• Journal of Plant Biotechnology
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• v.3 no.3
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• pp.145-149
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• 2001

This study was conducted to analyze the morphological characteristics such as flower color, flower type, flower diameter and flower stalk, and the main annual production yield, and genetic similarity of twenty four Gerbera species growing in Korea. Most of flower colors were pink. The numerical order of flower color was pink, orange, red, double-colored, and milk-white. Majority of flower types were sin81e or semidouble flowers. A few species were double flowers. flower diameters were from 7 ㎝ to 12 ㎝, showed significant differences compared to other characteristics. Flower stalks were ranged from 55 ㎝ to 65 ㎝. Only one species was the shortest as 55 ㎝. The others were similar size as about 65 ㎝. Main annual production yields were between 190 and 400 blossoms. Fifty seven reproducible polymorphic bands from eighty primers were used for analyses of genetic similarity. The genetic similarity of 24 collected Gerberas was largely classified into five groups. The average similarity coefficient was 0.72 ranged from 0.50 to 0.90. The highest similarity coefficient was shown between 'Sardana' with red/white flower color and double flower type, and 'Tamara' with orange flower color and double flower type as 0.90.

• Journal of Crop Science and Biotechnology
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• v.11 no.1
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• pp.39-44
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• 2008

Korean cultivated bramble, which is known as Bokbunja-ddal-gi is regarded to be originated from Korea native Rubus coreanus. However, little scientific evidence and significant morphological differences between Korean cultivated bramble(KCB) and R. coreanus throw doubt on the ancestry of KCB. This study was carried out to obtain phylogenetic information on KCB by comparing its nuclear genomic background with those of R. coreanus, black(R. occidentalis) and red(R. idaeus) raspberry, blackberry(R. lanciniatus) and R. crataegifolius. A total of 99 random amplified polymorphic DNA(RAPD) markers were generated and used for phylogenetic analysis of 76 Rubus accessions. Accessions of each species were grouped into each distinct subclade by the RAPD markers at a similarity coefficient of about 0.59. The KCB subclade formed a clade with R. occidentalis and R. crataegifolius subclades at a similarity coefficient of 0.47. The R. coreanus subclade formed a clade with R. idaeus, R. lanciniatus and R. crataegifolius subclades at a similar similarity coefficient. Only one KCB accession from Hoengsung was included in R. coreanus subclade. The accession shows leaf and flower characteristics different from the rest of the KCB accessions. The phylogenetic relationship inferred from the RAPD markers suggests that the nuclear genomic background of KCB accessions which show morphological similarity to black raspberry is more closely related to black raspberry than to R. coreanus. This brings about the need for close scientific evaluations on the ancestry of KCB at both morphological and molecular levels.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.3
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• pp.287-293
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• 2012

This paper presents an efficient image registration method by measuring the similarity, which is based on multi-dimensional intensity fluctuation. Multi-dimensional intensity which considers 4 directions of the image, is applied to reflect more properties in similarity decision. And an intensity fluctuation is also applied to measure comprehensively the similarity by considering a change in brightness between the adjacent pixels of image. The normalized cross-correlation(NCC) is calculated by considering an intensity fluctuation to each of 4 directions. The 5 correlation coefficients based on the NCC have been used to measure the registration, which are total NCC, the arithmetical mean and a simple product on the correlation coefficient of each direction and on the normalized correlation coefficient by the maximum NCC, respectively. The proposed method has been applied to the problem for registrating the 22 face images of 243*243 pixels and the 9 person images of 500*500 pixels, respectively. The experimental results show that the proposed method has a superior registration performance that appears the image properties well. Especially, the arithmetical mean on the correlation coefficient of each direction is the best registration measure.

• Horticultural Science & Technology
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• v.18 no.2
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• pp.103-106
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• 2000

The genetic relationship of Bletilla striata native to Korea and Japan was investigated using random amplified polymorphic DNA (RAPD). The 156 reproducible DNA bands, consisted of 58 polymorphic and 98 monomorphic bands, were obtained by polymerase chain reaction (PCR) with selected 10 random primers. The 8 Bletilla lines have been classified into three groups according to the similarity coefficient obtained by RAPD analysis. The dendrogram showed overall correlationship between similarity coefficient of 0.48 and 0.84. The first group included A (Bletilla striata native to Korea), B (Bletilla striata variegated and native to Korea in Mokpo), C (Bletilla striata variegated and native to Korea), D and E (Bletilla striata native to Japan). In this group, it was showed that B and C had the most similar genetic relationship. The similarity coefficient between D and E was 0.77. D and E had a very close resemblance in plant height and flower color with A native to Korea, respectively. The second group included only G (dwarf Bletilla native to Japan) and had a different morphological character compared to other cultivars. The last group included F and H (dwarf and variegated Bletilla native to Japan) and they had a similarity of variegation.

• The Korean Journal of Mycology
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• v.26 no.1 s.84
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• pp.1-7
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• 1998

In order to find relationship within and between entomopathogenic species, analysis of protein band pattern in mycelia of 25 isolates was conducted by UPGMA. The results allowed differentiation of three groups on 85% similarity coefficient. Similarity coefficient within C. militaris was $0.787{\sim}1.000$, C. kyushuensis was 0.958-1.000 and C. pruinosa was 0.993-1.000. C210 and C298 isolates which had somewhat immersed perithecia, comparable to other C. militaris isolates, had 91% similarity. C108, C225-1 and C228 isolates pathogenic on Lepidopterous larvae had 89% similarity. Closely related species to C. militaris were C. kyushuensis and C. pruinosa. And similarity between C. pruinosa and C. kyushuensis was 88%. Similarity between C. bifusispora formed conidia on media and Paecilomyces tenuipes was 89%. C. scarabaeicola pathogenic specifically on adult Scarabaeidae had 82% similarity with above two species. C118 identified as C. militaris showed different protein banding patterns.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2587-2605
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• 2022

In order to efficiently detect community structure in complex networks, community detection algorithms can be designed from the perspective of node similarity. However, the appropriate parameters should be chosen to achieve community division, furthermore, these existing algorithms based on the similarity of common neighbors have low discrimination between node pairs. To solve the above problems, a noval community detection algorithm using closeness similarity based on common neighbor node clustering entropy is proposed, shorted as CSCDA. Firstly, to improve detection accuracy, common neighbors and clustering coefficient are combined in the form of entropy, then a new closeness similarity measure is proposed. Through the designed similarity measure, the closeness similar node set of each node can be further accurately identified. Secondly, to reduce the randomness of the community detection result, based on the closeness similar node set, the node leadership is used to determine the most closeness similar first-order neighbor node for merging to create the initial communities. Thirdly, for the difficult problem of parameter selection in existing algorithms, the merging of two levels is used to iteratively detect the final communities with the idea of modularity optimization. Finally, experiments show that the normalized mutual information values are increased by an average of 8.06% and 5.94% on two scales of synthetic networks and real-world networks with real communities, and modularity is increased by an average of 0.80% on the real-world networks without real communities.

• Asia pacific journal of information systems
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• v.18 no.1
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• pp.79-96
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• 2008

One of the roles of the Semantic Web services is to execute dynamic intra-organizational services including the integration and interoperation of business processes. Since different organizations design their processes differently, the retrieval of similar semantic business processes is necessary in order to support inter-organizational collaborations. Most approaches for finding services that have certain features and support certain business processes have relied on some type of logical reasoning and exact matching. This paper presents our approach of using imprecise matching for expanding results from an exact matching engine to query the OWL(Web Ontology Language) MIT Process Handbook. MIT Process Handbook is an electronic repository of best-practice business processes. The Handbook is intended to help people: (1) redesigning organizational processes, (2) inventing new processes, and (3) sharing ideas about organizational practices. In order to use the MIT Process Handbook for process retrieval experiments, we had to export it into an OWL-based format. We model the Process Handbook meta-model in OWL and export the processes in the Handbook as instances of the meta-model. Next, we need to find a sizable number of queries and their corresponding correct answers in the Process Handbook. Many previous studies devised artificial dataset composed of randomly generated numbers without real meaning and used subjective ratings for correct answers and similarity values between processes. To generate a semantic-preserving test data set, we create 20 variants for each target process that are syntactically different but semantically equivalent using mutation operators. These variants represent the correct answers of the target process. We devise diverse similarity algorithms based on values of process attributes and structures of business processes. We use simple similarity algorithms for text retrieval such as TF-IDF and Levenshtein edit distance to devise our approaches, and utilize tree edit distance measure because semantic processes are appeared to have a graph structure. Also, we design similarity algorithms considering similarity of process structure such as part process, goal, and exception. Since we can identify relationships between semantic process and its subcomponents, this information can be utilized for calculating similarities between processes. Dice's coefficient and Jaccard similarity measures are utilized to calculate portion of overlaps between processes in diverse ways. We perform retrieval experiments to compare the performance of the devised similarity algorithms. We measure the retrieval performance in terms of precision, recall and F measure? the harmonic mean of precision and recall. The tree edit distance shows the poorest performance in terms of all measures. TF-IDF and the method incorporating TF-IDF measure and Levenshtein edit distance show better performances than other devised methods. These two measures are focused on similarity between name and descriptions of process. In addition, we calculate rank correlation coefficient, Kendall's tau b, between the number of process mutations and ranking of similarity values among the mutation sets. In this experiment, similarity measures based on process structure, such as Dice's, Jaccard, and derivatives of these measures, show greater coefficient than measures based on values of process attributes. However, the Lev-TFIDF-JaccardAll measure considering process structure and attributes' values together shows reasonably better performances in these two experiments. For retrieving semantic process, we can think that it's better to consider diverse aspects of process similarity such as process structure and values of process attributes. We generate semantic process data and its dataset for retrieval experiment from MIT Process Handbook repository. We suggest imprecise query algorithms that expand retrieval results from exact matching engine such as SPARQL, and compare the retrieval performances of the similarity algorithms. For the limitations and future work, we need to perform experiments with other dataset from other domain. And, since there are many similarity values from diverse measures, we may find better ways to identify relevant processes by applying these values simultaneously.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.3
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• pp.579-586
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• 2018

With the beginning of the new era of bigdata, information extraction or prediction are an important research area. Here, we present the acquisition of semi-automatically curated large-scale biological database and the prediction of enzyme reaction annotation for analyzing the pharmacological activities of drugs. Because the xenobiotic metabolism of pharmaceutical drugs by cellular enzymes is an important aspect of pharmacology and medicine. In this study, we apply and analyze similarity models to predict bimolecular reactions between human enzymes and their corresponding substrates. Thirteen models select to reflect the characteristics of each cluster in the similarity model. These models compare based on sensitivity and AUC. Among the evaluation models, the Simpson coefficient model showed the best performance in predicting the reactivity between the enzymes. The whole similarity model implement as a web service. The proposed model can respond dynamically to the addition of reaction information, which will contribute to the shortening of new drug development time and cost reduction.

• The Journal of Industrial Distribution & Business
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• v.9 no.2
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• pp.39-45
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• 2018

Purpose - The purpose of this study is to examine the image similarity and attribute recognition of the top 10 rated spa destinations (Chungnam Deoksan, Chungnam Dogo, Busan Dongrae, Daejeon Yuseong, Chungnam Asan, Gyeongbuk Bomun, Chungbuk Suanbo, Gyeongnam Jangyu, Chungnam Onyang, & Gyeongbol Bugok) in Korea based on the visits to these spa places by the customers. Research design, data, and methodology - The survey of this study was conducted on the visitors to the top 10 spa destinations in Korea from April 8 ~ April 21, 2017, and a total of 300 questionnaires were distributed. Of them, effective questionnaires used in the final study were a total of 241. In this study, empirical analysis was made through frequency analysis, factor analysis, and multidimensional scaling ALSCAL(spinning symmetry for image similarity and rectangle for attributes recognition) by using the Statistics Package SPSS 24.0. Results - According to the analysis result of spa destination image similarity, the stress level was 0.16453 and the level of the stress was good. Moreover, the coefficient of determination (RSQ) was, which had a description of each aspect of the spa destination, 0.79908. According to the results of attribute recognition, the stress value of 0.11805 represents a degree of conformity, and the coefficient of determination(RSQ) appeared at 0.98665. Therefore, the results of this analysis are that the similarities between spa destinations and the attribute recognition of the spa destinations is a suitable model that is properly expressed in two dimensions. Conclusions - First, according to the analysis result of image similarity, Deoksan & Dogo spa revealed similar images, as well as the Dongrae and Yuseong spa, while on the contrary Asan, Bomun, Suanbo spa has different images from the rest. Second, according to the results of attribute recognition, Asan and Onyang spa has competitiveness in terms of accessibility to spa destination; Yuseong, Dongrae, Jangyu spa in terms of spa facilities, spa tourism conditions, and service & shopping conditions. while spa water quality and spa costs showed low attribute reflection for all 10 spas. Therefore, the spa visitors cannot recognize the differentiation of spa water quality and spa costs.

• Information Systems Review
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• v.9 no.1
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• pp.85-103
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• 2007

The purpose of this study is to evaluate the performance of collaborative filtering recommender algorithms for better prediction accuracy of the customer's preference. The accuracy of customer's preference prediction is compared through the MAE of neighborhood based collaborative filtering algorithm and correspondence mean algorithm. It is analyzed by using MovieLens 1 Million dataset in order to experiment with the prediction accuracy of the algorithms. For similarity, weight used in both algorithms, commonly, Pearson's correlation coefficient and vector similarity which are used generally were utilized, and as a result of analysis, we show that the accuracy of the customer's preference prediction of correspondence mean algorithm is superior. Pearson's correlation coefficient and vector similarity used in two algorithms are calculated using the preference rating of two customers' co-rated movies, and it shows that similarity weight is overestimated, where the number of co-rated movies is small. Therefore, it is intended to increase the accuracy of customer's preference prediction through expanding the number of the existing co-rated movies.