• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.23-38
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• 2012

There is a large difference between purchasing patterns in an online shopping mall and in an offline market. This difference may be caused mainly by the difference in accessibility of online and offline markets. It means that an interval between the initial purchasing decision and its realization appears to be relatively short in an online shopping mall, because a customer can make an order immediately. Because of the short interval between a purchasing decision and its realization, an online shopping mall transaction usually contains fewer items than that of an offline market. In an offline market, customers usually keep some items in mind and buy them all at once a few days after deciding to buy them, instead of buying each item individually and immediately. On the contrary, more than 70% of online shopping mall transactions contain only one item. This statistic implies that traditional data mining techniques cannot be directly applied to online market analysis, because hardly any association rules can survive with an acceptable level of Support because of too many Null Transactions. Most market basket analyses on online shopping mall transactions, therefore, have been performed by expanding the co-occurrence criteria of traditional association rule mining. While the traditional co-occurrence criteria defines items purchased in one transaction as concurrently purchased items, the expanded co-occurrence criteria regards items purchased by a customer during some predefined period (e.g., a day) as concurrently purchased items. In studies using expanded co-occurrence criteria, however, the criteria has been defined arbitrarily by researchers without any theoretical grounds or agreement. The lack of clear grounds of adopting a certain co-occurrence criteria degrades the reliability of the analytical results. Moreover, it is hard to derive new meaningful findings by combining the outcomes of previous individual studies. In this paper, we attempt to compare expanded co-occurrence criteria and propose a guideline for selecting an appropriate one. First of all, we compare the accuracy of association rules discovered according to various co-occurrence criteria. By doing this experiment we expect that we can provide a guideline for selecting appropriate co-occurrence criteria that corresponds to the purpose of the analysis. Additionally, we will perform similar experiments with several groups of customers that are segmented by each customer's average duration between orders. By this experiment, we attempt to discover the relationship between the optimal co-occurrence criteria and the customer's average duration between orders. Finally, by a series of experiments, we expect that we can provide basic guidelines for developing customized recommendation systems. Our experiments use a real dataset acquired from one of the largest internet shopping malls in Korea. We use 66,278 transactions of 3,847 customers conducted during the last two years. Overall results show that the accuracy of association rules of frequent shoppers (whose average duration between orders is relatively short) is higher than that of causal shoppers. In addition we discover that with frequent shoppers, the accuracy of association rules appears very high when the co-occurrence criteria of the training set corresponds to the validation set (i.e., target set). It implies that the co-occurrence criteria of frequent shoppers should be set according to the application purpose period. For example, an analyzer should use a day as a co-occurrence criterion if he/she wants to offer a coupon valid only for a day to potential customers who will use the coupon. On the contrary, an analyzer should use a month as a co-occurrence criterion if he/she wants to publish a coupon book that can be used for a month. In the case of causal shoppers, the accuracy of association rules appears to not be affected by the period of the application purposes. The accuracy of the causal shoppers' association rules becomes higher when the longer co-occurrence criterion has been adopted. It implies that an analyzer has to set the co-occurrence criterion for as long as possible, regardless of the application purpose period.

• The Korean Society of Law and Medicine
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• v.16 no.2
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• pp.263-291
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• 2015

In May, 2015, a 68 years old man, who has been Middle East Saudi Arabia and the United Arab Emirates, had high fever, muscle aches, cough and shortness of breath. he went two local hospital near his house and the S Medical Center emergency center. He was diagnosed MERS(Middle East respiratory syndrome) and the diseases had put South Korea the fear of epidemics for three months. Especially, this disease has firstly reported in Middle East Asia in September 2012 and spreaded to twenty-six countries. In 21, July, 2015, European Center for disease prevention and control reported 533 people were died and in South Korea, 186 people were infected, 36 people were died and 16,693 people were isolated from MERS. South Korea government were faced into epidemic control and blamed from public. Especially, hospital acquired infection, disease control chain, opening of information, ventilation, lack of isolation bed, the problem of function of local health center, the issue of reparation for hospital and insurance cover rate, the classification of disease, the role of Korea Centers for disease control and prevention, the culture of visiting hospital to see sick people, the issue of hospital multiple room and other related social support policy. it is time to study and discuss to solve these problems. South Korea citizens felt fear and fright from MERS. What is wore, they thought the dieses were out of their government control. It was unusual case for word except Middle East Asia. numerous tourists canceled visiting korea. South korea economic were severly damaged especially, tourism industry. South korea government should admit that they had failed initial action against MERS and take full reasonability from any damages. The government have to open information to public in terms of epidemic diseases and try to prevent any other epidemic diseases and try to work with local governments.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.742-757
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• 2001

Since database management systems(DBMSS) have limited lock resources, transactions requesting locks beyond the limit mutt be aborted. In the worst carte, if such transactions are aborted repeatedly, the DBMS can become paralyzed, i.e., transaction execute but cannot commit. Lock escalation is considered a solution to this problem. However, existing lock escalation methods do not provide a complete solution. In this paper, we prognose a new lock escalation method, adaptive lock escalation, that selves most of the problems. First, we propose a general model for lock escalation and present the concept of the unescalatable look, which is the major cause making the transactions to abort. Second, we propose the notions of semi lock escalation, lock blocking, and selective relief as the mechanisms to control the number of unescalatable locks. We then propose the adaptive lock escalation method using these notions. Adaptive lock escalation reduces needless aborts and guarantees that the DBMS is not paralyzed under excessive lock requests. It also allows graceful degradation of performance under those circumstances. Third, through extensive simulation, we show that adaptive lock escalation outperforms existing lock escalation methods. The results show that, compared to the existing methods, adaptive lock escalation reduces the number of aborts and the average response time, and increases the throughput to a great extent. Especially, it is shown that the number of concurrent transactions can be increased more than 16 ~256 fold. The contribution of this paper is significant in that it has formally analysed the role of lock escalation in lock resource management and identified the detailed underlying mechanisms. Existing lock escalation methods rely on users or system administrator to handle the problems of excessive lock requests. In contrast, adaptive lock escalation releases the users of this responsibility by providing graceful degradation and preventing system paralysis through automatic control of unescalatable locks Thus adaptive lock escalation can contribute to developing self-tuning: DBMSS that draw a lot of attention these days.

• Korean Journal of Environment and Ecology
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• v.33 no.5
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• pp.506-514
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• 2019

Unlike natural ecosystems, the urban ecosystem proVides an interdependent enVironment in which wild organisms and urban people co-exist. Hornets (genus Vespa) appearing in urban green and parks haVe a positiVe effect on urban ecosystems, but they also cause ecosystem disserVices that cause physical and psychological discomforts to the urban people. Children's parks, for example, are Very popular among children and residents for easy accessibility, and hornets also use them as bases and habitats. HoweVer, there is still a lack of spatial analysis of habitats and appearance characteristics of hornets in children's parks. This study installed hornet traps in 27 children's parks in Cheonan from April to NoVember 2018 in consideration of the life cycle of hornets. We captured a total of fiVe Vespa species (Vespa crabro, V. analis, V. mandarinia, V. ducalis, and V. Velutina) for 32 weeks and analyzed the emergence of hornets in relation to the composition of seasonal characteristics, species characteristics, and enVironmental spatial information. We captured a total of 818 hornets during the study period. They included 290 V. analis (35.4%), 260 V. crabro (31.8%), 100 V. ducalis (12.1%), 87 V. mandaninia (10.6%), and 81 V. Velutina(9.9%). Most of the hornets showed a common feature that queen hornets were largely captured in May through June after they awake from hibernation, and the number of caught hornets decreased sharply beginning in mid-June, which was the cooperatiVe period. HoweVer, V. Velutina showed a seasonal specificity that more than 80% were captured beginning in the third week of October when other hornet species had already entered a decline phase. The analysis of the number of hornets caught in each spot in children's parks showed significant difference among the spots as 363 hornets (44.3%) were captured in top children's parks, and 35 hornets (4%) were captured in bottom children's parks. In particular, the mean NDVI (Normalized difference Vegetation index) of the top six children's parks was 0.79, and that of the bottom six children's parks was 0.38 (t=2.67*, *=p<0.05), indicating a significant difference. The frequency of capturing hornets was high when the ground around the children's parks was grass or bare land. This study is meaningful as a reference study that confirms the ecological characteristics of hornets appearing in green and parks in the city. We expect it to be a foundation for effectiVe urban green area management in the future.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.43-62
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• 2019

At one time, the anomaly detection sector dominated the method of determining whether there was an abnormality based on the statistics derived from specific data. This methodology was possible because the dimension of the data was simple in the past, so the classical statistical method could work effectively. However, as the characteristics of data have changed complexly in the era of big data, it has become more difficult to accurately analyze and predict the data that occurs throughout the industry in the conventional way. Therefore, SVM and Decision Tree based supervised learning algorithms were used. However, there is peculiarity that supervised learning based model can only accurately predict the test data, when the number of classes is equal to the number of normal classes and most of the data generated in the industry has unbalanced data class. Therefore, the predicted results are not always valid when supervised learning model is applied. In order to overcome these drawbacks, many studies now use the unsupervised learning-based model that is not influenced by class distribution, such as autoencoder or generative adversarial networks. In this paper, we propose a method to detect anomalies using generative adversarial networks. AnoGAN, introduced in the study of Thomas et al (2017), is a classification model that performs abnormal detection of medical images. It was composed of a Convolution Neural Net and was used in the field of detection. On the other hand, sequencing data abnormality detection using generative adversarial network is a lack of research papers compared to image data. Of course, in Li et al (2018), a study by Li et al (LSTM), a type of recurrent neural network, has proposed a model to classify the abnormities of numerical sequence data, but it has not been used for categorical sequence data, as well as feature matching method applied by salans et al.(2016). So it suggests that there are a number of studies to be tried on in the ideal classification of sequence data through a generative adversarial Network. In order to learn the sequence data, the structure of the generative adversarial networks is composed of LSTM, and the 2 stacked-LSTM of the generator is composed of 32-dim hidden unit layers and 64-dim hidden unit layers. The LSTM of the discriminator consists of 64-dim hidden unit layer were used. In the process of deriving abnormal scores from existing paper of Anomaly Detection for Sequence data, entropy values of probability of actual data are used in the process of deriving abnormal scores. but in this paper, as mentioned earlier, abnormal scores have been derived by using feature matching techniques. In addition, the process of optimizing latent variables was designed with LSTM to improve model performance. The modified form of generative adversarial model was more accurate in all experiments than the autoencoder in terms of precision and was approximately 7% higher in accuracy. In terms of Robustness, Generative adversarial networks also performed better than autoencoder. Because generative adversarial networks can learn data distribution from real categorical sequence data, Unaffected by a single normal data. But autoencoder is not. Result of Robustness test showed that he accuracy of the autocoder was 92%, the accuracy of the hostile neural network was 96%, and in terms of sensitivity, the autocoder was 40% and the hostile neural network was 51%. In this paper, experiments have also been conducted to show how much performance changes due to differences in the optimization structure of potential variables. As a result, the level of 1% was improved in terms of sensitivity. These results suggest that it presented a new perspective on optimizing latent variable that were relatively insignificant.

• Korea Science and Art Forum
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• v.37 no.4
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• pp.297-308
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• 2019

Air pollution due to the fine dust level updating every day, and the problem of indoor air pollution due to ventilation difficulties and indoor discharge pollutants is also serious. In order to improve the indoor air quality, the air purification effect using the plants is prominent. In this study was started to investigated the living environment of modern people, the risk of indoor air pollution and the improvement function of plants, and to activate plant application. The purpose of this study is to analyze the main functions and design status of domestic and overseas plant - related applications, and to understand the actual use of modern plant applications and to help them learn more convenient plant - related knowledge. Therefore, this paper attempted to establish a basis for suggesting a new plant application by conducting a survey on the health effects of indoor air pollution and user awareness of plant - related applications. The results and contents of the study are as follows. First, as a theoretical review, indoor air pollution is more dangerous to modern people who have a high proportion of indoor living time and adversely affects their health. In order to solve such a problem, it has been shown that air conditioning and stress reduction can be effectively achieved by placing plants in the indoor space. Second, the analysis of the previous study shows the risk of indoor air pollution and its adverse effects on health. In addition, I have been able to find some researches related to the improvement of the indoor air by using the air purifying plants, and I can see the improvement of the user's behavior through the development or improvement of the application. Third, as a result of the survey on the status of domestic and overseas plant application, the main function of the application having high installation number was watering notification, provision of basic information of plants, and most of the functions were plant discerment through cameras. Fourth, most of the survey respondents have either raised or raised plants. Those who have little experience with plant applications have also shown positive feedback in the future on the use of plant-related applications. In addition, due to social problems such as air pollution, air purification using plants and functional plants showed high interest. Based on these results, we propose the need for a multi-functional plant application that can improve the indoor air pollution and facilitate the provision of information related to it.

• Ecology and Resilient Infrastructure
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• v.7 no.4
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• pp.388-395
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• 2020

Exotic species have been imported for economic purposes, but more recently, an increasing number of animals are imported as pets. With the increasing popularity of two species of turtles, Mauremys sinensis and Pseudemys concinna, the number of pet turtle owners has gradually increased since 2014. The number of turtles increased by 180 in 2017 and 281 in 2019. However, these turtle species have been abandoned to nature, owing to their long lifespans and the changes in conditions of pet owners. The two turtle species have been designated as invasive alien species (AIS) in Korea considering their ecological risks, and the Biological Diversity Act prohibits their release. The owners of Mauremys sinensis and Pseudemys concinna are required to submit the "Application for Approval of Breeding and Grace for AIS" document. In this study, the breeding conditions for the two turtle species were investigated by analyzing the information in the submitted applications for six months (e.g., the suitability of breeding facilities, number of turtles, breeding period, type of pet adoption, and local district of pet owner). A total of 614 cases were analyzed. Because only 58% of breeders provided suitable breeding conditions, breeding information and responsible pet ownership training should be offered to prevent abandonment in natural ecosystems. In addition, continuous monitoring is necessary to prepare for potential problems caused by the lack of information in many applications and the one-off licensing policy.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.30-43
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• 2018

Many studies have been carried out to retrieve images using colors, shapes, and textures which are characteristic of images. In addition, there is also progress in research related to the disease images of the crop. In this paper, to be a help to identify the disease occurred in crops grown in the agricultural field, we propose a similarity-based crop disease search system using the diseases image of horticulture crops. The proposed system improves the similarity retrieval performance compared to existing ones through the combination descriptor without using a single descriptor and applied the weight based calculation method to provide users with highly readable similarity search results. In this paper, a total of 13 Descriptors were used in combination. We used to retrieval of disease of six crops using a combination Descriptor, and a combination Descriptor with the highest average accuracy for each crop was selected as a combination Descriptor for the crop. The retrieved result were expressed as a percentage using the calculation method based on the ratio of disease names, and calculation method based on the weight. The calculation method based on the ratio of disease name has a problem in that number of images used in the query image and similarity search was output in a first order. To solve this problem, we used a calculation method based on weight. We applied the test image of each disease name to each of the two calculation methods to measure the classification performance of the retrieval results. We compared averages of retrieval performance for two calculation method for each crop. In cases of red pepper and apple, the performance of the calculation method based on the ratio of disease names was about 11.89% on average higher than that of the calculation method based on weight, respectively. In cases of chrysanthemum, strawberry, pear, and grape, the performance of the calculation method based on the weight was about 20.34% on average higher than that of the calculation method based on the ratio of disease names, respectively. In addition, the system proposed in this paper, UI/UX was configured conveniently via the feedback of actual users. Each system screen has a title and a description of the screen at the top, and was configured to display a user to conveniently view the information on the disease. The information of the disease searched based on the calculation method proposed above displays images and disease names of similar diseases. The system's environment is implemented for use with a web browser based on a pc environment and a web browser based on a mobile device environment.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.47-64
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• 2021

The global spread of COVID-19 around the world has not only affected many parts of our daily life but also has a huge impact on many areas, including the economy and society. As the number of confirmed cases and deaths increases, medical staff and the public are said to be experiencing psychological problems such as anxiety, depression, and stress. The collective tragedy that accompanies the epidemic raises fear and anxiety, which is known to cause enormous disruptions to the behavior and psychological well-being of many. Long-term negative emotions can reduce people's immunity and destroy their physical balance, so it is essential to understand the psychological state of COVID-19. This study suggests a method of monitoring medial news reflecting current days which requires striving not only for physical but also for psychological quarantine in the prolonged COVID-19 situation. Moreover, it is presented how an easier method of analyzing social media networks applies to those cases. The aim of this study is to assist health policymakers in fast and complex decision-making processes. News plays a major role in setting the policy agenda. Among various major media, news headlines are considered important in the field of communication science as a summary of the core content that the media wants to convey to the audiences who read it. News data used in this study was easily collected using "Bigkinds" that is created by integrating big data technology. With the collected news data, keywords were classified through text mining, and the relationship between words was visualized through semantic network analysis between keywords. Using the KrKwic program, a Korean semantic network analysis tool, text mining was performed and the frequency of words was calculated to easily identify keywords. The frequency of words appearing in keywords of articles related to COVID-19 emotions was checked and visualized in word cloud 'China', 'anxiety', 'situation', 'mind', 'social', and 'health' appeared high in relation to the emotions of COVID-19. In addition, UCINET, a specialized social network analysis program, was used to analyze connection centrality and cluster analysis, and a method of visualizing a graph using Net Draw was performed. As a result of analyzing the connection centrality between each data, it was found that the most central keywords in the keyword-centric network were 'psychology', 'COVID-19', 'blue', and 'anxiety'. The network of frequency of co-occurrence among the keywords appearing in the headlines of the news was visualized as a graph. The thickness of the line on the graph is proportional to the frequency of co-occurrence, and if the frequency of two words appearing at the same time is high, it is indicated by a thick line. It can be seen that the 'COVID-blue' pair is displayed in the boldest, and the 'COVID-emotion' and 'COVID-anxiety' pairs are displayed with a relatively thick line. 'Blue' related to COVID-19 is a word that means depression, and it was confirmed that COVID-19 and depression are keywords that should be of interest now. The research methodology used in this study has the convenience of being able to quickly measure social phenomena and changes while reducing costs. In this study, by analyzing news headlines, we were able to identify people's feelings and perceptions on issues related to COVID-19 depression, and identify the main agendas to be analyzed by deriving important keywords. By presenting and visualizing the subject and important keywords related to the COVID-19 emotion at a time, medical policy managers will be able to be provided a variety of perspectives when identifying and researching the regarding phenomenon. It is expected that it can help to use it as basic data for support, treatment and service development for psychological quarantine issues related to COVID-19.