• Journal of The Korean Association of Information Education
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• v.20 no.2
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• pp.189-196
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• 2016

The PATROL instructional model only uses digital textbooks. PATROL is an acronym for Planning, Action, Tracking, Recommending, Ordering, and Leading. Teachers have a difficult time using current digital textbooks to determine how much time students spend using course materials. This is because current digital textbooks can only show the content of paper textbooks and display additional multimedia materials. In this study, digital textbook functions were designed based on the PATROL model in order to analyze students' learning situations, diagnose problems, and offer solutions. Digital textbook are based on learning analytics named SEE-PAD. SEE-PAD is composed of the following: Social network analysis; Evaluation and assEssment analysis; Predictive analysis; Adaptive learning analysis; and the analysis Dashboard. I drew and showed the use case and sequence diagrams of SEE-PAD to help design digital textbook functions.

• Culinary science and hospitality research
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• v.18 no.4
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• pp.47-58
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• 2012

This study, targeting the students of "K" university in Busan City area, was performed to draw the groups by food-related lifestyle types and to identify the correlation between each group's attributes of selecting places to eat out and obesity index. The purpose of the study was achieved by means of the PASW Statistic 18.0(Predictive Analytics Software) which conducted frequency analysis, factor analysis, reliability analysis, t-test, ${\chi}^2$-test, non-hierarchical cluster analysis and ANOVA. It turned out that the male university students were 175.59 cm tall and weigh 69.53 kg on average. And the female university students showed their average height of 162.81 cm and weight of 53.42 kg. When examined by the body mass index(BMI), male students were composed of 1.7% of underweight, 64.6% of normal weight, 19.7% of overweight and 14.0% of obese. As for the female students, 22.9% were classified as underweight, 62.7% as normal weight, 8.5% as overweight and 5.9% as obese. The food-related lifestyle categories were divided into five factors; health seeking type, safety seeking type, mood seeking type, taste seeking type, and western food seeking type. The four attributes of selecting places to eat out included quality of food and service, price reasonableness, accessibility and atmosphere, and experience to have eaten. With regard to food-related lifestyle, the groups were named by cluster 1 [careless diet group], Cluster 2 [health oriented group], and cluster3 [careless healthcare group]. In terms of the correlation between the clusters by food-related lifestyle and their attributes of selecting places to eat out, Cluster 1 had a high mean value in experience to have eaten, Cluster 2 quality of food and service, Cluster 3 accessibility and atmosphere.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.155-170
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• 2018

Predicting a company's financial bankruptcy is traditionally one of the most crucial forecasting problems in business analytics. In previous studies, prediction models have been proposed by applying or combining statistical and machine learning-based techniques. In this paper, we propose a novel intelligent prediction model based on the simulated annealing which is one of the well-known optimization techniques. The simulated annealing is known to have comparable optimization performance to the genetic algorithms. Nevertheless, since there has been little research on the prediction and classification of business decision-making problems using the simulated annealing, it is meaningful to confirm the usefulness of the proposed model in business analytics. In this study, we use the combined model of simulated annealing and machine learning to select the input features of the bankruptcy prediction model. Typical types of combining optimization and machine learning techniques are feature selection, feature weighting, and instance selection. This study proposes a combining model for feature selection, which has been studied the most. In order to confirm the superiority of the proposed model in this study, we apply the real-world financial data of the Korean companies and analyze the results. The results show that the predictive accuracy of the proposed model is better than that of the naïve model. Notably, the performance is significantly improved as compared with the traditional decision tree, random forests, artificial neural network, SVM, and logistic regression analysis.

• Journal of The Korean Society of Integrative Medicine
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• v.5 no.1
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• pp.19-24
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• 2017

Purpose : The purpose of this study was to find out the difference motion of hip, knee and ankle joint during walking according to using walker on older people. Method : Korean older people of 34 subjects was participated in this study. Participants was measured joint motion on hip, knee and ankle joint during both conditions (walking with walker and without walker). The measured data were analyzed using independent t-test to investigate the difference of joint motion on the both condition. The statistical analyses were performed using Predictive Analytics Soft Ware (PASW) for windows(Ver. 19) and p-value less than .05 were considered significant for all cases. Result : The study showed that more joint motion on hip flexion and ankle pronation is increased by using walker. And hip extension, knee external rotation and ankle plantar flexion is decreased by using walker. Conclusion : This study suggest that using walker on older people was change the motion of the lower limb joint during walking. Therefore, It is necessary to develop a new walker that can reduce dependency and ensure stability on older people during walking.

• Korean Journal of Health Education and Promotion
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• v.30 no.1
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• pp.27-39
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• 2013

Objectives: The purpose of the study was to test a model of physical activity of elderly living in Korea, determine significant factors contributing the physical activity, and examine significant paths in the model. Methods: A cross-sectional study was conducted using a convenience sample of 207 elderly men and women, aged 60 and older, residing in Busan Metropolitan City. Data were collected from July to August 2009 and analyzed using Predictive Analytics Software (PASW) and Analysis of a Moment Structures (AMOS). Results: The fitness of the modified model was confirmed to be appropriate (${\chi}^2$ = 55.61, ${\chi}^2$/df = 1.32, p = .078, RMSEA = .04, GFI = .96, AGFI = .91, NFI = .90, NNFI = .94, CFI = .97, PNFI = .48). The elder's age, previous exercise behavior, and self-efficacy were significant in explaining the variance in their physical activity. We found that (a) perceived health status, perceived benefits, perceived barriers, and social support directly affected self-efficacy; (b) previous exercise behavior and perceived health status directly affected perceived benefits; (c) previous exercise behavior directly affected perceived barriers; and (d) and education level, extent of pocket money, and economic level directly affected social support. Conclusions: The younger the age, the more previous exercise experience, and the higher the self-efficacy, the more S. Korean elders demonstrated improved physical activity.

• Genomics & Informatics
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• v.11 no.4
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• pp.186-190
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• 2013

The advances in electronic medical records (EMRs) and bioinformatics (BI) represent two significant trends in healthcare. The widespread adoption of EMR systems and the completion of the Human Genome Project developed the technologies for data acquisition, analysis, and visualization in two different domains. The massive amount of data from both clinical and biology domains is expected to provide personalized, preventive, and predictive healthcare services in the near future. The integrated use of EMR and BI data needs to consider four key informatics areas: data modeling, analytics, standardization, and privacy. Bioclinical data warehouses integrating heterogeneous patient-related clinical or omics data should be considered. The representative standardization effort by the Clinical Bioinformatics Ontology (CBO) aims to provide uniquely identified concepts to include molecular pathology terminologies. Since individual genome data are easily used to predict current and future health status, different safeguards to ensure confidentiality should be considered. In this paper, we focused on the informatics aspects of integrating the EMR community and BI community by identifying opportunities, challenges, and approaches to provide the best possible care service for our patients and the population.

• Journal of the Korean Dietetic Association
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• v.25 no.1
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• pp.44-58
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• 2019

Predicting the number of meals in a foodservice organization is an important decision-making process that is essential for successful food production, such as reducing the amount of residue, preventing menu quality deterioration, and preventing rising costs. Compared to other demand forecasts, the menu of dietary personnel includes diverse menus, and various dietary supplements include a range of side dishes. In addition to the menus, diverse subjects for prediction are very difficult problems. Therefore, the purpose of this study was to establish a method for predicting the number of meals including predictive modeling and considering various factors in addition to menus which are actually used in the field. For this purpose, 63 variables in eight categories such as the daily available number of people for the meals, the number of people in the time series, daily menu details, weekdays or seasons, days before or after holidays, weather and temperature, holidays or year-end, and events were identified as decision variables. An ensemble model using six prediction models was then constructed to predict the number of meals. As a result, the prediction error rate was reduced from 10%~11% to approximately 6~7%, which was expected to reduce the residual amount by approximately 40%.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.507-513
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• 2022

This study developed a model for predicting the number of VOD uses of IPTV, an online market in the film industry. A machine learning-based prediction model was established using the VOD usage data collected by the Korean Film Council from 2017 to 2021. Through literature research and cluster analysis, the difference between the offline market and the online market is revealed, and a new category of VOD usage is proposed. The purpose is to help IPTV companies establish marketing strategies as well as support decision-making by developing a machine learning-based VOD usage prediction model.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.3
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• pp.635-644
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• 2024

Although the business model(BM) patents act as a creative bridge between technology and the marketplace, limited scholarly attention has been paid to the content analysis of BM patents. This study aims to contextualize converging BM patents by employing topic modeling technique and clustering highly marketable topics, which are expressed through a topic-market impact matrix. We relied on BM patent data filed between 2010 and 2022 to derive empirical insights into the commercial potential of emerging business models. Subsequently, nine topics were identified, including but not limited to "Data Analytics and Predictive Modeling" and "Mobile-Based Digital Services and Advertising." The 2x2 matrix allows to position topics based on the variables of topic growth rate and market impact, which is useful for prioritizing areas that require attention or are promising. This study differentiates itself by going beyond simple topic classification based on topic modeling, reorganizing the findings into a matrix format. T he results of this study are expected to serve as a valuable reference for companies seeking to innovate their business models and enhance their competitive positioning.

• Asia pacific journal of information systems
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• v.29 no.1
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• pp.35-49
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• 2019

The paper attempts to document the application of relevant Machine Learning (ML) models on Yelp (a crowd-sourced local business review and social networking site) dataset to analyze, predict and recommend business. Strategically using two cloud platforms to minimize the effort and time required for this project. Seven machine learning algorithms in Azure ML of which four algorithms are implemented in Databricks Spark ML. The analyzed Yelp business dataset contained 70 business attributes for more than 350,000 registered business. Additionally, review tips and likes from 500,000 users have been processed for the project. A Recommendation Model is built to provide Yelp users with recommendations for business categories based on their previous business ratings, as well as the business ratings of other users. Classification Model is implemented to predict the popularity of the business as defining the popular business to have stars greater than 3 and unpopular business to have stars less than 3. Text Analysis model is developed by comparing two algorithms, uni-gram feature extraction and n-feature extraction in Azure ML studio and logistic regression model in Spark. Comparative conclusions have been made related to efficiency of Spark ML and Azure ML for these models.