• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.95-108
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• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.

• Applied Biological Chemistry
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• v.50 no.2
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• pp.95-100
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• 2007

The cellulase gene of Bacillus licheniformis K11 which has plant growth-promoting activity by auxin and antagonistic ability by siderophore was cloned in pUC18 using PCR employing heterologous primers. The 1.6kb PCR fragment contained the full sequence of the cellulase gene, denoted celW which has been reported to encode a 499 amino acid protein. Similarity search in protein data base revealed that the cellulase from B. licheniformis K11 was more than 97% identical in amino acid sequence to those of various Bacillus spp. The cellulase protein from B. licheniformis K11, overproduced in E. coli DH5${\alpha}$ by the lac promoter on the vector, had apparent molecular weight of 55 kDa upon CMC-SDS-PAGE analysis. The protein not only had enzymatic activity toward carboxymethyl-cellulose (CMC), but also was able to degrade insoluble cellulose, such as Avicel and filter paper (Whatman$^{\circledR}$ No. 1). In addition, the cellulase could degrade a fungal cell wall of Phytophthora capsici. Consequently B. licheniformis K11 was able to suppress the peperblight causing P. capsici by its cellulase. Biochemical analysis showed that the enzyme had a maximum activity at 60$^{\circ}C$ and pH 6.0. Also, the enzyme activity was activated by Co$^{2+}$ of Mn$^{2+}$ but inhibited by Fe$^{3+}$ or Hg$^{2+}$. Moreover, enzyme activity was not inhibited by SDS or sodium azide.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.1
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• pp.25-36
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• 2008

Eye localization means localization of the center of the pupils, and is necessary for face recognition and related applications. Most of eye localization methods reported so far still need to be improved about robustness as well as precision for successful applications. In this paper, we propose a robust eye localization method using multi-scale Gabor feature vectors without big computational burden. The eye localization method using Gabor feature vectors is already employed in fuck as EBGM, but the method employed in EBGM is known not to be robust with respect to initial values, illumination, and pose, and may need extensive search range for achieving the required performance, which may cause big computational burden. The proposed method utilizes multi-scale approach. The proposed method first tries to localize eyes in the lower resolution face image by utilizing Gabor Jet similarity between Gabor feature vector at an estimated initial eye coordinates and the Gabor feature vectors in the eye model of the corresponding scale. Then the method localizes eyes in the next scale resolution face image in the same way but with initial eye points estimated from the eye coordinates localized in the lower resolution images. After repeating this process in the same way recursively, the proposed method funally localizes eyes in the original resolution face image. Also, the proposed method provides an effective illumination normalization to make the proposed multi-scale approach more robust to illumination, and additionally applies the illumination normalization technique in the preprocessing stage of the multi-scale approach so that the proposed method enhances the eye detection success rate. Experiment results verify that the proposed eye localization method improves the precision rate without causing big computational overhead compared to other eye localization methods reported in the previous researches and is robust to the variation of post: and illumination.

• Journal of Life Science
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• v.31 no.11
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• pp.1028-1036
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• 2021

Firefly luciferase (FLuc) can function as an efficient marker in the gene and viral therapies. Nonetheless, its clinical translation has been unaccomplished with the concerns on its exogenous nature and the similarity with human fatty acyl-CoA synthetase. In this study, we aimed to show safety of FLuc by conducting a set of preclinical experiments and a human use. Initially, FLuc permeability across the plasma membrane was investigated by delivering the FLuc-carrying viral vector, OTS-412, or the FLuc recombinant protein. After in vitro infection of OTS-412 into different cancer cell lines, FLuc activity was detected only in the cell lysates, but not in culture media. In addition, recombinant FLuc protein further showed the impermeability against the plasma membrane. Similar result was also observed in the in vivo experiment. After being injected into the VX2 tumor-bearing rabbit, the FLuc exclusively resided within the tumor tissue without being detected in the blood plasma or other organs. Human cancer cell lines originated from various organs were lysed and treated to the FLuc, and none of the human substrates was reactive against the FLuc. As a final step, FLuc recombinant protein was intravenously injected into a human. The luciferase was degraded with the half-life of 20 to 30 minutes in blood, and was untraceable from 1.5 hr after the injection. In addition, the blood plasma was nonresponsive against the fatty acids. Hematological analysis was also comparable between the pre- and post-injection. Altogether, our study collectively demonstrates the safety of the firefly luciferase.