• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.284-310
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• 2024

Identifying clinical pathways for disease diagnosis and treatment process recommendations are seriously decision-intensive tasks for health care practitioners. It requires them to rely on their expertise and experience to analyze various categories of health parameters from a health record to arrive at a decision in order to provide an accurate diagnosis and treatment recommendations to the end user (patient). Technological adaptation in the area of medical diagnosis using AI is dispensable; using expert systems to assist health care practitioners in decision-making is becoming increasingly popular. Our work architects a novel knowledge-based recommender system model, an expert system that can bring adaptability and transparency in usage, provide in-depth analysis of a patient's medical record, and prescribe diagnostic results and treatment process recommendations to them. The proposed system uses a set of parallel discrete fuzzy rule-based classifier systems, with each of them providing recommended sub-outcomes of discrete medical conditions. A novel knowledge-based combiner unit extracts significant relationships between the sub-outcomes of discrete fuzzy rule-based classifier systems to provide holistic outcomes and solutions for clinical decision support. The work establishes a model to address disease diagnosis and treatment recommendations for primary lung disease issues. In this paper, we provide some samples to demonstrate the usage of the system, and the results from the system show excellent correlation with expert assessments.

• The Journal of the Acoustical Society of Korea
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• v.27 no.6
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• pp.319-324
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• 2008

In this paper, we propose a novel approach to improve the performance of speech/music classification for the selectable mode vocoder (SMV) of 3GPP2 using the discriminative weight training which is based on the minimum classification error (MCE) algorithm. We first present an effective analysis of the features and the classification method adopted in the conventional SMV. And then proposed the speech/music decision rule is expressed as the geometric mean of optimally weighted features which are selected from the SMV. The performance of the proposed algorithm is evaluated under various conditions and yields better results compared with the conventional scheme of the SMV.

• Special Issue of the Society of Naval Architects of Korea
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• 2005.06a
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• pp.15-21
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• 2005

In this paper, the procedure of generation and application of nonlinear wave loads for structural design of large container carrier was described. Ship motion and wave load was calculated by modified strip method. Pressure acting on wetted hull surface was calculated taking into account of relative hull motion to the wave. Design wave height was determined based on the most sensitive wave length considering rule vertical wave bending moment at head sea or fellowing sea condition. And the enforced heeling angie concept which was introduced by Germanischer Lloyd (GL) classification had been used to simulate high torsional moment in way of fore hold parts similar to actual sea going condition. Using wave load generated from this dynamic load calculation, FE analyses were performed. With this result, yielding, buckling, hatch diagonal deflection and fatigue strength of hatch corners were reviewed based on the requirement of GL classification. The results of FE analysis show good compatibility with GL classification.

• Health Policy and Management
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• v.16 no.4
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• pp.128-146
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• 2006

The objective of this paper is to examine the proportion and characteristics of non-emergent patients at emergency departments. The observational survey was conducted using a structured form used by emergency medicine specialists or senior residents on June 7-20, 2005. 1,526 patients at ten emergency centers took part in this study. The structural form contained type of insurance, route and means of emergency department (ED) visit, triage based on the Manchester Triage Scale(MTS)-modified criteria, emergency level based on the government defined rule, type of emergency centers (Regional Emergency Medical Center; REMC, Local Emergency Medical Center; LEMC, Local Emergency Agency; LEA), as well as patient's general information. Data were analyzed using SAS statistical program(V.8.2). Descriptive analysis was performed to describe the magnitude of non-emergent patients. ${\chi}^2-analysis$ and logistic regression analysis was performed to identify the nonurgent patients' characteristics. In the MTS-modified criteria, we found a 15.3% rate of non-emergent patients. This rate differed from that of non-emergent patients obtained using government's rule. In particular, there were inaccuracies in the definition of government rule on non-emergent patients, so it is necessary to apply the new government rule regarding classification of non-emergent patients. There were significant differences in the rate of non-emergent patients according to type of ED, means of ED visit, time to visit, and insurance. Non-emergent patients are more likely to visit a D-type ED(LEA having less than 20,000 patients annually), not to use ambulance, to have 'Automobile Insurance, Industrial Accident Compensation Insurance, or pay out-of-pocket'. Non-emergent patients tend to visit ED due to illness rather than injury. Further studies on the development' of triage scale and reexamination of the government's rule on emergency visits are required for future policy in this area.

• Journal of Digital Contents Society
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• v.4 no.2
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• pp.181-189
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• 2003

The requirement of distributed multimedia applications is the need for sophisticated QoS(quality of service) management. In terms of distributed multimedia systems, the most important catagories for quality of service are a timeless, volume, and reliability In this paper, we discuss a method for increasing reliability through fault tolerance. We describe the design and implementation of the ECA running on distributed multimedia environment. ECA is a system is able to classify automatically a software error based on distributed multimedia. This papaer explains a performance analysis of an error classification system running on distributed multimedia environment using the rule-based DEVS modeling and simulation techniques. In DEVS, a system has a time base, inputs, states, outputs, and functions.

• The KIPS Transactions:PartD
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• v.15D no.6
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• pp.767-776
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• 2008

Microarray technology is extensively being used in experimental molecular biology field. Microarray experiments generate quantitative expression measurements for thousands of genes simultaneously, which is useful for the phenotype classification of many diseases. One of the two major problems in microarray data classification is that the number of genes exceeds the number of tissue samples. The other problem is that current methods generate classifiers that are accurate but difficult to interpret. Our paper addresses these two problems. We performed a direct integration of individual microarrays with same biological objectives by transforming an expression value into a rank value within a sample and generated rank-comparison decision rules with variable number of genes for cancer classification. Our classifier is an ensemble method which has k top scoring decision rules. Each rule contains a number of genes, a relationship among involved genes, and a class label. Current classifiers which are also ensemble methods consist of k top scoring decision rules. However these classifiers fix the number of genes in each rule as a pair or a triple. In this paper we generalized the number of genes involved in each rule. The number of genes in each rule is in the range of 2 to N respectively. Generalizing the number of genes increases the robustness and the reliability of the classifier for the class prediction of an independent sample. Also our classifier is readily interpretable, accurate with small number of genes, and shed a possibility of the use in a clinical setting.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.7
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• pp.283-290
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• 2015

In this paper, we propose a hybrid method for the recognition of answer types in the WiseQA system. The answer types are classified into two categories: the lexical answer type (LAT) and the semantic answer type (SAT). This paper proposes two models for the LAT detection. One is a rule-based model using question focuses. The other is a machine learning model based on sequence labeling. We also propose two models for the SAT classification. They are a machine learning model based on multiclass classification and a filtering-rule model based on the lexical answer type. The performance of the LAT detection and the SAT classification shows F1-score of 82.47% and precision of 77.13%, respectively. Compared with IBM Watson for the performance of the LAT, the precision is 1.0% lower and the recall is 7.4% higher.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.116-119
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• 2005

As to the synthetical estimation of land covering parameters or the compounded land covering classification for multi-resolution satellite data, former researches mainly adopted linear or nonlinear regression models to describe the regression relationship of land covering parameters caused by the degradation of spatial resolution, in order to improve the retrieval accuracy of global land covering parameters based on 1;he lower resolution satellite data. However, these methods can't authentically represent the complementary characteristics of spatial resolutions among different satellite data at arithmetic level. To resolve the problem above, a new compounded land covering classification method at arithmetic level for multi-resolution satellite data is proposed in this .paper. Firstly, on the basis of unsupervised clustering analysis of the higher resolution satellite data, the likelihood distribution scatterplot of each cover type is obtained according to multiple-to-single spatial correspondence between the higher and lower resolution satellite data in some local test regions, then Parzen window approach is adopted to derive the real likelihood functions from the scatterplots, and finally the likelihood functions are extended from the local test regions to the full covering area of the lower resolution satellite data and the global covering area of the lower resolution satellite is classified under the maximum likelihood rule. Some experimental results indicate that this proposed compounded method can improve the classification accuracy of large-scale lower resolution satellite data with the support of some local-area higher resolution satellite data.

• Journal of KIISE:Software and Applications
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• v.31 no.8
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• pp.999-1009
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• 2004

As a new approach to the diagnosis of cancers, bioinformatics attracts great interest these days. Machine teaming techniques have produced valuable results, but the field of medicine requires not only highly accurate classifiers but also the effective analysis and interpretation of them. Since gene expression data in bioinformatics consist of tens of thousands of features, it is nearly impossible to represent their relations directly. In this paper, we propose a method composed of a feature selection method and genetic programming. Rank-based feature selection is adopted to select useful features and genetic programming based arithmetic operators is used to generate classification rules with features selected. Experimental results on Lymphoma cancer dataset, in which the proposed method obtained 96.6% test accuracy as well as useful classification rules, have shown the validity of the proposed method.

• Journal of Korean Library and Information Science Society
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• v.19
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• pp.35-57
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• 1992

This study is an attempt to provide some helpful data for the design and the implementation of the expert system for the book-classification based on the analysis of various cases of the classification-expert system models. Following the introduction, the concepts and some features of an expert system were overviewed in the second chapter, on the basis of which the following concrete cases were introduced and analyzed in the third chapter : (1) ACN System for NC, (2) Expert System for NDC, (3) Expert System for UDC, (4) Herba Medica System, (5) Expert System for IPC, (6) Stratcyclode Project, (7) Expert System for Classification of INIS Database, (8) AutoBC System, and etc. In the conclusion, for the development of the classification-expert system, it was turned out that constructing a new system by using an AI language such as Prolog or LISP is more desirable than employing any one of expert system shells. Together it is necessary for the following requirements to be met : (1) The subject concept of a document elicited should be accurate. (2) Not only a domain knowledge but also the knowledge covering all the subjects should be represented in the knowledge-bases. (3) The knowledge-bases should be organized in such a way that the characteristics of the knowledge about classification should be well defined. (4) rule-base consisting of accurate rules about classification should be made. (5) It should be possible for classification code wanted to be generated immediately.