• Korean Journal of Occupational Health Nursing
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• v.13 no.1
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• pp.5-18
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• 2004

This study aims to investigate the use state of the health service computerization system in the occupational nursing field and the occupational nursers' satisfaction level, and provide basic data to promote the development of the health service computerization system for the nursing field. For this study, a questionnaire was provided to 118 occupational nurses who belong to Busan and Gyeongnam branches of KAOHN(Korean Association of Occupational Health Nurses) for 2 months (from Dec. 1, 2002 to Jan. 31, 2003). A tool of Choi Yong-Heui(2000) was used to investigate the satisfaction level of using the health service computerization system. The collected materials were analyzed in real number and percentage, average and standard deviation, t-test and ANOVA by using the SPSS WIN 10.0 program. This study is summarized as follows: 1. The average age was $31.99{\pm}5.58$ old in this study. The married were 54.2%. Participants who graduated from a junior college was 76.9%. The average service period was $4.48{\pm}4.68$ years. In service types, 79.7% of participants served in a health care center. The average service period was $3.22{\pm}2.89$ years. The service place which had 1000 workers or more was 35.6%. 2. Only 20.3% of participants in this study had a computer use education. 3. The field who participants used mostly was communication/internet, $3.29{\pm}.85$ hours in average. 4. 97.1% of occupational fields had computers and peripheral devices: 71.4% in pentium computer, 42.8% in the hard disk capacity of 20-29GB, 60.0% in 15 inch monitors, 86.2% in printers, 18.1% in digital cameras, 12.4% in LAN, and 9.5% in scanners. 80.1% of the occupational fields which were objects of study could use communication. 5. The occupational fields which did not introduced the health service computerization system were 62.8%. The main cause was attributable to entrepreneurs' insufficient recognition 66.6%. 51.5% of the entrepreneurs did not have an introduction plan. 37.2% of participating companies had the health service computerization system. 56.4% of them introduced it since the year 2000. 81.6% of the introduction motivation aimed to the efficiency of health service. The most issue upon introduction was insufficient understanding of a person in charge - 25.6%. The in-house development of the system covered 56.4%. 61.5% of the participants accepted their demands from the first stage of development. The direct effect of computerization showed the increase of 25.9% in the quickness and continuity of service treatment, and 25.9% in the serviceability of statistical treatment. 6. 22.0% of the participants had a computerization system use education. 69.2% of them had a in-house education. An educational method by nurses who used the computerization system was 76.9%. 92.3% of the education was helpful for practical duties. 7. An analysis of the computer use by health service fields showed that the medicine management in a health management field was 15.9%. the work environment measuring management in a work environment filed was 32.9%. the employment. general and special examination management in a heal th management field was 61.1 %. the various reports management in an administrative field was 64%. the health education data preparation management in an educational field was 58.0%. and the medicine and expendables management in an equipment management field was 51.6%. An analysis of the computerization system use showed that the various statistical data manage in a health management field was 13.0%. the work environment measuring management in a health management field was 34.8%. the personal disease management in a health management field was 51.9%. the heal education data preparation management in an educational field was 54.5%. and the equipment management of health care centers in an equipment management field was 52.6%. 8. 31.6% of the participants wanted that health service computerization system would include the generals of health services. 42.4% of the participants thought that first of all. the aggressive interest and investment of employers were required to build the health service computerization system. 9. The participants' satisfaction level on the computerization system use was $3.51{\pm}.57$ points. An analysis by each factor showed $3.62{\pm}.68$ points in a service change factor. $3.15{\pm}.63$ points in a computer program use factor, and $3.45{\pm}.71$ points in a continuous computerization use factor. 10. An analysis of the computerization system use by general characteristics of participants showed that the married (p = .022) had the satisfaction level higher than the unmarried. 11. The satisfaction level of the computerization system use by participants' computer use ability tended to be higher in proportion to the increase of computer use abilities in spreadsheet (F=2.606. p=.048). presentation (F=3.62. p=.012) and communication/internet(F=2.885. p=.0321. Based on the study results mentioned above. I will suggest as follows : The nationwide enlargement and repetition study is required for occupational nurses who serve in occupational nursing fields. The computerization system in a health service field is inferior comparing with other fields. The computerization system standard by business types and characteristics should be prepared through employers's aggressive participation and national support. Therefore various statistical data which occurs in occupational fields will be managed systematically and efficiently. A regular and systematic computer education plan for occupational nurses in charge of health services in the filed is urgently required to efficiently manage and improve the health of on-site workers.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.

• Journal of Korean Society of Archives and Records Management
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• v.1 no.1
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• pp.201-230
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• 2001

Conserving the recored cultural inheritance is actually the duty of all of us. Above all, the management and conservation of archives and documents is up to archivists who have technical knowledge about archival science. Archivists have to not only conserve archives and documents but also carry out classifying and appraising them in order to define them as current historic ones. The fundamental education about archival science is made up of history and law. Because Archive is the organisation which manage archives and documents produced by legal and administrative actions. Although there are still arguments about technical knowledge and degree archivists have to acquire, most of them prefer the studies related with history and emphasize legal studies to be the general boundary of archivits' ideology and trust. The training course about conservation of archives is conducted in about 9 National Archives of Torino, Milano, Venezia, Genova, Bologna, Parma, Roma, Napoli, Palermo. The training course in 19th was mostly based on the lectures of Phaleography, Diplomatics. There were not the education about archival science yet. Toward the end of 19th and 20th, people stressed the most basic subject in the training course of National Archive was not Phaleography and Diplomatics but archival science. The goal of archival science is to study the institution and organisation transferring archives and documents to Archive. And also it help archivists not wander about with ignorance of organisational and original procedures and divisions but know exactly theirs works. Like this, the studies on institution and organisation have got in the saddle as a branch of archival science since a few ten years. While archival science didn't evoke sympathy among people and experienced the tedious and difficult path in italy and other countries, Archive was managed by experts of other branches. As a result, there were a lot of faults in Archival Science. Specializing training course for Italian archivists came into being under the backdrop of Social Science Institute of Roma National University in 1925. The archival course of universities accomplished by the studies of history, law and economy. And such as Eugenio Casanova and Giorgio Cencetti were devoted archival science was abled to settle down in national archive. The training course for experts of 'archival science, 'Phaleography and Diplomatics' in National Archive of Bologna(Archivio di Stato di Bologna) is one of courses conducted in 17 National Archives in italy. This course is gratuitous and made up of 8 subjects(Archivistica, Paleografia, Diplomatica, Storia dell' Archivio, Notariato e documenti privati, istituzione medievale, istituzione moderna, istituzione contemporanea) students have to complete for two years. Students can receive the degree through passing twice written exam and once oral test. After department of Culture and education finally puts the marks of students, the chief Nationa Archive of Bologna confer the degree of 'archival science Phaleography and Diplomatics' on students passing the exams. This degree authenticates trainees' qualification which enables him to work at the archive in province, district and administrative capital city and archive of comunity and so on. Italian training course naturally leads archivists to keep in contact with valuable cultural inheritance through training in Archive. And it shows the intention to strengthen the affinity with each documents in the spot of archival management before training archivists. Also this is appraised as one of positive policies to conserve the local cultual inheritante in connection with the original qualitity of national archive with testify the history of each region. Traning course for archivist in Italy shows us the way how we have to prepare and proceed it. First, from producing documents to conserving than forever there has introduced 'original order that is to say a general rule to respect the first order given at the time producing documents'. Management of administrative documents is related consistently with one of historical documents. Second, the traning course for archivist is managing around 17 national archives. because italian national archive lay stress not or rducation of theory bus on train for archivest working in the first time of archival science. Third, diplomatics and phaleography for studies about historical document support archives. Forth, the studies on history id proceeding by cooperation between archivist and historian around archive. How our duties is non continuinf disputer who has to conserve and manage document and archives, but traing experts who having ability, vision and flexible thought, responsibility about archivals.