• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.129-142
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• 2016

Customer product reviews have become one of the important factors for purchase decision makings. Customers believe that reviews written by others who have already had an experience with the product offer more reliable information than that provided by sellers. However, there are too many products and reviews, the advantage of e-commerce can be overwhelmed by increasing search costs. Reading all of the reviews to find out the pros and cons of a certain product can be exhausting. To help users find the most useful information about products without much difficulty, e-commerce companies try to provide various ways for customers to write and rate product reviews. To assist potential customers, online stores have devised various ways to provide useful customer reviews. Different methods have been developed to classify and recommend useful reviews to customers, primarily using feedback provided by customers about the helpfulness of reviews. Most shopping websites provide customer reviews and offer the following information: the average preference of a product, the number of customers who have participated in preference voting, and preference distribution. Most information on the helpfulness of product reviews is collected through a voting system. Amazon.com asks customers whether a review on a certain product is helpful, and it places the most helpful favorable and the most helpful critical review at the top of the list of product reviews. Some companies also predict the usefulness of a review based on certain attributes including length, author(s), and the words used, publishing only reviews that are likely to be useful. Text mining approaches have been used for classifying useful reviews in advance. To apply a text mining approach based on all reviews for a product, we need to build a term-document matrix. We have to extract all words from reviews and build a matrix with the number of occurrences of a term in a review. Since there are many reviews, the size of term-document matrix is so large. It caused difficulties to apply text mining algorithms with the large term-document matrix. Thus, researchers need to delete some terms in terms of sparsity since sparse words have little effects on classifications or predictions. The purpose of this study is to suggest a better way of building term-document matrix by deleting useless terms for review classification. In this study, we propose neutrality index to select words to be deleted. Many words still appear in both classifications - useful and not useful - and these words have little or negative effects on classification performances. Thus, we defined these words as neutral terms and deleted neutral terms which are appeared in both classifications similarly. After deleting sparse words, we selected words to be deleted in terms of neutrality. We tested our approach with Amazon.com's review data from five different product categories: Cellphones & Accessories, Movies & TV program, Automotive, CDs & Vinyl, Clothing, Shoes & Jewelry. We used reviews which got greater than four votes by users and 60% of the ratio of useful votes among total votes is the threshold to classify useful and not-useful reviews. We randomly selected 1,500 useful reviews and 1,500 not-useful reviews for each product category. And then we applied Information Gain and Support Vector Machine algorithms to classify the reviews and compared the classification performances in terms of precision, recall, and F-measure. Though the performances vary according to product categories and data sets, deleting terms with sparsity and neutrality showed the best performances in terms of F-measure for the two classification algorithms. However, deleting terms with sparsity only showed the best performances in terms of Recall for Information Gain and using all terms showed the best performances in terms of precision for SVM. Thus, it needs to be careful for selecting term deleting methods and classification algorithms based on data sets.

• Resources Recycling
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• v.14 no.2
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• pp.3-9
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• 2005

A study on the air and gravity separation has been performed for the removal of chlorine containing materials from ASR of end-of-life vehicles. The gravity separation was also conducted on waste plastics collected from ASR. In this work, ASR were previously shredded to pass through 8 mm sieve prior to separation tests and the gravity separation of waste plastics was conducted for three different particle sizes. The two-stage air classification was conducted with the range of air flow rate of 9~20 M$^3$/hr at first stage and 25~34 M$^3$/hr at second stage, respectively. The fraction of overflow product was remarkably increased in the 2nd stage air classification because of high air flow rate while that of underflow product obtained from 1st stage air classification was found to be 62~66%. From the results of gravity separation on waste plastics, it was also found that the amount of the float product was much greater than sink product. It is believed that the gravity separation may be used very efficiently for the removal of calorine bearing materials from waste plastics.

• International journal of advanced smart convergence
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• v.11 no.1
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• pp.111-116
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• 2022

After COVID-19, the number of people with sleep disorders around the world is increasing. In particular, in the flow of the 4th industrial revolution, the differentiation of types and characteristics of the sleep industry is accelerating. Therefore, in this study, the characteristics of each type of sleep-related industry were reclassified from an industrial point of view, and based on this, an attempt was made to review the classification system that can help companies develop sleep products and improve related national systems. Based on the 10th standard industry classification, we compared input cost, value, and usability and analyzed common characteristics, treatments, and preventive effects based on this. A comprehensive taxonomy using matrix analysis was reviewed. As a result, in terms of cost (A), the most common sleeping products are general mattresses and general bedding. It is an IOT device (auxiliary device), and the value aspect (B, B/D) included sleep cafe, bedding rental and management service, and sleep consulting. In terms of utility (A/B), a total of 6 product groups including sleep aids (health functional foods) belong to this category, and in terms of treatment (A/C), a total of 3 product groups including sleep clinics (medical services) belong to this category. As for the product group (A/D) with both properties, it was found that non-insurance sleep treatment medical devices, sleep-related over-the-counter drugs, and some sleep monitoring applications belong to this category. Ultimately, it was found that the sleep industry classification enables the most active product development and composition according to the relative relationship between cost and utility, and treatment and utility. appeared to be necessary.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.203-214
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• 2023

The Korea Customs Service is efficiently handling business with an electronic customs system that can effectively handle one-stop business. This is the case and a more effective method is needed. Import and export require HS Code (Harmonized System Code) for classification and tax rate application for all goods, and item classification that classifies the HS Code is a highly difficult task that requires specialized knowledge and experience and is an important part of customs clearance procedures. Therefore, this study uses various types of data information such as product name, product description, and product image in the item classification request form to learn and develop a deep learning model to reflect information well based on Multimodal representation learning. It is expected to reduce the burden of customs duties by classifying and recommending HS Codes and help with customs procedures by promptly classifying items.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.713-716
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• 2005

In this paper, in order to identify and recognize attack patterns, we propose a Bayesian classification using frequent patterns. In theory, Bayesian classifiers guarantee the minimum error rate compared to all other classifiers. However, in practice this is not always the case owing to inaccuracies in the unrealistic assumption{ class conditional independence) made for its use. Our method addresses the problem of attribute dependence by discovering frequent patterns. It generates frequent patterns using an efficient FP-growth approach. Since the volume of patterns produced can be large, we propose a pruning technique for selection only interesting patterns. Also, this method estimates the probability of a new case using different product approximations, where each product approximation assumes different independence of the attributes. Our experiments show that the proposed classifier achieves higher accuracy and is more efficient than other classifiers.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.91-94
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• 2001

The exponential growth of the Internet usage has motivated the launching of many commercial business web sites. Internet as a purchasing medium shows several unique characteristics because of its customer- driven technologies and absence of physical products. Thus, new commercial medium provokes a reclassification of products. Twenty five types of commercial Products are empirically tested in the Internet retailing and found to be grouped into four categories. This classification framework is investigated in the view of involvement and web technology Furthermore, this paper proposes four business web implementation strategies - impressive, simple, sensory, and semantic - based on the product classification. Proposed guidelines on business web might increase customer satisfaction.

• Industrial Engineering and Management Systems
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• v.2 no.1
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• pp.35-44
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• 2003

Recovery of end-of-life (EOL) products is an environmentally and economically sound way to achieve many of the goals of sustainable development. Many product recovery systems are dependent upon destructive disassembly such as shredding, which undesirably causes a large volume of shredder dust and makes parts reuse impossible. Although non-destructive disassembly has been considered as an alternative for solving the problems, the classification of disassembled items has not been sufficiently investigated. In this paper, we propose a model that mathematically optimizes the disassembly and classification of EOL products. Based on the AND/OR graph that illustrates all possible disassembly sequences of a given product, we identify the physical properties that are considered as constraints in the model. As a result of the solution procedure, the recovery problem can be transformed into a mixed integer linear programming (MILP) model. We show an example that illustrates the concept of our model.

• Journal of the Korea Fashion and Costume Design Association
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• v.4 no.3
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• pp.33-42
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• 2002

The purpose of this study was to find out criteria for classifying fashion brand from consumer point of view in order to develop strategy of fashion brands and to manage brand effectively and systematically, and to suggest theoretical frame for application of these criteria. Survey was used as a research method. Subject were 422 age of 20-30 women living in and near Seoul. Questionnaires was developed to based on 37 classification criteria, and SPSS package program were used to analyze data. The results of this study were as follows: First, factor analysis considering 37 classification criteria identified 8 factors as classification criteria. They were the level of brand form, the level of product concept, the level of management item, the level of brand sales ability, the level of customer management, the level of brand advertizing and awareness, the level of brand value, the level of product lead ability. Second, the most important factor was the level of customer management, but comparatively factor of the level of brand sales ability the level of brand value was less important. Third, consumer cognized difference of criteria for classifying fashion brands. And the level of product lead ability was the most important factor in women's wear category and the level of brand form was in general casual wear category.

• Journal of the Korean Society of Costume
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• v.56 no.6 s.105
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• pp.153-167
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• 2006

This study was purposed to analyse the registration of Korean Fashion brands in China and to point out problems by the registration of analogous trademarks to Korean brand names. List of total 137 Korean national brands were used to collect trademarks in Chinese Trademark Office and each trademark was analysed by the nationally and the date of registration. Analogy of registered trademarks were classified by the common traits. In Result, only 61 Korean national brands were registered by Korean fashion firms in China and 37 Korean national brands were registered by Chinese firms or individuals in the same product classification or in the similar fashion product classification. And 22 Korean national brands out of 61 registered by Korean firms were also registered by Chinese firms, which may lead confusion and misidentification to Chinese consumers. Pre-registration by the Chinese firms f9r analogous or identical trademarks of the Korean fashion brand names in analogous product classification should be a serious entry barrier to Chinese market.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.82-84
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• 2021

Through the smart factory construction project, sensor data such as power, vibration, pressure, and temperature are collected from production facilities, and services such as predictive maintenance, defect prediction, and abnormality detection are developed through data analysis. In general, in the case of manufacturing data, because the imbalance between normal and abnormal data is extreme, an anomaly detection service is preferred. In this paper, FFT method is used to extract feature data of manufacturing data as a pre-stage of the anomaly detection service development. Using this method, we classified the produced products and confirmed results. In other words, after FFT of the representative pattern for each product, we verified whether product classification was possible or not, by calculating correlation coefficient.