• Journal of the Korea Society of Computer and Information
• /
• v.17 no.4
• /
• pp.163-172
• /
• 2012

Modern society, various great strength crimes are producing. After all crimes happen, it is most important that prevent crime beforehand than that cope. So, many research studied to prevent various crime. However, existing method of studies are to analyze and prevent by society and psychological factors. Therefore we wishes to achieve research to forecast crime by time using Markov chain method. We embody modelling for crime occurrence estimate by crime type time using crime occurrence number of item data that is collected about 5 great strength offender strength, murder, rape, moderation, violence. And examined propriety of crime occurrence estimate modelling by time that propose in treatise that compare crime occurrence type crime occurrence estimate price and actuality occurrence value. Our proposed crime occurrence estimate techniques studied to apply maximum value by critcal value about great strength crime such as strength, murder, rape etc. actually, and heighten crime occurrence estimate probability by using way to apply mean value about remainder crime in this paper. So, we wish to more study about wide crime case and as the crime occurrence estimate rate and actuality value by time are different in crime type hereafter applied examples investigating.

• IE interfaces
• /
• v.13 no.3
• /
• pp.503-511
• /
• 2000

The main theme of this research is a case of e-Business implementation in part manufacturing industry, especially in a PCB manufacturing company. The characteristics of part manufacturing industry are as follows. First, an ERP system runs as a legacy system that is ready to be combined with e-Business system. Secondly, the number of customers is very small. The customers are not many individuals but only a few big electronic enterprises that are strategically affiliated with the part manufacturing company. This means that the e-Business of the part manufacturing industry needs to focus on sharing pertinent information throughout the transactions with the customers, not on data-warehousing or data-mining customers' potential needs or requests. In this paper, we extracted e-Business opportunity domains from a PCB manufacturing company, a typical part manufacturing industry. We are intended to enhance information sharing between customers and the company, and provide functions of transactions necessary in the whole value chain from order to shipment. Implementing the e-Business system on the Web can increase the visibility of customers, and further, the company can be transformed into an extended enterprise where the relationship with the customers becomes very close and interleaved. Also, the Cyber Office functionality of the e-Business system can support the salespersons effectively, so that they can spend more time on customer satisfaction. Such efforts, in the future, can be a basis for active adaptation to the industry transformations such as forming e-community and participating in the marketplace.

• Journal of Distribution Science
• /
• v.15 no.1
• /
• pp.123-130
• /
• 2017

Purpose - This research aims to examine the effect of e-business adoption on firm's growth and profitability in the distribution industry. The value added from the distribution industry acts as the cost of other industries. As the distribution industry develops, its stage becomes shorter and the distribution margin becomes smaller. Therefore, e-business is expected to have a different effect on the distribution industry than other industries. Research design, data and methodology - The previous research generally used e-business adoption as an independent variable and firm's performance as a dependent variable. This study elaborated the model using a dynamic panel model that includes the performance variable of the previous year as an independent variable. By employing system GMM (Generalized Method of Moments), the endogeneity problem in the dynamic panel model can be solved. For the analysis, I extracted the distribution companies as the raw data in the National Statistical Office's Business Activity Survey over the period 2006 to 2012. Results - The growth rate of firms adopting e-business was 0.299%p higher than that of the non-adopter. However, only ERP (Enterprise Resource Planning), KMS (Knowledge Management System) and SCM (Supply Chain Management) contributed positively to the growth rate. In the case of profitability, it was 0.04%p higher than the distribution companies that did not adopt e-business. ERP and LMS (Learning Management System) improve profitability, while SCM reduces profitability. Consequently, while ERP improves both growth and profitability, SCM improves growth but reduces profitability. In addition, KMS improves firm's growth only, and LMS does only profitability, showing that each e-business has a differentiated effect. Conclusions - Since the distribution industry has different characteristics from manufacturing and other service industries, the introduction of e-business may not guarantee the growth and profitability of distribution companies. Careful introduction considering the characteristics of the distribution industry is required. In particular, it is necessary to select an e-business meeting the characteristics and needs of a distribution company, and thereafter, it is required for the company's own efforts to internalize it within the system.

• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.14
• /
• pp.6155-6158
• /
• 2015

Background: The human leukocyte antigen-G (HLA-G) gene is highly expressed in cancer pathologies and is one strategy used by tumor cells to escape immune surveillance. A 14-bp insertion/deletion (InDel) polymorphism of the HLA-G gene has been suggested to be associated with HLA-G mRNA stability and the expression of HLA-G. The aim of present study was to assess any genetic association between this polymorphism and breast cancer among Iranian-Azeri women. Materials and Methods: In this study 227 women affected with breast cancer, in addition to 255 age-sex and ethnically matched healthy individuals as the control group, participated. Genotyping was performed using polymerase chain reaction and electrophoresis assays. The data were compiled according to the genotype and allele frequencies, compared using the Chi-square test. Statistical significance was set at P<0.05. Results: In this case-control study, no significant difference was found between the case and control groups at allelic and genotype levels, although there is a slightly higher allele frequency of HLA-G 14bp deletion in breast cancer affected group. However,when the stage I subgroup was compared with stage II plus stage III subgroup of affected breast cancer, a significant difference was seen with the 14 bp deletion allele frequency. The stage II-III subgroup patients had higher frequency of deletion allele (57.4% vs 45.8%) than stage I cases (${\chi}^2=4.16$, p-value=0.041). Conclusions: Our data support a possible action of HLA-G 14bp InDel polymorphism as a potential genetic risk factor for progression of breast cancer. This finding highlights the necessity of future studies of this gene to establish the exact role of HLA-G in progression steps of breast cancer.

• International Journal of Computer Science & Network Security
• /
• v.23 no.9
• /
• pp.1-7
• /
• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
• /
• v.23 no.8
• /
• pp.210-216
• /
• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• Journal of Korea Trade
• /
• v.27 no.2
• /
• pp.96-114
• /
• 2023

Purpose - This paper elucidates a nexus between the occurrence of rare disaster events and the volatility of economic growth by distinguishing the likelihood of rare events from stochastic volatility. We provide new empirical facts based on a quarterly time series. In particular, we focus on the role of financial liberalization in spreading the economic crisis in developing countries. Design/methodology - We use quarterly data on consumption expenditure (real per capita consumption) from 44 countries, including advanced and developing countries, ending in the fourth quarter of 2020. We estimate the likelihood of rare event occurrences and stochastic volatility for countries using the Bayesian Markov chain Monte Carlo (MCMC) method developed by Barro and Jin (2021). We present our estimation results for the relationship between rare disaster events, stochastic volatility, and growth volatility. Findings - We find the global common disaster event, the COVID-19 pandemic, and thirteen country-specific disaster events. Consumption falls by about 7% on average in the first quarter of a disaster and by 4% in the long run. The occurrence of rare disaster events and the volatility of gross domestic product (GDP) growth are positively correlated (4.8%), whereas the rare events and GDP growth rate are negatively correlated (-12.1%). In particular, financial liberalization has played an important role in exacerbating the adverse impact of both rare disasters and financial market instability on growth volatility. Several case studies, including the case of South Korea, provide insights into the cause of major financial crises in small open developing countries, including the Asian currency crisis of 1998. Originality/value - This paper presents new empirical facts on the relationship between the occurrence of rare disaster events (or stochastic volatility) and growth volatility. Increasing data frequency allows for greater accuracy in assessing a country's specific risk. Our findings suggest that financial market and institutional stability can be vital for buffering against rare disaster shocks. It is necessary to preemptively strengthen the foundation for financial stability in developing countries and increase the quality of the information provided to markets.

• Journal of Korea Society of Industrial Information Systems
• /
• v.21 no.4
• /
• pp.67-78
• /
• 2016

Recently many countries around the world are actively promoting smart city projects to address various urban problems such as traffic congestion, housing shortage, and energy scarcity. Due to development of the Internet of Things (IoT), the development of a smart city with sustainability, convenience, and environment-friendliness was enabled through the effective control and reuse of urban resources. The purpose of this study is to analyze the technical trends of IoT and present a development plan for smart city which is one of the applications of the IoT. To this end, the news articles of the Electronic Times between 2013 and 2015were analyzed using the text mining technique and smart city development cases of other countries were investigated. The analysis results revealed the close relationships of big data, cloud, platforms, and sensors with smart city. For the successful development of a smart city, first, all the interested parties in the city must work together to create new values throughout the entire process of value chain. Second, they must utilize big data and disclose public data more actively than they are doing now. This study has made academic contribution in that it has presented a big data analysis method and stimulated follow-up studies. For the practical contribution, the results of this study provided useful data for the policy making of local governments and administrative agencies for smart city development. This study may have limitations in the incorporation of the total trends because only the news articles of the Electronic Times were selected to analyze the technical trends of the IoT.

• Tuberculosis and Respiratory Diseases
• /
• v.68 no.5
• /
• pp.280-285
• /
• 2010

Background: $QuantiFERON^{(R)}$-TB Gold In Tube (QFT-G IT) has been used for diagnosing latent tuberculosis infection and active tuberculosis (TB) since 2007. However, there has not been enough data on QFT-G IT for universal use in children. In this study, we evaluated the clinical usefulness of the QFT-G IT in pediatric practice. Methods: We retrospectively reviewed the clinical records of 70 patients younger than 18 years of age who had taken QFT-G IT and had a tuberculin skin test (TST) between July 2007 and July 2009 at Wonju Christian Hospital. The subjects were divided into two groups, asymptomatic TB exposure group and disease group. Four patients who were taking immunosuppressants during the study period were excluded. Results: A total of 66 immunocompetent children were included in this study. Among 27 asymptomatic children who had contact histories of TB, 6 (22.2%) were found to be positive by QFT-G IT. Eleven (40.7%) and 5 (18.5%) children were found to be positive by TST with cutoff values of ${\geq}5mm$ and ${\geq}10mm$, respectively. Agreement was fair to good between QFT-G IT and TST (${\kappa}=0.59$: cutoff value ${\geq}5mm$, ${\kappa}=0.7$: cutoff value ${\geq}10mm$). In disease group, 14 patients (35.9%) were diagnosed with active tuberculosis, 8/14 (57.1%) were positive on TST and 9/14 (64.3%) on QFT-G IT. The positive rate of acid-fast bacilli smear, TB-polymerase chain reaction, and culture for tuberculosis was 11% (1/9), 27.3% (3/11) and 33.3% (3/9), respectively. Conclusion: Our data support that the QFT-G IT can be used as an additional diagnostic tool for latent and active tuberculosis infection in children.

• Asian Pacific Journal of Cancer Prevention
• /
• v.15 no.9
• /
• pp.4049-4054
• /
• 2014

Background: Race and ethnicity are significant factors in predicting survival time of breast cancer patients. In this study, we applied advanced statistical methods to predict the survival of White non-Hispanic female breast cancer patients, who were diagnosed between the years 1973 and 2009 in the United States (U.S.). Materials and Methods: Demographic data from the Surveillance Epidemiology and End Results (SEER) database were used for the purpose of this study. Nine states were randomly selected from 12 U.S. cancer registries. A stratified random sampling method was used to select 2,000 female breast cancer patients from these nine states. We compared four types of advanced statistical probability models to identify the best-fit model for the White non-Hispanic female breast cancer survival data. Three model building criterion were used to measure and compare goodness of fit of the models. These include Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC). In addition, we used a novel Bayesian method and the Markov Chain Monte Carlo technique to determine the posterior density function of the parameters. After evaluating the model parameters, we selected the model having the lowest DIC value. Using this Bayesian method, we derived the predictive survival density for future survival time and its related inferences. Results: The analytical sample of White non-Hispanic women included 2,000 breast cancer cases from the SEER database (1973-2009). The majority of cases were married (55.2%), the mean age of diagnosis was 63.61 years (SD = 14.24) and the mean survival time was 84 months (SD = 35.01). After comparing the four statistical models, results suggested that the exponentiated Weibull model (DIC= 19818.220) was a better fit for White non-Hispanic females' breast cancer survival data. This model predicted the survival times (in months) for White non-Hispanic women after implementation of precise estimates of the model parameters. Conclusions: By using modern model building criteria, we determined that the data best fit the exponentiated Weibull model. We incorporated precise estimates of the parameter into the predictive model and evaluated the survival inference for the White non-Hispanic female population. This method of analysis will assist researchers in making scientific and clinical conclusions when assessing survival time of breast cancer patients.