• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.253-266
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• 2018

This paper proposes a methodology applying sequence tagging methodology to improve the performance of NER(Named Entity Recognition) used in QA system. In order to retrieve the correct answers stored in the database, it is necessary to switch the user's query into a language of the database such as SQL(Structured Query Language). Then, the computer can recognize the language of the user. This is the process of identifying the class or data name contained in the database. The method of retrieving the words contained in the query in the existing database and recognizing the object does not identify the homophone and the word phrases because it does not consider the context of the user's query. If there are multiple search results, all of them are returned as a result, so there can be many interpretations on the query and the time complexity for the calculation becomes large. To overcome these, this study aims to solve this problem by reflecting the contextual meaning of the query using Bidirectional LSTM-CRF. Also we tried to solve the disadvantages of the neural network model which can't identify the untrained words by using ontology knowledge based feature. Experiments were conducted on the ontology knowledge base of music domain and the performance was evaluated. In order to accurately evaluate the performance of the L-Bidirectional LSTM-CRF proposed in this study, we experimented with converting the words included in the learned query into untrained words in order to test whether the words were included in the database but correctly identified the untrained words. As a result, it was possible to recognize objects considering the context and can recognize the untrained words without re-training the L-Bidirectional LSTM-CRF mode, and it is confirmed that the performance of the object recognition as a whole is improved.

• Radiation Oncology Journal
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• v.17 no.4
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• pp.299-306
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• 1999

Purpose : The human genetic disorder ataxia-telangiectasia (AT) is a multisystem disease characterized by extreme radiosensitivity. The recent identification of the gene mutated in AT, ATM, and the demonstration that it encodes a homologous domain of phosphatidylinositol 3-kinase (PI3-K), the catalytic subunit of an enzyme involved in transmitting signals from the cell surface to the nucleus, provide support for a role of this gene in signal transduction. Although ionizing radiation was known to induce c-fos transcription, nothing is known about how ATM or PKCI mediated signal transduction pathway modulates the c-fos gene transcription and gene expression. Here we have studied the effect of PKCI on radiation sensitivity and c-fos transcription in normal and AT cells. Materials and Methods: Normal (LM217) and AT (AT5BIVA) cells were transfected with PKCI expression plasmid and the overexpression and integration of PKCI was evaluated by northern blotting and polymerase chain reaction, respectively. 5 Gy of radiation was exposed to LM and AT cells transfected with PKCI expression plasmid and cells were harvested 48 hours after radiation and investigated apoptosis with TUNEL method. The c-fos transcription activity was studied by performing CAT assay of reporter gene after transfection of c-fos CAT plasmid into AT and LM cells. Results: Our results demonstrate for the first time a role of PKCI on the radiation sensitivity and c-fos expression in LM and AT cells. PKCI increased radiation induced apoptosis in LM cells but reduced apoptosis in AT cells. The basal c-fos transcription activity is 70 times lower in AT cells than that in LM cells. The c-fos transcription activity was repressed by overexpression of PKCI in LM cells but not in AT cells. After induction of c-fos by Ras protein, overexpression of PKCI repressed c-fos transcription in LM cells but not in AT cells Conclusion: Overexpression of PKCI increased radiation sensitivity and repressed c-fos transcription in LM cells but not in AT cells. The results may be a. reason of increased radiation sensitivity of AT cells. PKCI may be involved in an ionizing radiation induced signal transduction pathway responsible for radiation sensitivity and c-fos transcription. The data also provided evidence for novel transcriptional difference between LM and AT cells.

• Journal of Arbitration Studies
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• v.13 no.2
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• pp.459-488
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• 2004

General interest in the out-of-court dispute resolution system are mounting in Korea, and the spread of ADR(alternative dispute resolution) is the worldwide trend. In addition, it was confirmed that the resolution of disputes by ADR such as the decision based on arbitration made by the Prime Ministerial Administrative Decision Committee is no longer in exclusive possession of the civil case. The activation of ADR could lead to the smooth agreement between parties by getting away from the once-for-all mode of decision such as the dismissal of the application or the cancellation of disposal and the like in relation to administrative cases for the years. In consequence, it is anticipated that the administrative litigation that applicants have filed by not responding to the administrative decision would greatly reduce in the future. But, it would be urgent to provide for the legal ground of the ADR system through the revision of related laws to take root in our society because ADR has no legal binding power relating to the administrative case due to the absence of its legal grounds. The fundamental reason for having hesitated to introduce ADR in relation to the administrative case for the years is the protective interest of the third party as well as the public interest that would follow in case the agreement on the dispute resolution between parties brings the dispute to a termination in the domain of the public law. The disputes related to the contract based on the public law and the like that take on a judicial character as the administrative act have been settled within the province of ADR by applying the current laws such as the Civil Arbitration Law, Mediation Law, but their application to the administrative act of the administrative agency that takes on a character of the public law has been hesitated. But as discussed earlier, there are laws and regulations that has the obscure distinction between public and private laws. But there is no significant advantage in relation to the distinction between public and private laws. To supplement and cure these defects it is necessary to include the institutional arrangement for protection of the rights and benefits of the third party, for example the provision of the imposition of the binding power on the result of ADR between parties, in enacting its related law. It can be said that the right reorganization of the out-of-court dispute resolution system in relation to the administrative case corresponds with the ideology of public administration for cooperaton in the Administrative Law. It is high time to discuss within what realm the out-of-court dispute resolution system, alternative dispute resolution system, can be accepted and what binding power is imposed on its result, not whether it is entirely introduced into the administrative case. It is thought that the current Civil Mediation Law or Arbitration Law provides the possibility of applying arbitration or mediation only to the civil case, thereby opening the possibility of arbitration in the field of the intellectual property right law. For instance, the act of the state is not required in establishing the rights related to the secret of business or copyrights. Nevertheless, the disputes arising from or in connection with the intellectual property rights law is seen as the administrative case, and they are excluded from the object of arbitration or mediation, which is thought to be improper. This is not an argument for unconditionally importing ADR into the resolution of administrative cases. Most of the Korean people are aware that the administrative litigation system is of paramount importance as the legal relief for administrative cases. Seeing that there is an independent administrative decision system based on the Administrative Decision Law other than administrative litigation in relation to administrative cases, the first and foremost task is the necessity for the shift in thinking of people, followed by consideration of the plan for relief of the rights through the improvement of the administrative decision system. Then, it is necessary to formulate the plan for the formal introduction and activation of ADR. In this process, energetic efforts should be devoted to introducing diverse forms of ADR procedures such as settlement conference, case evaluation, mini-trial, summary jury trial, early neutral evaluation adopted in the US as the method of dispute resolution other than compromise, conciliation, arbitration and mediation

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.133-148
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• 2014

Recently, with the advent of various information channels, the number of has continued to grow. The main cause of this phenomenon can be found in the significant increase of unstructured data, as the use of smart devices enables users to create data in the form of text, audio, images, and video. In various types of unstructured data, the user's opinion and a variety of information is clearly expressed in text data such as news, reports, papers, and various articles. Thus, active attempts have been made to create new value by analyzing these texts. The representative techniques used in text analysis are text mining and opinion mining. These share certain important characteristics; for example, they not only use text documents as input data, but also use many natural language processing techniques such as filtering and parsing. Therefore, opinion mining is usually recognized as a sub-concept of text mining, or, in many cases, the two terms are used interchangeably in the literature. Suppose that the purpose of a certain classification analysis is to predict a positive or negative opinion contained in some documents. If we focus on the classification process, the analysis can be regarded as a traditional text mining case. However, if we observe that the target of the analysis is a positive or negative opinion, the analysis can be regarded as a typical example of opinion mining. In other words, two methods (i.e., text mining and opinion mining) are available for opinion classification. Thus, in order to distinguish between the two, a precise definition of each method is needed. In this paper, we found that it is very difficult to distinguish between the two methods clearly with respect to the purpose of analysis and the type of results. We conclude that the most definitive criterion to distinguish text mining from opinion mining is whether an analysis utilizes any kind of sentiment lexicon. We first established two prediction models, one based on opinion mining and the other on text mining. Next, we compared the main processes used by the two prediction models. Finally, we compared their prediction accuracy. We then analyzed 2,000 movie reviews. The results revealed that the prediction model based on opinion mining showed higher average prediction accuracy compared to the text mining model. Moreover, in the lift chart generated by the opinion mining based model, the prediction accuracy for the documents with strong certainty was higher than that for the documents with weak certainty. Most of all, opinion mining has a meaningful advantage in that it can reduce learning time dramatically, because a sentiment lexicon generated once can be reused in a similar application domain. Additionally, the classification results can be clearly explained by using a sentiment lexicon. This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of movie reviews. Additionally, various parameters in the parsing and filtering steps of the text mining may have affected the accuracy of the prediction models. However, this research contributes a performance and comparison of text mining analysis and opinion mining analysis for opinion classification. In future research, a more precise evaluation of the two methods should be made through intensive experiments.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.125-139
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• 2013

Big data refers to the data that cannot be processes with conventional contemporary data technologies. As smart devices and social network services produces vast amount of data, big data attracts much attention from researchers. There are strong demands form governments and industries for bib data as it can create new values by drawing business insights from data. Since various new technologies to process big data introduced, academic communities also show much interest to the big data domain. A notable advance related to the big data technology has been in various fields. Big data technology makes it possible to access, collect, and save individual's personal data. These technologies enable the analysis of huge amounts of data with lower cost and less time, which is impossible to achieve with traditional methods. It even detects personal information that people do not want to open. Therefore, people using information technology such as the Internet or online services have some level of privacy concerns, and such feelings can hinder continued use of information systems. For example, SNS offers various benefits, but users are sometimes highly exposed to privacy intrusions because they write too much personal information on it. Even though users post their personal information on the Internet by themselves, the data sometimes is not under control of the users. Once the private data is posed on the Internet, it can be transferred to anywhere by a few clicks, and can be abused to create fake identity. In this way, privacy intrusion happens. This study aims to investigate how perceived personal information overload in SNS affects user's risk perception and information privacy concerns. Also, it examines the relationship between the concerns and user resistance behavior. A survey approach and structural equation modeling method are employed for data collection and analysis. This study contributes meaningful insights for academic researchers and policy makers who are planning to develop guidelines for privacy protection. The study shows that information overload on the social network services can bring the significant increase of users' perceived level of privacy risks. In turn, the perceived privacy risks leads to the increased level of privacy concerns. IF privacy concerns increase, it can affect users to from a negative or resistant attitude toward system use. The resistance attitude may lead users to discontinue the use of social network services. Furthermore, information overload is mediated by perceived risks to affect privacy concerns rather than has direct influence on perceived risk. It implies that resistance to the system use can be diminished by reducing perceived risks of users. Given that users' resistant behavior become salient when they have high privacy concerns, the measures to alleviate users' privacy concerns should be conceived. This study makes academic contribution of integrating traditional information overload theory and user resistance theory to investigate perceived privacy concerns in current IS contexts. There is little big data research which examined the technology with empirical and behavioral approach, as the research topic has just emerged. It also makes practical contributions. Information overload connects to the increased level of perceived privacy risks, and discontinued use of the information system. To keep users from departing the system, organizations should develop a system in which private data is controlled and managed with ease. This study suggests that actions to lower the level of perceived risks and privacy concerns should be taken for information systems continuance.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.1
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• pp.67-83
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• 2023

The social venture start-up phenomenon is found from the perspectives of social enterprise and for-profit enterprise. This study aims to fundamentally explore the start-up phenomenon of social ventures from these two perspectives. Considering the lack of prior research that researched both social and commercial perspectives at the same time, this paper analyzed using grounded theory approach of Strauss & Corbin(1998), an inductive research method that analyzes based on prior research and interview data. In order to collect data for this study, eight corporate representatives currently operating social ventures were interviewed and data and phenomena were analyzed. This progressed to a theoretical saturation where no additional information was derived. The analysis results of this study using the grounded theory approach are as follows. As a result of open coding and axial coding, 147 concepts and 70 subcategories were derived, and 18 categories were derived through the final abstraction process. In the selective coding, 'expansion of social venture entry in the social domain' and 'expansion of social function of for-profit companies' were selected as key categories, and a story line was formed around this. In this study, we saw that it is necessary to conduct academic research and analysis on the competitive factors required for companies that pursue the values of two conflicting relationships, such as social ventures, to survive with competitiveness. In practice, concepts such as collaboration with for-profit companies, value combination, entrepreneurship competency and performance improvement, social value execution competency reinforcement, communication strategy, for-profit enterprise value investment, and entrepreneur management competency were derived. This study explains the social venture phenomenon for social enterprises, commercial enterprises, and entrepreneurs who want to enter the social venture field. It is expected to provide the implications necessary for successful social venture startups.