Bibliographic and network analysis of environmental impacts to animal contagious diseases
2
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
The applications of artificial intelligence (AI) can provide useful solutions to animal infectious diseas-es and their impact on humans. The advent of AI learning algorithms and recognition technologies is especially advantageous in applied studies, including the detection, analysis, impact assessment, simulation, and prediction of environmental impacts on malignant animal epidemics. To this end, this study specifically focused on environmental pollution and animal diseases. While the number of related studies is rapidly increasing, the research trends, evolution, and collaboration in this field are not yet well-established. We analyzed the bibliographic data of 1191 articles on AI applications to environmental pollution and animal diseases during the period of 2000∼2019; these articles were collected from the Web of Science (WoS). The results revealed that PR China and the United States are the leaders in research production, impact, and collaboration. Finally, we provided research di-rections and practical implications for the incorporation of AI applications to address environmental impacts on animal diseases.
Abstract
The applications of artificial intelligence (AI) can provide useful solutions to animal infectious diseas-es and their impact on humans. The advent of AI learning algorithms and recognition technologies is especially advantageous in applied studies, including the detection, analysis, impact assessment, simulation, and prediction of environmental impacts on malignant animal epidemics. To this end, this study specifically focused on environmental pollution and animal diseases. While the number of related studies is rapidly increasing, the research trends, evolution, and collaboration in this field are not yet well-established. We analyzed the bibliographic data of 1191 articles on AI applications to environmental pollution and animal diseases during the period of 2000∼2019; these articles were collected from the Web of Science (WoS). The results revealed that PR China and the United States are the leaders in research production, impact, and collaboration. Finally, we provided research di-rections and practical implications for the incorporation of AI applications to address environmental impacts on animal diseases.
참고문헌 (35)
- Abdallah M, "Artificial intelligence applications in solid waste management : a systematic research review", Waste Management, 109, pp.231~246 (2020)
- Altizer S, "Animal migration and infectious disease risk", Science, 331, pp.296~302 (2011)
- Andrade DF, "Past and emerging topics related to electronic waste management : top countries, trends, and perspectives", Environmental Science and Pollution Research, 26, pp.17135~17151 (2019)
- Bean, R., "How big data is empowering ai and machine learning at scale", MIT sloan management review (2017)
- Cath C, "Artificial intelligence and the ‘good society’ : the US, EU, and UK approach", Science and engineering ethics, 24, pp.505~528 (2018)
- D’Amato G, "Climate change and air pollution", Allergo Journal, 23, pp.32~38 (2014)
- Dhital S, "Bibliometric analysis of global research on air pollution and human health : 1998~2017", Environmental Science and Pollution Research, 26, pp.13103~13114 (2019)
- Ellegaard O, "The bibliometric analysis of scholarly production : How great is the impact?", Scientometrics, 105, pp.1809~1831 (2015)
- Fan M, "A review on experimental design for pollutants removal in water treatment with the aid of artificial intelligence", Chemosphere, 200, pp.330~343 (2018)
- Fu HZ, "A bibliometric analysis of solid waste research during the period 1993~2008", Waste Management, 30, pp.2410~2417 (2010)
- Gao J, "Comparative analysis between international research hotspots and national-level policy keywords on artificial intelligence in China from 2009 to 2018", Sustainability, 11, pp.6574~ (2019)
- Guo K, "Global research on soil contamination from 1999 to 2012 : a bibliometric analysis", Acta Agriculturae Scandinavica, Section B—Soil & Plant Science, 64, pp.377~391 (2014)
- Haenlein M, "A brief history of artificial intelligence : On the past, present, and future of artificial intelligence", California management review, 61, pp.5~14 (2019)
- He K, "Knowledge domain and emerging trends of agricultural waste management in the field of social science : a scientometric review", Science of the total environment, 670, pp.236~244 (2019)
- Ho YS, "Bibliometric analysis of biosorption technology in water treatment research from 1991 to 2004", International Journal of Environment and pollution, 34, pp.1~13 (2008)
- Hou H, "The structure of scientific collaboration networks in Scientometrics", Scientometrics, 75, pp.189~202 (2008)
- Hu J, "A historical review and bibliometric analysis of research on lead in drinking water field from 1991 to 2007", Science of the total environment, 408, pp.1738~1744 (2010)
- Li Y, "Sources of atmospheric pollution : a bibliometric analysis", Scientometrics, 112, pp.1025~1045 (2017)
- Losacco C, "Particulate matter air pollution and respiratory impact on humans and animals", Environmental Science and Pollution Research, 25, pp.33901~33910 (2018)
- Mao G, "Research on biomass energy and environment from the past to the future : a bibliometric analysis", Science of The Total Environment, 635, pp.1081~1090 (2018)
- Mo X, "A novel air quality early-warning system based on artificial intelligence", International Journal of Environmental Research and Public Health, 16, pp.3505~ (2019)
- OECD, "Artificial Intelligence in Society" (2019), OECD publishing
- 오지선, "Bibliometric analysis on the evolution of knowledge structure of African swine fever", 한국동물위생학회지, 44(4), pp.257~270 (2021), 한국동물위생학회[DOI]
- Ouyang W, "Heavy metal loss from agricultural watershed to aquatic system : A scientometrics review", Science of the Total Environment, 637, pp.208~220 (2018)
- Padilla FM, "Global trends in nitrate leaching research in the 1960~2017 period", Science of the Total Environment, 643, pp.400~413 (2018)
- Patz JA, "Climate change and human health: risks and responses 2", pp.103~132 (2003)
- Seinfeld JH, "From air pollution to climate change", Atmospheric chemistry and physics, pp.1326~ (1998)
- Sun J, "A historical review and bibliometric analysis of research on estuary pollution", Marine Pollution Bulletin, 64, pp.13~21 (2012)
- Tomašev N, "AI for social good : unlocking the opportunity for positive impact", Nature Communications, 11, pp.1~6 (2020)
- Yang B, "Mapping the scientific research on non-point source pollution : a bibliometric analysis", Environmental Science and Pollution Research, 24, pp.4352~4366 (2017)
- Yang L, "Global trends of solid waste research from 1997 to 2011 by using bibliometric analysis", Scientometrics, 96(1), pp.133~146 (2013)
- Ye Z, "Tackling environmental challenges in pollution controls using artificial intelligence : a review", Science of the Total Environment, 699, pp.134279~ (2020)
- Zhao L, "Application of artificial intelligence to wastewater treatment : a bibliometric analysis and systematic review of technology, economy, management, and wastewater reuse", Process Safety and Environmental Protection, 133, pp.169~182 (2020)
- Zheng T, "A bibliometric analysis of industrial wastewater research : current trends and future prospects", Scientometrics, 105, pp.863~882 (2015)
- Zhou W, "Is There Always a Negative Causality between Human Health and Environmental Degradation? Current Evidence from Rural China", International Journal of Environmental Research and Public Health, 19, pp.10561~ (2022)
