• Information Systems Review
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• v.22 no.3
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• pp.1-14
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• 2020

This study aims to contribute to the existing literature in creativity from the viewpoint of neuro-physiological analysis. Further, we looked at emotional influences on creativity within a business problem-solving context that implemented the use of a cognitive map in exploring creativity. For this purpose, we measured brain cortical activity as people solved a business strategy problem to explore the neural mechanisms of "insight problems" that are influenced by distinct emotions. Through an Electroencephalography (EEG) analysis of 34 qualified participants, we investigated the relationship between emotions and business problem-solving creativity (BPSC). Insightful results were derived such that participants primed in a negative condition evoked higher temporal alpha band activity compared to those primed in the positive condition. Meanwhile, there were no significant differences between two priming conditions on the other band activities. Therefore, this study sheds a very positive light on the scholarly value of conducting rigorous studies about the relationship between emotional states and BPSC status.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.143-163
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• 2016

The demographics of Internet users are the most basic and important sources for target marketing or personalized advertisements on the digital marketing channels which include email, mobile, and social media. However, it gradually has become difficult to collect the demographics of Internet users because their activities are anonymous in many cases. Although the marketing department is able to get the demographics using online or offline surveys, these approaches are very expensive, long processes, and likely to include false statements. Clickstream data is the recording an Internet user leaves behind while visiting websites. As the user clicks anywhere in the webpage, the activity is logged in semi-structured website log files. Such data allows us to see what pages users visited, how long they stayed there, how often they visited, when they usually visited, which site they prefer, what keywords they used to find the site, whether they purchased any, and so forth. For such a reason, some researchers tried to guess the demographics of Internet users by using their clickstream data. They derived various independent variables likely to be correlated to the demographics. The variables include search keyword, frequency and intensity for time, day and month, variety of websites visited, text information for web pages visited, etc. The demographic attributes to predict are also diverse according to the paper, and cover gender, age, job, location, income, education, marital status, presence of children. A variety of data mining methods, such as LSA, SVM, decision tree, neural network, logistic regression, and k-nearest neighbors, were used for prediction model building. However, this research has not yet identified which data mining method is appropriate to predict each demographic variable. Moreover, it is required to review independent variables studied so far and combine them as needed, and evaluate them for building the best prediction model. The objective of this study is to choose clickstream attributes mostly likely to be correlated to the demographics from the results of previous research, and then to identify which data mining method is fitting to predict each demographic attribute. Among the demographic attributes, this paper focus on predicting gender, age, marital status, residence, and job. And from the results of previous research, 64 clickstream attributes are applied to predict the demographic attributes. The overall process of predictive model building is compose of 4 steps. In the first step, we create user profiles which include 64 clickstream attributes and 5 demographic attributes. The second step performs the dimension reduction of clickstream variables to solve the curse of dimensionality and overfitting problem. We utilize three approaches which are based on decision tree, PCA, and cluster analysis. We build alternative predictive models for each demographic variable in the third step. SVM, neural network, and logistic regression are used for modeling. The last step evaluates the alternative models in view of model accuracy and selects the best model. For the experiments, we used clickstream data which represents 5 demographics and 16,962,705 online activities for 5,000 Internet users. IBM SPSS Modeler 17.0 was used for our prediction process, and the 5-fold cross validation was conducted to enhance the reliability of our experiments. As the experimental results, we can verify that there are a specific data mining method well-suited for each demographic variable. For example, age prediction is best performed when using the decision tree based dimension reduction and neural network whereas the prediction of gender and marital status is the most accurate by applying SVM without dimension reduction. We conclude that the online behaviors of the Internet users, captured from the clickstream data analysis, could be well used to predict their demographics, thereby being utilized to the digital marketing.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.125-141
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• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, total misclassification cost is more affected by FNE rather than FPE. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.

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

• Development and Reproduction
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• v.6 no.2
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• pp.117-122
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• 2002

Dopamine(DA), norepinephrine(NE), and epinephrine(E) belong to a class of neurotransmitters known as catecholamine (CA) which are synthesized and secreted by mammalian brain and adrenal medulla. CA regulate several behavior patterns connected with breeding, and regulate GnRH-gonadotropin hormone axis' vitality between hypothalamus-pituitary gland linking with reproduction freeze. The present study examined effects of sex steroid hormone on the transcriptional activities of CA biosynthesis enzymes, tyrosine hydroxylase(TH), dopamine $\beta$ -hydroxylase(DBH), and phenylethaolamine-N-methyl transferase(PNMT). Mature female rats were ovariectomized(OVX) and implanted with 17 $\beta$-estradiol(E$_2$: 500 $\mu\textrm{g}$/ml) or sesame oil. Forty-eight hours after implantation all the animals were sacrificed. Total RNAs were extracted immediately and were applied to semi-quantitative reverse transcription-polymerase chain reaction(RT-PCR). The expression level of TH was appeared by hypothalamus > SNc> adrenal medulla orders in OVX＋Oil group, and by SNc > hypothalamus) adrenal medulla orders in OVX+E$_2$ group. Treatment with E$_2$ significantly increased TH expression in SNc and adrenal medulla but in hypothalamus, the reduced TH expression was observed. The expression level of DBH was appeared by adrenal medulla > SNc > hypothalamus orders in OVX＋Oil group and in OVX＋E$_2$ group. Administration of E$_2$ significantly reduced DBH expression in SNc, and increased in adrenal medulla. Two cDNA products, large(PNMT1) and small(PMNTs) species of 110bp difference, were amplified in SNc and hypothalamus, but only PNMTs was observed in adenal medulla. The PNMTs expression level was in the order of adrenal medulla > hypothalamus > SNc in both OVX＋Oil and OVX＋E$_2$ group. The PNMTs expression in SNc and adrenal medulla was significantly increased byE$_2$. The present report demonstrated that estrogen effects on transcriptional activities for CA biosynthethic enzymes were tissue specific in adrenal medulla as well as different region of brain. These results suggest that it might be crucial relationship between the type of estrogen receptor and CA enzyme gene expression.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Tuberculosis and Respiratory Diseases
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• v.40 no.6
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• pp.683-693
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• 1993

Background: During movement the major inputs to nervous system come from firstly the muscle and joint to maintain posture and motion and secondly the chemoreceptors and baroreceptors to adjust the cardiovascular and respiratory function. Their complex relationships are generally studied for many years but the direct relation between the joint and respiratory system is not studied thoroughly until now. So this experiment was performed to determine whether the natural movement of knee joint can cause the enhancement of respiratory function by observation of the changes of respiratory rate, phrenic nerve activity and inspiratory neuron activity during the stimulation of knee joint in cat anesthetized with $\alpha$-chloralose. Method: Twenty six male adult cats were used and the extracelluar recording using bipolar platinum electrode and carbon filament electrode was done to record the changes in the activities of phrenic nerve and inspiratory neuron movement of knee joint, injection of chemicals into the joint cavity and electrical stimulation of articular nerve were done. Results: The 60 Hz. could not but 120 Hz. flexion-extension movement of knee joint increased respiratory rate(R.R.), tidal neural activity(TNA) and minute neural activity(MNA). Intra-articular injection of lactate could not increase R.R. but significantly increase TNA and MNA which represented the enhanced respiratory function. Injection of potassium chloride showed similar effects with the case of lactate but the duration of effect was shorter. The electrical stimulation of medial articular nerve with IV strength which could activate only group I and II afferents showed increased TNA and MNA during stimulation but 20 V stimulation which could activate all the afferents increased all the respiratory parameters. The changes of inspiratory neuron activity by knee joint stimulation was similar to that of phrenic nerve. Conclusion: The respiratory center could be directly stimulated by the activation of group I and II articular afferents and it seemed that the magnitude of the respiratory center enhancement is proportional to the amount of sensory information from the knee joint. These facts might suggest that the respiratory function could be enhanced even by the normal movement of knee joint.

• Journal of Korean Neurosurgical Society
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• v.29 no.8
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• pp.985-994
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• 2000

Objective : There are some advantages of trigeminal evoked potential(TEP) recording compared to other somatosensory evoked potential(SSEP) recordings. The trigeminal sensory pathway has a pure sensory nerve branch, a broader receptive field in cerebral cortex, and a shorter pathway. Despite these advantages, there is little agreement as to what constitutes a normal response and what wave forms truly characterize the intraoperative TEP. This study presents the normative data of TEP recorded on the epidural surface of the rat with a platinum ball electrode. Materials & Methods : Under general anesthesia with urethane, the adult Sprague-Dawley male rats(300-350g) were given electrical stimulation with two stainless steel electrodes which were inserted into the subcutaneous layer of the area around whiskers. A reference electrode was positioned in the temporalis muscle ipsilateral to the recording site. Results : TEPs were recorded in the Par I area of somatosensory cortex and recorded most apparently on the point of 2mm posterior from the bregma and 6mm lateral from the midline. The typical wave form consisted of 5 peaks (N1-P1-N2-P2-N3 according to emerging order, upward negativity). Each latency to corresponding peaks was not influenced by the different intensities of stimulation, especially from 1 to 5mA. Average latencies of 5 peaks were in the following order ; 7.7, 11.1, 15, 22.3, 29.4ms. There was also no significant difference between latencies before and after administration of muscle relaxant(pancuronium). For the electrophysiological localization of recorded waves, the action potential of a single unit was recorded with glass microelectrode(filled with 2M NaCl, $3-5M{\Omega}$) in the thalamus of rat. A sharp wave was recorded in the VPM nucleus, in which the latency was shorter than that of N1. This suggests that all 5 peaks were generated by neural activities in the suprathalamic pathway. Conclusion : In terms of recording near-field potentials, our data also suggests that TEP in the rat may be superior to other SSEPs. In overall, these results may afford normative data for the studies of supratentorial lesions such as hydrocephalus or cerebral ischemia which can have an influence on near-field potentials.

• Biomolecules & Therapeutics
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• v.23 no.6
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• pp.597-603
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• 2015

Synthetic cannabinoids JWH-018 and JWH-250 in 'herbal incense' also called 'spice' were first introduced in many countries. Numerous synthetic cannabinoids with similar chemical structures emerged simultaneously and suddenly. Currently there are not sufficient data on their adverse effects including neurotoxicity. There are only anecdotal reports that suggest their toxicity. In the present study, we evaluated the neurotoxicity of two synthetic cannabinoids (JWH-081 and JWH-210) through observation of various behavioral changes and analysis of histopathological changes using experimental mice with various doses (0.1, 1, 5 mg/kg). In functional observation battery (FOB) test, animals treated with 5 mg/kg of JWH-081 or JWH-210 showed traction and tremor. Their locomotor activities and rotarod retention time were significantly (p<0.05) decreased. However, no significant change was observed in learning or memory function. In histopathological analysis, neural cells of the animals treated with the high dose (5 mg/kg) of JWH-081 or JWH-210 showed distorted nuclei and nucleus membranes in the core shell of nucleus accumbens, suggesting neurotoxicity. Our results suggest that JWH-081 and JWH-210 may be neurotoxic substances through changing neuronal cell damages, especially in the core shell part of nucleus accumbens. To confirm our findings, further studies are needed in the future.