• Clinical and Experimental Reproductive Medicine
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• v.35 no.4
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• pp.309-317
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• 2008

Objectives: The aim of this study was to evaluate the usefulness of Anti-mullerian hormone (AMH) as a predictive marker for ovarian response and cycle outcome in IVF cycles. Methods: From Jan., to Aug., 2007, 111 patients undergoing IVF/ICSI stimulated by short or antagonist protocol were selected. On cycle day 3, basal serum AMH level and FSH level were measured. The correlation between basal serum AMH or FSH, and COH outcome was analyzed and IVF outcome was compared according to the AMH levels. To determine the threshold value of AMH for poor- and hyper-response, ROC curve was analyzed. Results: Serum AMH showed higher correlation coefficient (r=0.792, p<0.001) with the number of retrieved mature oocyte than serum FSH (r=-0.477, p<0.001). According to ovarian response, FSH and AMH leves showed significant differences among poor, normal, and hyperresponder. For predicting poor (${\leq}2$ oocytes) and hyperresponse (${\geq}17$ oocyets), AMH cut-off values were 0.5 ng/ml (the sensitivity 88.9% and the specificity 89.5%) and 2.5 ng/ml (sensitivity 85.7%, specificity 87.0%), respectively. According to the AMH level, patients were divided into 3 groups: low (${\leq}0.60\;ng/ml$), normal ($0.60{\sim}2.60\;ng/ml$), and high AMH (${\geq}2.60\;ng/ml$). The number of retrieved mature oocytes was significantly higher ($2.7{\pm}2.2$, $8.1{\pm}4.8$, $16.5{\pm}5.7$) and total gonadotropin dose was lower ($3530.5{\pm}1251.0$, $2957.1{\pm}1057.6$, and $2219.2{\pm}751.9\;IU$) in high AMH group (p<0.001). There was no significant difference in fertilization rates and pregnancy rates (23.8%, 34.0%, 37.5%) among the groups. Conclusions: Basal serum AMH level correlated better with the number of retrieved mature oocytes than FSH level, suggesting its usefulness for predicting ovarian response. However, IVF outcome was not significantly different according to the AMH levels. Serum AMH level presented good cut-off value for poor- or hyper-responders, therefore it could be useful in prediction of cycle cancellation, gonadotropin dose, and OHSS risk in IVF cycles.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.113-125
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• 2013

Every company wants to know customer's requirement and makes an effort to meet them. Cause that, communication between customer and company became core competition of business and that important is increasing continuously. There are several strategies to find customer's needs, but VOC (Voice of customer) is one of most powerful communication tools and VOC gathering by several channels as telephone, post, e-mail, website and so on is so meaningful. So, almost company is gathering VOC and operating VOC system. VOC is important not only to business organization but also public organization such as government, education institute, and medical center that should drive up public service quality and customer satisfaction. Accordingly, they make a VOC gathering and analyzing System and then use for making a new product and service, and upgrade. In recent years, innovations in internet and ICT have made diverse channels such as SNS, mobile, website and call-center to collect VOC data. Although a lot of VOC data is collected through diverse channel, the proper utilization is still difficult. It is because the VOC data is made of very emotional contents by voice or text of informal style and the volume of the VOC data are so big. These unstructured big data make a difficult to store and analyze for use by human. So that, the organization need to automatic collecting, storing, classifying and analyzing system for unstructured big VOC data. This study propose an intelligent VOC analyzing system based on opinion mining to classify the unstructured VOC data automatically and determine the polarity as well as the type of VOC. And then, the basis of the VOC opinion analyzing system, called domain-oriented sentiment dictionary is created and corresponding stages are presented in detail. The experiment is conducted with 4,300 VOC data collected from a medical website to measure the effectiveness of the proposed system and utilized them to develop the sensitive data dictionary by determining the special sentiment vocabulary and their polarity value in a medical domain. Through the experiment, it comes out that positive terms such as "칭찬, 친절함, 감사, 무사히, 잘해, 감동, 미소" have high positive opinion value, and negative terms such as "퉁명, 뭡니까, 말하더군요, 무시하는" have strong negative opinion. These terms are in general use and the experiment result seems to be a high probability of opinion polarity. Furthermore, the accuracy of proposed VOC classification model has been compared and the highest classification accuracy of 77.8% is conformed at threshold with -0.50 of opinion classification of VOC. Through the proposed intelligent VOC analyzing system, the real time opinion classification and response priority of VOC can be predicted. Ultimately the positive effectiveness is expected to catch the customer complains at early stage and deal with it quickly with the lower number of staff to operate the VOC system. It can be made available human resource and time of customer service part. Above all, this study is new try to automatic analyzing the unstructured VOC data using opinion mining, and shows that the system could be used as variable to classify the positive or negative polarity of VOC opinion. It is expected to suggest practical framework of the VOC analysis to diverse use and the model can be used as real VOC analyzing system if it is implemented as system. Despite experiment results and expectation, this study has several limits. First of all, the sample data is only collected from a hospital web-site. It means that the sentimental dictionary made by sample data can be lean too much towards on that hospital and web-site. Therefore, next research has to take several channels such as call-center and SNS, and other domain like government, financial company, and education institute.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.19-33
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• 2013

In this research, a proposed Dynamic Virtual Ontology using Tags (DyVOT) supports dynamic search of resources depending on user's requirements using tags from social web driven resources. It is general that the tags are defined by annotations of a series of described words by social users who usually tags social information resources such as web-page, images, u-tube, videos, etc. Therefore, tags are characterized and mirrored by information resources. Therefore, it is possible for tags as meta-data to match into some resources. Consequently, we can extract semantic relationships between tags owing to the dependency of relationships between tags as representatives of resources. However, to do this, there is limitation because there are allophonic synonym and homonym among tags that are usually marked by a series of words. Thus, research related to folksonomies using tags have been applied to classification of words by semantic-based allophonic synonym. In addition, some research are focusing on clustering and/or classification of resources by semantic-based relationships among tags. In spite of, there also is limitation of these research because these are focusing on semantic-based hyper/hypo relationships or clustering among tags without consideration of conceptual associative relationships between classified or clustered groups. It makes difficulty to effective searching resources depending on user requirements. In this research, the proposed DyVOT uses tags and constructs ontologyfor effective search. We assumed that tags are extracted from user requirements, which are used to construct multi sub-ontology as combinations of tags that are composed of a part of the tags or all. In addition, the proposed DyVOT constructs ontology which is based on hierarchical and associative relationships among tags for effective search of a solution. The ontology is composed of static- and dynamic-ontology. The static-ontology defines semantic-based hierarchical hyper/hypo relationships among tags as in (http://semanticcloud.sandra-siegel.de/) with a tree structure. From the static-ontology, the DyVOT extracts multi sub-ontology using multi sub-tag which are constructed by parts of tags. Finally, sub-ontology are constructed by hierarchy paths which contain the sub-tag. To create dynamic-ontology by the proposed DyVOT, it is necessary to define associative relationships among multi sub-ontology that are extracted from hierarchical relationships of static-ontology. The associative relationship is defined by shared resources between tags which are linked by multi sub-ontology. The association is measured by the degree of shared resources that are allocated into the tags of sub-ontology. If the value of association is larger than threshold value, then associative relationship among tags is newly created. The associative relationships are used to merge and construct new hierarchy the multi sub-ontology. To construct dynamic-ontology, it is essential to defined new class which is linked by two more sub-ontology, which is generated by merged tags which are highly associative by proving using shared resources. Thereby, the class is applied to generate new hierarchy with extracted multi sub-ontology to create a dynamic-ontology. The new class is settle down on the ontology. So, the newly created class needs to be belong to the dynamic-ontology. So, the class used to new hyper/hypo hierarchy relationship between the class and tags which are linked to multi sub-ontology. At last, DyVOT is developed by newly defined associative relationships which are extracted from hierarchical relationships among tags. Resources are matched into the DyVOT which narrows down search boundary and shrinks the search paths. Finally, we can create the DyVOT using the newly defined associative relationships. While static data catalog (Dean and Ghemawat, 2004; 2008) statically searches resources depending on user requirements, the proposed DyVOT dynamically searches resources using multi sub-ontology by parallel processing. In this light, the DyVOT supports improvement of correctness and agility of search and decreasing of search effort by reduction of search path.

• Sim-seong Yeon-gu
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• v.26 no.1
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• pp.36-66
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• 2011

The goal of this article is to examine the connection between creativity of unconscious and religion. Jung criticized how Freud's approach in studying the unconscious as a scientific inquiry focuses on the unconscious as reflecting only those which is repressed by the ego. Jung conceived of the unconscious as encompassing not only the repressed but also the variety of other psychic materials that have not reached the threshold of the consciousness in its range. Moreover, since human psyche is as individualistic as is a collective phenomenon, the collective psyche is thought to be pervasive at the bottom of the psychic functioning and the conscious and the personal unconscious comprising the upper level of the psychic functioning. Through clinical and personal experience, Jung had come to a realization that the unconscious has the self-regulatory function. The unconscious can make "demands" and also can retract its demands. Jung saw this as the autonomous function of the unconscious. And this autonomous unconscious creates, through dreams and fantasies, images that include an abundance of ideas and feelings. These creative images the unconscious produces assist and lead the "individuation process" which leads to the discovery of the Self. Because this unconscious process compensates the conscious ego, it has the necessary ingredients for self-regulation and can function in a creative and autonomous fashion. Jung saw religion as a special attitude of human psyche, which can be explained by careful and diligent observation about a dynamic being or action, which Rudolph Otto called the Numinosum. This kind of being or action does not get elicited by artificial or willful action. On the contrary, it takes a hold and dominates the human subject. Jung distinguished between religion and religious sector or denomination. He explained religious sector as reflecting the contents of sanctified and indoctrinated religious experiences. It is fixated in the complex organization of ritualized thoughts. And this ritualization gives rise to a system that is fixated. There is a clear goal in the religious sector to replace intellectual experiences with firmly established dogma and rituals. Religion as Jung experienced is the attitude of contemplation about Numinosum, which is formed by the images of the collective unconscious that is propelled by the creativity and autonomy of the unconscious. Religious sector is a religious community that is formed by these images that are ritualized. Jung saw religion as the relationship with the best or the uttermost value. And this relationship has a duality of being involuntary and reflecting free will. Therefore people can be influenced by one value, overcome with the unconscious being charged with psychic energy, or could accept it on a conscious level. Jung saw God as the dominating psychic element among humans or that psychic reality itself. Although Jung grew up in the atmosphere of the traditional Swiss reformed church, it does not seem that he considered himself to be a devoted Christian. To Jung, Christianity is a habitual, ritualized institution, which lacked vitality because it did not have the intellectual honesty or spiritual energy. However, Jung's encounter with the dramatic religious experience at age 12 through hallucination led him to perceive the existence of living god in his unconscious. This is why the theological questions and religious problems in everyday life became Jung's life-long interest. To this author, the reason why Jung delved into problems with religion has to do with his personal interest and love for the revival of the Christian church which had lost its spiritual vitality and depth and had become heavily ritualized.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.113-127
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• 2016

Among the techniques for recommendation, collaborative filtering (CF) is commonly recognized to be the most effective for implementing recommender systems. Until now, CF has been popularly studied and adopted in both academic and real-world applications. The basic idea of CF is to create recommendation results by finding correlations between users of a recommendation system. CF system compares users based on how similar they are, and recommend products to users by using other like-minded people's results of evaluation for each product. Thus, it is very important to compute evaluation similarities among users in CF because the recommendation quality depends on it. Typical CF uses user's explicit numeric ratings of items (i.e. quantitative information) when computing the similarities among users in CF. In other words, user's numeric ratings have been a sole source of user preference information in traditional CF. However, user ratings are unable to fully reflect user's actual preferences from time to time. According to several studies, users may more actively accommodate recommendation of reliable others when purchasing goods. Thus, trust relationship can be regarded as the informative source for identifying user's preference with accuracy. Under this background, we propose a new hybrid recommender system that fuses CF and social network analysis (SNA). The proposed system adopts the recommendation algorithm that additionally reflect the result analyzed by SNA. In detail, our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and trust relationship information between users when calculating user similarities. For this, our system creates and uses not only user-item rating matrix, but also user-to-user trust network. As the methods for calculating user similarity between users, we proposed two alternatives - one is algorithm calculating the degree of similarity between users by utilizing in-degree and out-degree centrality, which are the indices representing the central location in the social network. We named these approaches as 'Trust CF - All' and 'Trust CF - Conditional'. The other alternative is the algorithm reflecting a neighbor's score higher when a target user trusts the neighbor directly or indirectly. The direct or indirect trust relationship can be identified by searching trust network of users. In this study, we call this approach 'Trust CF - Search'. To validate the applicability of the proposed system, we used experimental data provided by LibRec that crawled from the entire FilmTrust website. It consists of ratings of movies and trust relationship network indicating who to trust between users. The experimental system was implemented using Microsoft Visual Basic for Applications (VBA) and UCINET 6. To examine the effectiveness of the proposed system, we compared the performance of our proposed method with one of conventional CF system. The performances of recommender system were evaluated by using average MAE (mean absolute error). The analysis results confirmed that in case of applying without conditions the in-degree centrality index of trusted network of users(i.e. Trust CF - All), the accuracy (MAE = 0.565134) was lower than conventional CF (MAE = 0.564966). And, in case of applying the in-degree centrality index only to the users with the out-degree centrality above a certain threshold value(i.e. Trust CF - Conditional), the proposed system improved the accuracy a little (MAE = 0.564909) compared to traditional CF. However, the algorithm searching based on the trusted network of users (i.e. Trust CF - Search) was found to show the best performance (MAE = 0.564846). And the result from paired samples t-test presented that Trust CF - Search outperformed conventional CF with 10% statistical significance level. Our study sheds a light on the application of user's trust relationship network information for facilitating electronic commerce by recommending proper items to users.

• Journal of Korean Society of Forest Science
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• v.109 no.2
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• pp.157-168
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• 2020

The sensitivity of forest soil to acidification in the Republic of Korea (ROK) was evaluated based on pH_H2O, cation exchange capacity (CEC), and base saturation (BS). Sensitivity to acidification was categorized into three grades: adequate level (AL, pH ≧ 4.2, CEC ≧ 15cmol/kg, BS ≧ 15%), caution level (CL, at least one indicator is below AL), and severe Level (SL, all three indicators are below AL). Soil samples were collected from the 65 stationary monitoring plots (40 × 40 ㎢), distributed throughout ROK. Only 19% of soil samples were classified as AL, while 66% and 15% were CL and SL, respectively. The median of pH_H2O, CEC, BS, and Ca/Al indicator in AL soils was pH 4.64, 20.7cmol/kg, 29%, and 6.3, respectively. Moreover, BC_ex (K⁺, Mg²⁺, Ca²⁺) and available phosphorus (AP) concentration compared with a threshold value and molar ratio of BC_ex and AP to total nitrogen (TN) was high. This indicates that AL soils have a good nutrient condition. The molar Ca/Al ratio, an indicator for toxicity of exchangeable aluminum (Al_ex), was more than 1, indicating no negative impact of Al_ex on plant growth. On the contrary, the median of pH_H2O, CEC, and BS in SL soils was pH 4.02, 13.2cmol/kg, and 10%, respectively. The Ca/Al index was less than 0.6, which indicates that negative impacts of Al_ex on plants were high. Furthermore, both the concentration of BC_ex in SL soils and the BC_ex/TN ratio were the lowest among the three acidity degrees. This shows that SLsoils can be degraded by soil acidification compared with less acidic soils.