• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.3
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• pp.203-209
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• 2015

This paper discusses the effectiveness of using Landsat 8 TIR (Thermal Infrared) band images to improve the accuracy of landuse/landcover classification of urban areas. According to classification results for the study area using diverse band combinations, the classification accuracy using an image fusion process in which the TIR band is added to the visible and near infrared band was improved by 4.0%, compared to that using a band combination that does not consider the TIR band. For urban area landuse/landcover classification in particular, the producer’s accuracy and user’s accuracy values were improved by 10.2% and 3.8%, respectively. When MLC (Maximum Likelihood Classification), which is commonly applied to remote sensing images, was used, the TIR band images helped obtain a higher discriminant analysis in landuse/landcover classification.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06c
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• pp.447-451
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• 2010

In this paper, we propose a simple but effective algorithm to increase the speed of Emphysema region classification. Emphysema region classification method based on CT image consumes a lot of time because of the large number of subregions due to the large size of CT image. Some of the sub-regions contain no Emphysema and the classification of these regions is worthless. To speed up the classification process, we create an algorithm to select Emphysema region candidates and only use these candidates in the Emphysema region classification instead of all of the sub-regions. First, the lung region is detected. Then we threshold the lung region and only select the dark pixels because Emphysema only appeared in the dark area of the CT image. Then the thresholded pixels are clustered into a region that called the Emphysema pre-detected region or Emphysema region candidate. This region is then divided into sub-region for the Emphysema region classification. The experimental result shows that Emphysema region classification using predetected Emphysema region decreases the size of lung region which will result in about 84.51% of time reduction in Emphysema region classification.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.143-148
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• 2022

Brain tumor classification is an important process that allows doctors to plan treatment for patients based on the stages of the tumor. To improve classification performance, various CNN-based architectures are used for brain tumor classification. Existing methods for brain tumor segmentation suffer from overfitting and poor efficiency when dealing with large datasets. The enhanced CNN architecture proposed in this study is based on U-Net for brain tumor segmentation, RefineNet for pattern analysis, and SegNet architecture for brain tumor classification. The brain tumor benchmark dataset was used to evaluate the enhanced CNN model's efficiency. Based on the local and context information of the MRI image, the U-Net provides good segmentation. SegNet selects the most important features for classification while also reducing the trainable parameters. In the classification of brain tumors, the enhanced CNN method outperforms the existing methods. The enhanced CNN model has an accuracy of 96.85 percent, while the existing CNN with transfer learning has an accuracy of 94.82 percent.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.258-266
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• 2023

Aiming at the problems of poor customer satisfaction and poor accuracy of customer classification, this paper proposes a customer classification model based on speech recognition. First, this paper analyzes the temporal data characteristics of customer demand data, identifies the influencing factors of customer demand behavior, and determines the process of feature extraction of customer voice signals. Then, the emotional association rules of customer demands are designed, and the classification model of customer demands is constructed through cluster analysis. Next, the Euclidean distance method is used to preprocess customer behavior data. The fuzzy clustering characteristics of customer demands are obtained by the fuzzy clustering method. Finally, on the basis of naive Bayesian algorithm, a customer demand classification model based on speech recognition is completed. Experimental results show that the proposed method improves the accuracy of the customer demand classification to more than 80%, and improves customer satisfaction to more than 90%. It solves the problems of poor customer satisfaction and low customer classification accuracy of the existing classification methods, which have practical application value.

• The Transactions of the Korea Information Processing Society
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• v.3 no.4
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• pp.855-865
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• 1996

In this paper, we propose a automation of the classification process for reusable software component and construction method of structured software components library. In order to efficient and automatic classification of software component, we decide the facets to represent characteristics of software component by acquiring semantic and syntactic information from software components descriptions in natural language, and compose the software component identifier or automatic extract terms corresponds to each facets. And then, in order to construct the structured software components library, we sore in the near location with software components of similar characteristic according to semantic similarity of the classified software components. As the result of applying proposed method, we can easily identify similar software components, the classification process of software components become simple, and the software components store in the structured software components library.

• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.764-772
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• 2003

The main thrust of our talk will be based on our experience in developing and applying an attribute-based classification technique in the context of an operational answer set driven retrieval system. To alleviate the difficulty and reduce the cost of manually constructing and maintaining answer sets, i.e., knowledge base, we have devised a new method of automating the answer document selection process by using the notion of attribute-based classification, which is in and of itself novel. We attempt to explain through experiments how helpful the proposed method is for the knowledge base construction process.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.23 no.1
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• pp.47-63
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• 2012

The purpose of this study is to improve the faceted navigation function of next-generation library catalogs(NGC) using the tables in a classification scheme. Because the tables provide forms or contents of materials systematically, faceted navigation based on the tables can be as effective as faceted navigation based on language, publishing date, or class-level subject facet. Therefore, class numbers based on the Korean Decimal Classification (KDC) were examined and the characteristics of schedules and tables were analyzed. As a result, suggestions to improve faceted navigation was provided. Moreover, the method using the tables does not need additional resources to derive facets because the facet analysis process is always carried out in the classification process.

• Journal of Ship and Ocean Technology
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• v.8 no.2
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• pp.41-60
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• 2004

Ship design process requires lots of complicated analyses for determining a large number of design variables. Due to its complexity, the process is divided into several tractable designs or analysis problems. The interdependent relationship requires repetitive works. This paper employs collaborative optimization (CO), one of the multidisciplinary design optimization (MDO) techniques, for treating such complex relationship. CO guarantees disciplinary autonomy while maintaining interdisciplinary compatibility due to its bi-level optimization structure. However, the considerably increased computational time and the slow convergence have been reported as its drawbacks. This paper proposes the use of an approximation model in place of the disciplinary optimization in the system-level optimization. Neural network classification is employed as a classifier to determine whether a design point is feasible or not. Kriging is also combined with the classification to make up for the weakness that the classification cannot estimate the degree of infeasibility. For the purpose of enhancing the accuracy of a predicted optimum and reducing the required number of disciplinary optimizations, an approximation management framework is also employed in the system-level optimization.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1167-1175
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• 2018

Microarray data plays an essential role in diagnosing and detecting cancer. Microarray analysis allows the examination of levels of gene expression in specific cell samples, where thousands of genes can be analyzed simultaneously. However, microarray data have very little sample data and high data dimensionality. Therefore, to classify microarray data, a dimensional reduction process is required. Dimensional reduction can eliminate redundancy of data; thus, features used in classification are features that only have a high correlation with their class. There are two types of dimensional reduction, namely feature selection and feature extraction. In this paper, we used k-means algorithm as the clustering approach for feature selection. The proposed approach can be used to categorize features that have the same characteristics in one cluster, so that redundancy in microarray data is removed. The result of clustering is ranked using the Relief algorithm such that the best scoring element for each cluster is obtained. All best elements of each cluster are selected and used as features in the classification process. Next, the Random Forest algorithm is used. Based on the simulation, the accuracy of the proposed approach for each dataset, namely Colon, Lung Cancer, and Prostate Tumor, achieved 85.87%, 98.9%, and 89% accuracy, respectively. The accuracy of the proposed approach is therefore higher than the approach using Random Forest without clustering.

• Journal of the Society of Disaster Information
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• v.10 no.1
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• pp.15-24
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• 2014

The study presents work information classification system of apartment house which can organize all construction management services throughout the planning and management of a construction such as the decomposition of the design process, the assembly of construction process and cost estimating, etc. In addition, the study suggested a way to connect work information classification system based on a relational database in working order and built a conceptual model for automated cost estimating by utilizing established data base. A conceptual model for automated cost estimating will resolve the fundamental problems of the existing cost estimating system and will be able to take advantage of scientific cost estimating system at the construction site of apartment house.