• Journal of Korean Society of Forest Science
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• v.75 no.1
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• pp.1-18
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• 1986

This study was initiated to estimate productivity of Quercus variabilis stand. However the practical objective of this study was to provide some information to establish the basis of selecting the suitable site for Quercus variabilis. The productivity measured in terms of DBH, height, basal area and stem volume was hypothesized, respectively, to be a function of a group of factors. This study considered 32 factors, 20 of which were related to the forest environmental factors such as tree age, latitude, percent slope, etc. and the rest of which were related to soil factors such as soil moisture, total nitrogen, available $P_2O_5$, etc. The data on 4 productivity measurements of Quercus variabilis growth and related factors cited were collected from 99 sample plots in Kyeongbook and chungbook provinces. Some factors considered were, in nature, discrete variables and the others continuous variables. Each kind of factor was classified into 3 or 4 categories and total numbers of such categories were eventually amounted to 110. Then each category was treated as an independent variable. This is amounted to saying that individual variable was treated a dummy variable and assigned a value 1 or 0. However the first category of each factor was deleted from the normal equation for statistical consideration. First of all, each of 4 productivity measurements of Quercus variabilis growth was regressed and, at the same time, those 110 categories. Secondly, the partial correlation coefficients were measured between each pair of 4 productivity measurements and 32 individual foctors. Finally, the relative scores were estimated in order to derive the category ranges. The result of these statistical analyses could be summarized as follows: 1) Growth measurement in terms of height seems to be a more significant criterion for estimation of productivity of Quercus variabilis. 2) Productivity of forest on stocked land may better be estimated in terms of forest environmental factors, on the other hand, that of unstocked land may be estimated in terms of physio-chemical factors of soil. 3) The factors that a strongly positive relation to all growth factors of tree are age group, effective soil, soil moisture, etc. This implies that these factors might effectively be used for criteria for selecting the suitable site for Quercus variabilis. 4) Parent rock, latitude, total nitrogen, age group, effective soil depth, soil moisture, organic matter, etc., had more significant category range for tree growth. Therefore, the suitable site for Quercus variabilis may be selected, based on this information. In conclusion, the above results obtained by the multivariable analysis can be not only the important criteria for estimating the growth of Quercus variabilis but also the useful guidance for selecting the suitable sites and performing the rational of Quercus variabilis forest.

• Journal of the Korean Society of School Health
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• v.8 no.2
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• pp.219-232
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• 1995

This study was conducted to examine both usage rate of standard sized desks and chairs for the middle and high school students and pain complant of students who use standard-size desk & chair in Taegu, Korea, by means of questionnaires with 1,201 students of both male and female middle and high schools in Taegu area from March 20 to April 19, 1995. The result of this study is summarized as follows. It was mostly shown that the desks and chairs used by those middle and high school students were 1-3 higher than their standard sizes, and that they also preferred a little higher size with respect to their desired sizes. The rate of students who use the standard size showed that the desk accounted for 30.5%, and chair for 21.0%, that the size bigger than the standard accounted for 61.3%, respectively, and 65.2, and that the size smaller than the standard accounted for 8.2%, respectively, and 13.8%. The using rate of the standard sized for the middle school students indicated that their desk accounted for 44.1%, and their chair for 26.0% which were higher than 16.1% and 14.7% for the high school students. Then, the rate of the male students indicated that their desk accounted for 31.5% and their chair for 24.5% which were higher than 29.6% and 17.6% of the female students. In addition, the using rate of the standard size for the public schools showed that the desk accounted for 34.2% and chair for 24.5% which were also higher than 27.1% and 17.5% of the private schools. It was shown, however, that the using rate of the standard size for both groups was lower. The most inconvenient factor in the usage of their desks appeared in such orders as their wear, narrow drawers, too low height and uneven face, while the factor in their chairs did in such orders as too hard chair body the surface and back part, wear, lower and higher height and narrow width. Their physical pains resulting from usage of those desks and chairs showed that the male and female middle school students' complaint rate of pains in their neck and shoulder accounted for 32.1%, respectively, and 36.0% which were highest, while those high school students' complaint rate in their waist accounted for 37.9%, respectively, and 44.1% which were hight. It was also shown that the bigger their height, the higher their complaint rate of pain in the waist, and that their complaint rate in the shoulder and neck was totally higher. When using the standard-sized desks and chairs, their complaint rate of pain in the shoulder and neck accounted for 25.4%, respectively, and 23.8%. As compared with them, when using the desks or chairs bigger than the standard size, their complaint rate accounted for 31.5%, respectively, and 31.8% which were high while it did 26.5% and 28.9% when using them smaller than the standard size which were also high, the usage of those standard-sized desks and chairs indicated lower complaint rate of pain in their waist than used the desks and chairs bigger or smaller than the standard size. The rate of the middle and high school students who use their standard size is very low and the size of their desks and chairs are quite different from those they hope to use and many students appeal their discomfort with their desks and chairs. Therefore, the school should try to provide the desks and chairs of the various students' standard sizes in consideration of their physical condition and it also should try to get extra desks and chairs of various sizes according to the students' standard size and their preference.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.69-93
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• 2017

The development of ICT has leaded the diversification and changes of supplies and demands in markets. It also caused the creations of a variety of values which are differentiated from those in the existing market. Therefore, a new-type market is created, which can include multi-value chains which are from ICT-based created markets as well as the existing markets. We defined the platform as the new-type market. In the platform, the multi-value chains can be coexisted with multi-values. In true market, when a new-type value chain entered into an existing market, it is general that it can be conflicted with the existing value chain in the market. The conflicted problem among multi-value chains in a market is caused by the sharing of limited market resources like suppliers, consumers, services or products among the value chains. In other words, if there are multi-value chains in the platform, then it is possible to have conflictions, overlapping, creations or losses of values among the value chains. To solve the problem, we introduce coexistence factors to reduce the conflictions to reach market equilibrium in the platform. In the other hand, it is possible to lead the creations of differentiated values from the existing market and to augment the total market values in the platform. In the early era of ICT development, ICT was introduced for improvement of efficiency and effectiveness of the value chains in the existing market. However, according to the changed role of ICT from the supporter to the promotor of the market, ICT became to lead the variations of the value chains and creations of various values in the markets. For instance, Uber Taxi created a new value chain with ICT-based new-type service or products with new resources like new suppliers and consumers. When Uber and Traditional Taxi services are playing at the same time in Taxi service platform, it is possible to create values or make conflictions among values between the new and old value chains. In this research, like Uber and traditional taxi services, if there are conflictions among the multi-value chains, then it is necessary to minimize the conflictions in the platform for the coexistence of multi-value chains which can create the value-added values in the platform. So, it is important to predict and discuss the possible conflicted problems between new and old value chains. The confliction should be solved to reach market equilibrium with multi-value chains in the platform. That is, we discuss the possibility of the coexistence of multi-value chains in the platform which are comprised of a variety of suppliers and customers. To do this, especially we are focusing on the healthcare markets. Nowadays healthcare markets are popularized in global market as well as domestic. Therefore, there are a lot of and a variety of healthcare services like Traditional-, Tele-, or Intelligent- healthcare services and so on. It shows that there are multi-suppliers, -consumers and -services as components of each different value chain in the same platform. The platform can be shared by different values that are created or overlapped by confliction and loss of values in the value chains. In this research, as was said, we focused on the healthcare services to show if a platform can be shared by different value chains like traditional-, tele-healthcare and intelligent-healthcare services and products. Additionally, we try to show if it is possible to increase the value of each value chain as well as the total value of the platform. As the result, it is possible to increase of each value of each value chain as well as the total value in the platform. Finally, we propose a coexistence model to overcome such problems and showed the possibility of coexistence between the value chains through experimentation.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.47-67
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• 2017

Steel plate faults is one of important factors to affect the quality and price of the steel plates. So far many steelmakers generally have used visual inspection method that could be based on an inspector's intuition or experience. Specifically, the inspector checks the steel plate faults by looking the surface of the steel plates. However, the accuracy of this method is critically low that it can cause errors above 30% in judgment. Therefore, accurate steel plate faults diagnosis system has been continuously required in the industry. In order to meet the needs, this study proposed a new steel plate faults diagnosis system using Simultaneous MTS (S-MTS), which is an advanced Mahalanobis Taguchi System (MTS) algorithm, to classify various surface defects of the steel plates. MTS has generally been used to solve binary classification problems in various fields, but MTS was not used for multiclass classification due to its low accuracy. The reason is that only one mahalanobis space is established in the MTS. In contrast, S-MTS is suitable for multi-class classification. That is, S-MTS establishes individual mahalanobis space for each class. 'Simultaneous' implies comparing mahalanobis distances at the same time. The proposed steel plate faults diagnosis system was developed in four main stages. In the first stage, after various reference groups and related variables are defined, data of the steel plate faults is collected and used to establish the individual mahalanobis space per the reference groups and construct the full measurement scale. In the second stage, the mahalanobis distances of test groups is calculated based on the established mahalanobis spaces of the reference groups. Then, appropriateness of the spaces is verified by examining the separability of the mahalanobis diatances. In the third stage, orthogonal arrays and Signal-to-Noise (SN) ratio of dynamic type are applied for variable optimization. Also, Overall SN ratio gain is derived from the SN ratio and SN ratio gain. If the derived overall SN ratio gain is negative, it means that the variable should be removed. However, the variable with the positive gain may be considered as worth keeping. Finally, in the fourth stage, the measurement scale that is composed of selected useful variables is reconstructed. Next, an experimental test should be implemented to verify the ability of multi-class classification and thus the accuracy of the classification is acquired. If the accuracy is acceptable, this diagnosis system can be used for future applications. Also, this study compared the accuracy of the proposed steel plate faults diagnosis system with that of other popular classification algorithms including Decision Tree, Multi Perception Neural Network (MLPNN), Logistic Regression (LR), Support Vector Machine (SVM), Tree Bagger Random Forest, Grid Search (GS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The steel plates faults dataset used in the study is taken from the University of California at Irvine (UCI) machine learning repository. As a result, the proposed steel plate faults diagnosis system based on S-MTS shows 90.79% of classification accuracy. The accuracy of the proposed diagnosis system is 6-27% higher than MLPNN, LR, GS, GA and PSO. Based on the fact that the accuracy of commercial systems is only about 75-80%, it means that the proposed system has enough classification performance to be applied in the industry. In addition, the proposed system can reduce the number of measurement sensors that are installed in the fields because of variable optimization process. These results show that the proposed system not only can have a good ability on the steel plate faults diagnosis but also reduce operation and maintenance cost. For our future work, it will be applied in the fields to validate actual effectiveness of the proposed system and plan to improve the accuracy based on the results.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.23-46
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• 2017

Although there have been cases of evaluating the value of specific companies or projects which have centralized on developed countries in North America and Europe from the early 2000s, the system and methodology for estimating the economic value of individual technologies or patents has been activated on and on. Of course, there exist several online systems that qualitatively evaluate the technology's grade or the patent rating of the technology to be evaluated, as in 'KTRS' of the KIBO and 'SMART 3.1' of the Korea Invention Promotion Association. However, a web-based technology valuation system, referred to as 'STAR-Value system' that calculates the quantitative values of the subject technology for various purposes such as business feasibility analysis, investment attraction, tax/litigation, etc., has been officially opened and recently spreading. In this study, we introduce the type of methodology and evaluation model, reference information supporting these theories, and how database associated are utilized, focusing various modules and frameworks embedded in STAR-Value system. In particular, there are six valuation methods, including the discounted cash flow method (DCF), which is a representative one based on the income approach that anticipates future economic income to be valued at present, and the relief-from-royalty method, which calculates the present value of royalties' where we consider the contribution of the subject technology towards the business value created as the royalty rate. We look at how models and related support information (technology life, corporate (business) financial information, discount rate, industrial technology factors, etc.) can be used and linked in a intelligent manner. Based on the classification of information such as International Patent Classification (IPC) or Korea Standard Industry Classification (KSIC) for technology to be evaluated, the STAR-Value system automatically returns meta data such as technology cycle time (TCT), sales growth rate and profitability data of similar company or industry sector, weighted average cost of capital (WACC), indices of industrial technology factors, etc., and apply adjustment factors to them, so that the result of technology value calculation has high reliability and objectivity. Furthermore, if the information on the potential market size of the target technology and the market share of the commercialization subject refers to data-driven information, or if the estimated value range of similar technologies by industry sector is provided from the evaluation cases which are already completed and accumulated in database, the STAR-Value is anticipated that it will enable to present highly accurate value range in real time by intelligently linking various support modules. Including the explanation of the various valuation models and relevant primary variables as presented in this paper, the STAR-Value system intends to utilize more systematically and in a data-driven way by supporting the optimal model selection guideline module, intelligent technology value range reasoning module, and similar company selection based market share prediction module, etc. In addition, the research on the development and intelligence of the web-based STAR-Value system is significant in that it widely spread the web-based system that can be used in the validation and application to practices of the theoretical feasibility of the technology valuation field, and it is expected that it could be utilized in various fields of technology commercialization.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.37-51
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• 2015

Due to the development of internet technology and the rapid increase of internet data, various studies are actively conducted on how to use and analyze internet data for various purposes. In particular, in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of the current application of structured data. Especially, there are various studies on sentimental analysis to score opinions based on the distribution of polarity such as positivity or negativity of vocabularies or sentences of the texts in documents. As a part of such studies, this study tries to predict ups and downs of stock prices of companies by performing sentimental analysis on news contexts of the particular companies in the Internet. A variety of news on companies is produced online by different economic agents, and it is diffused quickly and accessed easily in the Internet. So, based on inefficient market hypothesis, we can expect that news information of an individual company can be used to predict the fluctuations of stock prices of the company if we apply proper data analysis techniques. However, as the areas of corporate management activity are different, an analysis considering characteristics of each company is required in the analysis of text data based on machine-learning. In addition, since the news including positive or negative information on certain companies have various impacts on other companies or industry fields, an analysis for the prediction of the stock price of each company is necessary. Therefore, this study attempted to predict changes in the stock prices of the individual companies that applied a sentimental analysis of the online news data. Accordingly, this study chose top company in KOSPI 200 as the subjects of the analysis, and collected and analyzed online news data by each company produced for two years on a representative domestic search portal service, Naver. In addition, considering the differences in the meanings of vocabularies for each of the certain economic subjects, it aims to improve performance by building up a lexicon for each individual company and applying that to an analysis. As a result of the analysis, the accuracy of the prediction by each company are different, and the prediction accurate rate turned out to be 56% on average. Comparing the accuracy of the prediction of stock prices on industry sectors, 'energy/chemical', 'consumer goods for living' and 'consumer discretionary' showed a relatively higher accuracy of the prediction of stock prices than other industries, while it was found that the sectors such as 'information technology' and 'shipbuilding/transportation' industry had lower accuracy of prediction. The number of the representative companies in each industry collected was five each, so it is somewhat difficult to generalize, but it could be confirmed that there was a difference in the accuracy of the prediction of stock prices depending on industry sectors. In addition, at the individual company level, the companies such as 'Kangwon Land', 'KT & G' and 'SK Innovation' showed a relatively higher prediction accuracy as compared to other companies, while it showed that the companies such as 'Young Poong', 'LG', 'Samsung Life Insurance', and 'Doosan' had a low prediction accuracy of less than 50%. In this paper, we performed an analysis of the share price performance relative to the prediction of individual companies through the vocabulary of pre-built company to take advantage of the online news information. In this paper, we aim to improve performance of the stock prices prediction, applying online news information, through the stock price prediction of individual companies. Based on this, in the future, it will be possible to find ways to increase the stock price prediction accuracy by complementing the problem of unnecessary words that are added to the sentiment dictionary.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.53-77
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• 2015

Mobile commerce provides a convenient shopping experience in which users can buy products without the constraints of time and space. Mobile commerce has already set off a mega trend in Korea. The market size is estimated at approximately 15 trillion won (KRW) for 2015, thus far. In the Korean market, social commerce and open market are key components. Social commerce has an overwhelming open market in terms of the number of users in the Korean mobile commerce market. From the point of view of the industry, quick market entry, and content curation are considered to be the major success factors, reflecting the rapid growth of social commerce in the market. However, academics' empirical research and analysis to prove the success rate of social commerce is still insufficient. Henceforward, it is to be expected that social commerce and the open market in the Korean mobile commerce will compete intensively. So it is important to conduct an empirical analysis to prove the differences in user experience between social commerce and open market. This paper is an exploratory study that shows a comparative analysis of social commerce and the open market regarding user experience, which is based on the mobile users' reviews. Firstly, this study includes a collection of approximately 10,000 user reviews of social commerce and open market listed Google play. A collection of mobile user reviews were classified into topics, such as perceived usefulness and perceived ease of use through LDA topic modeling. Then, a sentimental analysis and co-occurrence analysis on the topics of perceived usefulness and perceived ease of use was conducted. The study's results demonstrated that social commerce users have a more positive experience in terms of service usefulness and convenience versus open market in the mobile commerce market. Social commerce has provided positive user experiences to mobile users in terms of service areas, like 'delivery,' 'coupon,' and 'discount,' while open market has been faced with user complaints in terms of technical problems and inconveniences like 'login error,' 'view details,' and 'stoppage.' This result has shown that social commerce has a good performance in terms of user service experience, since the aggressive marketing campaign conducted and there have been investments in building logistics infrastructure. However, the open market still has mobile optimization problems, since the open market in mobile commerce still has not resolved user complaints and inconveniences from technical problems. This study presents an exploratory research method used to analyze user experience by utilizing an empirical approach to user reviews. In contrast to previous studies, which conducted surveys to analyze user experience, this study was conducted by using empirical analysis that incorporates user reviews for reflecting users' vivid and actual experiences. Specifically, by using an LDA topic model and TAM this study presents its methodology, which shows an analysis of user reviews that are effective due to the method of dividing user reviews into service areas and technical areas from a new perspective. The methodology of this study has not only proven the differences in user experience between social commerce and open market, but also has provided a deep understanding of user experience in Korean mobile commerce. In addition, the results of this study have important implications on social commerce and open market by proving that user insights can be utilized in establishing competitive and groundbreaking strategies in the market. The limitations and research direction for follow-up studies are as follows. In a follow-up study, it will be required to design a more elaborate technique of the text analysis. This study could not clearly refine the user reviews, even though the ones online have inherent typos and mistakes. This study has proven that the user reviews are an invaluable source to analyze user experience. The methodology of this study can be expected to further expand comparative research of services using user reviews. Even at this moment, users around the world are posting their reviews about service experiences after using the mobile game, commerce, and messenger applications.

• Radiation Oncology Journal
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• v.19 no.2
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• pp.87-94
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• 2001

Purpose : To evaluate the role of LINAC-based stereotactic radiosurgery (SRS) in the management of meningiomas, we reviewed clinical response, image response, neurological deficits for patients treated at our institution. Methods and materials : Between February 1995 and December 1999, twenty-six patients were treated with SRS. Seven patients had undergone prior resection. Nineteen patients received SRS as the initial treatment. There were 7 male and 19 female patients. The median age was 51 years (range, $14\~67\;years$). At least one clinical symptom presented at the time of SRS in 17 patients and cranial neuropathy was seen in 7 patients. The median tumor volume was $4.7\;cm^3\;(range,\;0.7\~16.5\;m^3)$. The mean marginal dose was 15 Gy (range, $10\~20\;Gy$), delivered to the $80\%$ isodose surface (range, $46\~90\%$). The median clinical and imaging follow-up periods were 27 months (range, 1-71 months) and 25 months (range, $1\~52\;months$), respectively. Results : Of 14 patients who had clinical follow-up of one year or longer, thirteen patients $(93\%)$ were improved clinically at follow-up examination. Clinical symptom worsened in one patient at 4 months after SRS as a result of intratumoral edema, who underwent surgical resection at 7 months. OF 14 patients who had radiologic follow-up of one year or longer, tumor volume decreased in 7 patients $(50\%)$ at a median of 11 months (range, $6\~25\;months$), remained stable in 6 patients $(43\%)$, and increased in one patient $(7\%)$, who underwent surgical resection at 44 months. New radiation-induced neurological deficits developed in six patients $(23\%)$. Five patients $(19\%)$ had transient neurological deficits, completely resolved by conservative treatment including steroid therapy. Radiation-induced brain necrosis developed in one patient $(3.8\%)$ at 9 months after SRS who followed by surgical resection of tumor and necrotic tissue. Conclusions : LINAC-based SRS proves to be an effective and safe management strategy for small to moderate sized meningiomas, inoperable, residual, and recurrent, but long-term follow-up will be necessary to fully evaluate its efficacy. To reduce the radiation-induced neurological deficit for large size meningioma and/or in the proximity of critical and neural structure, more delicate treatment planning and optimal decision of radiation dose will be necessary.

• Radiation Oncology Journal
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• v.19 no.2
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• pp.127-135
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• 2001

Purpose : This study is a prospective randomized clinical trial comparing the efficacy and complication of anti-emetic drugs for prevention of nausea and vomiting after radiotherapy which has moderate emetogenic potential. The aim of this study was to investigate whether the anti-emetic efficacy of ondansetron $(Zofran^{\circledR})$ 8 mg bid dose (Group O) is better than the efficacy of metoclopramide 5 mg lid dose (Group M) in patients undergoing fractionated radiotherapy to the abdominal region. Materials and Methods : Study entry was restricted to those patients who met the following eligibility criteria: histologically confirmed malignant disease; no distant metastasis; performance status of not more than ECOG grade 2; no previous chemotherapy and radiotherapy. Between March 1997 and February 1998, 60 patients enrolled in this study. All patients signed a written statement of informed consent prior to enrollment. Blinding was maintained by dosing identical number of tablets including one dose of matching placebo for Group O. The extent of nausea, appetite loss, and the number of emetic episodes were recorded everyday using diary card. The mean score of nausea, appetite loss and the mean number of emetic episodes were obtained in a weekly interval. Results : Prescription error occurred in one patient. And diary cards have not returned in 3 patients due to premature refusal of treatment. Card from one patient was excluded from the analysis because she had a history of treatment for neurosis. As a result, the analysis consisted of 55 patients. Patient characteristics and radiotherapy characteristics were similar except mean age was $52.9{\pm}11.2$ in group M, $46.5{\pm}9.5$ in group O. The difference of age was statistically significant. The mean score of nausea, appetite loss and emetic episodes in a weekly interval was higher in group M than O. In group M, the symptoms were most significant at 5th week. In a panel data analysis using mixed procedure, treatment group was only significant factor detecting the difference of weekly score for all three symptoms. Ondansetron $(Zofran^{\circledR})$ 8 mg bid dose and metoclopramide 5 mg lid dose were well tolerated without significant side effects. There were no clinically important changes In vital signs or clinical laboratory parameters with either drug. Conclusion : Concerning the fact that patients with younger age have higher emetogenic potential, there are possibilities that age difference between two treatment groups lowered the statistical power of analysis. There were significant difference favoring ondansetron group with respect to the severity of nausea, vomiting and loss of appetite. We concluded that ondansetron is more effective anti-emetic agents in the control of radiotherapy-induced nausea, vomiting, loss of appetite without significant toxicity, compared with commonly used drug, i.e., metoclopramide. However, there were patients suffering emesis despite the administration of ondansetron. The possible strategies to improve the prevention and the treatment of radiotherapy-induced emesis must be further studied.