• Journal of Intelligence and Information Systems
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• v.9 no.2
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• pp.19-38
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• 2003

In this research, we propose the mechanism to develop self-evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most researchers had tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, this approach had some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, knowledge engineers had tried to develop an automatic knowledge extraction mechanism. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference engine. Our proposed mechanism has five advantages. First, it can extract and reduce the specific domain knowledge from incomplete database by using data mining technology. Second, our proposed mechanism can manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it can construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems) module. Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy relationships. Fifth, RDB-driven forward and backward inference time is shorter than the traditional text-oriented inference time.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.153-156
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• 2005

In this paper, a framework for implementing UFIS (Unified Fuzzy rule-based knowledge Inference System) is presented. First, fuzzy clustering and fuzzy rules deal with the presence of the knowledge in DB (DataBase) and its value is presented with a value between 0 and 1. Second, RDB (Relational DB) and SQL queries provide more flexible functionality fur knowledge management than the conventional non-fuzzy knowledge management systems. Therefore, the obtained fuzzy rules offer the user additional information to be added to the query with the purpose of guiding the search and improving the retrieval in knowledge base and/ or rule base. The framework can be used as DM (Data Mining) and ES (Expert Systems) development and easily integrated with conventional KMS (Knowledge Management Systems) and ES.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.521-529
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• 2020

Forest fires are one of the most important environmental risks that have adverse effects on many aspects of life, such as the economy, environment, and health. The early detection, quick prediction, and rapid response of forest fires can play an essential role in saving property and life from forest fire risks. For the rapid discovery of forest fires, there is a method using meteorological data obtained from local sensors installed in each area by the Meteorological Agency. Meteorological conditions (e.g., temperature, wind) influence forest fires. This study evaluated a Data Mining (DM) approach to predict the burned area of forest fires. Five DM models, e.g., Stochastic Gradient Descent (SGD), Support Vector Machines (SVM), Decision Tree (DT), Random Forests (RF), and Deep Neural Network (DNN), and four feature selection setups (using spatial, temporal, and weather attributes), were tested on recent real-world data collected from Gyeonggi-do area over the last five years. As a result of the experiment, a DNN model using only meteorological data showed the best performance. The proposed model was more effective in predicting the burned area of small forest fires, which are more frequent. This knowledge derived from the proposed prediction model is particularly useful for improving firefighting resource management.

• Asian-Australasian Journal of Animal Sciences
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• v.25 no.4
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• pp.462-470
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• 2012

The availability and efficient use of the feed resources in India are the primary drivers to maximize productivity of Indian livestock. Feed security is vital to the livestock management, extent of use, conservation and productivity enhancement. Assessment and forecasting of livestock feed resources are most important for effective planning and policy making. In the present study, 40 years of data on crop production, land use pattern, rainfall, its deviation from normal, area under crop and yield of crop were collected and modeled to forecast the likely production of feed resources for the next 20 years. The higher order auto-regressive (AR) models were used to develop efficient forecasting models. Use of climatic variables (actual rainfall and its deviation from normal) in combination with non-climatic factors like area under each crop, yield of crop, lag period etc., increased the efficiency of forecasting models. From the best fitting models, the current total dry matter (DM) availability in India was estimated to be 510.6 million tonnes (mt) comprising of 47.2 mt from concentrates, 319.6 mt from crop residues and 143.8 mt from greens. The availability of DM from dry fodder, green fodder and concentrates is forecasted at 409.4, 135.6 and 61.2 mt, respectively, for 2030.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.3
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• pp.927-930
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• 2012

Background and Objectives: Artificial neural networks (ANNs) are flexible and nonlinear models which can be used by clinical oncologists in medical research as decision making tools. This study aimed to predict distant metastasis (DM) of colorectal cancer (CRC) patients using an ANN model. Methods: The data of this study were gathered from 1219 registered CRC patients at the Research Center for Gastroenterology and Liver Disease of Shahid Beheshti University of Medical Sciences, Tehran, Iran (January 2002 and October 2007). For prediction of DM in CRC patients, neural network (NN) and logistic regression (LR) models were used. Then, the concordance index (C index) and the area under receiver operating characteristic curve (AUROC) were used for comparison of neural network and logistic regression models. Data analysis was performed with R 2.14.1 software. Results: The C indices of ANN and LR models for colon cancer data were calculated to be 0.812 and 0.779, respectively. Based on testing dataset, the AUROC for ANN and LR models were 0.82 and 0.77, respectively. This means that the accuracy of ANN prediction was better than for LR prediction. Conclusion: The ANN model is a suitable method for predicting DM and in that case is suggested as a good classifier that usefulness to treatment goals.

• Journal of Digital Convergence
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• v.17 no.8
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• pp.249-255
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• 2019

Type 2 diabetes mellitus(T2DM) is included in metabolic disorders characterized by hyperglycemia, which causes many complications, and requires long-term treatment resulting in massive medical expenses each year. There have been many studies to solve this problem, but the existing studies have not been accurate by learning and predicting the data at specific time point. Thus, this study proposed a model using RNN to increase the accuracy of prediction of T2DM. This work propose a T2DM prediction model based on Korean Genome and Epidemiology study(Ansan, Anseong Korea). We trained all of the data over time to create prediction model of diabetes. To verify the results of the prediction model, we compared the accuracy with the existing machine learning methods, LR, k-NN, and SVM. Proposed prediction model accuracy was 0.92 and the AUC was 0.92, which were higher than the other. Therefore predicting the onset of T2DM by using the proposed diabetes prediction model in this study, it could lead to healthier lifestyle and hyperglycemic control resulting in lower risk of diabetes by alerted diabetes occurrence.

• Journal of the Korean Society for Marine Environment & Energy
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• v.16 no.1
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• pp.17-24
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• 2013

In order to have market competitiveness in local and global areas, Domestic Marine Leisure Industry business, which is a latecomer in the Marine Leisure industry, should retain a strong market adaptability by reducing time and cost that are required for work of planning, designing, and preparation for product development. To meet above requirements, it is essential that integrated system control extensive marine leisure industry. After ensuring integrated information by figuring out the systematic link between related-industries, the core of this research is to secure information classifications that are not just in the flow of simple serial order, but in that of integration and object-oriented information classifications. For this end, we examine other similar cases in industries using real information system applied to industrial production and Product Lifecycle Management (PLM), Product Data Management (PDM), Digital Manufacturing (DM) and applying the same methodology to review practical application in order to construct the information system, and Work Breakdown Structure (WBS), compared with the case studies. Through this basic task for the marine leisure industry classification system configuration (Work Breakdown Structure, WBS) and utilizing information of driving real companies of marine leisure industry, a unique area of MLWBS (Marine Leisure Work Breakdown Structure, MLWBS) is configured. This Marine Leisure Work Breakdown Structure can be used in various areas of applications like products, design information, engineering, production, purchasing, sales, marketing, AS, utilizing various forms of customer support.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.501-510
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• 2019

This study aimed to find health related lifestyle factors that influence glycemic control for diabetes mellitus (DM) management strategies. This study used nested case-control design with matching variables that were not controlled by individuals such as age, sex, insulin or oral hypoglycemic agent (OHA) use, disease duration, education level and household income. This study analyzed 983 subjects with type 2 DM who enrolled in the $7^{th}$ (2016-2017) Korean National Health and Nutrition Examination Survey (KNHANES). The target HbA1c level of controlled glucose was defined as less than 6.5%, and 289 (30%) were achieved. Conditional multivariable logistic regression analysis was performed to find self-control factors associated with HbA1c levels. The results statistically significant for variables such as duration of diabetes, insulin or OHA use in overall cohort and body mass index (BMI), smoking and fundus Examination in matched cohort. These results are expected to provide as evidence for the intensive care criteria(disease duration, drug use) and lifestyle management strategy(BMI, smoking, fundus examination).

• Convergence Security Journal
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• v.17 no.5
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• pp.87-92
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• 2017

MANET is a network composed itself because mobile nodes are connected wirelessly. It has been applied to various fields for group communication. However, the dynamic topology by the movement of the nodes causes routing failure frequently because it is difficult to maintain the position information of the nodes participating in the group communication. Also, it has a problem that network performance is decreased due to high overhead for managing information of member nodes. In this paper, we propose a multicast technique using location-based 2-tier virtual group that is flexible and reliable in management of member nodes. The network is composed of cellular zones and the virtual group is constructed using the location information of the nodes in the proposed technique. The virtual group management node is selected to minimize the overhead of location information management for member nodes in the virtual group. In order to improve the reliability for management of member nodes and multicast data transmission, it excludes the gateway node with low transfer rate when setting the route after the packet transmission rate of the member nodes is measured. The excellent performance of the proposed technique can be confirmed through comparative experiments with AMroute method and PAST-DM method.

• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.582-587
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• 2007

ERP system is a good one because it provides required data to the Board of Directors at the right time, but needs to collect many data in this system. Nevertheless, increase in data leads to the system's quality deterioration which makes companies to carry out quality improvement. In order to solve quality deterioration problem, a company's quality improvement director must execute under acknowledgement of the relationships between sectors to be improved, which are DBMS, Application, System, Data Management, Archiving, and Reorganization. But in many cases, these relationships are ignored due to massive size of each of the sectors, resulting fragmental quality improvement operation. This case paper proposes a solution to effectively solve quality deterioration problem created by the massive data produced while operating ERP System(constructed by SAP package and web). First, it defines the sectors where quality improvements are vital, and lists out things to be considered. Then, by analysing the working process of these sectors, proposes the most efficient order of the improvement process. This case will eventually help the company's quality improvement director to execute quality improvement most effectively without trials and errors, which is this paper's ultimate goal.