Animal Infectious Diseases Prevention through Big Data and Deep Learning (빅데이터와 딥러닝을 활용한 동물 감염병 확산 차단)
-
- Journal of Intelligence and Information Systems
- /
- v.24 no.4
- /
- pp.137-154
- /
- 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 is based on the research of the first year, which is the National Research Foundation of Korea's R&D subject for middle-grade researchers. In this study, the practical curriculum development of the education for love - an according to the psychoanalytic perspectives of F. Dolto(1908-1988) - is suggested as follows. The first is 'the reconstruction of the directions of curriculum and its specific aims in accordance with such directions.' The reconstruction of the directions of curriculum aims at leading our future generation to live as a subject of desire through the mutual-communication of love. The second is 'the reconstruction of the tasks of curriculum and its specific contents in accordance with such tasks.' The reconstruction of the tasks of curriculum pursuit to help our future generation through the converting the education for love into the paradigm of desire of Agape to live as a subject of desire forming a whole personality and practicing the desire of Agape in daily life. as a source of desire. According to these aims, the reconstruction of directions of curriculum are presented as following: firstly, 'curriculum for the mutual-communication between subjects of love' and secondly, 'curriculum for the subject of desire'. The reconstruction of tasks of curriculum are like these: firstly, 'converting the education for love into the paradigm of desire of Agape', and secondly, 'forming a whole personality through the education for love'. Thus, with respect to two specific aims in accordance with the reconstruction of directions are suggested like these: Firstly, 'constructing a subject as a speaking existence' and secondly, 'realizing the subject as the autonomous source of desire'. In the two specific contents in accordance with the reconstruction of tasks are presented as following: Firstly, 'realizing the truth of the desire of Agape'.' Secondly, 'practicing the desire of Agape in daily life.' The third is 'the reconstruction of curriculum by life cycle' are suggested. They include the fetal life, infants and preschool children life, and childhood life. In further study, it is required to contain adolescent period. It will be useful to help them to recover their self-esteem with the experience of true love, especially, out-of-school young generation overcome negative perspectives and prejudice in the society, and challenges to their dreams and future through proper utilization of the study outcome. The outcome of this study, which presented practical curriculum development of the education for love based on the understanding of psychoanalytic 'desire of subject' can be used as basic teaching materials for our future generations. Furthermore, the results can be used as a resource for educating ministers and lay leaders in the religious world to build capabilities to heal their inner side as well as the society that is tainted with various forms of conflict. These include general conflicts, anger, pleasure and addiction, depression and suicide, violence and murder, etc. The study outcome can contribute to the prevention of antisocial incidents against humanity that have recently been occurring in our free-semester system implemented in all middle schools across the country to be operated effectively. For example, it is possible to provide the study results as lecture and teaching materials for 'character camp' (self-examination and self-esteem improvement training) and 'family healing camp' (solution of a communication gap between family members and love communication training), which help students participate in field trip activities and career exploration activities voluntarily, independently, and creatively. Ultimately, it can visibly present the convergent research performance by providing the study outcome as preliminary data for the development of lecture videos and materials including infant care and preschool education, parental education, family consultation education, and holistic healing education. Support from the religious world, including the central government and local governments, are urgently required in order for such educational possibilities to be fulfilled both in the society and the fields of church education and to be actively linked to follow-up studies.
From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (