• Journal of Information Technology Services
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• v.11 no.1
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• pp.211-221
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• 2012

Many metric-based classification models have been proposed to predict fault-proneness of software module. This paper presents two prediction models using Bayesian classifier which is one of the most popular modern classification algorithms. Bayesian model based on Bayesian probability theory can be a promising technique for software quality prediction. This is due to the ability to represent uncertainty using probabilities and the ability to partly incorporate expert's knowledge into training data. The two models, Na$\ddot{i}$veBayes(NB) and Bayesian Belief Network(BBN), are constructed and dimensionality reduction of training data and test data are performed before model evaluation. Prediction accuracy of the model is evaluated using two prediction error measures, Type I error and Type II error, and compared with well-known prediction models, backpropagation neural network model and support vector machine model. The results show that the prediction performance of BBN model is slightly better than that of NB. For the data set with ambiguity, although the BBN model's prediction accuracy is not as good as the compared models, it achieves better performance than the compared models for the data set without ambiguity.

• Agricultural and Biosystems Engineering
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• v.4 no.1
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• pp.1-7
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• 2003

Internal defects such as browning of the flesh and blackening and rot of the ovary of pear can be easily developed because of the inadequate environmental conditions during the storage and distribution of fruit. The quality assurance system for the agricultural product is to be settled in Korea. All defected agricultural products should be excluded prior to the distribution to enhance the commercial values. However, early stage on-line defect detection of agricultural product is very difficult and even more difficult in a case of the internal defects. The goal of this research is to develop a system that can detect and classify internal defects of agricultural produce on-line using VIS/NIR transmittance spectroscopy. And Shingo pear, which is one of the famous species of Korean pear, was used for the experiment. Soft independence modeling of class analogy (SIMCA) algorithm was employed to analyze the transmittance spectroscopic data qualitatively. On-line classification system was constructed and classification model was developed and validated. As a result, the correct classification rate (CCR) using the developed classification model was 96.1 ％.

• Journal of Korean Society of Water and Wastewater
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• v.26 no.5
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• pp.637-647
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• 2012

Recent series of flooding events in urban area has brought a growing concern on storage facilities as a major stormwater management method. The Korean Ministry of Environment has announced diverse plans to tackle the problem, including plans for multi-purpose storages which deal both the stormwater and wastewater. Even though storages can be categorized for different perspectives, classification of possible storages in urban area has not been throughly studied so far. This study investigated diverse references of urban storages and suggested systematic classifications on structural, functional and some other basis. Structural classification mainly concerns structural shape of facilities and includes (1)Cisterns & Rain barrels, (2)Forebays, (3)Dry basins, (4)Wet basins and (5)Constructed wetland. Those functions can be (1)flood prevention (2)water quality control and (3)reuse of stored water. Other criteria that categorize storages depend on (1)height, (2)location, (3)configuration, (4)depth, (5)site of the installation and (6)shape.

• International Journal of Quality Innovation
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• v.3 no.2
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• pp.113-131
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• 2002

The relationships among multi-dimensional data (such as medical examination data) with ambiguity and variation are difficult to explore. The traditional approach to building a data classification system requires the formulation of rules by which the input data can be analyzed. The formulation of such rules is very difficult with large sets of input data. This paper first describes two classification approaches using back-propagation (BP) neural network and Mahalanobis distance (MD) classifier, and then proposes two classification approaches for multi-dimensional feature selection. The first one proposed is a feature selection procedure from the trained back-propagation (BP) neural network. The basic idea of this procedure is to compare the multiplication weights between input and hidden layer and hidden and output layer. In order to simplify the structure, only the multiplication weights of large absolute values are used. The second approach is Mahalanobis-Taguchi system (MTS) originally suggested by Dr. Taguchi. The MTS performs Taguchi's fractional factorial design based on the Mahalanobis distance as a performance metric. We combine the automatic thresholding with MD: it can deal with a reduced model, which is the focus of this paper In this work, two case studies will be used as examples to compare and discuss the complete and reduced models employing BP neural network and MD classifier. The implementation results show that proposed approaches are effective and powerful for the classification.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.443-451
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• 2021

The classification standards for hotels in Vietnam are different from many other hotel classification standards in the world. This study aims to analyze customer reviews on the TripAdvisor website to develop a new algorithm for hotel rating that is independent of Vietnam's hotel classification standards. This method can be applied to individual hotels, or hotels of a region or the whole country, while online booking sites only rate individual hotels. Data was crawled from TripAdvisor with 22,287 reviews of 5 cities in Vietnam. This study used a statistical model to analyze the review dataset and build an algorithm to rate hotels according to aspects or hotel overall. The results have less rating deviation when compared to the TripAdvisor system. This study also supports hotel managers to regularly update the status of their hotels using data from customer reviews, from which, managers can strategize long-term solutions to improve the quality of the hotel in all aspects and attract more travelers to Vietnam. Moreover, this method can be developed into an automatic system to rate hotels and update the status of service quality more quickly, thus, saving time and costs.

• The Journal of Information Systems
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• v.27 no.2
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• pp.175-196
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• 2018

Purpose The purpose of this study is to identify problems of current educational facility data management and recommend a standardized terminology classification system as a solution. In addition, the research aims to present a preemptive and integrated disaster and safety management framework for educational facilities by seeking efficient business processes through secured data quality, systematic data management, and external data linkage and analysis. Design/methodology/approach A terminology classification system has been established through various processes including filtering and analysis of related data including laws, manuals, educational facilities accidents, and historical records. Furthermore, the terminology classification system has been further reviewed through several consultations with experts and practitioners. In addition, the accumulated data was refined according to the established standard terminology and an Excel database was developed. Based on the data, accident patterns occurred in educational facilities over the past 10 years were analyzed. Findings In the study, a template was developed to collect consistent data for the standardized disaster and safety management terminology classification system in educational facilities. In addition, the standardized data utilization methods are presented from the viewpoint of 'education facility disaster safety data management', 'data analysis and insight', 'business management through data', and 'leaping into big data management'.

• Quality Improvement in Health Care
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• v.12 no.2
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• pp.84-92
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• 2006

Background : To examine the difference between ICD-10 and The Korean standard classification of disease(oriental medicine), and to aim at improve the practical use as statistical data. It is one of the reason of disease classification. On that account we convert the many to many correspondence presenting classification of oriental medicine into many to one correspondence. Method : The study tracked out 155 patients discharged from the university hospital which is located in Gyeonggi Province and managing hospital and oriental medicine hospital from July to October this year. The period of this study was from August 1 to November 18. We compared correspondence between the two services' diagnosis(hospital services and oriental medicine hospital services) at the same time and attempted many to one correspondence classification. That is for production of statistical data. Result : We investigated the group which have had medical treatment experience of two kinds of services at the same time. The result of this investigation was that the same oriental medicine diagnosis used differently in western medicine diagnosis. 44.5% was accorded with western medicine diagnosis. Correspondence of the western medicine diagnose with the top of the Korean standard classification of disease(oriental medicine) list's western medicine diagnosis was 13.5%. For many to one correspondence classification for statistics, one western medicine diagnosis was selected for one oriental medicine diagnosis. In case of the main diagnosis(I sign) was not enough to explain oriental medicine diagnosis' characteristic, we chose multiple other diagnosis, so other diagnosis(II sign) about patient's cause of disease could be selected for supplement after we examined the patient's records. The statistics was possible with this many to one correspondence. Conclusion : The result of this study about correspondence between western medicine diagnoses and those of oriental medicine confirms that The Korean standard classification of disease(oriental medicine) is hard to be standardized with western medicine diagnosis. Therefore, according to this study, we use new many to one correspondence classification, multiple oriental medicine diagnoses with one ICD-10, which can be used by statistical data.

• Journal of Embryo Transfer
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• v.30 no.3
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• pp.161-170
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• 2015

These studies were conducted to establish the practical Hanwoo (Korean native cattle) improvement system through the combining of embryo transfer technology and confirming individual Hanwoo oocyte culture system and to investigate that correlation of Hanwoo carcass classification (intramuscular marbling) and oocyte donor for blastocyst production in vitro. In case of Hanwoo, the carcass meat quality grades were divided to grade $1^{{+}{+}}$, $1^{+}$, 1, 2, and 3 depends on fat distribution of longest muscle cross-sectional surface. As results, the numbers of follicular oocytes collected from individual fundamentally-registered Hanwoo yielded $1^{{+}{+}}$, $1^{+}$, 1, 2 and 3 meat quality were 9.5, 9.4, 8.5, 8.8 and 8.8 per ovary, respectively. The numbers of retrieval oocyte from follicles were significantly higher in the cattle yield $1^{{+}{+}}$, $1^{+}$ meat quality than in the cattle yield 1, 2 and 3 meat quality (p<0.05). The rates of blastocyst formation were 18.2, 21.3, 29.4, 30.9, and 31.5% in the cattle yield $1^{{+}{+}}$, $1^{+}$, 1, 2 and 3 meat quality of after in vitro maturation, respectively. It was significantly lower in the cattle yield $^{{+}{+}}$ and $1^{+}$ meat quality than in the cattle yield 1, 2 and 3 meat quality (p<0.05). In order to evaluate embryos quality, TUNNEL assay was conducted for each meat quality grade using blastocyst stage embryo on day 8. The results showed that apoptosis cell number was higher tendency in the cattle yield $1^{{+}{+}}$and $1^{+}$ meat quality (81 and 79, respectively) than in the cattle yield 1, 2 and 3 meat quality (51, 48 and 50, respectively) but there was no statistical significance in each group. After embryo transfer, the conception rate of recipient was 53.5 (23 out of 43), 52.1 (38 out of 73) and 58.0% (58 out of 100) in the meat quality of 1, $1^{+}$ and $1^{{+}{+}}$, respectively. These results showed that the conception rate was significantly higher in the cattle yield 1 meat quality than in the cattle yield $1^{{+}{+}}$, $1^{+}$, 2, and 3 meat quality (p<0.05). In summary, these results indicate that the application of confirming Hanwoo individual oocyte culture system and embryo transfer technology can make good use of the genetic resources conservation and improvement of Hanwoo. Relevance of the meat quality grade and reproductive ability of carcasses of Hanwoo will be considered to be one of the effective means for the associated research with obesity and reproduction.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.1
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• pp.27-38
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• 2017

Packet classification is one of the essential functionalities of Internet routers in providing quality of service. Since the arrival rate of input packets can be tens-of-millions per second, wire-speed packet classification has become one of the most challenging tasks. While traditional packet classification only reports a single matching result, new network applications require multiple matching results. Ternary content-addressable memory (TCAM) has been adopted to solve the multi-match classification problem due to its ability to perform fast parallel matching. However, TCAM has a fundamental issue: high power dissipation. Since TCAM is designed for a single match, the applicability of TCAM to multi-match classification is limited. In this paper, we propose a cost- and energy-efficient multi-match classification architecture that combines TCAM with a tuple space search algorithm. The proposed solution uses two small TCAM modules and requires a single-cycle TCAM lookup, two SRAM accesses, and several Bloom filter query cycles for multi-match classifications.

• Korean Journal of Remote Sensing
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• v.34 no.4
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• pp.671-680
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• 2018

Compared to the imagery produced by traditional satellites, PlanetScope satellite imagery has made it possible to easily capture remotely-sensed imagery every day through dozens or even hundreds of satellites on a relatively small budget. This study aimed to detect changed areas and update a land cover map using a PlanetScope image. To generate a classification map, pixel-based Random Forest (RF) classification was performed by using additional features, such as the Normalized Difference Water Index (NDWI) and the Normalized Difference Vegetation Index (NDVI). The classification result was converted to vector data and compared with the existing land cover map to estimate the changed area. To estimate the accuracy and trends of the changed area, the quantitative quality of the supervised classification result using the PlanetScope image was evaluated first. In addition, the patterns of the changed area that corresponded to the classification result were analyzed using the PlanetScope satellite image. Experimental results found that the PlanetScope image can be used to effectively to detect changed areas on large-scale land cover maps, and supervised classification results can update the changed areas.