• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.3
• /
• pp.853-857
• /
• 2015

Background: Gastric cancer is the most common cancer in the Iranian population. The aim of this study was to determine the effect of clinicopathological factors on prognosis by meta-analysis. Materials and Methods: A literature search was conducted using MEDLINE, EMBASE and Cochrane library and extensive literature search using the Persian databases until February 2011. Prospective follow up studies with multivariate analysis of overall survival of the patients with gastric cancer were included in this review. The data were analyzed by CMA.2. Publication bias are checked by funnel plot and data are shown as Forest plots. Results: From a total of 63 articles, 14 retrospective studies which examined 5 prognostic factors and involving 10,500 patients were included. Tumor size (>35mm) was the main significant factor predicting an unfavorable prognosis for the patients with gastric cancer (RR=1.829, p<0.001) followed by presence of distant metastases (RR=1.607, p<0.001), poor differentiation (RR=1.408, p<0.001) and male sex (RR=1.194, p<0.001). Lymph node metastases (RR=1.058, p=0.698) and moderate differentiation (RR=0.836, p=0.043) were not statistically significant as prognostic factors. Conclusions: This meta-analysis suggests that tumor size>35mm, poor differentiation, presence of distant metastasis and male gender are strongly associated with a poor prognosis in Iranian patients with gastric cancer.

• Journal of Electrical Engineering and Technology
• /
• v.7 no.1
• /
• pp.109-114
• /
• 2012

In this paper, we utilize training strategy of hidden Markov model (HMM) to use in versatile issues such as classification of time-series sequential data such as electric transient disturbance problem in power system. For this, an automatic means of optimizing HMMs would be highly desirable, but it raises important issues: model interpretation and complexity control. With this in mind, we explore the possibility of using genetic algorithm (GA) and harmony search (HS) algorithm for optimizing the HMM. GA is flexible to allow incorporating other methods, such as Baum-Welch, within their cycle. Furthermore, operators that alter the structure of HMMs can be designed to simple structures. HS algorithm with parameter-setting free technique is proper for optimizing the parameters of HMM. HS algorithm is flexible so as to allow the elimination of requiring tedious parameter assigning efforts. In this paper, a sequential data analysis simulation is illustrated, and the optimized-HMMs are evaluated. The optimized HMM was capable of classifying a sequential data set for testing compared with the normal HMM.

• Journal of Information Technology Applications and Management
• /
• v.20 no.3_spc
• /
• pp.231-257
• /
• 2013

The recent rise in the unstructured data generated by social media has resulted in an increasing need to collect, store, search, analyze, and visualize it. These data cannot be managed effectively by using traditional data analysis methodologies because of their vast volume and unstructured nature. Therefore, many attempts are being made to analyze these unstructured data (e.g., text files and log files) by using commercial and noncommercial analytical tools. Especially, the attempt to discover meaningful knowledge by using text mining is being made in business and other areas such as politics, economics, and cultural studies. For instance, several studies have examined pending national issues by analyzing large volumes of texts on various social issues. However, it is difficult to create satisfactory information services that can identify R&D documents on specific national issues from among the various R&D resources. In other words, although users specify some words related to pending national issues as search keywords, they usually fail to retrieve the R&D information they are looking for. This is usually because of the discrepancy between the terms defining pending national issues and the corresponding terms used in R&D documents. We need a mediating logic to overcome this discrep 'ancy so that we can identify and package appropriate R&D information on specific pending national issues. In this paper, we use association analysis and social network analysis to devise a mediator for bridging the gap between the keywords defining pending national issues and those used in R&D documents. Further, we propose a methodology for packaging R&D information services for pending national issues by using the devised mediator. Finally, in order to evaluate the practical applicability of the proposed methodology, we apply it to the NTIS(National Science & Technology Information Service) system, and summarize the results in the case study section.

• The Bulletin of The Korean Astronomical Society
• /
• v.39 no.1
• /
• pp.78.2-78.2
• /
• 2014

Many techniques for data analysis also based on gaussian noise assumption which is often valid in various situations. However, the sensitivity of gravitational wave searches are limited by their non-gaussian and non-stationary noise. We introduce various on-going efforts to overcome this limitation in Korean Gravitational Wave Group. First, artificial neural networks are applied to discriminate non-gaussian noise artefacts and gravitational-wave signals using auxiliary channels of a gravitational wave detector. Second, viability of applying Hilbert-Huang transform is investigated to deal with non-stationary data of gravitational wave detectors. We also report progress in acceleration of low-latency gravitational search using GPGPU.

• International Commerce and Information Review
• /
• v.13 no.1
• /
• pp.3-28
• /
• 2011

Recently marketing methods of small and medium exporting firms have changed from internet marketing using homepage, or e-catalogs, to search engine marketing. However, there is no specific proof of search engine marketing effectiveness. Therefore the purpose of this research is to explore marketing performance of search engine marketing(SEM) based on search engine optimization. In order to build an optimal SEM strategy, quantitative data are collected from the Google-analytics such as homepage visits, page views, and traffic source for three years. At the same time, this study has carried out a survey to measure the qualitative effectiveness. The result of this quantitative study suggests that the existing carryover effects and lag effects would be maintained through search engine optimization. Meanwhile, the qualitative survey shows that satisfaction and awareness of homepage have been improved after search engine optimization. This can support logically increase of homepage visiting ratio of quantitative analysis. Also exporting companies know very well, that traffic and page views have increased after search engine optimization.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.29 no.4D
• /
• pp.555-561
• /
• 2009

In lieu of section profile data, a fitting of the bored tunnel shape is more significant confirmation for maintenance of a tunnel. Before the permit on the completion of a tunnel, deformation of the completed tunnel with respect to the design model are considered. And deformation can be produced at continuously along the entire of the tunnel section. This study firstly includes an analysis of algebraic approach and test it with an observed field data. And then a number of methods, line search method, genetic algorithm, and pattern search methods, are compared with the 3D tunnel shape fitting. Algebraic methods can solve a simple circular cylinder type as like a railway tunnel. However, a more complex model (compound circular curve and non circular) as like a highway tunnel has to be solved with soft computing tools in the cause of conditional constraints. The genetic algorithm and pattern search methods are computationally more intensive, but they are more flexible at a complex condition. The line search method is fastest, but it needs a narrow bounds of the initial values.

• Journal of Oriental Neuropsychiatry
• /
• v.34 no.3
• /
• pp.235-245
• /
• 2023

Objectives: The aim of this study was to analyze public and researcher interests in suicide and related illnesses and acupuncture and acupressure treatment using Google Trends and some electronic databases. Methods: Search results for keywords "suicide," "acupuncture," "acupressure," and several illnesses related to suicide were analyzed in Google Trends from January 2004 to June 2023. Illnesses included anxiety, depression (including major depressive disorder), schizophrenia, bipolar disorder, post- traumatic stress disorder (PTSD), eating disorder (including anorexia nervosa and bulimia nervosa), substance use disorder, autism spectrum disorder, personality disorder (including borderline person- ality disorder), and chronic pain. Search results were extracted using relative search volume (RSV) scores between 0 and 100. Search terms were also searched in online databases, including PubMed, CNKI, and OASIS, to estimate the number of related studies, and descriptive analysis was conducted. Results: Google Trends analysis showed a strong positive correlation between the RSVs of "suicide and depression," "acupuncture and chronic pain," and "acupressure and PTSD." The electronic database search results produced numerous studies published on "suicide and depression," "acupuncture and depression," and "acupressure and anxiety." High interest in "suicide and depression," "acupuncture and chronic pain," and "acupressure and anxiety" was seen among the public and researchers. Interest in "suicide and chronic pain," "acupuncture and eating disorder," and "acupressure and PTSD" was higher in the public than among researchers, while "anxiety and suicide" and "anxiety and acu- puncture" showed opposite trends. Conclusions: The results of this research enable an understanding of public and researcher interest in suicide, acupuncture, acupressure, and suicide-related illnesses. The results also provide a basis for fu- ture research and examining public health implications in Korean medicine.

• Health Policy and Management
• /
• v.27 no.3
• /
• pp.241-246
• /
• 2017

Background: The purpose of this study is to investigate the trends on the induced abortion in Korea using social big-data and confirm whether there was time series trends and seasonal characteristics in induced abortion. Methods: From October 1, 2007 to October 24, 2016, we used Naver's data lab query, and the search word was 'induced abortion' in Korean. The average trend of each year was analyzed and the seasonality was analyzed using the cosinor model. Results: There was no significant changes in search volume of abortion during that period. Monthly search volume was the highest in May followed by the order of June and April. On the other hand, the lowest month was December followed by the order of January, and September. The cosinor analysis showed statistically significant seasonal variations (amplitude, 4.46; confidence interval, 1.46-7.47; p< 0.0036). The search volume for induced abortion gradually increased to the lowest point at the end of November and was the highest at the end of May and declined again from June. Conclusion: There has been no significant changes in induced abortion for the past nine years, and seasonal changes in induced abortion have been identified. Therefore, considering the seasonality of the intervention program for the prevention of induced abortion, it will be effective to concentrate on the induced abortion from March to May.

• Geomechanics and Engineering
• /
• v.36 no.1
• /
• pp.83-97
• /
• 2024

It is important to make reasonable prediction about the long-term deformation of high rockfill geostructures. However, the deformation is usually underestimated using the rockfill parameters obtained from laboratory tests due to different size effects, which make it necessary to identify parameters from in-situ monitoring data. This paper proposes a novel hybrid back-analysis method with a modified objective function defined for the time-dependent back-analysis problem. The method consists of two stages. In the first stage, an improved weighted average method is proposed to quickly narrow the search region; while in the second stage, an adaptive response surface method is proposed to iteratively search for the satisfactory solution, with a technique that can adaptively consider the translation, contraction or expansion of the exploration region. The accuracy and computational efficiency of the proposed hybrid back-analysis method is demonstrated by back-analyzing the long-term deformation of two high embankments constructed for airport runways, with the rockfills being modeled by a rheological model considering the influence of stress states on the creep behavior.

• KIPS Transactions on Software and Data Engineering
• /
• v.3 no.6
• /
• pp.215-218
• /
• 2014

In hashing-based methods for approximate nearest neighbors(ANN) search, by mapping data points to k-bit binary codes, nearest neighbors are searched in a binary embedding space. In this paper, we present a hashing method using a PCA-based clustering method, Principal Direction Divisive Partitioning(PDDP). PDDP is a clustering method which repeatedly partitions the cluster with the largest variance into two clusters by using the first principal direction. The proposed hashing method utilizes the first principal direction as a projective direction for binary coding. Experimental results demonstrate that the proposed method is competitive compared with other hashing methods.