• Journal of the Korean Society of Clothing and Textiles
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• v.16 no.2
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• pp.155-168
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• 1992

The purposes of this study were: (1) to identify the image dimensions of apparel stores according to how the consumers rate the importance of store attributes; (2) to classify the apparel stores in Seoul, Korea according to consumers' perception of the image attributes of their preferred store; (3) to develop a positioning map of the apparel stores according to their salient image dimensions; and (4) to classify the female adults in Seoul according to the criteria of their preferred store and to describe the characteristics of target customers according to storetype. 'A questionnaire was developed to measure store patronage, perceived importance of the store image attributes, perception of the store image attributes for the respondent's most frequently patronized store, and demographic information. Data from 520 female adults living in Seoul were analyzed. The results were as follows; 1. The image dimensions of fashion stores were product quality, shopping convenience, location, promotion, atmosphere, product information, design characteristics and price. 2. The apparel stores in Seoul were classified into five groups by the perception of store image, which were labeled as national chain store, designer store, specialty store, wholesale store and independent store, according to their discriminant characteristics. 3. According to the positioning map, product quality and location convenience were identified as the most important apparel store type patronage criteria. 4. The female adult group divided by store preference indicated significant differences in the perceived importance of store attributes. Each group showed multi-store patronage.

• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.2
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• pp.61-76
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• 1995

The "optimal" solution for a decision making problem should be the one that best reflects the decision-maker's preference. For MADM (Multi-Attribute Decision-Making) problems, however, finding an optimal solution is difficult, especially when the number of alternatives, or that of attributes is relatively large. Most of the existing mathematical approaches arrive at a final solution on the basis of many unrealistic assumptions, without reflecting the decision-maker's preference structure exactly. To remedy this, some interactive methods have been proposed, but most of them require a large amount of information growing exponentially as the number of alternatives, or that of attributes increases. Therefore it is difficult for the decision-maker to maintain consistency throughout the decision making process. In this paper, an interactive method which finds optimal solutions for deterministic MADM problems with many attributes and alternatives is proposed. Instead of considering all the attributes simultaneously, this method partitions all the attributes into several mutually independent subgroups and considers one of them at each of preordered steps, where the alternatives are eliminated until the optimal one is obtained. The efficiency of the method lies in the fact that the amount of neccessary information is reduced significantly, and even further if a suboptimal solution is acceptable to the decision-maker.ion-maker.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.135-144
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• 2020

In graph theory, the least-cost path is discovered by searching the shortest path between a start node and destination node. The least cost is calculated as a one-dimensional value that represents the difference in distance or price between two nodes, and the nodes and edges that comprise the lowest sum of costs between the linked nodes is the shortest path. However, it is difficult to determine the shortest path if each node has multiple attributes because the number of cost types that can appear is equal to the number of attributes. In this paper, a shortest path search scheme is proposed that considers multiple attributes using the Euclidean distance to satisfy various user requirements. In simulation, we discovered that the shortest path calculated using one-dimensional values differs from that calculated using the Euclidean distance for two-dimensional attributes. The user's preferences are reflected in multi attributes and it was different from one-dimensional attribute. Consequently, user requirements could be satisfied simultaneously by considering multiple attributes.

• Journal of The Korean Association For Science Education
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• v.35 no.2
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• pp.267-275
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• 2015

This research purports to find out the characteristics of Korean students cognitive attributes and compare it with that of high-achieving countries who took TIMSS 2011 based on the Cognitive Diagnostic Theory. Based on TIMSS 2011 Science framework, nine cognitive attributes were extracted and the researcher analyzed that 216 of the TIMSS 2011 science items require these attributes. This analysis was conducted to come up with a Q-matrix. After producing the Q-matrix, multi-level IRT was used to figure out each countries' characteristics for each of the cognitive attribute. According to the study results, four attributes, such as 'Use Models,' 'Interpret Information,' 'Draw Conclusions,' and 'Evaluate and justify' were easier attributes for Korean middle school students. However, the other five attributes such as 'Recall/Recognize', 'Explain', 'Classify', 'Integrate', 'Hypothesize and Design' were considered as harder attributes compared to other countries. Korean students also considered 'Interpret Information' as the easiest attributes, and 'Explain' as the hardest attributes of all. For Korean students, those attributes considered to be easy were the easiest and hard attributes as the hardest compared to other countries, showing very extreme cases. Therefore, to give students more meaningful learning experience, it is better to use all the attributes altogether rather than use specific attributes while constructing Science curriculum or textbooks.

• The KIPS Transactions:PartD
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• v.10D no.6
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• pp.927-934
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• 2003

Database query optimates the selectivety of a query to find the most efficient access plan. Multi-dimensional selectivity estimation technique is required for a query with multiple attributes because the attributes are not independent each other. Histogram is practically used in most commercial database products because it approximates data distributions with small overhead and small error rates. However, histogram is inadequate for a query with multiple attributes because it incurs high storage overhead and high error rates. In this paper, we propose a novel method for multi-dimentional selectivity estimation. Compressed information from a large number of small-sized histogram buckets is maintained using the discrete cosine transform. This enables low error rates and low storage overheads even in high dimensions. Extensive experimental results show adventages of the proposed approach.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.101-106
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• 2023

Chronic illnesses are among the most common serious problems affecting human health. Early diagnosis of chronic diseases can assist to avoid or mitigate their consequences, potentially decreasing mortality rates. Using machine learning algorithms to identify risk factors is an exciting strategy. The issue with existing feature selection approaches is that each method provides a distinct set of properties that affect model correctness, and present methods cannot perform well on huge multidimensional datasets. We would like to introduce a novel model that contains a feature selection approach that selects optimal characteristics from big multidimensional data sets to provide reliable predictions of chronic illnesses without sacrificing data uniqueness.[1] To ensure the success of our proposed model, we employed balanced classes by employing hybrid balanced class sampling methods on the original dataset, as well as methods for data pre-processing and data transformation, to provide credible data for the training model. We ran and assessed our model on datasets with binary and multivalued classifications. We have used multiple datasets (Parkinson, arrythmia, breast cancer, kidney, diabetes). Suitable features are selected by using the Hybrid feature model consists of Lassocv, decision tree, random forest, gradient boosting,Adaboost, stochastic gradient descent and done voting of attributes which are common output from these methods.Accuracy of original dataset before applying framework is recorded and evaluated against reduced data set of attributes accuracy. The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy on multi valued class datasets than on binary class attributes.[1]

• Archives of design research
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• no.16
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• pp.17-25
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• 1996

, \l!ernatives in multi dimensional decision prohlems generally possess numerous attrihutes by which they can be describ('d and compared, The ('\';dllation factors include all attributes that have Ic\'(']s specified by quantitative and qualitativc objectil'l'S, Howev'('f since qualitative factors arc difficul! to quantify as num('ral estimates, these factors have tended to bl' ignored without regard for their importance to human contrnl. In this study, the author adapted :j ('va]uation methods with critrria which have qualitative and qualitative attributes: the Intuitive Evaluation ~1cthods the Accumulativc' Evaluation Model the Benchmarking Evaluation Methods, and studied the corrC'iation between them, The results show that Ill(' :j Mrthods have reciprocal relationships under reliability (r=O, (XX)]] In order to removl' obstacles of desi!;n ev'aluation ( lots of timl' l'llnsumption, constr;lints of placc" difficulties of hu!;!' data procc'ssin!;), it is necessary to be developed a new ('va]uation syst('rn which could prov'idc' effective rat in!; of desi!;n v'alm's 10 make value judw'rnents, , \l!ernatives in multi dimensional decision prohlems generally possess numerous attrihutes by which they can be describ('d and compared, The ('\';dllation factors include all attributes that have Ic\'(']s specified by quantitative and qualitativc objectil'l'S, Howev'('f since qualitative factors arc difficul! to quantify as num('ral estimates, these factors have tended to bl' ignored without regard for their importance to human contrnl. In this study, the author adapted :j ('va]uation methods with critrria which have qualitative and qualitative attributes: the Intuitive Evaluation ~1cthods the Accumulativc' Evaluation Model the Benchmarking Evaluation Methods, and studied the corrC'iation between them, The results show that Ill(' :j Mrthods have reciprocal relationships under reliability (r=O, (XX)]] In order to removl' obstacles of desi!;n ev'aluation ( lots of timl' l'llnsumption, constr;lints of placc" difficulties of hu!;!' data procc'ssin!;), it is necessary to be developed a new ('va]uation syst('rn which could prov'idc' effective rat in!; of desi!;n v'alm's 10 make value judw'rnents,alm's 10 make value judw'rnents,

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.1
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• pp.66-71
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• 2007

To adapt environment changing high speed and improve rapidly response ability for variation of environment and reduce delay time of decision making inlet agents, the derivation of user's preference and alternative are required. In this paper, we propose an efficient coordination method of multi-agents based on fuzzy decision making with the solution proposed by agents in the view of Pareto optimality. Our method generates the optimal alternative by using weighted value. We compute importance of attributes of winner agent, then can obtain the priorities lot attributes. The result of our method is analyzed that of Yager's method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.5100-5119
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• 2018

Most of existing privacy-preserving multi-authorities attribute-based encryption schemes (PP-MA-ABE) only considers the privacy of the user identity (ID). However, in many occasions information leakage is caused by the disclosing of his/her some sensitive attributes. In this paper, we propose a collusion-resisting ciphertext-policy PP-MA-ABE (CRPP-MACP-ABE) scheme with hiding both user's ID and attributes in the cloud storage system. We present a method to depict anonymous users and introduce a managerial role denoted by IDM for the management of user's anonymous identity certificate ($AID_{Cred}$). The scheme uses $AID_{Cred}$ to realize privacy-preserving of the user, namely, by verifying which attribute authorities (AAs) obtain the blinded public attribute keys, pseudonyms involved in the $AID_{Cred}$ and then distributes corresponding private keys for the user. We use different pseudonyms of the user to resist the collusion attack launched by viciousAAs. In addition, we utilize IDM to cooperate with multiple authorities in producing consistent private key for the user to avoid the collusion attack launched by vicious users. The proposed CRPP-MACP-ABE scheme is proved secure. Some computation and communication costs in our scheme are finished in preparation phase (i.e. user registration). Compared with the existing schemes, our scheme is more efficient.

• Fashion & Textile Research Journal
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• v.15 no.5
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• pp.753-762
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• 2013

This study identifies meaningful factors that influence relationship switching intention and multi-channel use intention for department store customers. Two types of commitment (affective and calculative commitment) are proposed as mediators between satisfaction and behavioral intention. A web-based survey collected fashion product data from consumers who had a relationship with a department store. A total of 150 responses were analyzed by frequency analysis and reliability analysis, CFA, and SEM analysis with SPSS 18.0 and AMOS 18.0. Department store attributes were composed of four factors (product, salespersons, facilities and place, and price and promotion). Department store satisfaction was specified as a second order latent construct which was reflected in the satisfaction with the four department store attributes. Department store satisfaction had a significant negative influence on calculative commitment and a positive effect on affective commitment. Calculative commitment affected switching intention and affective commitment determined multichannel use intention. This study has implications for retail marketers that target fashion consumers and academics who research consumer behavior in retail settings. The most important result is that the affective commitment and the calculative commitment serve different functions. Department store managers need to make an increased effort to instigate an affective customer commitment in order to reduce switching intention.