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A Study of variables Related to Nursing Productivity (간호생산성에 관한 연구: 관련변수의 검증을 중심으로)

  • 박광옥
    • Journal of Korean Academy of Nursing
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    • v.24 no.4
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    • pp.584-596
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    • 1994
  • The objective of the study is to explore the relationships between the variables of nursing productivity on the framework of system del in the tertiary university based care hospital in Korea. Productivity is basically defined as the relation-ship between inputs and outputs. Under the proposition that the nursing unit is a system that produces nursing care output using personal and material resources through the nursing intervention and nursing care management. And this major conception of nursing productivity system comproises input, process and output and feed-back. These categorized variables are essential parts to produce desirable and meaningful out-put. While nursing personnel from head nurse to staff nurses cooperate with each other, the head nurse directs her subordinates to achieve the goal of nursing care unit. In this procedure, the head nurse uses the leadership of authority and benevolence. Meantime nursing productivity will be greatly influenced by environment and surrounding organizational structures, and by also the operational objectives, the policy and standards of procedures. For the study of nursing productivity one sample hospital with 15 general nursing care units was selected. Research data were collected for 3 weeks from May 31 to June 20 in 1993. Input variables were measured in terms of both the served and the server. And patient classification scores were measured drily by degree of nursing care needs that indicated patent case-mix. And also nurses' educational period for profession and clinical experience and the score of nurses' personality were measured as producer input variables by the questionnaires. The process varialbes act necessarily on leading input resources and result in desirable nursing outputs. Thus the head nurse's leadership perceived by her followers is defined as process variable. The output variables were defined as length of stay, average nursing care hours per patient a day the score of quality of nursing care, the score of patient satisfaction, the score of nurse's job satis-faction. The nursing unit was the basis of analysis, and various statistical analyses were used : Reliability analysis(Cronbach's alpha) for 5 measurement tools and Pearson-correlation analysis, multiple regression analysis, and canonical correlation analysis for the test of the relationship among the variables. The results were as follows : 1. Significant positive relationship between the score of patient classification and length of stay was found(r=.6095, p.008). 2. Regression coefficient between the score of patient classification and length of stay was significant (β=.6245, p=.0128), and variance explained was 39%. 3. Significant positive relationship between nurses’ educational period and length of stay was found(r=-.4546, p=.044). 5. Regression coefficient between nurses' educational period and the score of quality of nursing care was significant (β=.5600, p=.029), and variance explained was 31.4%. 6. Significant positive relationship between the score of head nurse's leadership of authoritic characteristics and the length of stay was found (r=.5869, p=.011). 7. Significant negative relationship between the score of head nurse's leadership of benevolent characteristics and average nursing care hours was found(r=-.4578, p=.043). 8. Regression coefficient between the score of head nurse's leadership of benevolent characteristics and average nursing care hours was significant(β=-.6912, p=.0043), variance explained was 47.8%. 9. Significant positive relationship between the score of the head nurse's leadership of benevolent characteristics and the score of nurses' job satis-faction was found(r=.4499, p=050). 10. A significant canonical correlation was found between the group of the independent variables consisted of the score of the nurses' personality, the score of the head nurse's leadership of authoritic characteristics and the group of the dependent variables consisted of the length of stay, average nursing care hours(Rc²=.4771, p=.041). Through these results, the assumed relationships between input variables, process variable, output variables were partly supported. In addition it is also considered necessary that-further study on the relationships between nurses' personality and nurses' educational period, between nurses' clinical experience including skill level and output variables in many research samples should be made.

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A Theory Construction on the Care Experience for Spouses of Patients with Chronic Illness (만성질환자 배우자의 돌봄 경험에 대한 이론 구축)

  • Choi, Kyung-Sook;Eun, Young
    • Journal of Korean Academy of Nursing
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    • v.30 no.1
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    • pp.122-136
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    • 2000
  • Chronic illness requiring attention and management during a long period of time puts great burden onto patients, their family and society. For patients with chronic illnesses, providing social support is the most important, and the fundamental support comes from their spouses. Amount and quality of support from spouses seems to differentiated according to the sex of patients. Female patients tend to believe that their spouses are not very supportive. Therefore, the researchers assessed the burden of husbands of female arthritis patients to discover the factors that result in greater burden. Also, they developed a theoretical model of husbands′ care for their wives through a qualitative research into husbands′ experience. Method 1: The study material was 650 female arthritis patients registered in an arthritis clinic. The questionnaire about the disease experience of female arthritis patients and the burden of husbands were sent. Returned questionnaires numbered 210(32.3%) and 27 were excluded because of inadequate answers. The remaining 183 questionnaires were analyzed. The mean age of the patients was 51 years and the mean age of spouses was 55 years. The mean marital period was 28 years. The average duration since diagnosis was 9.1 years. Education level was varied from primary school to graduate school, and average income/month was 1,517,300 won. Method 2: Initial questionnaire studies on the burden of husbands were performed. Among 183 responding husbands, 23 consented to participate for a qualitative research. Data was obtained by direct and telephone interviews. The mean age of participants was 58 years, and the educational level and socioeconomic status also varied. Result: 1. Husbands′ burden: The average burden was 57.68 with a range of 6-96. 2. Burden and general characteristics: The husband′s burden correlated with the age of the patients, numbers in the family, therapy methods, patient′s level of discomfort, patient′s disease severity, patient′s level of dependence and the husband′s understanding of the level of severity. 3. Linear correlation analysis on burden: The husbands′ burden is explained in 22.5% by husband′s recognition of level of severity and husbands′ age. 4. There were four patterns of the burden on husbands: both objectve burden and subjective burden were high(pattern I), both of objectve burden and subjective burden were low(pattern II), objective burden was high but subjective burden was low(pattern III), objective burden was low but subjective burden was high(pattern IV). The pattern was correlated with the family income, educational level of the patients and their husbands, therapy methods, patient′s level of discomfort, patient′s disease severity, patient′s level of dependence and husband′s understanding of level of severity. 5. The core category of the caring experience of the husbands with arthritis patients was "companionship". The causal factor was the patients′ experience due to symptoms : physical disfigurement, pain, immobility, limitation of house chores, and limitation of social activities. Contextural factors are husbands′ identification of housework and husbands′ concern about the disease. The mediating factors are economic problems, fear of aging, feeling of limitation and family support. The strategy for interaction is mind control and how to solve emotional stress. The "companionship" resulted from caring activities, participation of household activities, helping patients′ to coping with emotional experience. 6. Companionship is established through the process of entering intervention, and caring state of mind. Entering intervention is the phase of participation of therapy and involvement of houseworks. The caring phase consists of decision on therapy, providing therapy, providing direct care, and taking over the household role of wife. Through caring phase, the changing phase set a stage in which husbands consolidate the relationship with their wives, and are reminded of the meaning of marriage. As a result, in changing phase, husbands′ companionship is enhanced. In conclusion, nursing care of chronic illnesses should include a family member especially the spouse. All information on disease shoud be provided to patients and whole family member. Strong support should also be provided to overcome difficulties in taking over role of other sex. Then the quality of life of patients and families will be much improved.

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Validation of Satellite SMAP Sea Surface Salinity using Ieodo Ocean Research Station Data (이어도 해양과학기지 자료를 활용한 SMAP 인공위성 염분 검증)

  • Park, Jae-Jin;Park, Kyung-Ae;Kim, Hee-Young;Lee, Eunil;Byun, Do-Seong;Jeong, Kwang-Yeong
    • Journal of the Korean earth science society
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    • v.41 no.5
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    • pp.469-477
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    • 2020
  • Salinity is not only an important variable that determines the density of the ocean but also one of the main parameters representing the global water cycle. Ocean salinity observations have been mainly conducted using ships, Argo floats, and buoys. Since the first satellite salinity was launched in 2009, it is also possible to observe sea surface salinity in the global ocean using satellite salinity data. However, the satellite salinity data contain various errors, it is necessary to validate its accuracy before applying it as research data. In this study, the salinity accuracy between the Soil Moisture Active Passive (SMAP) satellite salinity data and the in-situ salinity data provided by the Ieodo ocean research station was evaluated, and the error characteristics were analyzed from April 2015 to August 2020. As a result, a total of 314 match-up points were produced, and the root mean square error (RMSE) and mean bias of salinity were 1.79 and 0.91 psu, respectively. Overall, the satellite salinity was overestimated compare to the in-situ salinity. Satellite salinity is dependent on various marine environmental factors such as season, sea surface temperature (SST), and wind speed. In summer, the difference between the satellite salinity and the in-situ salinity was less than 0.18 psu. This means that the accuracy of satellite salinity increases at high SST rather than at low SST. This accuracy was affected by the sensitivity of the sensor. Likewise, the error was reduced at wind speeds greater than 5 m s-1. This study suggests that satellite-derived salinity data should be used in coastal areas for limited use by checking if they are suitable for specific research purposes.

The Effects of Cognitive Bias on Entrepreneurial Opportunity Evaluations through Perceived Risks in Entrepreneurial Self-Efficacy (창업가의 인지편향이 지각된 위험과 조절된 창업효능감에 따라 창업기회평가에 미치는 영향)

  • Kim, Daeyop;Park, Jaehwan
    • Asia-Pacific Journal of Business Venturing and Entrepreneurship
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    • v.15 no.1
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    • pp.95-112
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    • 2020
  • This paper is to investigate how cognitive bias of college students and entrepreneurs relates to perceived risks and entrepreneurial opportunities that represent uncertainty, and how various cognitive bias and entrepreneurial efficacy In the same way. The purpose of this study is to find improvement points of entrepreneurship education for college students and to suggest problems and improvement possibilities in the decision making process of current entrepreneurs. This empirical study is a necessary to improve the decision-making of individuals who want to start a business at the time when various attempts are made to activate the start-up business and increase the sustainability of the existing SME management. And understanding of the difference in opportunity evaluation, and suggests that it is necessary to provide good opportunities together with the upbringing of entrepreneurs. In order to achieve the purpose of the study, questionnaires were conducted for college students and entrepreneurs. A total of 363 questionnaire data were obtained and demonstrated through structural equation modeling. This study confirms that there is some relationship between perceived risk and cognitive bias. Overconfidence and control illusions among cognitive bias have a significant relationship between perceived risk and wealth. Especially, it is confirmed that control illusion of college students has a significant relationship with perceived risk. Second, cognitive bias demonstrated some significant relationship with opportunity evaluation. Although we did not find evidence that excess self-confidence is related to opportunity evaluation, we have verified that control illusions and current status bias are related to opportunity evaluation. Control illusions were significant in both college students and entrepreneurs. Third, perceived risk has a negative relationship with opportunity evaluation. All students, regardless of whether they are college students or entrepreneurs, judge opportunities positively if they perceive low risk. Fourth, it can be seen from the college students 'group that entrepreneurial efficacy has a moderating effect between perceived risk and opportunity evaluation, but no significant results were found in the entrepreneurs' group. Fifth, the college students and entrepreneurs have different cognitive bias, and they have proved that there is a different relationship between entrepreneurial opportunity evaluation and perceived risk. On the whole, there are various cognitive biases that are caused by time pressure or stress on college students and entrepreneurs who have to make judgments in uncertain opportunities, and in this respect, they can improve their judgment in the future. At the same time, university students can have a positive view of new opportunities based on high entrepreneurial efficacy, but if they fully understand the intrinsic risks of entrepreneurship through entrepreneurial education and fully understand the cognitive bias present in direct entrepreneurial experience, You will get a better opportunity assessment. This study has limitations in that it is based on the fact that university students and entrepreneurs are integrated, and that the survey respondents are selected by the limited random sampling method. It is necessary to conduct more systematic research based on more faithful data in the absence of the accumulation of entrepreneurial research data. Second, the translation tools used in the previous studies were translated and the meaning of the measurement tools might not be conveyed due to language differences. Therefore, it is necessary to construct a more precise scale for the accuracy of the study. Finally, complementary research should be done to identify what competitive opportunities are and what opportunities are appropriate for entrepreneurs.

Review of a Plant-Based Health Assessment Methods for Lake Ecosystems (식물에 의한 호수생태계 건강성 평가법에 대한 고찰)

  • Choung, Yeonsook;Lee, Kyungeun
    • Korean Journal of Ecology and Environment
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    • v.46 no.2
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    • pp.145-153
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    • 2013
  • It is a global trend that the water management policy is shifting from a water quality-oriented assessment to the aquatic ecosystem-based assessment. The majority of aquatic ecosystem assessment systems were developed solely based on physicochemical factors (e.g., water quality and bed structure) and a limited number of organisms (e.g., plankton and benthic organisms). Only a few systems use plants for a health assessment, although plants are sensitive indicators reflecting long-term disturbances and alterations in water regimes. The development of an assessment system is underway to evaluate and manage lakes as ecosystem units in the Korean Ministry of Environment. We reviewed the existing multivariate health assessment methods of other leading countries, and discussed their applicability to Korean lakes. The application of multivariate assessment methods is costly and time consuming, in addition to the correlation problem among variables. However, a single variable is not available at this moment, and the multivariate method is an appropriate system due to its multidimensional evaluation and cumulative data generation. We, therefore, discussed multivariate assessment methods in three steps: selecting metrics, scoring metrics and assessing indices. In the step of selecting metrics, the best available metrics are species-related variables, such as composition and abundance, as well as richness and diversity. Indicator species, such as sensitive species, are the most frequently used in other countries, but their system of classification in Korea is not yet complete. In terms of scoring metrics, the lack of reference lakes with little anthropogenic impact make this step difficult, and therefore, the use of relative scores among the investigated lakes is a suitable alternative. Overall, in spite of several limitations, the development of a plant-based multivariate assessment method in Korea is possible using mostly field research data. Later, it could be improved based on qualitative metrics on plant species, and with the emergence of further survey data.

Corporate Bond Rating Using Various Multiclass Support Vector Machines (다양한 다분류 SVM을 적용한 기업채권평가)

  • Ahn, Hyun-Chul;Kim, Kyoung-Jae
    • Asia pacific journal of information systems
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    • v.19 no.2
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    • pp.157-178
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    • 2009
  • Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.

Rough Set Analysis for Stock Market Timing (러프집합분석을 이용한 매매시점 결정)

  • Huh, Jin-Nyung;Kim, Kyoung-Jae;Han, In-Goo
    • Journal of Intelligence and Information Systems
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    • v.16 no.3
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    • pp.77-97
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    • 2010
  • Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

Sentiment Analysis of Movie Review Using Integrated CNN-LSTM Mode (CNN-LSTM 조합모델을 이용한 영화리뷰 감성분석)

  • Park, Ho-yeon;Kim, Kyoung-jae
    • 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.

Reports on bionomical characteristics of Mellicta ambigua (여름어리표범나비(Mellicta ambigua (Menetries))의 생태적 특성에 관한 보고)

  • Kim, Se-Gwon;Nam, Gyoung-Pil;Kim, Nam-Ee;Bae, Kyoung-Sin;Choi, Young-Cheol;Lee, Sang-Hyun
    • Journal of Sericultural and Entomological Science
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    • v.52 no.2
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    • pp.110-116
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    • 2014
  • Recently the number of the butterflies, Mellicta ambigua, had been decreasing rapidly, and already disappeared at many habitat. In this studies, we investigated ecological environment of Mellicta ambigua for preparing of primary research data recovering habitat, and studied on bionomical characteristics. Two different habitat, Jindo and Inje, were selected for investigation of ecological environment. We investigated four times during 3-month, from June to August in 2012. In Jindo, we observed more than 100 butterflies and a lot of host plants, Melampyrum roseum var. japonicum. But only 5 butterflies and only a few host plants, Veronicastrum sibiricum were observed in Inje. We could not observe the eggs, the larva and pupa on the host plants at all. For finding of bionomical characteritics, we reared butterflies at natural conditions. Collected 3-female butterflies from Jindo laid 465 eggs on the leaves of 3-host plants, Veronicastrum sibiricum. 120 ~ 186 eggs per each female were laid in the shape of cluster. An egg was globular shape, 0.6 mm diameter and 0.7 mm height. The egg periods were $9.96{\pm}0.4days$ after ovipositioning, and the hatchability was 95.% at natural condition. The larval periods were $4.1{\pm}0.6days$ (1st instar), $2.1{\pm}1.0days$ (2nd), $8.1{\pm}0.7days$ (3rd), $239.2{\pm}10.9days$ (4th), $12.3{\pm}1.3days$ (5th), $17.1{\pm}1.1days$ (6th), $10.5{\pm}1.0days$ (7th) each other. The larva of 4th instar overwintered in the nest that had been made into the leaf of host plant with secreted thread as a group until early March next year. In the early March next year, overwintered larva went around their nest in search of host plants, and went to other host plants, Veronica persica and Plantago asiatica, sometimes. The overwintered larva of Mellicta ambigua could grow up on two other host plants normally. In the following experiment, the butterflies of Mellicta ambigua laid eggs on the leaves of Plantago asiatica, but the 1st instar larva from eggs died all. The headwidth of each developmental larval stage were $0.28{\pm}0.02mm$ (1st), $0.45{\pm}0.02mm$ (2nd), $0.58{\pm}0.02mm$ (3rd), $0.75{\pm}0.03mm$ (4th), $0.89{\pm}0.05mm$ (5th), $1.23{\pm}0.06mm$ (6th), $2.13{\pm}0.11mm$ (7th). The pupal ratio was 92.0%. The pupal period were $9.1{\pm}1.6days$, and the emergence rate was 88.6%. As a result we determined that Mellicta ambigua can rear at natural conditions. But indoor-rearing is considered to be difficult and not useful industrially, because they have long term larval stage and only one life cycle per an year.