• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Journal of Veterinary Clinics
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• v.24 no.4
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• pp.514-521
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• 2007

The sympathetic nerve block improves the blood flow in the innervated regions. For this region, the sympathetic nerve block has been performed in the neural and cerebral disorders. However, the cerebral blood flow regulation of the cranial cervical ganglion block in dogs have not been well defined and the correlation to the changes in the cerebral circulation and the changes in the electroencephalogram is not well defined in dogs yet. Therefore, we investigated the hypothesis that changes in the EEG could be affected by the changes in cerebral blood flow following the cranial cervical ganglion block in dogs. Twenty five beagle dogs were divided into 3 groups; group I(LCCGB, n=10) underwent left sided cranial cervical ganglion block using the 1% lidocaine, group II(L, n=10) injected the 1% lidocaine into the right or left sided digastricus muscle, group III(N/SCCGB, n=5, served as control) underwent the left sided cranial cervical ganglion block using saline. A statistical difference was not found between the control group and the LCCGB group in the 95% spectral edge frequency(SEF) and the median frequency(MF). In the relative band power, the $\delta$ frequency was decreased during 5-25 min, while the $\alpha$ frequency was increased during the same time(p<0.05). But the $\theta$ frequency and the $\beta$ frequency were not shown the significant changes compared with the control group during the same time(p<0.05). These results suggest that the left cranial cervical ganglion block does not induce the change of the cerebral blood flow and its effect is insignificant.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.139-157
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• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.71-89
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• 2016

Sentiment analysis is used for identifying emotions or sentiments embedded in the user generated data such as customer reviews from blogs, social network services, and so on. Various research fields such as computer science and business management can take advantage of this feature to analyze customer-generated opinions. In previous studies, the star rating of a review is regarded as the same as sentiment embedded in the text. However, it does not always correspond to the sentiment polarity. Due to this supposition, previous studies have some limitations in their accuracy. To solve this issue, the present study uses a supervised sentiment classification model to measure a more accurate sentiment polarity. This study aims to propose an advanced sentiment classifier and to discover the correlation between movie reviews and box-office success. The advanced sentiment classifier is based on two supervised machine learning techniques, the Support Vector Machines (SVM) and Feedforward Neural Network (FNN). The sentiment scores of the movie reviews are measured by the sentiment classifier and are analyzed by statistical correlations between movie reviews and box-office success. Movie reviews are collected along with a star-rate. The dataset used in this study consists of 1,258,538 reviews from 175 films gathered from Naver Movie website (movie.naver.com). The results show that the proposed sentiment classifier outperforms Naive Bayes (NB) classifier as its accuracy is about 6% higher than NB. Furthermore, the results indicate that there are positive correlations between the star-rate and the number of audiences, which can be regarded as the box-office success of a movie. The study also shows that there is the mild, positive correlation between the sentiment scores estimated by the classifier and the number of audiences. To verify the applicability of the sentiment scores, an independent sample t-test was conducted. For this, the movies were divided into two groups using the average of sentiment scores. The two groups are significantly different in terms of the star-rated scores.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Journal of Life Science
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• v.26 no.3
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• pp.282-288
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• 2016

Protein-protein interactions regulate the subcellular localization and function of receptors, enzymes, and cytoskeletal proteins. Proteins containing the postsynaptic density-95/disks large/zonula occludens-1 (PDZ) domain have potential to act as scaffolding proteins and play a pivotal role in various processes, such as synaptic plasticity, neural guidance, and development, as well as in the pathophysiology of many diseases. Multi-PDZ domain protein 1 (MUPP1), which has 13 PDZ domains, has a scaffolding function in the clustering of surface receptors, organization of signaling complexes, and coordination of cytoskeletal dynamics. However, the cellular function of MUPP1 has not been fully elucidated. In the present study, a yeast two-hybrid system was used to identify proteins that interacted with the N-terminal PDZ domain of MUPP1. The results revealed an interaction between MUPP1 and Wdpcp (formerly known as Fritz). Wdpcp was identified as a planar cell polarity (PCP) effector, which is known to have a role in collective cell migration and cilia formation. Wdpcp bound to the PDZ1 domain but not to other PDZ domains of MUPP1. The C-terminal end of Wdpcp was essential for the interaction with MUPP1 in the yeast two-hybrid assay. This interaction was further confirmed in a glutathione S-transferase (GST) pull-down assay. When coexpressed in HEK-293T cells, Wdpcp was coimmunoprecipitated with MUPP1. In addition, MUPP1 colocalized with Wdpcp at the same subcellular region in cells. Collectively, these results suggest that the MUPP1-Wdpcp interaction could modulate actin cytoskeleton dynamics and polarized cell migration.

• Korean Journal of Veterinary Research
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• v.43 no.1
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• pp.11-24
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• 2003

This experimental studies was to investigate the location of PNS and CNS labeled neurons following injection of 2% WGA-HRP and pseudorabies virus (PRY), Bartha strain, into the ductus deferens of rats. After survival times 4-5 days following injection of 2% WGA-HRP and PRV, the rats were perfused, and their brain, spinal cord, sympathetic ganglia and spinal ganglia were frozen sectioned ($30{\mu}m$). These sections were stained by HRP histochemical and PRY inummohistochemical staining methods, and observed with light microscope. The results were as follows ; 1. The location of sympathetic ganglia projecting to the ductus deferens were observed in pelvic ganglion, inferior mesenteric ganglion and L1-6 lwnbar sympathetic ganglia. 2. The location of spinal ganglia projecting to the ductus deferens were observed in T13-L6 spinal ganglia. 3. The PRY labeled neurons projecting to the ductus deferens were observed in lateral spinal nucleus, lamina I, II and X of cervical segments. In thoracic segments, PRY labeled neurons were observed in dorsomedial part of lamina I, II and III, and dorsolateral part of lamina IV and V. Densely labeled neurons were observed in intermediolateral nucleus. In first lumbar segment, labeled neurons were observed in intermediolateral nucleus and dorsal commisural nucleus. In sixth lumbar segment and sacral segments, dense labeled neurons were observed in sacral parasympathetic nuc., lamina IX and X. 4. In the medulla oblongata, PRV labeled neurons projecting to the ductus deferens were observed in the trigeminal spinal nuc., A1 noradrenalin cells/C1 adrenalin cells/caudoventrolateral reticular nuc., rostroventrolateral reticular nuc., area postrema, nuc. tractus solitarius, raphe obscurus nuc., raphe pallidus nuc., raphe magnus nuc., parapyramidal nuc., lateral reticular nuc., gigantocellular reticular nuc.. 5. In the pons, PRV labeled neurons projecting to the ductus deferens were ohserved in parabrachial nuc., Kolliker-Fuse nuc., locus cooruleus, subcooruleus nuc. and AS noradrenalin cells. 6. In midbrain, PRV labeled neurons projecting to the ductus deferens were observed in periaqueductal gray substance, substantia nigra and dorsal raphe nuc.. 7. In the diencephalon, PRV labeled neurons projecting to the ductus deferens were observed in paraventricular hypahalamic nuc., lateral hypothalamic nuc., retrochiasmatic nuc. and ventromedial hypothalamic nuc.. 8. In cerebrum, PRV labeled neurons projecting to the ductus deferens were observed in area 1 of parietal cortex. These results suggest that WGA-HRP labeled neurons of the spinal cord projecting to the rat ductus deferens might be the first-order neurons related to the viscero-somatic sensory and sympathetic postganglionic neurons, and PRV labeled neurons of the brain and spinal cord may be the second and third-order neurons response to the movement of smooth muscles in ductus deferens. These PRV labeled neurons may be central autonomic center related to the integration and modulation of reflex control linked to the sensory and motor system monitaing the internal environment. These observations provide evidence for previously unknown projections from ductus deferens to spinal cord and brain which may be play an important neuroanatornical basic evidence in the regulation of ductus deferens function.

• Korean Journal of Plant Tissue Culture
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• v.27 no.6
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• pp.423-428
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• 2000

In 1997 when cloned sheep Dolly and soon after Polly were born, it had become head-line news because in the former the nucleus that gave rise to the lamb came from cells of six-year-old adult sheep and in the latter case a foreign gene was inserted into the donor nucleus to make the cloned sheep produce human protein, factor IX, in e milk. In the last few years, once the realm of science fiction, cloned mammals especially in livestock have become almost commonplace. What the press accounts often fail to convey, however, is that behind every success lie hundreds of failures. Many of the nuclear-transferred egg cells fail to undergo normal cell divisions. Even when an embryo does successfully implant in the womb, pregnancy often ends in miscarriage. A significant fraction of the animals that are born die shortly after birth and some of those that survived have serious developmental abnormalities. Efficiency remains at less than one % out of some hundred attempts to clone an animal. These facts show that something is fundamentally wrong and enormous hurdles must be overcome before cloning becomes practical. Cloning researchers now tent to put aside their effort to create live animals in order to probe the fundamental questions on cell biology including stem cells, the questions of whether the hereditary material in the nucleus of each cell remains intact throughout development, and how transferred nucleus is reprogrammed exactly like the zygotic nucleus. Stem cells are defined as those cells which can divide to produce a daughter cell like themselves (self-renewal) as well as a daughter cell that will give rise to specific differentiated cells (cell-differentiation). Multicellular organisms are formed from a single totipotent stem cell commonly called fertilized egg or zygote. As this cell and its progeny undergo cell divisions the potency of the stem cells in each tissue and organ become gradually restricted in the order of totipotent, pluripotent, and multipotent. The differentiation potential of multipotent stem cells in each tissue has been thought to be limited to cell lineages present in the organ from which they were derived. Recent studies, however, revealed that multipotent stem cells derived from adult tissues have much wider differentiation potential than was previously thought. These cells can differentiate into developmentally unrelated cell types, such as nerve stem cell into blood cells or muscle stem cell into brain cells. Neural stem cells isolated from the adult forebrain were recently shown to be capable of repopulating the hematopoietic system and produce blood cells in irradiated condition. In plants although the term$\boxDr$ stem cell$\boxUl$is not used, some cells in the second layer of tunica at the apical meristem of shoot, some nucellar cells surrounding the embryo sac, and initial cells of adventive buds are considered to be equivalent to the totipotent stem cells of mammals. The telomere ends of linear eukaryotic chromosomes cannot be replicated because the RNA primer at the end of a completed lagging strand cannot be replaced with DNA, causing 5' end gap. A chromosome would be shortened by the length of RNA primer with every cycle of DNA replication and cell division. Essential genes located near the ends of chromosomes would inevitably be deleted by end-shortening, thereby killing the descendants of the original cells. Telomeric DNA has an unusual sequence consisting of up to 1,000 or more tandem repeat of a simple sequence. For example, chromosome of mammal including human has the repeating telomeric sequence of TTAGGG and that of higher plant is TTTAGGG. This non-genic tandem repeat prevents the death of cell despite the continued shortening of chromosome length. In contrast with the somatic cells germ line cells have the mechanism to fill-up the 5' end gap of telomere, thus maintaining the original length of chromosome. Cem line cells exhibit active enzyme telomerase which functions to maintain the stable length of telomere. Some of the cloned animals are reported prematurely getting old. It has to be ascertained whether the multipotent stem cells in the tissues of adult mammals have the original telomeres or shortened telomeres.

• Journal of Environmental Policy
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• v.12 no.3
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• pp.21-47
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• 2013

The aim is to analysis landslide vulnerability in Inje, Korea, using GCI(Geospatial Correlative Integration) and probability rainfalls based on geographic information system (GIS). In order to achieve this goal, identified indicators influencing landslides based on literature review. We include indicators of exposure to climate(rainfall probability), sensitivity(slope, aspect, curvature, geology, topography, soil drainage, soil material, soil thickness and soil texture) and adaptive capacity(timber diameter, timber type, timber density and timber age). All data were collected, processed, and compiled in a spatial database using GIS. Karisan-ri that had experienced 470 landslides by Typhoon Ewinia in 2006 was selected for analysis and verification. The 50% of landslide data were randomly selected to use as training data, while the other 50% being used for verification. The probability of landslides for target years (1 year, 3 years, 10 years, 50 years, and 100 years) was calculated assuming that landslides are triggered by 3-day cumulative rainfalls of 449 mm. Results show that number of slope has comparatively strong influence on landslide damage. And inclination of $25{\sim}30^{\circ}C$, the highest correlation landslide. Improved previous landslide vulnerability methodology by adopting GCI. Also, vulnerability map provides meaningful information for decision makers regarding priority areas for implementing landslide mitigation policies.