• Journal of Nutrition and Health
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• v.7 no.3
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• pp.1-13
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• 1974

The relative state of human iron storage may be ascertained more reliably through determination of the serum iron, iron binding capacity, transferrin saturation and absorption of radioactive iron in conjunction with studies of red cell morphology than from the study of red cell morphology alone. Recent investigations have shown that there is an increase in red cell protoporphyrin concentration in iron deficiency anemia. The significance of the red cell protoporphyrin has been discussed greatly during the years since its discovery. Two of the main factors which appear to influence the amaunt of protoporphyrin are increased erythropoiesis and factors interfering with the utilization of iron in the synthesis of hemoglobin, and iron deficiency. Recently Heller et al. have described a simplified method for blood protoporphyrin assay and this technique could be used assess nutritional iron status, wherein even minor insufficiencies are detectable as increased protoporphyrin concentrations. Based on the evaluation of the relationship between nutritional iron status and red cell protoporphyrin as an index suitable for the detection of the iron deficiency is described in this paper. RESULTS 1. Hemoglobin Concentrations and Anthropometric Measurements. The mean and standard deviations of the various anthropometric measurements of different age and sex groups are shown in table 1. There measurements have been compared with the Korean Standard. In the absence of local standards for arm circumference and skin-fold thickness over triceps, they have been compared with the standard from Jelliffe. Table 2,3, and 4 give anthropometric measurements and frequency (%) of anemia in children surveyed. The mean height of the children studid was 10 to 20 percent; below the Korean Standard. The distribution of height below 80 percent of the Standard was 21.2 percent, however, among anemic group this percentage was 27.7 percent. In general, the mean weight of the children was 10 to 15 percent below the Korean Standard. The percentage of children with weight less than 80 percent of the Standard was about 35 percent. But in the anemic group of the children, this percentage was 44 percent. The mean arm circumference was about 15 percent lower than the Jelliffe's standard. 61.2 percent of the children had values of arm circumference below 80 percent of the standard. Children with low hemoglobin levels, this percentage was 80 percent. The mean skinfold thickness over the triceps of the children studied was about 25 Percent lower than the Jelliffe's standard and 61.2 percent of the children had the value less than 80 percent of the standard. Among anemic children, this percentage was 70.8%. As may be seen from table 5, the mean hemoglobin concentration of the total group was 11.3g/100ml. Hemoglobin concentration was less than 11.0g/100ml. in 65(36.5%) of the 178 children. The degree of anemia in most of these children was mild with a hemoglobin level of less than 8.0g/100ml. found in only one child. In general, the prevalence of anemia was high in female children than male and decreased its frequency with increasing age. Relatively close relationship was observed between hemoglobin level and anthrophometric measurements especially high between arm circumference and skinfold thickness and hemoglobin but very low in height and low in weight and hemoglobin level, estimated by chi-square value. II. Serum iron, Transferrin saturation (1) Serum iron, and transferrin saturation Serum iron, transferrin saturation and red cell protoporphyrin concentrations were estimated in sub-sample of 84 children from 1 to 6 years and 24 older children between 7 and 13 years of age. The findings are presented in table 6. The mean serum iron concentration of the total group was 59ug/100ml. However, the level incrased with age from 36.6ug/100ml. (1-3years) to 80.8ug/100ml. (7-13 years). 60 percent of these children had a serum iron level less than 50ug/10ml. in the 1-3 years age group and 31.4 percent for 4-6 years group. These contrast with the finding of 12.5 percent anemic children in the 7-13 years age group. The mean transferrin saturation for the total group was 18.1 percent and frequency of anemia by transferrin saturation was observed same pattern as serum iron concentration. (2) Red cell protoporphyrin concentrations. (a) Red cell protoporphrin levels of children: Red cell protoporphyrin and other biochemical data are shown in table 4. The mean concentration in red cell of all children was fround 46.3ug/100ml. RBC. and differences with age groups were observed; in the age group 1-3 years, the mean concentration was $59.5{\pm}32.14$ ug/100ml. RBC; 4-6 years $44.1{\pm}22.57$ ug/100ml. RBC. and 7-13 years, $39.0{\pm}13.56$ ug/100ml. RBC. (b) Normal protoporphyrin values in adults: It was observed that in 10 normal adult males studied here the level of protoporphyrin in red cell ranged from 18 to 54 ug/100ml. RBC. and the mean concentration was $47.5{\sim}14.47$ ug/100ml. RBC. Other biochemical determination made on the same subjects are presented in table 8. (c) Red tell protoporphyrin concentration of occupational blood donors: The results of analyses for red cell protoporphyrin as well as serum iron, transferrin saturation and hemoglobin in the 76 blood donors are presented in table 7 and 8. In this experiment, donors were selected at random, however, most of them bled repeatedly because of poor economic situation, I doubt. Table 9 shows the distribution of red cell protoporphyrin concentration and hemoglobin concentration of occupational donors. The mean hemoglobin value for the total was 11.9 g/100 ml. When iron deficiency anemia is defined as a transferrin saturation below 15%, prevalence of anemia was 47.4 percent and the mean serum iron was 27.1ug/100ml. and red cell protoporphyrin, 168.3ug/100ml. RBC. However, mean serum iron and protoporphyrin concentration of above 15% transferrin saturation were 11.6 ug/100 ml. and 58.8 ug/100 ml. RBC. respectively. The mean Protoporphyrin concentration of non-anemic (above 15% transferrin saturation) donors was slightly higher than the results of normal adult males.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.141-156
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• 2013

Social media is a representative form of the Web 2.0 that shapes the change of a user's information behavior by allowing users to produce their own contents without any expert skills. In particular, as a new communication medium, it has a profound impact on the social change by enabling users to communicate with the masses and acquaintances their opinions and thoughts. Social media data plays a significant role in an emerging Big Data arena. A variety of research areas such as social network analysis, opinion mining, and so on, therefore, have paid attention to discover meaningful information from vast amounts of data buried in social media. Social media has recently become main foci to the field of Information Retrieval and Text Mining because not only it produces massive unstructured textual data in real-time but also it serves as an influential channel for opinion leading. But most of the previous studies have adopted broad-brush and limited approaches. These approaches have made it difficult to find and analyze new information. To overcome these limitations, we developed a real-time Twitter trend mining system to capture the trend in real-time processing big stream datasets of Twitter. The system offers the functions of term co-occurrence retrieval, visualization of Twitter users by query, similarity calculation between two users, topic modeling to keep track of changes of topical trend, and mention-based user network analysis. In addition, we conducted a case study on the 2012 Korean presidential election. We collected 1,737,969 tweets which contain candidates' name and election on Twitter in Korea (http://www.twitter.com/) for one month in 2012 (October 1 to October 31). The case study shows that the system provides useful information and detects the trend of society effectively. The system also retrieves the list of terms co-occurred by given query terms. We compare the results of term co-occurrence retrieval by giving influential candidates' name, 'Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn' as query terms. General terms which are related to presidential election such as 'Presidential Election', 'Proclamation in Support', Public opinion poll' appear frequently. Also the results show specific terms that differentiate each candidate's feature such as 'Park Jung Hee' and 'Yuk Young Su' from the query 'Guen Hae Park', 'a single candidacy agreement' and 'Time of voting extension' from the query 'Jae In Moon' and 'a single candidacy agreement' and 'down contract' from the query 'Chul Su Ahn'. Our system not only extracts 10 topics along with related terms but also shows topics' dynamic changes over time by employing the multinomial Latent Dirichlet Allocation technique. Each topic can show one of two types of patterns-Rising tendency and Falling tendencydepending on the change of the probability distribution. To determine the relationship between topic trends in Twitter and social issues in the real world, we compare topic trends with related news articles. We are able to identify that Twitter can track the issue faster than the other media, newspapers. The user network in Twitter is different from those of other social media because of distinctive characteristics of making relationships in Twitter. Twitter users can make their relationships by exchanging mentions. We visualize and analyze mention based networks of 136,754 users. We put three candidates' name as query terms-Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn'. The results show that Twitter users mention all candidates' name regardless of their political tendencies. This case study discloses that Twitter could be an effective tool to detect and predict dynamic changes of social issues, and mention-based user networks could show different aspects of user behavior as a unique network that is uniquely found in Twitter.