• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.73-85
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• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.79-104
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• 2020

Recently, as deep learning has attracted attention, the use of deep learning is being considered as a method for solving problems in various fields. In particular, deep learning is known to have excellent performance when applied to applying unstructured data such as text, sound and images, and many studies have proven its effectiveness. Owing to the remarkable development of text and image deep learning technology, interests in image captioning technology and its application is rapidly increasing. Image captioning is a technique that automatically generates relevant captions for a given image by handling both image comprehension and text generation simultaneously. In spite of the high entry barrier of image captioning that analysts should be able to process both image and text data, image captioning has established itself as one of the key fields in the A.I. research owing to its various applicability. In addition, many researches have been conducted to improve the performance of image captioning in various aspects. Recent researches attempt to create advanced captions that can not only describe an image accurately, but also convey the information contained in the image more sophisticatedly. Despite many recent efforts to improve the performance of image captioning, it is difficult to find any researches to interpret images from the perspective of domain experts in each field not from the perspective of the general public. Even for the same image, the part of interests may differ according to the professional field of the person who has encountered the image. Moreover, the way of interpreting and expressing the image also differs according to the level of expertise. The public tends to recognize the image from a holistic and general perspective, that is, from the perspective of identifying the image's constituent objects and their relationships. On the contrary, the domain experts tend to recognize the image by focusing on some specific elements necessary to interpret the given image based on their expertise. It implies that meaningful parts of an image are mutually different depending on viewers' perspective even for the same image. So, image captioning needs to implement this phenomenon. Therefore, in this study, we propose a method to generate captions specialized in each domain for the image by utilizing the expertise of experts in the corresponding domain. Specifically, after performing pre-training on a large amount of general data, the expertise in the field is transplanted through transfer-learning with a small amount of expertise data. However, simple adaption of transfer learning using expertise data may invoke another type of problems. Simultaneous learning with captions of various characteristics may invoke so-called 'inter-observation interference' problem, which make it difficult to perform pure learning of each characteristic point of view. For learning with vast amount of data, most of this interference is self-purified and has little impact on learning results. On the contrary, in the case of fine-tuning where learning is performed on a small amount of data, the impact of such interference on learning can be relatively large. To solve this problem, therefore, we propose a novel 'Character-Independent Transfer-learning' that performs transfer learning independently for each character. In order to confirm the feasibility of the proposed methodology, we performed experiments utilizing the results of pre-training on MSCOCO dataset which is comprised of 120,000 images and about 600,000 general captions. Additionally, according to the advice of an art therapist, about 300 pairs of 'image / expertise captions' were created, and the data was used for the experiments of expertise transplantation. As a result of the experiment, it was confirmed that the caption generated according to the proposed methodology generates captions from the perspective of implanted expertise whereas the caption generated through learning on general data contains a number of contents irrelevant to expertise interpretation. In this paper, we propose a novel approach of specialized image interpretation. To achieve this goal, we present a method to use transfer learning and generate captions specialized in the specific domain. In the future, by applying the proposed methodology to expertise transplant in various fields, we expected that many researches will be actively conducted to solve the problem of lack of expertise data and to improve performance of image captioning.

• Journal of radiological science and technology
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• v.35 no.2
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• pp.165-172
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• 2012

We investigated the vascular characteristics of tumors and normal tissue using perfusion CT in the rabbit brain tumor model. The VX2 carcinoma concentration of $1{\times}10^7$ cells/ml(0.1ml) was implanted in the brain of nine New Zealand white rabbits (weight: 2.4kg-3.0kg, mean: 2.6kg). The perfusion CT was scanned when the tumors were grown up to 5mm. The tumor volume and perfusion value were quantitatively analyzed by using commercial workstation (advantage windows workstation, AW, version 4.2, GE, USA). The mean volume of implanted tumors was $316{\pm}181mm^3$, and the biggest and smallest volumes of tumor were 497 $mm^3$ and 195 $mm^3$, respectively. All the implanted tumors in rabbits are single-nodular tumors, and intracranial metastasis was not observed. In the perfusion CT, cerebral blood volume (CBV) were $74.40{\pm}9.63$, $16.08{\pm}0.64$, $15.24{\pm}3.23$ ml/100g in the tumor core, ipsilateral normal brain, and contralateral normal brain, respectively ($p{\leqq}0.05$). In the cerebral blood flow (CBF), there were significant differences between the tumor core and both normal brains ($p{\leqq}0.05$), but no significant differences between ipsilateral and contralateral normal brains ($962.91{\pm}75.96$ vs. $357.82{\pm}12.82$ vs. $323.19{\pm}83.24$ ml/100g/min). In the mean transit time (MTT), there were significant differences between the tumor core and both normal brains ($p{\leqq}0.05$), but no significant differences between ipsilateral and contralateral normal brains ($4.37{\pm}0.19$ vs. $3.02{\pm}0.41$ vs. $2.86{\pm}0.22$ sec). In the permeability surface (PS), there were significant differences among the tumor core, ipsilateral and contralateral normal brains ($47.23{\pm}25.45$ vs. $14.54{\pm}1.60$ vs. $6.81{\pm}4.20$ ml/100g/min)($p{\leqq}0.05$). In the time to peak (TTP) were no significant differences among the tumor core, ipsilateral and contralateral normal brains. In the positive enhancement integral (PEI), there were significant differences among the tumor core, ipsilateral and contralateral brains ($61.56{\pm}16.07$ vs. $12.58{\pm}2.61$ vs. $8.26{\pm}5.55$ ml/100g). ($p{\leqq}0.05$). In the maximum slope of increase (MSI), there were significant differences between the tumor core and both normal brain($p{\leqq}0.05$), but no significant differences between ipsilateral and contralateral normal brains ($13.18{\pm}2.81$ vs. $6.99{\pm}1.73$ vs. $6.41{\pm}1.39$ HU/sec). Additionally, in the maximum slope of decrease (MSD), there were significant differences between the tumor core and contralateral normal brain($p{\leqq}0.05$), but no significant differences between the tumor core and ipsilateral normal brain($4.02{\pm}1.37$ vs. $4.66{\pm}0.83$ vs. $6.47{\pm}1.53$ HU/sec). In conclusion, the VX2 tumors were implanted in the rabbit brain successfully, and stereotactic inoculation method make single-nodular type of tumor that was no metastasis in intracranial, suitable for comparative study between tumors and normal tissues. Therefore, perfusion CT would be a useful diagnostic tool capable of reflecting the vascularity of the tumors.

• Korean Journal of Soil Science and Fertilizer
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• v.21 no.3
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• pp.316-338
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• 1988

To identify the physiological phenomena on the sudden wilting of japonica/indica crossed varieties, Pot experiment was carried out under the heavy N application with various levels of potassium in Japan. The results obtained are as follows. 1. Sudden wilting was occurred in both varieties used, Yushin and Milyang 23. The former showed a higher degree than the latter. 2. Sudden wilting was occurred into two types, one at early ripening stage and the other at late ripening stage. The former type was found in the field with low potassium supply and the latter was seemed to be related to varietal wilting tolerence. 3. By the investigation of concerning the effective tillering rate and the change of dry weight of each organ at the heading stage, it was inferred that the growth status from young panicle formation stage to heading stage were related to sudden wilting tolerence. 4. Manganese content at heading stage, ratio of Fe/Mn and Fe. Fe/Mn in stern at late ripening stage and $K_2$ O/N ratio of stem at harvesting stage were recognized as the specific factors in connection with sudden wilting. Mn content in the sudden wilting rice plant was already in creased remarkably at heading stage. In relation to root age and absoption characteristics of Mn, the senility of root before heading stage was inferred as the cause of increase the value of Fe/Mn or Fe. Fe/Mn. 5. The $K_2$ O/N ratio of culm at harvesting stage was lower in upper node than lower node in relation to sudden wilting. And it was well accordance with the fact that the symptoms of sudden wilting proceeded from upper leaf to lower leaf. These phenomenon was different from the usual one that the effect of potassium deficiency was more remarkable in lower node than upper node. 6. All varieties which have a condition of potassium deficiency have a high degree of nitrogen content of leaves at heading stage and the $K_2$ O/N ratio of each organ was low, Especialy, $K_2$ O/N ratio is much lower in sheath and culm than leaves.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.111-136
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• 2018

In this paper, we propose a methodology to extract answer information about queries from various types of unstructured documents collected from multi-sources existing on web in order to expand knowledge base. The proposed methodology is divided into the following steps. 1) Collect relevant documents from Wikipedia, Naver encyclopedia, and Naver news sources for "subject-predicate" separated queries and classify the proper documents. 2) Determine whether the sentence is suitable for extracting information and derive the confidence. 3) Based on the predicate feature, extract the information in the proper sentence and derive the overall confidence of the information extraction result. In order to evaluate the performance of the information extraction system, we selected 400 queries from the artificial intelligence speaker of SK-Telecom. Compared with the baseline model, it is confirmed that it shows higher performance index than the existing model. The contribution of this study is that we develop a sequence tagging model based on bi-directional LSTM-CRF using the predicate feature of the query, with this we developed a robust model that can maintain high recall performance even in various types of unstructured documents collected from multiple sources. The problem of information extraction for knowledge base extension should take into account heterogeneous characteristics of source-specific document types. The proposed methodology proved to extract information effectively from various types of unstructured documents compared to the baseline model. There is a limitation in previous research that the performance is poor when extracting information about the document type that is different from the training data. In addition, this study can prevent unnecessary information extraction attempts from the documents that do not include the answer information through the process for predicting the suitability of information extraction of documents and sentences before the information extraction step. It is meaningful that we provided a method that precision performance can be maintained even in actual web environment. The information extraction problem for the knowledge base expansion has the characteristic that it can not guarantee whether the document includes the correct answer because it is aimed at the unstructured document existing in the real web. When the question answering is performed on a real web, previous machine reading comprehension studies has a limitation that it shows a low level of precision because it frequently attempts to extract an answer even in a document in which there is no correct answer. The policy that predicts the suitability of document and sentence information extraction is meaningful in that it contributes to maintaining the performance of information extraction even in real web environment. The limitations of this study and future research directions are as follows. First, it is a problem related to data preprocessing. In this study, the unit of knowledge extraction is classified through the morphological analysis based on the open source Konlpy python package, and the information extraction result can be improperly performed because morphological analysis is not performed properly. To enhance the performance of information extraction results, it is necessary to develop an advanced morpheme analyzer. Second, it is a problem of entity ambiguity. The information extraction system of this study can not distinguish the same name that has different intention. If several people with the same name appear in the news, the system may not extract information about the intended query. In future research, it is necessary to take measures to identify the person with the same name. Third, it is a problem of evaluation query data. In this study, we selected 400 of user queries collected from SK Telecom 's interactive artificial intelligent speaker to evaluate the performance of the information extraction system. n this study, we developed evaluation data set using 800 documents (400 questions * 7 articles per question (1 Wikipedia, 3 Naver encyclopedia, 3 Naver news) by judging whether a correct answer is included or not. To ensure the external validity of the study, it is desirable to use more queries to determine the performance of the system. This is a costly activity that must be done manually. Future research needs to evaluate the system for more queries. It is also necessary to develop a Korean benchmark data set of information extraction system for queries from multi-source web documents to build an environment that can evaluate the results more objectively.

• The Journal of Engineering Geology
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• v.29 no.1
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• pp.51-68
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• 2019

This study performs to evaluate adaptability of seashore filtering type seawater-intake which adapts dua1 filter well alternative for direct seawater-intake. This study varies filter condition of seashore free surface aquifer which is composed of sand layer then installs real size dual filter well and single filter well to evaluate water permeability and proper pumping amount according to filter condition. According to result of step aquifer test, it is analysed that 110.3% synergy effect of water permeability coefficient is happened compare to single filter since dual filter well has better improvement. dual filter has higher water permeability coefficient compare to same pumping amount, this means dual filter has more improved water permeability than single filter. According to analysis result of continuous aquifer test, it is evaluated that dual filter well (SD1200) has higher water permeability than single filter well (SS800) by analysis of water permeability coefficient using monitoring well and gauging well, it is also analysed dual filter has 110.7% synergy effect of water permeability coefficient. As a evaluation result of pumping amount according to analysis of water level dropping rate, it is analysed that dual filter well increased 122.8% pumping amount compare to single filter well when water level dropping is 2.0 m. As a result of calculating proper pumping amount using water level dropping rate, it is analysed that dual filter well shows 136.0% higher pumping amount compare to single filter well. It is evaluated that proper pumping amount has 122.8~160% improvement compare to single filter, pumping amount improvement rate is 139.6% compare to averaged single filter. In other words, about 40% water intake efficiency can be improved by just installation of dual filter compare to normal well. Proper pumping amount of dual filter well using inflection point is 2843.3 L/min and it is evaluated that daily seawater intake amount is about $4,100m^3/day$ (${\fallingdotseq}4094.3m^3/day$) in one hole of dual filter well. Since it is possible to intake plenty of water in one hole, higher adaptability is anticipated. In case of intaking seawater using dual filter well, no worries regarding damages on facilities caused by natural disaster such as severe weather or typhoon, improvement of pollution is anticipated due to seashore sand layer acts like filter. Therefore, It can be alternative of environmental issue for existing seawater intake technique, can save maintenance expenses related to installation fee or damages and has excellent adaptability in economic aspect. The result of this study will be utilized as a basic data of site demonstration test for adaptation of riverside filtered water of upcoming dual filter well and this study is also anticipated to present standard of well design and construction related to riverside filter and seashore filter technique.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.29 no.2
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• pp.81-89
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• 2011

The results of tracking the symbolism of plants in the introduction factors of Sokhwa(folk painting) are as the following. 1. The term Sokhwa(俗畵) is not only a type of painting with a strong local customs, but also carries a symbolic meaning and was discovered in "Donggukisanggukjip" of Lee, Gyu-Bo(1268~1241) in the Goryo era as well as the various usage in the "Sok Dongmunseon" in the early Chosun era, "Sasukjaejip" of Gang, Hee-mang(1424~1483), "Ilseongrok(1786)" in the late Chosun era, "Jajeo(自著)" of Yoo, Han-joon(1732~1811), and "Ojuyeonmunjangjeonsango(五洲衍文長箋散稿)" of Lee, Gyu-gyung(1788~?). Especially, according to the Jebyungjoksokhwa allegation〈題屛簇俗畵辯證說〉in the Seohwa of the Insa Edition of Ojuyeonmunjangjeonsango, there is a record that the "people called them Sokhwa." 2. Contemporarily, the Korean Sokhwa underwent the prehistoric age that primitively reflected the natural perspective on agricultural culture, the period of Three States that expressed the philosophy of the eternal spirits and reflected the view on the universe in colored pictures, the Goryo Era that religiously expressed the abstract shapes and supernatural patterns in spacein symbolism, and the Chosun Era that established the traditional Korean identity of natural perspective, aesthetic values and symbolism in a complex integration in the popular culture over time. 3. The materials that were analyzed in 1,009 pieces of Korean Sokhwa showed 35 species of plants, 37 species of animals, 6 types of natural objects and other 5 types with a total of 83 types. 4. The shape aesthetics according to the aesthetic analysis of the plants in Sokhwa reflect the primitive world view of Yin/yang and the Five Elements in the peony paintings and dynamic refinement and biological harmonies in the maehwado; the composition aesthetics show complex multi-perspective composition with a strong noteworthiness in the bookshelf paintings, a strong contrast of colors with reverse perspective drawing in the battlefield paintings, and the symmetric beauty of simple orderly patterns in nature and artificial objects with straight and oblique lines are shown in the leisurely reading paintings. In terms of color aesthetics, the five colors of directions - east, west, south, north and the center - or the five basic colors - red, blue, yellow, white and black - are often utilized in ritual or religious manners or symbolically substitute the relative relationships with natural laws. 5. The introduction methods in the Korean Sokhwa exceed the simple imitation of the natural shapes and have been sublimated to the symbolism that is related to nature based on the colloquial artistic characteristics with the suspicion of the essence in the universe. Therefore, the symbolism of the plants and animals in the Korean Sokhwas is a symbolic recognition system, not a scientific recognition system with a free and unique expression with a complex interaction among religious, philosophical, ecological and ideological aspects, as a identity of the group culture of Koreans where the past and the future coexist in the present. This is why the Koran Sokhwa or the folk paintings can be called a cultural identity and can also be interpreted as a natural and folk meaningful scenic factor that has naturally integrated into our cultural lifestyle. However, the Sokhwa(folk paintings) that had been closely related to our lifestyle drastically lost its meaning and emotions through the transitions over time. As the living lifestyle predominantly became the apartment culture and in the historical situations where the confusion of the identity has deepened, the aesthetic and the symbolic values of the Sokhwa folk paintings have the appropriateness to be transmitted as the symbolic assets that protect our spiritual affluence and establish our identity.

• The Journal of Korean Philosophical History
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• no.52
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• pp.393-424
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• 2017

This study investigates the relationship between South Korean youths' Confucian values and sense of citizen ethics while presenting outlook on the sense of citizen ethics based on the theory of human nature. The purpose of this study, by doing so, is to present educational measures. For this purpose, empirical research method was applied in this study. In the empirical study, youths were surveyed and the answers were statistically analyzed and discussed with a view to achieve the study purpose. In the empirical research part of the study, Korean youths' awareness on Confucian values was examined along with its relationship with the sense of citizen ethics. The effect of Confucian values on sense of citizen ethics and their relationship were analyzed to evaluate the receptivity of youths on Confucian ideas and usefulness of sense of citizen ethics. This study investigated a total of final 311 sets of data from male and female students at middle and high schools located in Seoul, Gyeonggi, South Korea. First, to identify the youths' Confucian values and level of sense of citizen ethics, descriptive statistical analysis was conducted. As a result, the survey subjects were found to have, concerning the Confucian values, world view M=3.54, human relations view M=3.66, morality cultivation M=3.76, and social order M=3.45, higher than 3.0 to represent positive levels. The morality cultivation, in particular, was recorded the highest among all whereas the social order was relatively lower, which represents the degree of relying on Confucian values to establish social order. Second, the sub-variables of Confucian values were verified according to the personal characteristics of the surveyed youths and differences in their entire perception was investigated. As a result, according to gender, morality cultivation was found higher in female students (M=3.85) than in male students (M=3.64). According to the subjective economic level of their household, world view was found higher in upper class (M=3.98) than middle-low class (M=3.25) and low class (M=3.22) while human relations view was found higher in middle-upper class (M=3.79) than low class (M=3.46). As for the family type, morality cultivation was found higher in extended family (M=3.83) than nuclear family (M=3.62); and social order was higher in extended family (M=3.54) than nuclear family (M=3.36). Third, to verify the study theme of identifying the effects of youths' Confucian values on sense of citizen morality, hierarchical regression analysis was employed in this study, which used the multi-level model of multiple regression analysis. As a result, the Confucian values was found to have significant positive (+) correlations with the entire sense of citizen ethics in order of human relations view(${\beta}=.499$), world view(${\beta}=.412$), social order(${\beta}=.341$), and morality cultivation(${\beta}=.241$). Confucian value showed significant positive (+) correlations with autonomy in order of morality cultivation(${\beta}=.458$), human relations view(${\beta}=.454$), social order(${\beta}=.362$), and world view(${\beta}=.158$). Confucian values was found to have significant positive (+) correlations with community spirit in order of human relations view(${\beta}=.295$), social order(${\beta}=.281$), and morality cultivation(${\beta}=.232$). As shown in the findings above, youths' Confucian values was found to have significant positive (+) effects on the sense of citizen ethics. It is noted that the higher the Confucian values, the more positive the sense of citizen ethics would be. Consequentially, the Confucian values was identified to play an important role in the sense of citizen ethics in the modern society. Based on this analysis, this study presented specific measures - the necessity and possibility of education on sense of citizen ethics under the theory of human nature. To this end, this study proposed to find an optimal interface between the contemporary sense of citizen ethics and Confucian ethics through the respect for human life and nature, man of virtue as the ideal human model, and united society as a desirable society model.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.

• Journal of Practical Agriculture & Fisheries Research
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• v.24 no.3
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• pp.15-24
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• 2022

In 2000, a total of 23,794ha of forest was lost due to the East Coast forest fire, and about 70% of the damaged area was concentrated in Samcheok. In 2001, artificial restoration and natural restoration were implemented in the damaged area. This study was conducted to understand the current vegetation structure 21 years after the restoration of forest fire damage in the Samcheok, Gumbong Mountain area. As a result of classifying the vegetation community, it was divided into three communities: Quercus variabilis-Pinus densiflora community, Pinus densiflora-Quercus mongolica community, and Pinus thunbergii community. Quercus variabilis, Pinus densiflora, and Pinus thunbergii planted in the artificial restoration site were found to continue to grow as dominant species in the local vegetation after restoration. As for the species diversity index of the community, the Quercus variabilis-Pinus densiflora community dominated by deciduous broad-leaf trees showed the highest, and the coniferous forest Pinus thunbergii community showed the lowest. Vegetation in areas affected by forest fires is greatly affected by reforestation tree species, and 21 years later, it has shown a tendency to recover to the forest type before forest fire. In order to establish DataBase for effective restoration and to prepare monitoring data, it is necessary to construct data through continuous vegetation survey on the areas affected by forest fires.