• Asia Marketing Journal
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• 제20권1호
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• pp.49-68
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• 2018

It is appropriate and useful to interpret some product classification schemes as buyer behavior models; such classifications permit investigations of discrepancies between classification predictions and actual buyer behavior. We review existing product classifications and identify underlying behavioral assumptions of various classification schemes that have been used in the marketing discipline for more than nine decades. Recognizing the irrelevance of existing product classifications for current products, we propose a new reclassification framework by incorporating transparency concepts. Based on this extended product classification, we highlight the potential roles of product classification study as an important link between consumer research and marketing strategy, emphasizing behavioral implications.

• 전기학회논문지
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• 제59권12호
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• pp.2314-2318
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• 2010

For human-robot interaction, a robot should recognize the meaning of human behavior. In the case of static behavior such as face expression and sign language, the information contained in a single image is sufficient to deliver the meaning to the robot. In the case of dynamic behavior such as gestures, however, the information of sequential images is required. This paper proposes behavior classification by using fuzzy classifier to deliver the meaning of dynamic behavior to the robot. The proposed method extracts feature points from input images by a skeleton model, generates a vector space from a differential image of the extracted feature points, and uses this information as the learning data for fuzzy classifier. Finally, we show the effectiveness and the feasibility of the proposed method through experiments.

• 한국축산시설환경학회지
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• 제5권3호
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• pp.139-148
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• 1999

This study was conducted to develop an image processing system which can classify the pig's thermoregulatory behavior under the different environmental conditions. The 4 pigs of 25kg were housed in the environmentally controlled chamber(1.4m$\times$2.2m floor space). Postural behavior of the pigs was captured with an CCD color camera. The raw behavioral images were processed by thresholoding, reduction, separation of slightly contacted pigs, labeling, noise removal, computation of number of labels, and classification of the pig's behavior. The correct classification rate of the image processing system was 97.8%(88 out of 90 testing images). The results of this study showed that the image processing system could be used for a behavior-based automatic environmental controller.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1998년도 추계학술대회
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• pp.255-256
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• 1998

In this paper, we propose the multi-axial classification system using parallel coding method that is systemic and flexible properties for representing dental clinical behavior. The methodology and organization of this thesis as follows. First, an analysis of other classification systems. Second, the domain of medical behavior and axises using selected elements was were determined. Third, the new code system is constructed of these common factors in properties of prediction of hierarchy, brevity, simplicity, flexibility and mnemonic usage. Finally, the framework of classification system for dental was made using multi-axial code system. The result of the this study, the eight bases axis of multi-axial code system is composed and can be basic information of research for construction of classification system of all medical domain.

• 한국경영과학회지
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• 제26권4호
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• pp.55-70
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• 2001

Artificial neural network(ANN) models have been widely used for the classification problems in business such as bankruptcy prediction, credit evaluation, etc. Although the application of ANN to classification of consumers＇ choice behavior is a promising research area, there have been only a few researches. In general, most of the researches have reported that the classification performance of the ANN models were better than conventional statistical model Because the survey data on consumer behavior may include much noise and missing data, ANN model will be more robust than conventional statistical models welch need various assumptions. The purpose of this paper is to study the potential of the ANN model for forecasting consumers＇ choice behavior based on survey data. The data was collected by questionnaires to the shoppers of department stores and discount stores. Then the correct classification rates of the ANN models for the training and test sample with that of multiple discriminant analysis(MDA) and logistic regression(Logit) model. The performance of the ANN models were betted than the performance of the MDA and Logit model with respect to correct classification rate. By using input variables identified as significant in the stepwise MDA, the performance of the ANN models were improved.

• IEIE Transactions on Smart Processing and Computing
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• 제5권2호
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• pp.94-99
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• 2016

Application layer attacks have for years posed an ever-serious threat to network security, since they always come after a technically legitimate connection has been established. In recent years, cyber criminals have turned to fully exploiting the web as a medium of communication to launch a variety of forbidden or illicit activities by spreading malicious automated software (auto-ware) such as adware, spyware, or bots. When this malicious auto-ware infects a network, it will act like a robot, mimic normal behavior of web access, and bypass the network firewall or intrusion detection system. Besides that, in a private and large network, with huge Hypertext Transfer Protocol (HTTP) traffic generated each day, communication behavior identification and classification of auto-ware is a challenge. In this paper, based on a previous study, analysis of auto-ware communication behavior, and with the addition of new features, a method for classification of HTTP auto-ware communication is proposed. For that, a Not Only Structured Query Language (NoSQL) database is applied to handle large volumes of unstructured HTTP requests captured every day. The method is tested with real HTTP traffic data collected through a proxy server of a private network, providing good results in the classification and detection of suspicious auto-ware web access.

• 한국안전학회지
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• 제39권1호
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• pp.82-95
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• 2024

Analyzing the aftermath of events at domestic nuclear power plants brings in the question: "Why do workers not comply with the prescribed procedures?" The current investigation of nuclear power plant events identifies their reasons considering the factors affecting the workers' behaviors. However, there are some complications to it: in addition to confirming the action such as an error or a violation, there is a limit to identifying the intention of the actor. To overcome this limitation, the study analyzed and examined the reasons for non-compliance identified in nuclear power plant events by Reason's rule-related behavior classification. For behavior analysis, I selected unit behaviors for events that are related to human and organizational factors and occurred at domestic nuclear power plants since 2017, and then I applied the rule-related behavior classification introduced by Reason (2008). This allowed me to identify the intentions by classifying unit behaviors according to quality and compliance with the rules. I also identified the factors that influenced unit behaviors. The analysis showed that most often, non-compliance only pursued personal goals and was based on inadequate risk appraisal. On the other hand, the analysis identified cases where it was caused by such factors as poorly written procedures or human system interfaces. Therefore, the probability of non-compliance can be reduced if these factors are properly addressed. Unlike event investigation techniques that struggle to identify the reasons for employee behavior, this study provides a new interpretation of non-compliance in nuclear power plant events by examining workers' intentions based on the concept of rule-related behavior classification.

• Journal of Information Processing Systems
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• 제19권2호
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• pp.258-266
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• 2023

Aiming at the problems of poor customer satisfaction and poor accuracy of customer classification, this paper proposes a customer classification model based on speech recognition. First, this paper analyzes the temporal data characteristics of customer demand data, identifies the influencing factors of customer demand behavior, and determines the process of feature extraction of customer voice signals. Then, the emotional association rules of customer demands are designed, and the classification model of customer demands is constructed through cluster analysis. Next, the Euclidean distance method is used to preprocess customer behavior data. The fuzzy clustering characteristics of customer demands are obtained by the fuzzy clustering method. Finally, on the basis of naive Bayesian algorithm, a customer demand classification model based on speech recognition is completed. Experimental results show that the proposed method improves the accuracy of the customer demand classification to more than 80%, and improves customer satisfaction to more than 90%. It solves the problems of poor customer satisfaction and low customer classification accuracy of the existing classification methods, which have practical application value.

• 한국디자인학회:학술대회논문집
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• 한국디자인학회 1995년도 디자인 학 연구 1995년 추계 학술연구 발표 대회 개요 집
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• pp.36-37
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• 1995

• Journal of information and communication convergence engineering
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• 제3권4호
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• pp.209-212
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• 2005

This study proposes hybrid case-based reasoning and genetic algorithms model for customer classification. In this study, vertical and horizontal dimensions of the research data are reduced through integrated feature and instance selection process using genetic algorithms. We applied the proposed model to customer classification model which utilizes customers' demographic characteristics as inputs to predict their buying behavior for the specific product. Experimental results show that the proposed model may improve the classification accuracy and outperform various optimization models of typical CBR system.