• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.327-351
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• 2023

This paper aims to derive success factors that successfully lead an artificial intelligence (AI) project and prioritize importance. To this end, we first reviewed prior related studies to select success factors and finally derived 17 factors through expert interviews. Then, we developed a hierarchical model based on the TOE framework. With a hierarchical model, a survey was conducted on experts from AI-using companies and experts from supplier companies that support AI advice and technologies, platforms, and applications and analyzed using AHP methods. As a result of the analysis, organizational and technical factors are more important than environmental factors, but organizational factors are a little more critical. Among the organizational factors, strategic/clear business needs, AI implementation/utilization capabilities, and collaboration/communication between departments were the most important. Among the technical factors, sufficient amount and quality of data for AI learning were derived as the most important factors, followed by IT infrastructure/compatibility. Regarding environmental factors, customer preparation and support for the direct use of AI were essential. Looking at the importance of each 17 individual factors, data availability and quality (0.2245) were the most important, followed by strategy/clear business needs (0.1076) and customer readiness/support (0.0763). These results can guide successful implementation and development for companies considering or implementing AI adoption, service providers supporting AI adoption, and government policymakers seeking to foster the AI industry. In addition, they are expected to contribute to researchers who aim to study AI success models.

• 한국노년학
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• v.29 no.2
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• pp.669-682
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• 2009

The purpose of this study was to explore the role of professional identity as mediating variable in the relationship between job- and organization- related factors and job satisfaction. This study surveyed social workers who worked at 24 senior welfare facilities in Daegu·Gyeoungbuk province from Aug. 1. to Aug. 30. 2006. A total of 137 questionnaires were collected using on-site survey (response rate 76.7%). Statistical analyses were performed using SPSS 12.0 for Windows. Descriptive analysis and frequency analysis were performed on overall measurement items and hierarchical regression analysis was conducted to test the mediating effect of professional identity. The reliability of statements was acceptable since the coefficient alphas were > .70. Results of hierarchical regression showed that professional identity was verified as a partial mediator in the relationship between factors related with job and organization and job satisfaction. As the population ages, there will be an increasing need for professional social workers effectively to work with and help care for the elderly. This study highlighted that job- and organization- related factors, namely self-regulations and social supports, are significantly related with job satisfaction of social workers. Especially, such effect was more significantly apparent in high professional identity which is playing a partial mediator. This result implies that there is potential to change work environments of social workers ensuring a delegation of power and responsibility. Therefore, efforts should be made to improve the promotion system and connect social worker as servant with community through diverse service learning programs.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.281-286
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• 2003

A large number of features are collected for problem solving in real life, but to utilize ail the features collected would be difficult. It is not so easy to collect of correct data about all features. In case it takes advantage of all collected data to learn, complicated learning model is created and good performance result can't get. Also exist interrelationships or hierarchical relations among the features. We can reduce feature's number analyzing relation among the features using heuristic knowledge or statistical method. Heuristic technique refers to learning through repetitive trial and errors and experience. Experts can approach to relevant problem domain through opinion collection process by experience. These properties can be utilized to reduce the number of feature used in learning. Experts generate a new feature (highly abstract) using raw data. This paper describes machine learning model that reduce the number of features used in learning using heuristic function and use abstracted feature by neural network's input value. We have applied this model to the win/lose prediction in pro-baseball games. The result shows the model mixing two techniques not only reduces the complexity of the neural network model but also significantly improves the classification accuracy than when neural network and heuristic model are used separately.

• Korean Journal of Childcare and Education
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• v.11 no.2
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• pp.109-129
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• 2015

This study is to examine the effects of smartphone addiction on the mothers' parenting efficacy and parenting attitude. This study consisted of 203 smartphone using mothers of preschoolers in Gyonggi-do. The measurements included smartphone addiction scale(National Information Society Agency, 2011), the parenting efficacy scale(Choi & Chung, 2001), and the parenting attitude scale(Bae, 2005). Theses analyses were included in the Pearson correlation coefficient, the T-test, and the Hierarchical regression analysis. The findings indicated that the younger mothers showed higher levels of smartphone addiction. The results of smartphone addiction subscales on parenting efficacy indicated that the disturbance of adaptive functioning was related with general parenting ability, healthy parenting ability, communication ability, and learning guidance ability. The results of smartphone addiction subscales on parenting attitude showed that the disturbance of adaptive functioning was positively related with rejective parenting attitude and virtual life orientation was negatively associated with affective and autonomous parenting attitude. These findings can emphasize parents' appropriate smartphone use, and be useful resources to develop and utilize the programs of positive parenting efficacy and parenting attitude.

• Journal of Digital Convergence
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• v.11 no.12
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• pp.295-302
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• 2013

Many reported methods assume that the people in an image or an image sequence have been identified and localization. People detection is one of very important variable to affect for the system's performance as the basis technology about the detection of other objects and interacting with people and computers, motion recognition. In this paper, we present an efficient linear discriminant for multi-view people detection. Our approaches are based on linear discriminant. We define training data with fisher Linear discriminant to efficient learning method. People detection is considerably difficult because it will be influenced by poses of people and changes in illumination. This idea can solve the multi-view scale and people detection problem quickly and efficiently, which fits for detecting people automatically. In this paper, we extract people using fisher linear discriminant that is hierarchical models invariant pose and background. We estimation the pose in detected people. The purpose of this paper is to classify people and non-people using fisher linear discriminant.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1129-1135
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• 2016

The NH-NongHyup network and servers were paralyzed in 2011, in the 2013 3.20 cyber attack happened and classified documents of Korea Hydro & Nuclear Power Co. Ltd were leaked on december in 2015. All of them were conducted by a foreign country. These attacks were planned for a long time compared to the script kids attacks and the techniques used were very complex and sophisticated. However, no successful solution has been implemented to defend an APT attacks(Advanced Persistent Threat Attacks) thus far. We will use big data analytics to analyze whether or not APT attacks has occurred. This research is based on the data collected through ISAC monitoring among 3 hierarchical Korean Defense System. First, we will introduce related research about big data analytics and machine learning. Then, we design two big data analytics models to detect an APT attacks. Lastly, we will present an effective response method to address a detected APT attacks.

• Journal of KIISE
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• v.44 no.1
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• pp.44-50
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• 2017

Pedestrian detection, an important component of autonomous driving and driving assistant system, has been extensively studied for many years. In particular, image based pedestrian detection methods such as Hierarchical classifier or HOG and, deep models such as ConvNet are well studied. The evaluation score has increased by the various methods. However, pedestrian detection requires high sensitivity to errors, since small error can lead to life or death problems. Consequently, further reduction in pedestrian detection error rate of autonomous systems is required. We proposed a new method to detect pedestrians and reduce the error rate by using the Faster R-CNN with new developed pedestrian training data sets. Finally, we compared the proposed method with the previous models, in order to show the improvement of our method.

• Journal of KIISE
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• v.44 no.12
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• pp.1290-1295
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• 2017

Document classification, a task of classifying the category of each document based on text, is one of the fundamental areas for natural language processing. Document classification may be used in various fields such as topic classification and sentiment classification. Neural network models for document classification can be divided into two categories: word-level models and character-level models that treat words and characters as basic units respectively. In this study, we propose a neural network model that combines character-level and word-level models to improve performance of document classification. The proposed model extracts the feature vector of each word by combining information obtained from a word embedding matrix and information encoded by a character-level neural network. Based on feature vectors of words, the model classifies documents with a hierarchical structure wherein recurrent neural networks with attention mechanisms are used for both the word and the sentence levels. Experiments on real life datasets demonstrate effectiveness of our proposed model.

• Korea Journal of Hospital Management
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• v.18 no.2
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• pp.33-56
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• 2013

The purpose of this study is to analyze the change of hospitals that patients safety and quality improvement by accreditation process and to examine the impact or interrelation of leadership, organizational culture, hospital management activities and recognition of hospital management performances. The data were collected through a review of the literature, and selfadministered survey with a structured questionnaires to 714 subjects from several medical staff members, administration staff members, nursing staff members, medical technicians and other staff members working in 23 accredited hospitals in Korea. In this analysis hierarchical multiple regression and structural equation model were used. The conclusion of this study provides a theoretical model for understanding organizational changes brought about by accreditation system. Factor on improvement of efficiency and raise the morale, rather than increase of medical income and reduce of the cost factors, had a stronger influence on the accreditation process. In the future, the hospital's participation to induce the accreditation program voluntarily will come up with an alternative policy concern about financial perspective. Also, the hospitals which preparing accreditation program to achieve the goal efficiently, will make use of transformational leadership through enhancing individual consideration and intellectual development to leading members participation. Additionally, non-accredited hospitals should aim at professional culture by innovative and creative approaches, and inviting members to learning and growth in the organization.

• Human Ecology Research
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• v.53 no.2
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• pp.119-130
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• 2015

In this study, we examined the effects of a mother's reading motivation on the home literacy environment. Seven hundred fifty-seven mothers with three to five year old children participated in this study and completed the Adult Motivation for Reading Scale and the Home Literacy Environment Questionnaire. The subcategories of the Adult Motivation for Reading Scale are "reading as part of self," "reading efficacy," "reading for recognition," and "reading to do well in other realms." The Home Literacy Environment Questionnaire has three subcategories, namely reading books, reading behavior and modeling of parents, and literacy learning. The mean, standard deviation, one-way analysis of variance (ANOVA), and hierarchical multiple regression analysis were used to analyze the data. The results showed that (1) the home literacy environment was significantly different depending on the mother's education and family income levels, (2) the mother's reading motivation also differed significantly depending on the mother's education and family income levels, and (3) the mother's reading motivation has a significant explanatory effect on the home literacy environment. In addition, the mothers falling into the reading motivation subcategories of "reading part of self" or "reading to do well in other realms" were found to enrich their home literacy environment. Therefore, this study demonstrates that the mother's reading motivation is an important factor affecting the home literacy environment.