• 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.

• The Korean Journal of Nuclear Medicine
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• v.34 no.1
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• pp.62-73
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• 2000

Purpose: Esophageal cancer patients have a difficulty in the intake of meals through the blocked esophageal lumen, which is caused by an ingrowth of cancer cells and largely influences on the prognosis. It is reported that esophageal cancer has a very low survival rate due to the lack of nourishment and immunity as the result of this. In this study a new radioactive stent, which prevents tumor ingrowth and restenosis by additional radiation treatment, has been developed. Materials and Methods: Using ${\ulcorner}HANARO{\lrcorner}$ research reactor, the radioactive stent assembly ($^{166}Ho$-SA) was prepared by covering the metallic stent with a radioactive sleeve by means of a post-irradiation and pre-irradiation methods. Results: Scanning electron microscopy and autoradiography exhibited that the distribution of $^{165/166}Ho\;(NO_3)$ compounds in polyurethane matrix was homogeneous. A geometrical model of the esophagus considering its structural properties, was developed for the computer simulation of energy deposition to the esophageal wall. The dose distributions of $^{166}Ho$-stent were calculated by means of the EGS4 code system. The sources are considered to be distributed uniformly on the surface in the form of a cylinder with a diameter of 20 mm and length of 40 mm. As an animal experiment, when radioactive stent developed in this study was inserted into the esophagus of a Mongrel dog, tissue destruction and widening of the esophageal lumen were observed. Conclusion: We have developed a new radioactive stent comprising of a radioactive tubular sleeve covering the metallic stent, which emits homogeneous radiation. If it is inserted into the blocked or narrowed lumen, it can lead to local destruction of the tumor due to irradiation effect with dilatation resulting from self-expansion of the metallic property. Accordingly, it is expected that restenosis esophageal lumen by the continuous ingrowth and infiltration of cancer after insertion of our radioactive stent will be decreased remarkably.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.107-120
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• 2014

This study proposes a novel recommender system using the structural hole analysis to reflect qualitative and emotional information in recommendation process. Although collaborative filtering (CF) is known as the most popular recommendation algorithm, it has some limitations including scalability and sparsity problems. The scalability problem arises when the volume of users and items become quite large. It means that CF cannot scale up due to large computation time for finding neighbors from the user-item matrix as the number of users and items increases in real-world e-commerce sites. Sparsity is a common problem of most recommender systems due to the fact that users generally evaluate only a small portion of the whole items. In addition, the cold-start problem is the special case of the sparsity problem when users or items newly added to the system with no ratings at all. When the user's preference evaluation data is sparse, two users or items are unlikely to have common ratings, and finally, CF will predict ratings using a very limited number of similar users. Moreover, it may produces biased recommendations because similarity weights may be estimated using only a small portion of rating data. In this study, we suggest a novel limitation of the conventional CF. The limitation is that CF does not consider qualitative and emotional information about users in the recommendation process because it only utilizes user's preference scores of the user-item matrix. To address this novel limitation, this study proposes cluster-indexing CF model with the structural hole analysis for recommendations. In general, the structural hole means a location which connects two separate actors without any redundant connections in the network. The actor who occupies the structural hole can easily access to non-redundant, various and fresh information. Therefore, the actor who occupies the structural hole may be a important person in the focal network and he or she may be the representative person in the focal subgroup in the network. Thus, his or her characteristics may represent the general characteristics of the users in the focal subgroup. In this sense, we can distinguish friends and strangers of the focal user utilizing the structural hole analysis. This study uses the structural hole analysis to select structural holes in subgroups as an initial seeds for a cluster analysis. First, we gather data about users' preference ratings for items and their social network information. For gathering research data, we develop a data collection system. Then, we perform structural hole analysis and find structural holes of social network. Next, we use these structural holes as cluster centroids for the clustering algorithm. Finally, this study makes recommendations using CF within user's cluster, and compare the recommendation performances of comparative models. For implementing experiments of the proposed model, we composite the experimental results from two experiments. The first experiment is the structural hole analysis. For the first one, this study employs a software package for the analysis of social network data - UCINET version 6. The second one is for performing modified clustering, and CF using the result of the cluster analysis. We develop an experimental system using VBA (Visual Basic for Application) of Microsoft Excel 2007 for the second one. This study designs to analyzing clustering based on a novel similarity measure - Pearson correlation between user preference rating vectors for the modified clustering experiment. In addition, this study uses 'all-but-one' approach for the CF experiment. In order to validate the effectiveness of our proposed model, we apply three comparative types of CF models to the same dataset. The experimental results show that the proposed model outperforms the other comparative models. In especial, the proposed model significantly performs better than two comparative modes with the cluster analysis from the statistical significance test. However, the difference between the proposed model and the naive model does not have statistical significance.

• Resources Recycling
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• v.17 no.4
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• pp.21-29
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• 2008

Waste shell powder was added to the high density polyethylene(HDPE), and resultant mechanical properties and flame retardancy were analyzed in terms of shell content. Compatibilizer(PE-g-MA) was used to enhance the mechanical properties of the prepared HDPE/shell composites, and several flame retardant agents($Al_2O_3$, $Sb_2O_3$) were utilized to improve flame retardancy. Addition of the compatibilizer resulted in an improved mechanical properties due to the increased interfacial bonding between HDPE matrix and shell powder. In the case of impact strength, it even reached to the impact strength of pure HDPE. Also the addition of the flame retardant agents did not exhibit mechanical property decrease. UL-94 flammability test on the prepared HDPE/shell composites indicated that at 40wt% of shell only inclusion, time to ignite the flame and the total time of flame duration increased. When flame retardant agents mixed with shell powder were added to the HDPE matrix, improved flame retardancy was observed. Generally, flame retardancy effect of $Al_2O_3$ was better than $Sb_2O_3$. UL-94 V-0 classification was observed for the specimens with $Al_2O_3$ and compatibilizer at more than 40wt% shell, and also for specimens with $Sb_2O_3$ and compatibilizer at all shell content.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.2
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• pp.239-247
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• 2023

The FIDIC Red Book is an international standard contract condition in which the Employer designs and the Contractor performs the construction. The Engineer of FIDIC Red Book shall agree or determine any matter or Claim in accordance with Sub-Clause 3.7 neutrally, not as an agent of the Employer. This study aimed to derive Key Risk Sub-Clauses out of 49 Sub-Clauses that the Engineer of FIDIC Red Book recently revised in 18 years shall agree or determine according to Sub-Clause 3.7 using the Delphi method. A panel of 35 experts with more than 10 years of experience and expertise in international construction contracts was formed, and through total three Delphi surveys, errors and biases were prevented in the judgment process to improve reliability. As for the research method, 49 Sub-Clauses that engineers shall agree on or determine according to Sub-Clause 3.7 of the FIDIC Red Book were investigated through the analysis of contract conditions. In order to evaluate the probability and impact of contractual risk for each 49 Sub-Clause, the Delphi survey conducted repeatedly a closed-type survey three times on a Likert 10-point scale. The results of the first Delphi survey were delivered during the second survey, and the results of the second survey were delivered to the third survey, which was re-evaluated in the direction of increasing the consensus of experts' opinions. The reliability of the Delphi 3rd survey results was verified with the COV value of the coefficient of variation. The PI Risk Matrix was applied to the average value of risk probability and impact of each of the 49 Sub-Clauses and finally, 9 Key Risk Sub-Clauses that fell within the extreme risk range were derived.

• Korean Journal of Construction Engineering and Management
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• v.24 no.2
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• pp.59-69
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• 2023

FIDIC White Book is a Model Services Agreement between the Client and the Consultant. This study aimed to derive the Key Risk Sub-Clauses out of 63 Sub-Clauses of General Conditions of the FIDIC White Book by using the Delphi technique. A panel of 40 experts with more than 10 years of experience and expertise in overseas construction services agreements and FIDIC White Book was formed, and the reliability was improved in the direction of increasing the consensus of experts through a total of three Delphi survey processes. In the first Delphi survey, a closed-type survey was conducted on the impact of risk among 63 Sub-Clauses of General Conditions on a Likert 5-point scale, and 26 main risk Sub-Clauses were derived. The Content Validity of the results of the first Delphi survey was verified with the CVR value. In the 2nd and 3rd Delphi surveys, a closed-type survey was conducted on a Likert 10-point scale for 26 main risk Sub-Clauses and the risk possibility and impact of each main risk Sub-Clause were evaluated. The reliability of the 3rd Delphi survey result was verified with the COV value. Total 14 Key Risk Sub-Clauses were derived by applying the average risk possibility and impact of each of the 26 main risk Sub-Clauses to the PI Risk Matrix. The results of deriving Key Risk Sub-Clauses showed that agreement on specific scope of service, delay management, and change management were the most important. As a result of this study, from a practical point of view, consultants of consulting companies provide guidelines that should be reviewed to minimize contractual risks when signing service contracts with clients. From an academic point of view, the direction of research on deriving key risks related to service contracts for consultants participating in overseas construction is presented.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.1
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• pp.83-89
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• 2010

Purpose: The Wide Beam Reconstruction (WBR) algorithms that UltraSPECT, Ltd. (U.S) has provides solutions which improved image resolution by eliminating the effect of the line spread function by collimator and suppression of the noise. It controls the resolution and noise level automatically and yields unsurpassed image quality. The aim of this study is WBR of whole body bone scan in usefulness of clinical application. Materials and Methods: The standard line source and single photon emission computed tomography (SPECT) reconstructed spatial resolution measurements were performed on an INFINA (GE, Milwaukee, WI) gamma camera, equipped with low energy high resolution (LEHR) collimators. The total counts of line source measurements with 200 kcps and 300 kcps. The SPECT phantoms analyzed spatial resolution by the changing matrix size. Also a clinical evaluation study was performed with forty three patients, referred for bone scans. First group altered scan speed with 20 and 30 cm/min and dosage of 740 MBq (20 mCi) of $^{99m}Tc$-HDP administered but second group altered dosage of $^{99m}Tc$-HDP with 740 and 1,110 MBq (20 mCi and 30 mCi) in same scan speed. The acquired data was reconstructed using the typical clinical protocol in use and the WBR protocol. The patient's information was removed and a blind reading was done on each reconstruction method. For each reading, a questionnaire was completed in which the reader was asked to evaluate, on a scale of 1-5 point. Results: The result of planar WBR data improved resolution more than 10%. The Full-Width at Half-Maximum (FWHM) of WBR data improved about 16% (Standard: 8.45, WBR: 7.09). SPECT WBR data improved resolution more than about 50% and evaluate FWHM of WBR data (Standard: 3.52, WBR: 1.65). A clinical evaluation study, there was no statistically significant difference between the two method, which includes improvement of the bone to soft tissue ratio and the image resolution (first group p=0.07, second group p=0.458). Conclusion: The WBR method allows to shorten the acquisition time of bone scans while simultaneously providing improved image quality and to reduce the dosage of radiopharmaceuticals reducing radiation dose. Therefore, the WBR method can be applied to a wide range of clinical applications to provide clinical values as well as image quality.