• Language and Information
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• v.17 no.2
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• pp.1-27
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• 2013

This paper presents a unified approach to multiple nominative and accusative constructions in Korean. We identify 16 semantic relations holding between two consecutive noun phrases (NPs) in multiple case marking constructions, and propose each semantic relation as a licensing condition on double case marking. We argue that the multiple case marking constructions are merely the sequences of double case marking, which are formed by dextrosinistrally sequencing the pairs of the same-case marked NPs of same or different type. Some appealing consequences of this proposal include a new comprehensive classification of the sequences of same-case NPs and a straightforward account of some long standing problems such as how the additional same-case NPs are licensed, and in what respects the multiple nominative marking and the multiple accusative marking are alike and different from each other.

• Quality Improvement in Health Care
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• v.12 no.2
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• pp.84-92
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• 2006

Background : To examine the difference between ICD-10 and The Korean standard classification of disease(oriental medicine), and to aim at improve the practical use as statistical data. It is one of the reason of disease classification. On that account we convert the many to many correspondence presenting classification of oriental medicine into many to one correspondence. Method : The study tracked out 155 patients discharged from the university hospital which is located in Gyeonggi Province and managing hospital and oriental medicine hospital from July to October this year. The period of this study was from August 1 to November 18. We compared correspondence between the two services' diagnosis(hospital services and oriental medicine hospital services) at the same time and attempted many to one correspondence classification. That is for production of statistical data. Result : We investigated the group which have had medical treatment experience of two kinds of services at the same time. The result of this investigation was that the same oriental medicine diagnosis used differently in western medicine diagnosis. 44.5% was accorded with western medicine diagnosis. Correspondence of the western medicine diagnose with the top of the Korean standard classification of disease(oriental medicine) list's western medicine diagnosis was 13.5%. For many to one correspondence classification for statistics, one western medicine diagnosis was selected for one oriental medicine diagnosis. In case of the main diagnosis(I sign) was not enough to explain oriental medicine diagnosis' characteristic, we chose multiple other diagnosis, so other diagnosis(II sign) about patient's cause of disease could be selected for supplement after we examined the patient's records. The statistics was possible with this many to one correspondence. Conclusion : The result of this study about correspondence between western medicine diagnoses and those of oriental medicine confirms that The Korean standard classification of disease(oriental medicine) is hard to be standardized with western medicine diagnosis. Therefore, according to this study, we use new many to one correspondence classification, multiple oriental medicine diagnoses with one ICD-10, which can be used by statistical data.

• Journal of the Korea Society of Computer and Information
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• v.23 no.6
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• pp.1-7
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• 2018

A multi-label classification is to find multiple labels associated with the input pattern. Multi-label classification can be achieved by extending conventional single-label classification. Common extension techniques are known as Binary relevance, Label powerset, and Classifier chains. However, most of the extended multi-label naive bayes classifier has not been able to accurately estimate posterior probabilities because it does not reflect the label dependency. And the remaining extended multi-label naive bayes classifier has a problem that it is unstable to estimate posterior probability according to the label selection order. To estimate posterior probability well, we propose a new posterior probability estimation method that reflects the probability between all labels and labels efficiently. The proposed method reflects the correlation between labels. And we have confirmed through experiments that the extended multi-label naive bayes classifier using the proposed method has higher accuracy then the existing multi-label naive bayes classifiers.

• Korean Journal of Remote Sensing
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• v.19 no.5
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• pp.381-392
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• 2003

Multi-temporal approaches using sequential data acquired over multiple years are essential for satisfactory discrimination between many land-cover classes whose signatures exhibit seasonal trends. At any particular time, the response of several classes may be indistinguishable. A harmonic model that can represent seasonal variability is characterized by four components: mean level, frequency, phase and amplitude. The trigonometric components of the harmonic function inherently contain temporal information about changes in land-cover characteristics. Using the estimates which are obtained from sequential images through spectral analysis, seasonal periodicity can be incorporates into multi-temporal classification. The Normalized Difference Vegetation Index (NDVI) was computed for one week composites of the Advanced Very High Resolution Radiometer (AVHRR) imagery over the Korean peninsula for 1996 ～ 2000 using a dynamic technique. Land-cover types were then classified both with the estimated harmonic components using an unsupervised classification approach based on a hierarchical clustering algorithm. The results of the classification using the harmonic components show that the new approach is potentially very effective for identifying land-cover types by the analysis of its multi-temporal behavior.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.1-6
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• 2021

Recently, pre-trained convolutional neural network CNNs have been widely used and applied for medical image classification. These models can utilised in three different ways, for feature extraction, to use the architecture of the pre-trained model and to train some layers while freezing others. In this study, the ResNet18 pre-trained CNNs model is used for feature extraction, followed by the support vector machine for multiple classes to classify medical images from multi-classes, which is used as the main classifier. Our proposed classification method was implemented on Kvasir and PH2 medical image datasets. The overall accuracy was 93.38% and 91.67% for Kvasir and PH2 datasets, respectively. The classification results and performance of our proposed method outperformed some of the related similar methods in this area of study.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.101-110
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• 2022

Histopathological analysis of biopsy specimens is still used for diagnosis and classifying the brain tumors today. The available procedures are intrusive, time consuming, and inclined to human error. To overcome these disadvantages, need of implementing a fully automated deep learning-based model to classify brain tumor into multiple classes. The proposed CNN model with an accuracy of 92.98 % for categorizing tumors into five classes such as normal tumor, glioma tumor, meningioma tumor, pituitary tumor, and metastatic tumor. Using the grid search optimization approach, all of the critical hyper parameters of suggested CNN framework were instantly assigned. Alex Net, Inception v3, Res Net -50, VGG -16, and Google - Net are all examples of cutting-edge CNN models that are compared to the suggested CNN model. Using huge, publicly available clinical datasets, satisfactory classification results were produced. Physicians and radiologists can use the suggested CNN model to confirm their first screening for brain tumor Multi-classification.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.551-552
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• 2021

In this paper, we present a multi-class image scene classification method based on transformation learning. ImageNet classifies multiple classes of natural scene images by relying on pre-trained network models on large image datasets. In the experiment, we obtained excellent results by classifying the optimized ResNet model on Kaggle's Intel Image Classification data set.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.1
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• pp.27-38
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• 2017

Packet classification is one of the essential functionalities of Internet routers in providing quality of service. Since the arrival rate of input packets can be tens-of-millions per second, wire-speed packet classification has become one of the most challenging tasks. While traditional packet classification only reports a single matching result, new network applications require multiple matching results. Ternary content-addressable memory (TCAM) has been adopted to solve the multi-match classification problem due to its ability to perform fast parallel matching. However, TCAM has a fundamental issue: high power dissipation. Since TCAM is designed for a single match, the applicability of TCAM to multi-match classification is limited. In this paper, we propose a cost- and energy-efficient multi-match classification architecture that combines TCAM with a tuple space search algorithm. The proposed solution uses two small TCAM modules and requires a single-cycle TCAM lookup, two SRAM accesses, and several Bloom filter query cycles for multi-match classifications.

• Journal of Korean Library and Information Science Society
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• v.29
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• pp.241-272
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• 1998

The purpose of this paper is to examine the analytic-assembling(faceted analysis) methods applied in enumerative-hierarchical classification schemes. (mainly in KDC) The methods are summarized as follows : 1. For the enumerative-hierarchical classification schemes, in principle the subjects are divided into subdivisions by only one facet at the same level, and step by step. However some subjects, for example 'library and information science' 'education' and others in KDC, are divided into subdivisions by multiple facets at same level like Colon Classification. 2. Most of enumerative-hierarchical classification schemes have various kinds of auxiliary tables, such as standard subdivisions, areas, periods, and languages. Each of them is considered as foci by a facet applied to subdivide all kinds of subjects or some special subjects into lower level. 3. To classify the compound subjects with phase relation, KDC provides ready-made classification numbers or notes that says 'divide by 001-999'(whole subjects) of 'divide by xxx-xxx'(limited scope of subjects). The ready-made compound subjects, or subdividing by whole or limited scope of subjects are similar to representation of phase relation in Colon Classification. Yet these analytic-assembling methods in KDC are needed to be supplemented and amended. Subdividing methods for faceted analysis have to be unified through the whole schedule. The auxiliary tables should be enlarged and subdivided more specifically. And for representation of phase relation, the linking signs can be useful in KDC as well as UDC and other analytic-assembling classification schemes like Colon Classification.

• International Journal of Knowledge Content Development & Technology
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• v.2 no.1
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• pp.51-65
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• 2012

While science and technology information service portals and heterogeneous databases produced in Korea and other countries are integrated, methods of connecting the unique classification systems applied to each database have been studied. Results of technologists' research, such as, journal articles, patent specifications, and research reports, are organically related to each other. In this case, if the most basic and meaningful classification systems are not connected, it is difficult to achieve interoperability of the information and thus not easy to implement meaningful science technology information services through information convergence. This study aims to address the aforementioned issue by analyzing mapping systems between classification systems in order to design a structure to connect a variety of classification systems used in the academic information database of the Korea Institute of Science and Technology Information, which provides science and technology information portal service. This study also aims to design a mapping system for the classification systems to be applied to actual science and technology information services and information management systems.