• International Journal of Industrial Entomology and Biomaterials
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• v.8 no.1
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• pp.101-106
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• 2004

The process of removal of gummy proteinous material sericin from silk is commonly called as degumming loss or boil-off loss ratio. In the present study, the boil-off loss ratio in the cocoon shells of twelve multivoltine${\times}$bivoltine hybrids and their parents were analysed. Inheritance pattern of boil-off loss ratio was analysed in crosses involving high and low boil-off loss parents, F$_1$s, F$_2$s and back-crosses by parent off spring regression analysis. Heterosis and heterobeltiosis was also analysed for this character, Highly significant (P＞0.01) variations were observed in eight out of ten multivoltine and two out of five bivoltine parents indicating the presence of genetic variation in the expression of boil-off loss ratio. Among F$_1$ hybrids, ten hybrids expressed significant (P＞0.01) variations when compared with control hybrid PM${\times}$NB$_4$D$_2$. Significant negative heterosis was expressed in three multi ${\times}$ bi hybrids viz., BL67${\times}$CSR$_{101}$, 96A${\times}$CSR$_{19}$ and 96C${\times}$CSR$_{19}$, which is desirable for this character, whereas expression of heterobeltiosis was significant only with one hybrid, 96C${\times}$CSR$_{18}$ in desired direction. Studies on inheritance pattern showed that the character is heritable and contribution percentage of female and male in the ratio of 50.9: 49.1 and it appears that both the parents are influencing in the expression of boil-on loss ratio in silkworm. Based on the overall performance and evaluation by multiple trait evaluation index and also considering the expression of the boil-off loss ratio three hybrids vix., BL67 ${\times}$ CSR$_{101}$, 96A${\times}$CSR$_{19}$ and 96C${\times}$CSR$_{18}$ were found superior and recommended for commercial exploitation.n.ion.n.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.109-118
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• 1993

Development process of agricultural technology has been studied with a case study of Korean agriculture. Technological is considered as a transformer of inputs into outputs and hence technological appropriateness, an important aspect of agricultural development strategies, is considered as a dynamic concepts. Considering the concept of agricultural system as a delivery system for providing essential materials and services to producers and consumers, it has been divided into two major groups of dimensions vis. external challenge dimensions and internal response dimensions. Market, investment and agro-ecosystem constitute the external challenge dimensions : whereas trade , technology as well as production and resources allocation constitute internal response dimensions. The system manager is responsible for maintaining equilibrium in the mentioned six sub-systems. Two kinds of alternatives paths of technological development viz. land saving technology and labour saving technolog have been studied. Technology is considered as a combination of four basic components viz. facilities, abilities, facts and frameworks. Adoption of innovation in agriculture depends on profitability, awareness, risk aversion, financial capacity, institutional infrastructure, availability of physical inputs and adaptability to the local conditions. For a cast study of Korea, changes in the agricultural system through external challenge dimensions are investigated. The impacts of industrialization on agro-ecosystem reported are shift of labour from the agricultural sector to non-agricultural sectors and continuously increasing demand of farm the agricultural sector to non-agricultural sectors accompanied by increase in land prices. The impacts on the commodity market discussed are shift in demand from rice, barley and other cereals to meat , dairy products and vegetables : and increasing in supply capacity of agricultural inputs. The process of agricultural development from 1962 to 19 1 9 (i.e. from start of the first to the end of the sixth five year plan) are also discussed in details with several policy measures taken. The trend of agricultural income and productivity are also analyzed. The main cause of increase in the agricultural income is considered as increase in labour productivity. The study revealed that during the span of 1965-88, holding size has not changed significantly, but both the land and labour productivity increased and so did the agricultural income. R&D activities in Korea have changed over time in three stages vix. import of improved technology, localization by adaptive research and technological mastery. For the new technology to be made affordable to farmers, policy measures like fertilizer and food grain exchange system, dual price system in rice and barely and loan for machinery were strengthened.

• Information Systems Review
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• v.23 no.1
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• pp.23-43
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• 2021

Recently, many studies have been conducted to increase the accuracy of stock price prediction by analyzing candlestick charts using artificial intelligence techniques. However, these studies failed to consider the time-series characteristics of candlestick charts and to take into account the emotional state of market participants in data learning for stock price prediction. In order to overcome these limitations, this study produced input data by combining volatility index and candlestick charts to consider the emotional state of market participants, and used the data as input for a new method proposed on the basis of combining variantion autoencoder (VAE) and attention mechanisms for considering the time-series characteristics of candlestick chart. Fifty firms were randomly selected from the S&P 500 index and their stock prices were predicted to evaluate the performance of the method compared with existing ones such as convolutional neural network (CNN) or long-short term memory (LSTM). The results indicated the method proposed in this study showed superior performance compared to the existing ones. This study implied that the accuracy of stock price prediction could be improved by considering the emotional state of market participants and the time-series characteristics of the candlestick chart.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.