• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.123-132
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• 2013

As the smartphones are equipped with various sensors such as the accelerometer, GPS, gravity sensor, gyros, ambient light sensor, proximity sensor, and so on, there have been many research works on making use of these sensors to create valuable applications. Human activity recognition is one such application that is motivated by various welfare applications such as the support for the elderly, measurement of calorie consumption, analysis of lifestyles, analysis of exercise patterns, and so on. One of the challenges faced when using the smartphone sensors for activity recognition is that the number of sensors used should be minimized to save the battery power. When the number of sensors used are restricted, it is difficult to realize a highly accurate activity recognizer or a classifier because it is hard to distinguish between subtly different activities relying on only limited information. The difficulty gets especially severe when the number of different activity classes to be distinguished is very large. In this paper, we show that a fairly accurate classifier can be built that can distinguish ten different activities by using only a single sensor data, i.e., the smartphone accelerometer data. The approach that we take to dealing with this ten-class problem is to use the ensemble of nested dichotomy (END) method that transforms a multi-class problem into multiple two-class problems. END builds a committee of binary classifiers in a nested fashion using a binary tree. At the root of the binary tree, the set of all the classes are split into two subsets of classes by using a binary classifier. At a child node of the tree, a subset of classes is again split into two smaller subsets by using another binary classifier. Continuing in this way, we can obtain a binary tree where each leaf node contains a single class. This binary tree can be viewed as a nested dichotomy that can make multi-class predictions. Depending on how a set of classes are split into two subsets at each node, the final tree that we obtain can be different. Since there can be some classes that are correlated, a particular tree may perform better than the others. However, we can hardly identify the best tree without deep domain knowledge. The END method copes with this problem by building multiple dichotomy trees randomly during learning, and then combining the predictions made by each tree during classification. The END method is generally known to perform well even when the base learner is unable to model complex decision boundaries As the base classifier at each node of the dichotomy, we have used another ensemble classifier called the random forest. A random forest is built by repeatedly generating a decision tree each time with a different random subset of features using a bootstrap sample. By combining bagging with random feature subset selection, a random forest enjoys the advantage of having more diverse ensemble members than a simple bagging. As an overall result, our ensemble of nested dichotomy can actually be seen as a committee of committees of decision trees that can deal with a multi-class problem with high accuracy. The ten classes of activities that we distinguish in this paper are 'Sitting', 'Standing', 'Walking', 'Running', 'Walking Uphill', 'Walking Downhill', 'Running Uphill', 'Running Downhill', 'Falling', and 'Hobbling'. The features used for classifying these activities include not only the magnitude of acceleration vector at each time point but also the maximum, the minimum, and the standard deviation of vector magnitude within a time window of the last 2 seconds, etc. For experiments to compare the performance of END with those of other methods, the accelerometer data has been collected at every 0.1 second for 2 minutes for each activity from 5 volunteers. Among these 5,900 ($=5{\times}(60{\times}2-2)/0.1$) data collected for each activity (the data for the first 2 seconds are trashed because they do not have time window data), 4,700 have been used for training and the rest for testing. Although 'Walking Uphill' is often confused with some other similar activities, END has been found to classify all of the ten activities with a fairly high accuracy of 98.4%. On the other hand, the accuracies achieved by a decision tree, a k-nearest neighbor, and a one-versus-rest support vector machine have been observed as 97.6%, 96.5%, and 97.6%, respectively.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.

• Journal of Nutrition and Health
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• v.48 no.2
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• pp.180-191
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• 2015

Purpose: There is a lack of data on the energy cost of children's everyday activities, adult values are often used as surrogates. In addition, the influence of body weight on the energy cost of activity when expressed as metabolic equivalents (METs) has not been vigorously explored. Methods: In this study 20 elementary school students 9~12 years of age completed 18 various physical activities while energy expenditure was measured continuously using a portable telemetry gas exchange system ($K_4b^2$, Cosmed, Rome, Italy). Results: The average age was 10.4 years and the average height and weight was 145.1 cm and 43.6 kg, respectively. Oxygen consumption ($VO_2$), energy expenditure and METs at the time of resting of the subjects were 5.41 mL/kg/min, 1.44 kcal/kg/h, and 1.5 METs, respectively. METs values by 18 physical activities were as follows: Homework and reading books (1.6 METs), playing game with a mobile phone or video while sitting (1.6 METs), watching TV while sitting on a comfortable chair (1.7 METs), playing video game or mobile phone game while standing (1.9 METs), sweeping a room with a broom (2.7 METs) and playing a board game (2.8 METs) belong to light intensity physical activities. By contrary, speedy walking and running were 6.6 and 6.7 METs, respectively, which belong to high intensity physical activities over 6.0 METs. When the effect of body weight on physical activity energy expenditure was determined, $R^2$ values increased with 0.116 (playing a game at sitting), 0.176 (climbing up and down stairs), 0.246 (slow walking), and 0.455 (running), which showed that higher activity intensity increased explanation power of body weight on METs value. Conclusion: This study is important for direct evaluation of energy expenditure by physical activities of children, and it could be used directly for revising and complementing the existing activity classification table to fit for children.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.39-53
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• 2013

The emergence of the internet technology and SNS has increased the information flow and has changed the way people to communicate from one-way to two-way communication. Users not only consume and share the information, they also can create and share it among their friends across the social network service. It also changes the Social Media behavior to become one of the most important communication tools which also includes Social TV. Social TV is a form which people can watch a TV program and at the same share any information or its content with friends through Social media. Social News is getting popular and also known as a Participatory Social Media. It creates influences on user interest through Internet to represent society issues and creates news credibility based on user's reputation. However, the conventional platforms in news services only focus on the news recommendation domain. Recent development in SNS has changed this landscape to allow user to share and disseminate the news. Conventional platform does not provide any special way for news to be share. Currently, Social News Service only allows user to access the entire news. Nonetheless, they cannot access partial of the contents which related to users interest. For example user only have interested to a partial of the news and share the content, it is still hard for them to do so. In worst cases users might understand the news in different context. To solve this, Social News Service must provide a method to provide additional information. For example, Yovisto known as an academic video searching service provided time dependent metadata from the video. User can search and watch partial of video content according to time dependent metadata. They also can share content with a friend in social media. Yovisto applies a method to divide or synchronize a video based whenever the slides presentation is changed to another page. However, we are not able to employs this method on news video since the news video is not incorporating with any power point slides presentation. Segmentation method is required to separate the news video and to creating time dependent metadata. In this work, In this paper, a time dependent metadata-based framework is proposed to segment news contents and to provide time dependent metadata so that user can use context information to communicate with their friends. The transcript of the news is divided by using the proposed story segmentation method. We provide a tag to represent the entire content of the news. And provide the sub tag to indicate the segmented news which includes the starting time of the news. The time dependent metadata helps user to track the news information. It also allows them to leave a comment on each segment of the news. User also may share the news based on time metadata as segmented news or as a whole. Therefore, it helps the user to understand the shared news. To demonstrate the performance, we evaluate the story segmentation accuracy and also the tag generation. For this purpose, we measured accuracy of the story segmentation through semantic similarity and compared to the benchmark algorithm. Experimental results show that the proposed method outperforms benchmark algorithms in terms of the accuracy of story segmentation. It is important to note that sub tag accuracy is the most important as a part of the proposed framework to share the specific news context with others. To extract a more accurate sub tags, we have created stop word list that is not related to the content of the news such as name of the anchor or reporter. And we applied to framework. We have analyzed the accuracy of tags and sub tags which represent the context of news. From the analysis, it seems that proposed framework is helpful to users for sharing their opinions with context information in Social media and Social news.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.51-69
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• 2020

Now, it is a stylized fact that a small number of technology firms such as Apple, Alphabet, Microsoft, Amazon, Facebook and a few others have become larger and dominant players in an industry. Coupled with the rise of these leading firms, we have also observed that a large number of young firms have become an acquisition target in their early IPO stages. This indeed results in a sharp decline in the number of new entries in public exchanges although a series of policy reforms have been promulgated to foster competition through an increase in new entries. Given the observed industry trend in recent decades, a number of studies have reported increased concentration in most developed countries. However, it is less understood as to what caused an increase in industry concentration. In this paper, we uncover the mechanisms by which industries have become concentrated over the last decades by tracing the changes in industry concentration associated with a firm's status change in its early IPO stages. To this end, we put emphasis on the case in which firms are acquired shortly after they went public. Especially, with the transition to digital-based economies, it is imperative for incumbent firms to adapt and keep pace with new ICT and related intelligent systems. For instance, after the acquisition of a young firm equipped with AI-based solutions, an incumbent firm may better respond to a change in customer taste and preference by integrating acquired AI solutions and analytics skills into multiple business processes. Accordingly, it is not unusual for young ICT firms become an attractive acquisition target. To examine the role of M&As involved with young firms in reshaping the level of industry concentration, we identify a firm's status in early post-IPO stages over the sample periods spanning from 1990 to 2016 as follows: i) being delisted, ii) being standalone firms and iii) being acquired. According to our analysis, firms that have conducted IPO since 2000s have been acquired by incumbent firms at a relatively quicker time than those that did IPO in previous generations. We also show a greater acquisition rate for IPO firms in the ICT sector compared with their counterparts in other sectors. Our results based on multinomial logit models suggest that a large number of IPO firms have been acquired in their early post-IPO lives despite their financial soundness. Specifically, we show that IPO firms are likely to be acquired rather than be delisted due to financial distress in early IPO stages when they are more profitable, more mature or less leveraged. For those IPO firms with venture capital backup have also become an acquisition target more frequently. As a larger number of firms are acquired shortly after their IPO, our results show increased concentration. While providing limited evidence on the impact of large incumbent firms in explaining the change in industry concentration, our results show that the large firms' effect on industry concentration are pronounced in the ICT sector. This result possibly captures the current trend that a few tech giants such as Alphabet, Apple and Facebook continue to increase their market share. In addition, compared with the acquisitions of non-ICT firms, the concentration impact of IPO firms in early stages becomes larger when ICT firms are acquired as a target. Our study makes new contributions. To our best knowledge, this is one of a few studies that link a firm's post-IPO status to associated changes in industry concentration. Although some studies have addressed concentration issues, their primary focus was on market power or proprietary software. Contrast to earlier studies, we are able to uncover the mechanism by which industries have become concentrated by placing emphasis on M&As involving young IPO firms. Interestingly, the concentration impact of IPO firm acquisitions are magnified when a large incumbent firms are involved as an acquirer. This leads us to infer the underlying reasons as to why industries have become more concentrated with a favor of large firms in recent decades. Overall, our study sheds new light on the literature by providing a plausible explanation as to why industries have become concentrated.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.36 no.4
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• pp.39-49
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• 2018

"Annals of The Joseon Dynasty" is a book recording the Joseon Dynasty's historical facts in an annalistic format. The King's amusement activities through "Annals of The Joseon Dynasty" which were established by the Ye-ak(禮樂) system were analyzed. The results are as follows. The king's amusement activities that were performed during the Joseon Dynasty period could be classified as state banquets, military banquets, and banquets for play. The analysis of the king's amusement activity was divided into five stages. The characteristic of [1 period : King Taejo~Sejo(Yejong)] was dominated the military banquets of the Goryeo Dynasty. Neo-Confucianism is the establishment of political and social turning of the ballast, considerations of military culture, culture, and Hoeryeyeon Jinpungjeong, a cloud of dust and elders banquets such as Giroyeon and Yangnoyeon on the nature of the party. A lasting ordinance was institutionalized[2 period : King Seongjong~Jungjong]. In the chopper and jeongyujaeran, Hong Kyung Rae led a royal amusement activities are stagnant, often produce isolated storage compute in the gloomy situation[3 period : King Injong~Hyeonjong]. Revival period is pride of the amusement activity through the culture of Joseon Dynasty royal culture [4 period : King Sukjong~Jeongjo]. The throne, crashed due to political power is an ebb of royal amusement activities, while also rapidly waning[5 period : King Seonjo~Seonjong]. During the early Joseon Dynasty, hunting took place around the forest area northeast of Hanyang and during King Seongjong's period, it took place closer to the capital city, while in Lord Yeonsan's period, it was expanded to a 39 kilometer radius area from the palace, and banquets such as various forms of entertainment of Cheoyongmu, and Flower-viewing. The Joseon kings who enjoyed hunting were King Sejong, Sejo, Seongjong, Yeonsan, and Jungjong. Most of hunting objects were tigers, bears, deer and roe deer, leopards, boars, their animals and falconry took, and the purpose of the hunting was to perform ancestral rites to the royal ancestry or the royal tombs. Lord Yeonsan's hunting activities had negative effects after King Jungjong the king's hunting activity decreased sharply. However, there were also positive aspects of Lord Yeonsan's Prohibition of cutting woods ect. In conclusion, the expansion of the King's garden(庭:courtyard${\rightarrow}$園:privacy garden${\rightarrow}$苑:king's garden${\rightarrow}$苑?:national hunting park) is evident which starts from formal and informal activities that took place in Oejo, Chijo, and Yeonjo, which went further to the separate and secret gardens, and then even further, thus setting the amusement activity area as a 39 kilometer radius range from Hanyang.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.8 no.1
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• pp.129-177
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• 1970

For the purpose of establishing the systematized technical scheme of the cooperative rice cultivation which has most significant impact to improve rice productivity and the farm management, the author have studied the cultivation practices, and the variation of rice growth and yield between the cooperative rice cultivation and the individual rice cultivation at random selected 18 paddy fields. The author also have investigated through comparative method on the cultivation practices, management, organization and operation scheme of the two different rice cultivation methods at 460 paddy fields. The economic feasibility has been ana lysed and added in this report. The results obtained from this study are summarized as follows; 1. In the nursery, the average amount of fertilizer application, especially, phosphate and potassium, and the frequency of chemicals spray for the disease, insect and pest control at the cooperative rice cultivation are significantly higher than those of the individual rice cultivation. 2. The cultivation techniques of the cooperative rice farming after the transplanting can be characterized by a) the earlier transplanting of rice, b) the denser hills per unit area and the lesser number of seedlings per hill, c) the application of larger quantities of fertilizer including nitrogen, phosphate and potassium, d) more divided application of fertilizers, split doses of the nitrogen and potassium, e) the increased frequencies of the chemicals spray for the prevention of disease, insect and pest damages. 3. The rate of lodging in the cooperative rice cultivation was slightly higher than that of the individual rice cultivation, however, the losses of rice yield owing to the occurrence of rice stem borer and grass leaf roller in the cooperative rice cultivation were lower than that of the individual rice cultivation. 4. The culm length, panicle length, straw weight and grain-straw ratio are respectively higher at the cooperative rice cultivation, moreover, the higher variation of the above factors due to different localities of the paddy fields found at the individual rice cultivation. 5. The number of panicles, number of flowers per panicle and the weight of 1, 000 grains, those contributing components to the rice yield were significantly greater in the cooperative rice cultivation, however, not clear difference in the maturing rate was observed. The variation coefficient of the yield component in the cooperative cultivation showed lower than that or the individual rice cultivation. 6. The average yield of brown rice per 10 are in the cooperative rice cultivation obtained 459.0 kilograms while that of the individual rice cultivation brought 374.8 kilograms. The yield of brown rice in the cooperative rice cultivation increased 84.2 kilogram per 10 are over the individual rice cultivation. With lower variation coefficient of the brown rice yield in the cooperative rice cultivation, it can be said that uniformed higher yield could be obtained through the cooperative rice cultivation. 7. Highly significant positive correlations shown between the seeding date and the number of flowers per panicle, the chemical spray and the number of flowers per panicle, the transplanting date and the number of flowers per panicle, phosphate application and yield, potassium application and maturing rate, the split application of fertilizers and yield. Whilst the significant negative correlation was shown between the transplanting date and the maturing rate 8. The results of investigation from 480 paddy fields obtained through comparative method on the following items are identical in general with those obtained at 18 paddy fields: Application of fertilizers, chemical spray for the control of disease, insects and pests both in the nursery and the paddy field, transplanting date, transplanting density, split application of fertilizers and yield n the paddy fields. a) The number of rice varieties used in the cooperative rice cultivation were 13 varieties while the individual rice cultivation used 47 varieties. b) The cooperative rice cultivation has more successfully adopted improved cultivation techniques such as the practice of seed disinfection, adoption of recommended seeding amount, fall ploughing, application of red soil, introduction of power tillers, the rectangular-type transplanting, midsummer drainage and the periodical irrigation. 9. The following results were also obtained from the same investigation and they are: a) In the cooperative rice cultivation, the greater part of the important practices have been carried out through cooperative operation including seed disinfection, ploughing, application of red soil and compost, the control of disease, insects and pests, harvest, threshing and transportation of the products. b) The labor input to the nursery bed and water control in the cooperative rice cultivation was less than that of the individual rice cultivation while the higher rate of labor input was resulted in the red soil and compost application. 10. From the investigation on the organization and operation scheme of the cooperative rice cultivation, the following results were obtained: a) The size of cooperative rice cultivation farm was varied from. 3 ha to 7 ha and 5 ha farm. occupied 55.9 percent of the total farms. And a single cooperative farm was consisted of 10 to 20 plots of paddies. b) The educational back ground of the staff members involved in the cooperative rice cultivation was superior than that of the individual rice cultivation. c) All of the farmers who participated to the questionaires have responded that the cooperative rice cultivation could promise the increased rice yield mainly through the introduction of the improved method of fertilizer application and the effective control of diseases, insects and pests damages. And the majority of farmers were also in the opinion that preparation of the materials and labor input can be timely carried out and the labor requirement for the rice cultivation possibly be saved through the cooperative rice cultivation. d) The farmers who have expressed their wishes to continue and to make further development of the cooperative rice cultivation was 74.5 percent of total farmers participated to the questionaires. 11. From the analysis of economical feasibility on the two different methods of cultivation, the following results were obtained: a) The value of operation cost for the compost, chemical fertilizers, agricultural chemicals and labor input in the cooperative rice cultivation was respectively higher by 335 won, 199 won, 288 won and 303 won over the individual rice cultivation. However, the other production costs showed no distinct differences between the two cultivation methods. b) Although the total value of expenses for the fertilizers, agricultural chemicals, labor input and etc. in the cooperative rice cultivation were approximately doubled to the amount of the individual rice cultivation, the net income, substracted operation costs from the gross income, was obtained 24, 302 won in the cooperative rice cultivation and 20, 168 won was obtained from the individual rice cultivation. Thereby, it can be said that net income from the cooperative rice cultivation increased 4, 134 won over the individual rice cultivation. It was revealed in this study that the cooperative rice cultivation has not only contributed to increment of the farm income through higher yield but also showed as an effective means to introduce highly improved cultivation techniques to the farmers. It may also be concluded, therefore, the cooperative rice cultivation shall continuously renovate the rice production process of the farmers.