• International Journal of Computer Science & Network Security
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• 제21권4호
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• pp.272-276
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• 2021

Presently, Online / Offline Users are facing cyber attacks every day. These cyber attacks affect user's performance, resources and various daily activities. Due to this critical situation, attention must be given to prevent such users through cyber attacks. The objective of this research paper is to improve the IDS systems by using machine learning approach to develop a hybrid model which controls the cyber attacks. This Hybrid model uses the available KDD 1999 intrusion detection dataset. In first step, Hybrid Model performs feature optimization by reducing the unimportant features of the dataset through decision tree, support vector machine, genetic algorithm, particle swarm optimization and principal component analysis techniques. In second step, Hybrid Model will find out the minimum number of features to point out accurate detection of cyber attacks. This hybrid model was developed by using machine learning algorithms like PSO, GA and ELM, which trained the system with available data to perform the predictions. The Hybrid Model had an accuracy of 99.94%, which states that it may be highly useful to prevent the users from cyber attacks.

• 복합신소재구조학회 논문집
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• 제2권2호
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• pp.1-6
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• 2011

본 연구는 하이브리드 FRP로 보강된 철근 콘크리트 보의 구조거동 예측을 목표로 구조해석을 수행하여 기존에 발표된 실험 연구 데이터와 비교하였다. 보다 정확한 구조해석을 위하여 현존하는 다양한 부착강도 모델을 검토한 후, 이 중 콘크리트 피복분리를 예측하는 Teng and Yao model과 FRP 탈락 현상을 예측할 수 있는 Smith and Teng model을 유한요소 해석 모델에 포함시켰다. 비선형 재료 및 형상 역시 구조해석 모델에 포함되었으며 이렇게 해석된 결과는 실험결과와 비교하여 유사한 경향을 나타냈다. 그러나 다양한 하이브리드 FRP로 보강한 철근 콘크리트 보의 파괴모드를 보다 정확하게 예측하기 위하여 현존하는 수치식의 수정 및 도입이 필요하다.

• 한국경영과학회지
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• 제30권4호
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• pp.27-43
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• 2005

This paper presents a new hybrid data mining technique using error pattern modeling to improve classification accuracy when the data type of a target variable is binary. The proposed method increases prediction accuracy by combining two different supervised learning methods. That is, the algorithm extracts a subset of training cases that are predicted inconsistently by both methods, and models error patterns from the cases. Based on the error pattern model, the Predictions of two different methods are merged to generate final prediction. The proposed method has been tested using practical 10 data sets. The analysis results show that the performance of proposed method is superior to the existing methods such as artificial neural networks and decision tree induction.

• 한국생물공학회:학술대회논문집
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• 한국생물공학회 2000년도 춘계학술발표대회
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• pp.133-136
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• 2000

In recent years, hybrid neural network approaches which combine neural networks and mechanistic models have been gaining considerable interests. These approaches are potentially very efficient to obtain more accurate predictions of process dynamics by combining mechanistic and neural models in such a way that the neural network model properly captures unknown and nonlinear parts of the mechanistic model. In this work, such an approach was applied in the modeling of a full-scale coke wastewater treatment process. First, a simplified mechanistic model was developed based on the Activated Sludge Model No.1 and the specific process knowledge, Then neural network was incorporated with the mechanistic model to compensate the errors between the mechanistic model and the process data. Simulation and actual process data showed that the hybrid modeling approach could predict accurate process dynamics of industrial wastewater treatment plant. The promising results indicated that the hybrid modeling approach could be a useful tool for accurate and cost-effective modeling of biochemical processes.

• 한국데이타베이스학회:학술대회논문집
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• 한국데이타베이스학회 2010년도 춘계국제학술대회
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• pp.181-185
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• 2010

The current method for accessing interconnection charges in Korea, called a hybrid model in this paper, mixes a top-down with a bottom-up LRIC model. The method has given stable charges so far. However, according to the fundamental changes of the market, policy, and network technology in the telecommunications industry, it requires analyzing the validity of the method. We investigate the problems of the top-clown, bottom-up, and hybrid model used in Korea and analyze their effect on regulation policy.

• Journal of Information Technology Applications and Management
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• 제15권1호
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• pp.225-242
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• 2008

PM (Inconsistency Pattern Modeling) is a hybrid supervised learning technique using the inconsistence pattern of input variables in mining data sets. The IPM tries to improve prediction accuracy by combining more than two different supervised learning methods. The previous related studies have shown that the IPM was superior to the single usage of an existing supervised learning methods such as neural networks, decision tree induction, logistic regression and so on, and it was also superior to the existing combined model methods such as Bagging, Boosting, and Stacking. The objectives of this paper is explore the characteristics of the IPM. To understand characteristics of the IPM, three experiments were performed. In these experiments, there are high performance improvements when the prediction inconsistency ratio between two different supervised learning techniques is high and the distance among supervised learning methods on MDS (Multi-Dimensional Scaling) map is long.

• Journal of Electrical Engineering and Technology
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• 제8권3호
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• pp.464-471
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• 2013

This paper proposes a hybrid searching algorithm based on parameter identification for power system load models. Hybrid searching was performed by the combination of particle swarm optimization (PSO) and a complex method, which enhances the convergence of solutions closer to minima and takes advantage of global searching with PSO. In this paper, the load model of interest is composed of a ZIP model and a third-order model for induction motors for stability analysis, and parameter sets are obtained that best-fit the output measurement data using the hybrid search. The origin of the hybrid method is to further apply the complex method as a local search for finding better solutions using the selected particles from the performed PSO procedure.

• 한국경영과학회지
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• 제24권4호
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• pp.1-12
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• 1999

This paper presents a DEA-AHP hybrid model to evaluate and select R&D projects. AHP collects and processes information on the weights of evaluation criteria. The processed information is used as an input for DEA/AR model. Only desirable number of projects are selected by the hybrid model. The model is examined by an example generated from a real data set.

• Journal of the Korean Data and Information Science Society
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• 제22권3호
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• pp.467-476
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• 2011

우리나라의 건강보험제도권 내 해당되지 않은 상해상병 진료건 중 국민건강보험으로 부당 잘못 청구되는 진료건을 적발하여, 환수조치하기 위해서는 정확한 상해상병 조사대상자 선정이 필요하다. 그러나, 국민건강보험공단의 한정된 인력으로 증가하는 상해조사관련 업무량을 보다 효율적으로 대처하고, 수행하기 위해서는 상해요인조사 업무 효율화 및 환수 결정율 제고를 위한 조사대상자 발췌기준의 고도화 방안을 마련해야 한다. 이에 본 연구에서는 상해상병 유형에 대해 일정금액 이상 진료건의 발췌 등과 같은 과거의 발췌기준에서 데이터마이닝 기법과 같은 통계적 모형과 업무규칙을 함께 적용한 하이브리드 모형으로서 상해상병 조사대상자 선정기준을 제시하고자하였다.

• 대한간호학회지
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• 제26권2호
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• pp.290-303
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• 1996

There is a need to define the concept of suffering more appropriate in the context of Korean culture. This research is an attempt to analyze and develop the concept of suffering by applying the Hybrid Model suggested by Schwartz-Barcott and Kim. The data were collected from March 20, 1995 to September 17,1995. The subjects of the study were eight persons including in-patients and out-patients of a general hospital who were diagnosed as having cancer and those resting in sanatoria for natural treatment of cancer. Qualitative research methods of in-depth interview and participant observation were used for data collection. The contents of the interviews were recorded on tape. Data-analysis progressed according to the 3 phases suggested by the Hybrid Model. For each case, in-depth interview data and participant observation data were included and the attributes of suffering revealed in these data were analyzed. Finally, by summarizing the results from each case, the attributes of suffering, its dimensions, definition, and processes observed in the field were suggested. According to the results of the study, the followlng new definition of suffering is suggested : Suffering is a fundamental and inevitable experience of all human beings. When each individual experiences loss, damage, and pain which threaten one's personal integrity, suffering is perceived differently among each individual depending on their personal inner factors, one's significant others, exterior circumstances and stimuli, and the ultimate meaning of life. Suffering brings severe and unendurable distress which accompany despair, powerlessness, anxiety, bitterness, fear, anguish, guilt, depression, withdrawal and anger. The results of this study suggest that the more responsibility and burden a cancer patient felt, the more suffering she/he experienced and it tended to be more relevant to one's significant others and exterior circumstances and stimuli : the less responsibility and burden a cancer patient had, the less suffering she/he experienced and it tended to be related to one's inner factors. These findings have implications for nursing profession. When caring for patients who experience suffering, nurses need to consider the influence of responsibility, burden, and each dimension of suffering. Moreover, appropriate nursing interventions aimed at relieving pain and satisfying the spiritual need of patients experiencing loss need to be developed and implemented more widely.