Animal Infectious Diseases Prevention through Big Data and Deep Learning (빅데이터와 딥러닝을 활용한 동물 감염병 확산 차단)
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- Journal of Intelligence and Information Systems
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- v.24 no.4
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- pp.137-154
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- 2018
Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.
This study was conducted to survey the situation of direct rice seeding in Honam province in Korea to investigate problems and seek countermeasure of weed control in direct rice seeding. The total area of direct rice seeding in the south-western part of Korea (Chonbuk, Chonnam, and Chungnam) was 1650.8ha (732.1ha for direct seeding in dry field and 918.7ha for direct seeding in flooding field) in 1992. The followings are summary of the study. 1. In case of direct rice seeding in dry field, butachlor EC and G at 3 to 5 DAS was mostly selected by farmers to control weeds in dry field. Benthiocarb or chlornitrofen was also used in few cases. At 10 to 14 DAS just before rice emergence, tank misture of butachlor EC and paraquat was treated by some farmers. At 35 to 40 days, after flooding mixture of sulfonylurea derivatives was sequentially applied. Surviving weeds including barnyardgrass were finally controlled by mixture of bentazon+quinclorac WP foliage application. 2. In case of direct rice seeding in flooding field, weed control were mostly unsuccessful partially due to wrong selection of herbicide and missing the optimum application time. Three relatively successful weed control in the survey were summarized as follows. 1) Oxadiazon EC, butachlor or benthiocarb were treated just after puddling(5 to 7 days before seeding). then mixture of bentazone+quinclorac WP or sulfonylurea derivatives was sequently applied to control remaining weeds at 20 days after seeding. 2) Mixtures of bensulfuronmethyl+dimepiperate G, pyrazosulfuronethyl+molinate G, or bensulfuronmethyl+mefenacet+dymron G were applied at 11 days after puddling when barnyardgrass were at 2.0 leaf stage. Phytotoxicity was not found in case of mixture of bensulfuronmethyl+dimepiperate G but found in the other two cases but disappeared later. 3) Mixtures of bensulfuronmethyl+quinclorac G., pyrazosulfuronethyl+quinclorac G or betazone and quinclorac G were treated after 18 to 20 days after puddling when barnyardgrass was within 3.0 leaf stage. It showed good weed control in both annuals and perrenials without phytotoxicity. On the contrary, other sulfonylurea derivatives such as middle periodic herbicide showed poor weed control against barnyardgrass, so that sequential treatment of bentazone+quinclorac WP mixture was required. 3. Herbicidal characteristics and optimum application time of 45 rigistered herbicides in Korea were analyzed to discover new substitute for quinclorac mixture, that showed excellent weed control against barnyardgrass at its 3 leaf stage or older. The analysis revealed that 70% of herbicides were for preemergence and the others were post periodic herbicide. Most farmers favor to apply herbicide when rice seedlings completely rooted, at this time barnyardgrass are at 2.5-3.0 leaf stage. Therefore herbicide of which optimum application time had long is required. In this study. 6 middle periodic herbicides among sulfonylurea derivatives and 2 quinclorac mixture were selected and evaluated their weeding spectrums at different leaf stage of barnyardgrass in both soil application in flooding condition and foliage application in dry paddy field. The order of weeding spectrum in magnitude was as follows : bentazone+quinclorac WP> bentazone + quinclorac G>bensulfuronmethyl + quinclorac G>pyrazosulfuronethyl + quinclorac G> pyrazosulfuronethyl + Molinate G>bensulfuronmethyl + mefenacet + dymron G>bensulfuronmethyl + mefenacet G>bensulfuron methyl+benthiocarb G. The above results coincided with that of the survey. In conclusion, there is no proper substitute for quinclorac mixrure, which can control barnyardgrass at 3.0 leaf stage or even older. Therefore quinclorac should be supplied continuously to farmers in order to anchor direct rice seeding in Korea. Author suggested the followings to eastablish direct rice seeding technology effectively and quickly : 1) A tentatively named "The research committee for direct rice seeding" which was composed of farmers. researchers and goberment. should be eastablished to cooperate effectively. 2) Development of a pricise direct rice seeding machine for both dry and flooding paddy field. which is workable regardless of condition and varieties of seeds. 3) Study on protecting rice seed and seedling from sparrows. 4) Systematic studies of weed control techniques in direct rice seeding to standardize herbicide application. 5) Studies on farm-land reformation. techniques of precise land preparation. and direct rice seeding using an airplane.
Brand has received much attention from considerable marketing research. When consumers consume product or services, they are exposed to a lot of brand related stimuli. These contain brand personality, brand experience, brand identity, brand communications and so on. A special kind of new crisis occasionally confronting companies' brand management today is the brand related rumor. An important influence on consumers' purchase decision making is the word-of-mouth spread by other consumers and most decisions are influenced by other's recommendations. In light of this influence, firms have reasonable reason to study and understand consumer-to-consumer communication such as brand rumor. The importance of brand rumor to marketers is increasing as the number of internet user and SNS(social network service) site grows. Due to the development of internet technology, people can spread rumors without the limitation of time, space and place. However relatively few studies have been published in marketing journals and little is known about brand rumors in the marketplace. The study of rumor has a long history in all major social science. But very few studies have dealt with the antecedents and consequences of any kind of brand rumor. Rumor has been generally described as a story or statement in general circulation without proper confirmation or certainty as to fact. And it also can be defined as an unconfirmed proposition, passed along from people to people. Rosnow(1991) claimed that rumors were transmitted because people needed to explain ambiguous and uncertain events and talking about them reduced associated anxiety. Especially negative rumors are believed to have the potential to devastate a company's reputation and relations with customers. From the perspective of marketer, negative rumors are considered harmful and extremely difficult to control in general. It is becoming a threat to a company's sustainability and sometimes leads to negative brand image and loss of customers. Thus there is a growing concern that these negative rumors can damage brands' reputations and lead them to financial disaster too. In this study we aimed to distinguish antecedents of brand rumor transmission and investigate the effects of brand rumor characteristics on rumor spread intention. We also found key components in personal acceptance of brand rumor. In contextualist perspective, we tried to unify the traditional psychological and sociological views. In this unified research approach we defined brand rumor's characteristics based on five major variables that had been found to influence the process of rumor spread intention. The five factors of usefulness, source credibility, message credibility, worry, and vividness, encompass multi level elements of brand rumor. We also selected product involvement as a control variable. To perform the empirical research, imaginary Korean 'Kimch' brand and related contamination rumor was created and proposed. Questionnaires were collected from 178 Korean samples. Data were collected from college students who have been experienced the focal product. College students were regarded as good subjects because they have a tendency to express their opinions in detail. PLS(partial least square) method was adopted to analyze the relations between variables in the equation model. The most widely adopted causal modeling method is LISREL. However it is poorly suited to deal with relatively small data samples and can yield not proper solutions in some cases. PLS has been developed to avoid some of these limitations and provide more reliable results. To test the reliability using SPSS 16 s/w, Cronbach alpha was examined and all the values were appropriate showing alpha values between .802 and .953. Subsequently, confirmatory factor analysis was conducted successfully. And structural equation modeling has been used to analyze the research model using smartPLS(ver. 2.0) s/w. Overall, R2 of adoption of rumor is .476 and R2 of intention of rumor transmission is .218. The overall model showed a satisfactory fit. The empirical results can be summarized as follows. According to the results, the variables of brand rumor characteristic such as source credibility, message credibility, worry, and vividness affect argument strength of rumor. And argument strength of rumor also affects rumor intention. On the other hand, the relationship between perceived usefulness and argument strength of rumor is not significant. The moderating effect of product involvement on the relations between argument strength of rumor and rumor W.O.M intention is not supported neither. Consequently this study suggests some managerial and academic implications. We consider some implications for corporate crisis management planning, PR and brand management. This results show marketers that rumor is a critical factor for managing strong brand assets. Also for researchers, brand rumor should become an important thesis of their interests to understand the relationship between consumer and brand. Recently many brand managers and marketers have focused on the short-term view. They just focused on strengthen the positive brand image. According to this study we suggested that effective brand management requires managing negative brand rumors with a long-term view of marketing decisions.