• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5568-5587
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• 2018

This research uses artificial intelligence methods for computer network intrusion detection system modeling. Primary classification is done using self-organized maps (SOM) in two levels, while the secondary classification of ambiguous data is done using Sugeno type Fuzzy Inference System (FIS). FIS is created by using Adaptive Neuro-Fuzzy Inference System (ANFIS). The main challenge for this system was to successfully detect attacks that are either unknown or that are represented by very small percentage of samples in training dataset. Improved algorithm for SOMs in second layer and for the FIS creation is developed for this purpose. Number of clusters in the second SOM layer is optimized by using our improved algorithm to minimize amount of ambiguous data forwarded to FIS. FIS is created using ANFIS that was built on ambiguous training dataset clustered by another SOM (which size is determined dynamically). Proposed hybrid model is created and tested using NSL KDD dataset. For our research, NSL KDD is especially interesting in terms of class distribution (overlapping). Objectives of this research were: to successfully detect intrusions represented in data with small percentage of the total traffic during early detection stages, to successfully deal with overlapping data (separate ambiguous data), to maximize detection rate (DR) and minimize false alarm rate (FAR). Proposed hybrid model with test data achieved acceptable DR value 0.8883 and FAR value 0.2415. The objectives were successfully achieved as it is presented (compared with the similar researches on NSL KDD dataset). Proposed model can be used not only in further research related to this domain, but also in other research areas.

• Journal of Korea Multimedia Society
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• v.25 no.6
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• pp.769-782
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• 2022

A study on agricultural automation became more important. In Korea, sweet persimmon farmers spend a lot of time and effort on classifying profitable persimmons. In this paper, we propose and implement an efficient grading algorithm for persimmons before shipment. We gathered more than 1,750 images of persimmons, and the images were graded and labeled for classifications purpose. Our main algorithm is based on EfficientDet object detection model but we implemented more exquisite method for better classification performance. In order to improve the precision of classification, we adopted a machine learning algorithm, which was proposed by PyCaret machine learning workflow generation library. Finally we acquired an improved classification model with the accuracy score of 81%.

• Journal of Gastric Cancer
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• v.15 no.3
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• pp.183-190
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• 2015

Purpose: The Lauren classification system is a very commonly used pathological classification system of gastric adenocarcinoma. A recent study proposed that the Lauren classification should be modified to include the anatomical location of the tumor. The resulting three types were found to differ significantly in terms of genomic expression profiles. This retrospective cohort study aimed to evaluate the clinical significance of the modified Lauren classification (MLC). Materials and Methods: A total of 677 consecutive patients who underwent curative gastrectomy from January 2005 to December 2007 for histologically confirmed gastric cancer were included. The patients were divided according to the MLC into proximal non-diffuse (PND), diffuse (D), and distal non-diffuse (DND) type. The groups were compared in terms of clinical features and overall survival. Multivariate analysis served to assess the association between MLC and prognosis. Results: Of the 677 patients, 48, 358, and 271 had PND, D, and DND, respectively. Their 5-year overall survival rates were 77.1%, 77.7%, and 90.4%. Compared to D and PND, DND was associated with significantly better overall survival (both P<0.01). Multivariate analysis showed that age, differentiation, lympho-vascular invasion, T and N stage, but not MLC, were independent prognostic factors for overall survival. Multivariate analysis of early gastric cancer patients showed that MLC was an independent prognostic factor for overall survival (odds ratio, 5.946; 95% confidence intervals, 1.524~23.197; P=0.010). Conclusions: MLC is prognostic for survival in patients with gastric adenocarcinoma, in early gastric cancer. DND was associated with an improved prognosis compared to PND or D.

• Journal of the Korean Wood Science and Technology
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• v.47 no.3
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• pp.265-276
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• 2019

In our previous study, the LeNet3 model successfully classified images from the transverse surfaces of five Korean softwood species (cedar, cypress, Korean pine, Korean red pine, and larch). However, a practical limitation exists in our system stemming from the nature of the training images obtained from the transverse plane of the wood species. In real-world applications, it is necessary to utilize images from the longitudinal surfaces of lumber. Thus, we improved our model by training it with images from the longitudinal and transverse surfaces of lumber. Because the longitudinal surface has complex but less distinguishable features than the transverse surface, the classification performance of the LeNet3 model decreases when we include images from the longitudinal surfaces of the five Korean softwood species. To remedy this situation, we adopt ensemble methods that can enhance the classification performance. Herein, we investigated the use of ensemble models from the LeNet and MiniVGGNet models to automatically classify the transverse and longitudinal surfaces of the five Korean softwoods. Experimentally, the best classification performance was achieved via an ensemble model comprising the LeNet2, LeNet3, and MiniVGGNet4 models trained using input images of $128{\times}128{\times}3pixels$ via the averaging method. The ensemble model showed an F1 score greater than 0.98. The classification performance for the longitudinal surfaces of Korean pine and Korean red pine was significantly improved by the ensemble model compared to individual convolutional neural network models such as LeNet3.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2006.03a
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• pp.113-124
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• 2006

Despite the site classification method was improved in the previous study, the response spectrum would be required to be modified by adjusting the integration interval to calculate the site coefficients because the response spectra did not match well the average spectral accelerations obtained by site response analyses in the range of long periods. In this paper, new response spectra for each site categories were determined by adjusting the integration interval of long-period site coefficient $F_{v}$ from $0.4{\sim}2.0$ to $0.4{\sim}1.5$ second. It matched well the average spectral accelerations and new response spectrum, and it was also improved compared to the current site classification system.

• Investigative Magnetic Resonance Imaging
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• v.3 no.2
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• pp.179-187
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• 1999

Purpose : This paper introduces a computationally inexpensive context-dependent classification of multi-echo MRI with Bayes compound decision model. In order to produce accurate region segmentation especially in homogeneous area and along boundaries of the regions, we propose a classification method that uses contextual information of local enighborhood system in the image. Material and Methods : The performance of the context free classifier over a statistically heterogeneous image can be improved if the local stationary regions in the image are disassociated from each other through the mechanism of the interaction parameters defined at he local neighborhood level. In order to improve the classification accuracy, we use the contextual information which resolves ambiguities in the class assignment of a pattern based on the labels of the neighboring patterns in classifying the image. Since the data immediately surrounding a given pixel is intimately associated with this given pixel., then if the true nature of the surrounding pixel is known this can be used to extract the true nature of the given pixel. The proposed context-dependent compound decision model uses the compound Bayes decision rule with the contextual information. As for the contextual information in the model, the directional transition probabilities estimated from the local neighborhood system are used for the interaction parameters. Results : The context-dependent classification paradigm with compound Bayesian model for multi-echo MR images is developed. Compared to context free classification which does not consider contextual information, context-dependent classifier show improved classification results especially in homogeneous and along boundaries of regions since contextual information is used during the classification. Conclusion : We introduce a new paradigm to classify multi-echo MRI using clustering analysis and Bayesian compound decision model to improve the classification results.

• The Journal of Internal Korean Medicine
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• v.31 no.1
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• pp.1-10
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• 2010

Objectives : It is necessary that the international classification of diseases (ICD) be examined in order to comprise the third revision of the Korean Classification of Disease on Oriental Medicine (KCD-OM) and disease classification in the oriental internal medicine field. It is essential that the selection, classification and definition of disease and pattern names of oriental concepts in internal medicine be clear. Since 2008, the fifth revision of the Korean Classification of Disease (KCD-5) has been used in Korea. It was required to use the reference classification from the Oriental medicine area based on the ICD-10. Methods : In this review, the necessity for, meaning of and content of the third revision are briefly described. The ICD system was reviewed and KCD-OM was reconstructed. How diagnosis in the oriental internal medicine area had changed is discussed. Review and Results : In 1973, the disease classification of oriental medicine was established the basis on the contents of Dongeuibogam. It was irrespective of the ICD. As to the classification system in the Oriental internal medicine field, systemic disease was comprised of wind, cold, warm, wet, dryness, heat, spirit, ki, blood, phlegm and retained fluid, consumptive disease, etc. Diseases of internal medicine comprised a system according to the five viscera and the six internal organs and followed the classification system of Dongeuibogam. The first and second revisions were of the classification system based on the curriculum in 1979 and 1995. In 1979, in the first revision, geriatric disease and idiopathic types of disease were deleted, and skin disease was included among surgery diseases. This classification was expanded to 792 small classification items and 1,535 detailed classification items to the dozen disease classes. In 1995, in the second revision, it was adjusted to 644 small classes and 1,784 detailed classification items in the dozen disease classes. KCD-OM3 did KCD from this basis. It added and comprised the oriental medical doctor's concept names of diseases considering the special conditions in Korea. KCD-OM3 examined the KCD-OMsecond revised edition (1994). It improved the duplex classification, improper classifications, etc. It is difficult for us to separate the disease names and pattern names in oriental medicine. We added to the U code and made one classification system. By considering the special conditions in Korea, 169 codes (83 disease name codes, 86 pattern name codes) became the pre-existence classification and links among 306 U codes of KCD-OM3. 137 codes were newly added in the third revision. U code added 3 domains. These are composed of the disease name (U20-U33, 97 codes), the disease pattern name (U50-U79, 191 codes) and the constitution pattern name of each disease (U95-U98, 18 codes). Conclusion : The introduction of KCD-OM3 conforms to the diagnostic system by which oriental medical doctors examine classes used with the basic structure of the reference classification of WHO and raises the clinical study and academic activity of the Korean oriental medicine and makes the production of all kinds of nation statistical indices possible. The introduction of KCD-OM3 promotes the diagnostic system by which doctors of Oriental medicine examine classes using the association with KCD-5. It will raise the smoothness and efficiency of oriental medical treatment payments in the health insurance, automobile insurance, industrial accident compensation insurance, etc. In addition, internationally, the eleventh revision work of the ICD has been initiated. It needs to consider incorporating into the International Classification of Diseases some of every country's traditional medicine.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.218-223
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• 2011

Various research studies are underway to explore music classification by genre. Because sound professionals define the criterion of music to categorize differently each other, those classification is not easy to come up clear result. When a new genre is appeared, there is onerousness to renew the criterion of music to categorize. Therefore, music is classified by emotional adjectives, not genre. We classified music by light and shade in precedent study. In this paper, we propose the music classification system that is based on emotional adjectives to suitable search for atmosphere, and the classification criteria is three kinds; light and shade in precedent study, intense and placid, and grandeur and trivial. Variance Considered Machines that is an improved algorithm for Support Vector Machine was used as classification algorithm, and it represented 85% classification accuracy with the result that we tried to classify 525 songs.

• Journal of Physiology & Pathology in Korean Medicine
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• v.27 no.4
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• pp.383-390
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• 2013

In order to improve the integrating accuracy and to elevate the serviceability of the KM(Korean Medicine) ontology constructed by the Korea Institute of Oriental Medicine, this research simplified the many-to-many corresponding relationship between groups of methods of treatment and groups of accompanied symptoms from disease ontology and categorized systematically the relationship. We first extracted the combinations of methods of treatment and accompanied symptoms from the KM ontology, then categorized the attributes of combinations that their frequencies were over 10 times by analyzing KM terms definition and the basic KM theory. We constructed the classification hierarchy having 14 kinds of classification in 4 steps and extracted 450 meaningful combinations. This research improved the integrating accuracy and elevated the serviceability of KM information by the classification system.

• Journal of architectural history
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• v.21 no.6
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• pp.53-62
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• 2012

The purpose of this study is to present improvement for classification system of current neighborhood living facility to correspond rapid social change and various industries after understanding its status and problem. In current Building Standard Law, various kinds of buildings are classified for their structure, purpose of use, and building types. The Neighborhood Living Facility is divided into First Neighborhood Living Facility and Second Neighborhood Living Facility with applying area standards, according to facilities of convenience degree for neighborhood inhabitants. This classification, however, has problem in an arbitrary decision and applying of buildings without any definition or standards to adopt. And, there are some mixed neighborhood public functional facilities and amusement business affecting public morals among the Neighborhood Living Facility, so hazard environmental problems are also existed. According to the improved program, the study presents a prompt adoption of new facilities according to various industry increase, with minimum public discontent over adopted area standards. This study suggests making a clear scope through reclassification of Neighborhood Living Facility within the scope of the law on current Neighborhood Living Facility and an improvement plan of introducing necessary definitions on purpose of facility.