88.6% in BSR, p < 0.001; 52.1% vs. 79.4% in GR, p < 0.001), but the specificity did not differ significantly (66.6% vs. 66.4% in BSR, p = 0.238; 70.8% vs. 70.0% in GR, p = 0.689). The average reading time pooled across readers was significantly decreased by AI assistance for BSRs (82.73 vs. 73.04 seconds, p < 0.001) but increased in GRs (35.44 vs. 42.52 seconds, p < 0.001). Conclusion: AI-based software improved the performance of radiologists regardless of their experience and affected the reading time.

• Journal of Korean Society of Water and Wastewater
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• v.37 no.6
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• pp.425-435
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• 2023

Organoleptic parameters such as color, odor, and flavor influence consumer perception of drinking water quality. This study aims to evaluate the taste of the selected bottled and tap water samples using an electronic tongue (E-tongue) instead of a sensory test. Bottled and tap water's mineral components are related to the overall preference for water taste. Contrary to the sensory test, the potentiometric E-tongue method presented in this study distinguishes taste by measuring the mineral components in water, and the data obtained can be statistically analyzed. Eleven bottled water products from various brands and one tap water from I city in Korea were evaluated. The E-tongue data were statistically analyzed using multivariate statistical tools such as hierarchical clustering analysis (HCA), principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA). The results show that the E-tongue method can clearly distinguish taste discrimination in drinking water differing in water quality based on the ion-related water quality parameters. The water quality parameters that affect taste discrimination were found to be total dissolved solids (TDS), sodium (Na+), calcium (Ca²⁺), magnesium (Mg²⁺), sulfate (SO₄^2-), chloride (Cl^-), potassium (K⁺) and pH. The distance calculation of HCA was used to quantify the differences between 12 different types of drinking water. The proposed E-tongue method is a practical tool to quantitatively evaluate the differences between samples in water quality items related to the ionic components. It can be helpful in quality control of drinking water.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.461-466
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• 2024

Korean local governments operates the participatory budgeting system autonomously. This study is to classify these entities into clusters. Among the diverse machine learning methodologies(Neural Network, Rule Induction(CN2), KNN, Decision Tree, Random Forest, Gradient Boosting, SVM, Naïve Bayes), the Support Vector Machine technique emerged as the most efficacious in the analysis of 2022 Korean municipalities data. The first cluster C1 is characterized by minimal committee activity but a substantial allocation of participatory budgeting; another cluster C3 comprises cities that exhibit a passive stance. The majority of cities falls into the final cluster C2 which is noted for its proactive engagement in. Overall, most Korean local government operates the participatory busgeting system in good shape. Only a small number of cities is less active in this system. We anticipate that analyzing time-series data from the past decade in follow-up studies will further enhance the reliability of classifying local government types regarding participatory budgeting.

• Information Systems Review
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• v.26 no.1
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• pp.337-353
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• 2024

The purpose of this study is to investigate the relationship between consumer reviews and managerial responses, aiming to explore the necessity of webcare for efficiently managing consumer reviews. We intend to propose a methodology for effective webcare and to construct a webcare model using machine learning techniques based on an audiobook platform. In this study, we selected four audiobook platforms and conducted data collection and preprocessing for consumer reviews and managerial responses. We utilized techniques such as topic modeling, topic inconsistency analysis, and DBSCAN, along with various machine learning methods for analysis. The experimental results yielded significant findings in clustering managerial responses and predicting responses to consumer reviews, proposing an efficient methodology considering resource constraints and costs. This research provides academic insights by constructing a webcare model through machine learning techniques and practical implications by suggesting an efficient methodology, considering the limited resources and personnel of companies. The proposed webcare model in this study can be utilized as strategic foundational data for consumer engagement and providing useful information, offering both personalized responses and standardized managerial responses.

• Journal of Korean Society of Forest Science
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• v.98 no.3
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• pp.215-224
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• 2009

A review was given of the role of traditional morphometrics in plant morphological studies using 54 published studies in three major journals and others in Korea, such as Journal of Korean Forestry Society, Korean Journal of Plant Taxonomy, Korean Journal of Breeding, Korean Journal of Apiculture, Journal of Life Science, and Korean Journal of Plant Resources from 1997 to 2008. The two most commonly used techniques of data analysis, cluster analysis (CA) and principal components analysis (PCA) with other statistical tests were discussed. The common problem of PCA is the underlying assumptions of methods, like random sampling and multivariate normal distribution of data. The procedure was intended mainly for continuous data and was not efficient for data which were not well summarized by variances or covariances. Likewise CA was most appropriate for categorical rather than continuous data. Also, the CA produced clusters whether or not natural groupings existed, and the results depended on both the similarity measure chosen and the algorithm used for clustering. An additional problems of the PCA and the CA arised with both qualitative and quantitative data with a limited number of variables and/or too few numbers of samples. Some of these problems may be avoided if a certain number of variables (more than 20 at least) and sufficient samples (40-50 at least) are considered for morphometric analyses, but we do not think that the methods are all mighty tools for data analysts. Instead, we do believe that reasonable applications combined with focus on objectives and limitations of each procedure would be a step forward.

• The Journal of Fisheries Business Administration
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• v.40 no.2
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• pp.49-70
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• 2009

This study proviedes GARCH model(Bollerslev, 1986) to analyze the structural characteristics of price volatility in domestic aquacultural fish market of Korea. As a case study, flatfish and rock-fish are analyzed as major species with relatively high portion in an aspect of production volume among fish captured in Korea. For analyzing, this study uses daily market data (dating from Jan 1 2000 to June 30, 2008) published by the Noryangjin Fisheries Wholesale Market which is located in Seoul of Korea. This study performs normality test on trading volume and price volatility of flatfish and rock-fish as an advanced empirical approach. The normality test adopted is Jarque-Bera test statistic. As a result, first, a null hypothesis that "an empirical distribution follows normal distribution" was rejected in both fishes. The distribution of daily market data of them were not only biased toward positive(+) direction in terms of kurtosis and skewness, but also characterized by leptokurtic distribution with long right tail. Secondly, serial correlations were found in data on market trading volume and price volatility of two species during very long period. Thirdly, the results of unit root test and ARCH-LM test showed that all data of time series were very stationary and demonstrated effects of ARCH. These statistical characteristics can be explained as a reasonable ground for supporting the fitness of GARCH model in order to estimate conditional variances that reveal price volatility in empirical analysis. From empirical data analysis above, this study drew the following conclusions. First of all, from an empirical analysis on potential effects of seasonality and the day of week on price volatility of aquacultural fish, Monday effects were found in both species and Thursday and Friday effects were also found in flatfish. This indicates that Monday is effective in expanding price volatility of aquacultural fish market and also Monday has higher effects upon the price volatility of fish than other days of week have since it has more new information for weekend. Secondly, the empirical analysis led to a common conclusion that there was very high price volatility of flatfish and rock-fish. This points out that the persistency parameter($\lambda$), an index of possibility for current volatility to sustain similarly in the future, was higher than 0.8-equivalently nearly to 1-in both flatfish and rock-fish, which presents volatility clustering. Also, this study estimated and compared and model that hypothesized normal distributions in order to determine fitness of respective models. As a result, the fitness of GARCH(1, 1)-t model was better than model where the distribution of error term was hypothesized through-distribution due to characteristics of fat-tailed distribution, was also better than model, as described in the results of basic statistic analysis. In conclusion, this study has an important mean in that it was introduced firstly in Korea to investigate in price volatility of Korean aquacultural fishery products, although there was partially a limited of official statistic data. Therefore, it is expected that the results of this study will be useful as a reference material for making and assessing governmental policies. Also, it is looked forward that the results will be helpful to build a fishery business plan as and aspect of producer, and also to take timely measures to potential price fluctuations of fishery products in market. Hence, it is advisable that further studies related to such price volatility in fishery market will extend and evolve into a wider variety of articles and issues in near future.

• Journal of Environmental Impact Assessment
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• v.26 no.2
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• pp.160-170
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• 2017

The purpose of this study is to perform clustering of the habitat types and to identify the characteristics of species in the habitat types using mammal data (70,562) of the 3rd National Ecosystem Survey conducted from 2006 to 2012. The 15 habitat types recorded in the field-paper of the 3rd National ecosystem survey were reclassified, which was followed by the statistical analysis of mammal habitat types. In the habitat types cluster analysis, non-hierarchical cluster analysis (k-means cluster analysis), hierarchical cluster analysis, and non-metric multidimensional scaling method were applied to 14 habitat types recorded more than 30 times. A total of 7 Orders, 16 Families, and 39 Species of mammals were identified in the 3rd National Ecosystem Survey collected nationwide. When 11 clusters were classified by habitat types, the simple structure index was the highest (ssi = 0.07). As a result of the similarities and hierarchies between habitat types suggested by the hierarchical clustering analysis, the residential areas were the most different habitat types for mammals; the next following type was a cluster together with rivers and coasts. The results of the non-metric multidimensional scaling analysis demonstrated that both Mus musculus and Rattus norvegicus restrictively appeared in a residential area, which is the most discriminating habitat type. Lutra lutra restrictively appeared in coastal and river areas. In summary, according to our results, the mammalian habitat can be divided into the following four types: (1) the forest type (using forest as the main habitat and migration route); (2) the river type (using water as the main habitat); (3) the residence habitat (living near residential area); and (4) the lowland type (consuming grain or seeds as the main feeding resource).

• Journal of the Korean Society of Food Science and Nutrition
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• v.39 no.1
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• pp.54-63
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• 2010

Baicalin, a flavonoid, was shown to have diverse effects such as anti-inflammatory, anti-cancer, anti-viral, anti-bacterial and others. Recently, we found that the baicalin inhibits adipogenesis through the modulations of anti-adipogenic and pro-adipogenic factors of the adipogenesis pathway. In the present study, we further characterized the molecular mechanism of the anti-adipogenic effect of baicalin using microarray technology. Microarray analyses were conducted to analyze the gene expression profiles during the differentiation time course (0 day, 2 day, 4 day and 7 day) in 3T3-L1 cells with or without baicalin treatment. We identified a total of 3972 genes of which expressions were changed more than 2 fold. These 3972 genes were further analyzed using hierarchical clustering analysis, resulting in 20 clusters. Four clusters among 20 showed clearly up-regulated expression patterns (cluster 8 and cluster 10) or clearly down-regulated expression patterns (cluster 12 and cluster 14) by baicalin treatment for over-all differentiation period. The cluster 8 and cluster 10 included many genes which enhance cell proliferation or inhibit adipogenesis. On the other hand, the cluster 12 and cluster 14 included many genes which are related with proliferation inhibition, cell cycle arrest, cell growth suppression or adipogenesis induction. In conclusion, these data provide detailed information on the molecular mechanism of baicalin-induced inhibition of adipogenesis.

• Journal of the Korean Society for information Management
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• v.31 no.2
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• pp.57-77
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• 2014

As co-authorship has been prevalent within science communities, counting the credit of co-authors appropriately is an important consideration, particularly in the context of identifying the knowledge structure of fields with author-based analysis. The purpose of this study is to compare the characteristics of co-author credit counting methods by utilizing correlations, multidimensional scaling, and pathfinder networks. To achieve this purpose, this study analyzed a dataset of 2,014 journal articles and 3,892 cited authors from the Journal of the Architectural Institute of Korea: Planning & Design from 2003 to 2008 in the field of Architecture in Korea. In this study, six different methods of crediting co-authors are selected for comparative analyses. These methods are first-author counting (m1), straight full counting (m2), and fractional counting (m3), proportional counting with a total score of 1 (m4), proportional counting with a total score between 1 and 2 (m5), and first-author-weighted fractional counting (m6). As shown in the data analysis, m1 and m2 are found as extreme opposites, since m1 counts only first authors and m2 assigns all co-authors equally with a credit score of 1. With correlation and multidimensional scaling analyses, among five counting methods (from m2 to m6), a group of counting methods including m3, m4, and m5 are found to be relatively similar. When the knowledge structure is visualized with pathfinder network, the knowledge structure networks from different counting methods are differently presented due to the connections of individual links. In addition, the internal validity shows that first-author-weighted fractional counting (m6) might be considered a better method to author clustering. Findings demonstrate that different co-author counting methods influence the network results of knowledge structure and a better counting method is revealed for author clustering.

• Journal of Life Science
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• v.20 no.3
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• pp.411-415
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• 2010

Anisakis simplex is one of the parasitic nematodes, and has a complex life cycle in crustaceans, fish, squid or whale. When people eat under-processed or raw fish, it causes anisakidosis and also plays a critical role in inducing serious allergic reactions in humans. However, no web-based database on A. simplex at the level of DNA or protein has been so far reported. In this context, we constructed a web-based database for Anisakis research. To build up the web-based database for Anisakis research, we proceeded with the following measures: First, sequences of order Ascaridida were downloaded and translated into the multifasta format which was stored as database for stand-alone BLAST. Second, all of the nucleotide and EST sequences were clustered and assembled. And EST sequences were translated into amino acid sequences for Nuclear Localization Signal prediction. In addition, we added the vector, E. coli, and repeat sequences into the database to confirm a potential contamination. The web-based database gave us several advantages. Only data that agrees with the nucleotide sequences directly related with the order Ascaridida can be found and retrieved when searching BLAST. It is also very convenient to confirm contamination when making the cDNA or genomic library from Anisakis. Furthermore, BLAST results on the Anisakis sequence information can be quickly accessed. Taken together, the Web-based database on A. simplex will be valuable in developing species specific PCR markers and in studying SNP in A. simplex-related researches in the future.