• Journal of Intelligence and Information Systems
• /
• v.19 no.2
• /
• pp.55-71
• /
• 2013

The prediction model is the main factor affecting the performance of a knowledge-based system for bankruptcy prediction. Earlier studies on prediction modeling have focused on the building of a single best model using statistical and artificial intelligence techniques. However, since the mid-1980s, integration of multiple techniques (hybrid techniques) and, by extension, combinations of the outputs of several models (ensemble techniques) have, according to the experimental results, generally outperformed individual models. An ensemble is a technique that constructs a set of multiple models, combines their outputs, and produces one final prediction. The way in which the outputs of ensemble members are combined is one of the important issues affecting prediction accuracy. A variety of combination schemes have been proposed in order to improve prediction performance in ensembles. Each combination scheme has advantages and limitations, and can be influenced by domain and circumstance. Accordingly, decisions on the most appropriate combination scheme in a given domain and contingency are very difficult. This paper proposes a confidence-based selection approach as part of an ensemble bankruptcy-prediction scheme that can measure unified confidence, even if ensemble members produce different types of continuous-valued outputs. The present experimental results show that when varying the number of models to combine, according to the creation type of ensemble members, the proposed combination method offers the best performance in the ensemble having the largest number of models, even when compared with the methods most often employed in bankruptcy prediction.

• Korean Journal of Soil Science and Fertilizer
• /
• v.34 no.1
• /
• pp.55-63
• /
• 2001

N fertilizer recommendations to optimize with consideration to maximum crop yields, maximum profits, and minimum N losses to ground or runoff water, an advisory system. Nitrogen Expert System (NES), was developed. The system was to estimate the optimal rate of N fertilizer application cornfields in Illinois. NES was constructed using Smart Elements, a knowledge-based system that manages the expertise of human experts. NES was reinforced by addition of the effect of a productivity index (PI), soil organic matter content (SOM), and pre-sidedressing of nitrate concentration (PSNT) to the optimal N fertilizer recommendation. NES contains 49 rules, 1 class, 14 objects, and 2 properties. NES was successfully operated, showing N recommendations with inputs of three soil properties including PI, SOM, and PSNT. NES can reduce N loss to the environment, but adherence to the recommendations may also reduce farmers income. Therefore, NES will be more effective by evaluating both environmental damage assessment and other economic agricultural management parameters and other soil physico-chemical parameters.

• Korean small business review
• /
• v.42 no.3
• /
• pp.275-304
• /
• 2020

The purpose of this study is to present implications for revitalizing start-ups and contribute to enhancing the success rate of start-ups by clarifying factors and processes for converting workers with knowledge, experience and networks in related fields into entrepreneur. Based on the Shapero's Entrepreneurial Event Model, this study demonstrated whether the job insecurity and entrepreneurship of the workers were precipitating events of the entrepreneurial intention and whether the perceived desirability and feasibility of the entrepreneurial behaviour mediated between them. According to the results of the study, first, it was confirmed that job insecurity, innovativeness, and risk-taking of workers are factors that increase the entrepreneurial intention. Second, the indirect effect of perceived desirability between all components of job insecurity and entrepreneurial intentions was not significant, but all components of entrepreneurship appeared to improve entrepreneurial intention through perceived desirability. Third, it has been confirmed that job insecurity, innovativeness, and risk-taking strengthen the entrepreneurial intention through the perception of feasibility for entrepreneurial behavior. Through this study, it is confirmed that in order to convert workers into entrepreneur, it is necessary to strengthen entrepreneurship education and support for internal ventures for workers to increase their positive attitude and confidence in implementation. Therefore, it is expected to help solve job problems and revive the sluggish economy by contributing to boosting start-ups.

• Human Ecology Research
• /
• v.60 no.3
• /
• pp.331-344
• /
• 2022

The purpose of this study was to identify a support method for the introduction of metaverse-based classes (MBC) in home economics (HE) education. This was achieved by diagnosing the stages of concern and levels of use exhibited by HE teachers applying the concerns-based adoption model (CBAM). Questionnaires were sent to a convenience sample of middle- and high-school HE teachers using the KSDC (Korea Social Science Data Center). Overall, 271 responses were received, and the data were analyzed using KSDC E-STAT 3.0 and SPSS 28.0.1.1. The results were as follows: First, regarding the level of knowledge of MBC, the introductory level was the most common (139 respondents, 51.3%,), followed by the beginner level (81, 29.9%), the intermediate level (28, 10.3%,), the advanced level (12, 4.4%), and the master level (11, 4.1%). Average responses on a 5-point Likert scale to questions about the use of metaverse in HE classes were as follows: possibility (4.02), necessity (3.82), and usefulness (3.90). Second, HE teachers' stages of concern in MBC were as follows (in descending order): unconcerned - stage 0, and information - stage 1 (86.9), personal - stage 2 (85.6), management - stage 3 (80.9), collaboration - stage 5 (57.5), consequence - stage 4 (57.4), and refocusing - stage 6 (55.2). Third, the use of MBC was highest for orientation - level 1 (173 respondents, 63.8%), followed by non-use - level 0 (34, 12.5%), preparation - level 2 (29, 10.7%), mechanical - level 3 (15, 5.5%), refinement - level 5 (8, 3.0%), renewal - level 7 (8, 3.0%), routine - level 4 (3, 1.1%), and integration - level 6 (1, 0.4%). Many HE teachers had heard about MBC but were in the introductory level of not knowing what it is, and at the stage of being unconcerned or wanting to know about MBC. Of the 271 respondents, only 35 used metaverse in classes. Therefore, it is necessary to provide teacher training opportunities that provide basic information on the significance and implementation of MBC for HE teachers. Also, an MBC guideline book should be developed and distributed to HE teachers. Finally, a teacher community meeting is needed to share the expertise of teachers with substantial experience in using MBC.

• Information Systems Review
• /
• v.24 no.1
• /
• pp.1-19
• /
• 2022

Social media-based communication has become crucial part of our personal and official lives. Therefore, it is no surprise that social media sentiment analysis has emerged an important way of detecting potential customers' sentiment trends for all kinds of companies. However, social media sentiment analysis suffers from huge number of sentiment features obtained in the process of conducting the sentiment analysis. In this sense, this study proposes a novel method by using Bayesian Network. In this model MBFS (Markov Blanket-based Feature Selection) is used to reduce the number of sentiment features. To show the validity of our proposed model, we utilized online review data from Yelp, a famous social media about restaurant, bars, beauty salons evaluation and recommendation. We used a number of benchmarking feature selection methods like correlation-based feature selection, information gain, and gain ratio. A number of machine learning classifiers were also used for our validation tasks, like TAN, NBN, Sons & Spouses BN (Bayesian Network), Augmented Markov Blanket. Furthermore, we conducted Bayesian Network-based what-if analysis to see how the knowledge map between target node and related explanatory nodes could yield meaningful glimpse into what is going on in sentiments underlying the target dataset.

• Journal of Intelligence and Information Systems
• /
• v.19 no.2
• /
• pp.73-85
• /
• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.

• Asia pacific journal of information systems
• /
• v.19 no.3
• /
• pp.125-150
• /
• 2009

RFID(Radio Frequency IDentification) is a wireless frequency of recognition technology that can be used to recognize, trace, and identify people, things, and animals using radio frequency(RF). RFID will bring about many changes in manufacturing and distributions, among other areas. In accordance with the increasing importance of RFID techniques, great advancement has been made in RFID studies. Initially, the RFID research started as a research literature or case study. Recently, empirical research has floated on the surface for announcement. But most of the existing researches on RFID adoption have been restricted to a dichotomous measure of 'adoption vs. non-adoption' or adoption intention. In short, RFID research is still at an initial stage, mainly focusing on the research of the RFID performance, integration, and its usage has been considered dismissive. The purpose of this study is to investigate which factors are important for the RFID adoption and implementation with organizational resources. In this study, the organizational resources are classified into either finance resources or IT knowledge resources. A research model and four hypotheses are set up to identify the relationships among these variables based on the investigations of such theories as technological innovations, adoption stage, and organizational resources. In order to conduct this study, a survey was carried out from September 27, 2008 until October 23, 2008. The questionnaire was completed by 143 managers and workers from physical distribution and manufacturing companies related to the RFID in South Korea. 37 out of 180 surveys, which turned out unfit for the study, were discarded and the remaining 143(adoption stage 89, implementation stage 54) were used for the empirical study. The statistics were analyzed using Excel 2003 and SPSS 12.0. The results of the analysis are as follows. First, the adoption stage shows that perceived benefits, standardization, perceived cost savings, environmental uncertainty, and pressures from rival firms have significant effects on the intent of the RFID adoption. Further, the implementation stage shows that perceived benefits, standardization, environmental uncertainty, pressures from rival firms, inter-organizational cooperation, and inter-organizational trust have significant effects on the extent of the RFID use. In contrast, inter-organizational cooperation and inter-organizational trust did not show much impact on the intent of RFID adoption while perceived cost savings did not significantly affect the extent of RFID use. Second, in the adoption stage, financial issues had adverse effect on both inter-organizational cooperation and the intent against the RFID adoption. IT knowledge resources also had a deterring effect on both perceived cost savings and the extent of the RFID adoption. Third, in the implementation stage, finance resources had a moderate effect on environmental uncertainty and extent of RFID use while IT knowledge resources had also a moderate effect on perceived cost savings and the extent of the RFID use. Limitations and future research issues can be summarized as follows. First, it is difficult to say that the sample is large enough to be representative of the population. Second, because the sample of this study was conducted among manufacturers only, it may be limited in analyzing fully the effect on the industry as a whole. Third, in consideration of the fact that the organizational resources in the RFID study require a great deal of researches, this research may deem insufficient to fulfill the purpose that it initially set out to achieve. Future studies using performance research are, therefore, needed to help better understand the organizational level of the RFID adoption and implementation.

• Information Systems Review
• /
• v.21 no.4
• /
• pp.99-123
• /
• 2019

The software industry is a high value-added industry in the knowledge information age, and its importance is growing as it not only plays a key role in knowledge creation and utilization, but also secures global competitiveness. Among various SW available in today's business environment, Open Source Software(OSS) is rapidly expanding its activity area by not only leading software development, but also integrating with new information technology. Therefore, the purpose of this research is to empirically examine and analyze the effect of factors on the switching behavior to OSS. To accomplish the study's purpose, we suggest the research model based on "Push-Pull-Mooring" framework. This study empirically examines the two categories of antecedents for switching behavior toward OSS. The survey was conducted to employees at various firms that already switched OSS. A total of 268 responses were collected and analyzed by using the structural equational modeling. The results of this study are as follows; first, continuous maintenance cost, vender dependency, functional indifference, and SW resource inefficiency are significantly related to switch to OSS. Second, network-oriented support, testability and strategic flexibility are significantly related to switch to OSS. Finally, the results show that willingness to secures SW competitiveness has a moderating effect on the relationships between push factors and pull factor with exception of improved knowledge, and switch to OSS. The results of this study will contribute to fields related to OSS both theoretically and practically.

• Journal of Intelligence and Information Systems
• /
• v.17 no.4
• /
• pp.241-254
• /
• 2011

Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.

• Management & Information Systems Review
• /
• v.36 no.5
• /
• pp.27-44
• /
• 2017

The purpose of this study was to empirically verify the effect of social entrepreneurship on market orientation. total of 500 questionnaires were distributed to workers in social enterprise and preliminary social enterprise. 202 questionnaires were used for final validation of research model, The hypotheses set in this study were validated through SPSS18.0 and LISREL8.3 based on the research model. The results showed that all hypotheses were accepted, except for 5 hypotheses(Hypothesis 1-1, Hypothesis 1-2, Hypothesis 1-3, Hypothesis 1-6, Hypothesis 1-9). First, we examined the effect that empathy might have on market orientation in connection with social entrepreneurship. The results suggested that empathy did not have a statistically significant effect on customer-orientation, inter-department cooperation and coordination, and competitor orientation. Second, we examined the effect that innovativeness might have on market orientation in connection with social entrepreneurship. The results showed that innovativeness had a positive(+) effect on customer-orientation and inter-department cooperation and coordination but did not have a statistically significant effect on competitor-orientation. Third, we examined the effect that risk-taking might have on market orientation in connection with social entrepreneurship. The results implied that risk-taking had a positive(+) effect on customer-orientation and inter-department cooperation and coordination but did not have a statistically significant effect on competitor-orientation. Finally, the relationship among market orientation variables was like this: The inter-department cooperation and coordination had a positive(+) effect on both customer-orientation and competitor-orientation. The results of this study are expected to provide a useful basis for overall understanding about the effect of social entrepreneurship on market orientation and present important theoretical and practical implications.