• Journal of the Speleological Society of Korea
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• no.68
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• pp.23-73
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• 2005

Purpose of study; The purpose of this study is specifically classified as two parts. The one is to attempt the chronological annals of Quaternary topographic surface through the study over the formation process of alluvial surfaces in our country, setting forth the alluvial surfaces lower-parts of Han River area, as the basic deposit, and comparing it to the marginal landform surfaces. The other is to attempt the classification of micro morphology based on the and condition premising the land use as a link for the regional development in the lower-parts of Han river area. Reasons why selected the Lower-parts of Han river area as study objects: 1. The change of river course in this area is very serve both in vertical and horizontal sides. With a situation it is very easy to know about the old geography related to the formation process of topography. 2. The component materials of gravel, sand, silt and clay are deposited in this area. Making it the available data, it is possible to consider about not oかy the formation process of topography but alsoon the development history to some extent. 3. The earthen vessel, a fossil shell fish, bone, cnarcoal and sea-weed are included in the alluvial deposition in this area. These can be also valuable data related to the chronological annals. 4. The bottom set conglometate beds is also included in the alluvial deposits. This can be also valuable data related to the research of geomorphological development. 5. Around of this area the medium landform surface, lower landform surface, pediment and basin, are existed, and these enable the comparison between the erosion surfaces and the alluvial surfaces. Approach : 1. Referring to the change of river beds, I have calculated the vertical and horizontal differences comparing the topographic map published in 1916 with that published in 1966 and through the field work 2. In classifying the landform, I have applied the method of micro morphological classification in accordance with the synthetic index based upon the land conditions, and furthermore used the classification method comparing the topographic map published in 1916 and in that of 1966. 3. I have accorded this classification with the classification by mapping through appliying the method of classification in the development history for the field work making the component materials as the available data. 4. I have used the component materials, which were picked up form the outcrop of 10 places and bored at 5 places, as the available data. 5. I have referred to Hydrological survey data of the ministry of Construction (since 1916) on the overflow of Han-river, and used geologic map of Seoul metropolitan area. Survey Data, and general map published in 1916 by the Japanese Army Survbey Dept., and map published in 1966 by the Construction Research Laboratory and ROK Army Survey Dept., respectively. Conclusion: 1. Classification of Morphology: I have added the historical consideration for development, making the component materials and fossil as the data, to the typical consideration in accordance with the map of summit level, reliefe and slope distribution. In connection with the erosion surface, I have divided into three classification such as high, medium and low-,level landform surfaces which were classified as high and low level landform surfaces in past. furthermore I have divided the low level landform surface two parts, namely upper-parts(200-300m) and bellow-parts(${\pm}100m$). Accordingly, we can recognize the three-parts of erosion surface including the medium level landform surface (500-600m) in this area. (see table 22). In condition with the alluvial surfaces I have classified as two landform surfaces (old and new) which was regarded as one face in past. Meamwhile, under the premise of land use, the synthetic, micro morphological classification based upon the land condition is as per the draw No. 19-1. This is the quite new method of classification which was at first attempted in this country. 2. I have learned that the change of river was most severe at seeing the river meandering rate from Dangjung-ni to Nanjido. As you seee the table and the vertical and horizontal change of river beds is justly proportionable to the river meandering rate. 3. It can be learned at seeing the analysis of component materials of alluvial deposits that the component from each other by areas, however, in the deposits relationship upper stream, and between upper parts and below parts I couldn't always find out the regular ones. 4. Having earthern vessel, shell bone, fossil charcoal and and seaweeds includen in the component materials such as gravel, clay, sand and silt in Dukso and Songpa deposits area. I have become to attempt the compilation of chronicle as yon see in the table 22. 5. In according to hearing of basemen excavation, the bottom set conglomerate beds of Dukso beds of Dukso-beds is 7m and Songpa-beds is 10m. In according to information of dredger it is approx. 20m in the down stream. 6. Making these two beds as the standard beds, I have compared it to other beds. 7 The coarse sand beds which is covering the clay-beds of Dukso-beds and Nanjidobeds is shown the existence of so-called erosion period which formed the gap among the alluvial deposits of stratum. The former has been proved by the sorting, bedding and roundness which was supplied by the main stream and later by the branch stream, respectively. 8. If the clay-beds of Dukeo-bed and Songpa-bed is called as being transgressive overlap, by the Eustatic movement after glacial age, the bottom set conglomerate beds shall be called as being regressive overlap at the holocene. This has the closest relationship with the basin formation movement of Seoul besides the Eustatic movement. 9. The silt-beds which is the main component of deposits of flood plain, is regarded as being deposited at the Holocene in the comb ceramic and plain pottery ages. This has the closest relationship with the change of river course and river beds.

• 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.23 no.3
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• pp.95-118
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• 2017

Recent centrally the downtown area, the transaction between the row housing and multiplex housing is activated and platform services such as Zigbang and Dabang are growing. The row housing and multiplex housing is a blind spot for real estate information. Because there is a social problem, due to the change in market size and information asymmetry due to changes in demand. Also, the 5 or 25 districts used by the Seoul Metropolitan Government or the Korean Appraisal Board(hereafter, KAB) were established within the administrative boundaries and used in existing real estate studies. This is not a district classification for real estate researches because it is zoned urban planning. Based on the existing study, this study found that the city needs to reset the Seoul Metropolitan Government's spatial structure in estimating future housing prices. So, This study attempted to classify the area without spatial heterogeneity by the reflected the property price characteristics of row housing and Multiplex housing. In other words, There has been a problem that an inefficient side has arisen due to the simple division by the existing administrative district. Therefore, this study aims to cluster Seoul as a new area for more efficient real estate analysis. This study was applied to the hedonic model based on the real transactions price data of row housing and multiplex housing. And the K-Means Clustering algorithm was used to cluster the spatial structure of Seoul. In this study, data onto real transactions price of the Seoul Row housing and Multiplex Housing from January 2014 to December 2016, and the official land value of 2016 was used and it provided by Ministry of Land, Infrastructure and Transport(hereafter, MOLIT). Data preprocessing was followed by the following processing procedures: Removal of underground transaction, Price standardization per area, Removal of Real transaction case(above 5 and below -5). In this study, we analyzed data from 132,707 cases to 126,759 data through data preprocessing. The data analysis tool used the R program. After data preprocessing, data model was constructed. Priority, the K-means Clustering was performed. In addition, a regression analysis was conducted using Hedonic model and it was conducted a cosine similarity analysis. Based on the constructed data model, we clustered on the basis of the longitude and latitude of Seoul and conducted comparative analysis of existing area. The results of this study indicated that the goodness of fit of the model was above 75 % and the variables used for the Hedonic model were significant. In other words, 5 or 25 districts that is the area of the existing administrative area are divided into 16 districts. So, this study derived a clustering method of row housing and multiplex housing in Seoul using K-Means Clustering algorithm and hedonic model by the reflected the property price characteristics. Moreover, they presented academic and practical implications and presented the limitations of this study and the direction of future research. Academic implication has clustered by reflecting the property price characteristics in order to improve the problems of the areas used in the Seoul Metropolitan Government, KAB, and Existing Real Estate Research. Another academic implications are that apartments were the main study of existing real estate research, and has proposed a method of classifying area in Seoul using public information(i.e., real-data of MOLIT) of government 3.0. Practical implication is that it can be used as a basic data for real estate related research on row housing and multiplex housing. Another practical implications are that is expected the activation of row housing and multiplex housing research and, that is expected to increase the accuracy of the model of the actual transaction. The future research direction of this study involves conducting various analyses to overcome the limitations of the threshold and indicates the need for deeper research.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.117-140
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• 2019

As the aging phenomenon accelerates and various social problems related to the elderly of the vulnerable are raised, the need for effective elderly care solutions to protect the health and safety of the elderly generation is growing. Recently, more and more people are using Smart Toys equipped with ICT technology for care for elderly. In particular, log data collected through smart toys is highly valuable to be used as a quantitative and objective indicator in areas such as policy-making and service planning. However, research related to smart toys is limited, such as the development of smart toys and the validation of smart toy effectiveness. In other words, there is a dearth of research to derive insights based on log data collected through smart toys and to use them for decision making. This study will analyze log data collected from smart toy and derive effective insights to improve the quality of life for elderly users. Specifically, the user profiling-based analysis and elicitation of a change in quality of life mechanism based on behavior were performed. First, in the user profiling analysis, two important dimensions of classifying the type of elderly group from five factors of elderly user's living management were derived: 'Routine Activities' and 'Work-out Activities'. Based on the dimensions derived, a hierarchical cluster analysis and K-Means clustering were performed to classify the entire elderly user into three groups. Through a profiling analysis, the demographic characteristics of each group of elderlies and the behavior of using smart toy were identified. Second, stepwise regression was performed in eliciting the mechanism of change in quality of life. The effects of interaction, content usage, and indoor activity have been identified on the improvement of depression and lifestyle for the elderly. In addition, it identified the role of user performance evaluation and satisfaction with smart toy as a parameter that mediated the relationship between usage behavior and quality of life change. Specific mechanisms are as follows. First, the interaction between smart toy and elderly was found to have an effect of improving the depression by mediating attitudes to smart toy. The 'Satisfaction toward Smart Toy,' a variable that affects the improvement of the elderly's depression, changes how users evaluate smart toy performance. At this time, it has been identified that it is the interaction with smart toy that has a positive effect on smart toy These results can be interpreted as an elderly with a desire to meet emotional stability interact actively with smart toy, and a positive assessment of smart toy, greatly appreciating the effectiveness of smart toy. Second, the content usage has been confirmed to have a direct effect on improving lifestyle without going through other variables. Elderly who use a lot of the content provided by smart toy have improved their lifestyle. However, this effect has occurred regardless of the attitude the user has toward smart toy. Third, log data show that a high degree of indoor activity improves both the lifestyle and depression of the elderly. The more indoor activity, the better the lifestyle of the elderly, and these effects occur regardless of the user's attitude toward smart toy. In addition, elderly with a high degree of indoor activity are satisfied with smart toys, which cause improvement in the elderly's depression. However, it can be interpreted that elderly who prefer outdoor activities than indoor activities, or those who are less active due to health problems, are hard to satisfied with smart toys, and are not able to get the effects of improving depression. In summary, based on the activities of the elderly, three groups of elderly were identified and the important characteristics of each type were identified. In addition, this study sought to identify the mechanism by which the behavior of the elderly on smart toy affects the lives of the actual elderly, and to derive user needs and insights.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• The Journal of Korean Society for Radiation Therapy
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• v.30 no.1_2
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• pp.107-116
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• 2018

Purpose : Intensity-modulated radiation therapy(IMRT) has been widely used for radiation therapy of Prostate Cancer because it can reduce radiation adverse effects on normal tissues and deliver more dose to the Prostate than 3D radiation therapy. Volumetric modulated arc therapy(VMAT) has been widely used due to recent advances in equipment and treatment techniques. VMAT can reduce treatment time by up to 55 % compared to IMRT, minimizing motion error during treatment. Materials and Methods : In this study, compared the MU and DVH values of 10 patients with prostate cancer by classifying them into 4 groups with 5 LN-Prostate groups and 5 Only-Prostate. And DQA measurements were performed using ArcCHECK and MapCHECK. Results : The results of Target and OAR dose distribution of Prostate patients are as follows. $D_{max}$ was in the range of 100~110 % in 4 groups, and more than 110 % of hot spot was not seen. Only-Prostate ($P_1$, $P_2$) without LN had a satisfactory dose distribution for the target dose, but slightly better for 2 arc plan($P_2$) than 1 arc plan($P_1$). The target dose $D_{98%}$ distribution in the LN-Prostate ($P_{L1}$, $P_{L2}$) group showed better 2 arc plan($P_{L2}$) than 1 arc plan($P_{L1}$), But in the case of 1 arc plan($P_{L1}$), the target dose $D_{98%}$ value was not enough. In OAR, the dose distribution of 1 Arc($P_1$) Plan and 2 Arc($P_2$) Plan in the Only-Prostate ($P_1$, $P_2$) Group satisfied the prescribed dose value. But, The dose distribution of 1 arc($P_1$) was slightly higher. In LN-Prostate OAR, 1 Arc($P_{L1}$) Plan showed higher dose than the prescribed dose. The Gamma evaluation pass rate of ArcCHECK and MapCHECK calculated from the DQA measurements was slightly higher than 99 % and the mean error range of the point dose measurements using the CC04 ion chamber was less than 1 %. Conclusion : In this study, Only-Prostate ($P_1$, $P_2$) group, the dose of 2 Arc plan was better. However, considering the treatment time and MU value, 1 Arc treatment method was more suitable. In the LN-Prostate ($P_{L1}$, $P_{L2}$) group, 2 Arc($P_{L2}$) treatment method showed better results and satisfied with Target $D_{98%}$ and OAR prescription dose.

• (The) Research of the performance art and culture
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• no.35
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• pp.61-92
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• 2017

During the Japanese colonial era, traditional music performances were gradually diminishing and weakening in the particular condition of colonization. Meanwhile, from the time of enlightenment, Western genre performances were becoming vitalized with the influence of Western civilization that began to be spread steadily throughout the society. In that situation, traditional music performances tended to be mixed performances accompanied by Western ones, not independent performances. Mostly, they were accompanied by Western music, and also, they were performed along with other genres like plays, lectures, movies, dances, or magic, too. Such form of mixed performances accompanied by Western genres became even more vitalized in the 1920's and came to be positioned as a form of traditional music performances. Therefore, research on the forms of mixed performances between Western genres and traditional music is meaningful in examining the forms of traditional music performances that have not been studied in the history of Korean modern music and understanding the trends of traditional music performances which were generally found in the Japanese colonial era. However, such research has hardly been conducted concretely yet. Accordingly, concerning the forms of mixed performances between Western genres and traditional music in the 1920's, this author considered the background of vitalizing mixed performances between Western genres and traditional music mainly with newspaper articles of the time and their formal characteristics. Regarding the background of vitalizing the forms of mixed performances between Western genres and traditional music, from the 1920's, the forms of mixed performances between Western genres and traditional music became more vitalized than before. The causes of that may include the increase of groups hosting or sponsoring such performances from the 1920's and also the dramatic increase of such performances in general. Moreover, the increased performances were conducted in the forms of mixed performances mainly in order to satisfy the people's needs becoming diversified with the distribution of Western civilization. Concerning the formal characteristics of mixed performances between Western genres and traditional music, this researcher classified western genres performed with traditional music and examined what characteristics were found in such mixed performances of tradition music by the types of Western genres respectively. First, in the mixed performances type of western-type genre and traditional music, the number of programs for the western music had significant portion in general, and there were certain ensemble of the western music and traditional musical instrument that was rare at this period of time, and it also had the characteristics of classifying two genres to perform for each title or date. Second, in the mixed performances type of the drama and traditional music, the traditional music is directly participated in the drama with the similar type to the theater, or performed independently from the drama with the role of interlude performance for the stage conversion of the drama to have the characteristics of performing in audience publicity or entertainment. Third, in the mixed performances type of the lecture and traditional music, the traditional music is played before or after the lecture to play the role to set the atmosphere and entertainment for the lecture as displaying the feature to perform for the audience attraction. And, fourth, in the mixed performances type of the movie and traditional music, the traditional music sometimes directly participated in the movie or had the features of independent performance, and there was a characteristic to perform for the entertainment after showing a movie.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.

• 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.