• Journal of the Korean Institute of Landscape Architecture
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• v.40 no.2
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• pp.1-14
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• 2012

The purpose of this study is to derive a desirable riverfront construction plan to me for the activities of citizens through the evaluation and analysis of the urban riverfront space from the perspective of universal design. Therefore, previous studies were examined in order to induce evaluation tools that bhve been grafted from the universal design concept; in addition, a field survey was conducted in Shincheon, Daegu, which was selected as the study target, in order to induce problems and improvement directions from the perspective of universal design. The major results can be summarized as follows. (1) In the 'fairness' aspect, all the items such as installation of integrated functional signage, showing pictures, symbols, foreign language signs, and restroom entrances signage were determined to be 'All Unsuitable' for all sections; and therefore, it is necessary to improve the fairness of usage for everybody. (2) In the 'Functionality(Usability)' aspect, all items such as installation of bicycle paths beside access roads, installation of integrated functional signs, and night light signs were determined to be 'All Unsuitable' for all sections; therefore, it is necessary to improve the functionalities of these facilities. (3) In the 'Convenience' aspect, all items such as the installation of bicycle parking areas, continuous rest facilities, and back and ann support(handles) at resting facilities were determined to be 'All Unsuitable' for many sections; therefore, it is necessary to improve these facilities for the convenience of usage. (4) In the 'Information(Recognizability)' aspect, all items such as showing pictures, symbols, foreign languages and installation of night light signs, and restroom entrances signage were determined to be 'All Unsuitable' for all sections; therefore, it is necessary to improve the recognizability to minimize misunderstandings and confusion. (5) In the 'Safety' aspect, all items such as the installation of safe pedestrian paths in parking areas, using anti-slip and shock absorption materials on restroom floors, and the continuous installation of pedestrian paths that are separate from bicycle paths were determined to be 'All Unsuitable' for all sections; therefore, it is necessary to improve the safety to prevent accidents. (6) In the 'Amenity' aspect, access roads, parking areas, hygiene facilities, convenience facilities, and waterside facilities for many sections were determined to be 'All Unsuitable'; therefore, it is necessary to conduct more concentrated hygiene management. (7) In the 'Accessibility(Mobility)' aspect, all items such as the installation of safe pedestrian paths in parking areas, and continuous pedestrian paths that are separate from bicycle paths were determined to be 'All Unsuitable' for all sections; therefore, it is necessary to improve the accessibility to provide safety and convenience. (8) In the 'Durability' aspect, access roads, parking areas, rest facilities, convenience facilities, fitness facilities, and waterside facilities were determined to be 'All Unsuitable' for many sections, therefore, it is necessary to improve sunken or damaged areas by inspecting facilities by section.

• 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.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.