• Journal of Information Science Theory and Practice
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• v.6 no.1
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• pp.31-44
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• 2018

Purpose: This study aims to examine the research output in the field of nursing at the global level during the last two decades, viz., for the period 1996-2015, with special reference to India. Some of the aspects examined include the research growth in nursing literature during the period of study, leading nursing research countries at the regional and global level, and citation analysis. Scope and Methodology: This study is global in nature, but emphasis has also been laid on India's research contribution in nursing at the global level. Aspects like regional contribution to the nursing research output have also been assessed. This study is purely based on secondary data retrieved from SCImago Journal and Country Rankings. The figures in the study are based on one particular database and are not exhaustive; hence they simply reflect a trend in nursing research at the global level. Findings: During the period 1996 through 2015, a total of 550,490 research articles were published across the world by 212 nation states at an average of 2,596 articles from each individual country. On average, during the period of study, the number of nursing research publications grew at the rate of 7.36% each year. North America has emerged as one of the leading nursing research continents of the world by publishing 218,614 research articles, constituting 39.71% of the global nursing research output. The U.S. and U.K. are the world's two leading nursing research countries, which contributed 193,819 and 61,730 research articles respectively, comprising a 35.21% and 11.21% share of global nursing research output. India and China, apart from being the two fastest growing nursing research countries, have the potential to meet the global human resource demand in the field of nursing, given the skilled and trained human resource both these countries possess in nursing. Social Implication: There is always a need to share working knowledge in some professions and nursing is one of them. There cannot be a better medium than linking practice with theory through the research medium. Metric studies in turn help to get a better idea about the amount of work done in any given field at the national and international level, thus identifying the need thereof to improve upon those areas where there is research lag.

• Korean Journal of Ecology and Environment
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• v.56 no.3
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• pp.268-279
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• 2023

Nowadays, artificial intelligence model approaches such as machine and deep learning have been widely used to predict variations of water quality in various freshwater bodies. In particular, many researchers have tried to predict the occurrence of cyanobacterial blooms in inland water, which pose a threat to human health and aquatic ecosystems. Therefore, the objective of this study were to: 1) review studies on the application of machine learning models for predicting the occurrence of cyanobacterial blooms and its metabolites and 2) prospect for future study on the prediction of cyanobacteria by machine learning models including deep learning. In this study, a systematic literature search and review were conducted using SCOPUS, which is Elsevier's abstract and citation database. The key results showed that deep learning models were usually used to predict cyanobacterial cells, while machine learning models focused on predicting cyanobacterial metabolites such as concentrations of microcystin, geosmin, and 2-methylisoborneol (2-MIB) in reservoirs. There was a distinct difference in the use of input variables to predict cyanobacterial cells and metabolites. The application of deep learning models through the construction of big data may be encouraged to build accurate models to predict cyanobacterial metabolites.

• Journal of the Korean Society for information Management
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• v.41 no.1
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• pp.411-438
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• 2024

This study aims to identify and comprehensively view health information-related research trends using a bibliometric analysis. To this end, 1,193 papers from 2002 to 2023 related to "health information" were collected through the Korea Citation Index (KCI) database and analyzed in diverse aspects: research trends by period, academic fields, intellectual structure, and keyword changes. Results indicated that the number of papers related to health information continued to increase and has been decreasing since 2021. The main academic fields of health information-related research included "biomedical engineering," "preventive medicine/occupational environmental medicine," "law," "nursing," "library and information science," and "interdisciplinary research." Moreover, a co-word analysis was performed to understand the intellectual structure of research related to health information. As a result of applying the parallel nearest neighbor clustering (PNNC) algorithm to identify the structure and cluster of the derived network, four clusters and 17 subgroups belonging to them could be identified, centering on two conglomerates: "medical engineering perspective on health information" and "social science perspective on health information." An inflection point analysis was attempted to track the timing of change in the academic field and keywords, and common changes were observed between 2010 and 2011. Finally, a strategy diagram was derived through the average publication year and word frequency, and high-frequency keywords were presented by dividing them into "promising," "growth," and "mature." Unlike previous studies that mainly focused on content analysis, this study is meaningful in that it viewed the research area related to health information from an integrated perspective using various bibliometric methods.