• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.131-138
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• 2022

Novel Coronavirus (COVID-19) is viewed as one of the main general wellbeing theaters on the worldwide level all over the planet. Because of the abrupt idea of the flare-up and the irresistible force of the infection, it causes individuals tension, melancholy, and other pressure responses. The avoidance and control of the novel Covid pneumonia have moved into an imperative stage. It is fundamental to early foresee and figure of infection episode during this troublesome opportunity to control of its grimness and mortality. The entire world is investing unimaginable amounts of energy to fight against the spread of this lethal infection. In this paper, we utilized machine learning and deep learning techniques for analyzing what is going on utilizing countries shared information and for detecting the climate factors that effect on spreading Covid-19, such as humidity, sunny hours, temperature and wind speed for understanding its regular dramatic way of behaving alongside the forecast of future reachability of the COVID-2019 around the world. We utilized data collected and produced by Kaggle and the Johns Hopkins Center for Systems Science. The dataset has 25 attributes and 9566 objects. Our Experiment consists of two phases. In phase one, we preprocessed dataset for DL model and features were decreased to four features humidity, sunny hours, temperature and wind speed by utilized the Pearson Correlation Coefficient technique (correlation attributes feature selection). In phase two, we utilized the traditional famous six machine learning techniques for numerical datasets, and Dense Net deep learning model to predict and detect the climatic factor that aide to disease outbreak. We validated the model by using confusion matrix (CM) and measured the performance by four different metrics: accuracy, f-measure, recall, and precision.

• Smart Structures and Systems
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• v.29 no.1
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• pp.117-127
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• 2022

Data anomalies seriously threaten the reliability of the bridge structural health monitoring system and may trigger system misjudgment. To overcome the above problem, an efficient and accurate data anomaly detection method is desiderated. Traditional anomaly detection methods extract various abnormal features as the key indicators to identify data anomalies. Then set thresholds artificially for various features to identify specific anomalies, which is the artificial experience method. However, limited by the poor generalization ability among sensors, this method often leads to high labor costs. Another approach to anomaly detection is a data-driven approach based on machine learning methods. Among these, the bidirectional long-short memory neural network (BiLSTM), as an effective classification method, excels at finding complex relationships in multivariate time series data. However, training unprocessed original signals often leads to low computation efficiency and poor convergence, for lacking appropriate feature selection. Therefore, this article combines the advantages of the two methods by proposing a deep learning method with manual experience statistical features fed into it. Experimental comparative studies illustrate that the BiLSTM model with appropriate feature input has an accuracy rate of over 87-94%. Meanwhile, this paper provides basic principles of data cleaning and discusses the typical features of various anomalies. Furthermore, the optimization strategies of the feature space selection based on artificial experience are also highlighted.

• Journal of Pharmacopuncture
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• v.26 no.1
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• pp.38-43
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• 2023

Objectives: Psychiatric symptoms in epilepsy are very common, and the most common symptoms are depression, insomnia, and anxiety. These symptoms not only lower the quality of life of epilepsy patients, but also elevate the risk of epileptic seizures. There are no specific criteria for the available antiepileptic drugs to ameliorate these symptoms in patients with epilepsy, and there is a lack of evidence to support the efficacy and safety of existing drugs. The Shugan Jieyu capsule (SJC) is a traditional herbal medicine composed of Acanthopanax senticosus and Hypericum perforatum and is reported to be effective in relieving psychiatric symptoms. The purpose of this study was to assess the efficacy of SJC as a treatment for psychiatric symptoms in epilepsy patients. Methods: Electronic databases will be investigated for publications in English, Korean, Japanese, and Chinese. The participants of the study are epilepsy patients with psychiatric symptoms diagnosed using any validated criteria. All types of controls will be compared-placebo, conventional treatments, and no treatment-to groups treated with SJC or modified SJC. We will measure the degree of improvement in psychiatric symptoms and check epileptic symptoms, such as the frequency of seizures. The study selection and data extraction will be performed by two independent reviewers, who will also assess methodological quality using the risk-of-bias tool by Cochrane. We will use Review Manager software (RevMan) to carry out all statistical analyses. Results: This systematic review and meta-analysis will be performed in accordance with the PRISMA-P statement. Conclusion: This systematic review is the first study to assess the efficacy and safety of SJC for the treatment of psychiatric symptoms in epilepsy. We expect that this study will provide clinically applicable evidence for patients with epilepsy when selecting drug treatments.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.1
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• pp.17-31
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• 2009

The importance of software has been growing rapidly owing to the development of various Internet and e-business applications. The traditional approaches to software evaluation are based on the development process perspective, and their major concerns are no strongly related to use or customer-oriented evaluation of software. According to resource-based theory, company's resource is consisted of human, technology, market value, and finance. Customer satisfaction improved by product satisfaction and service satisfaction. Based on the previous studies the factors of human resources, technology, customer satisfaction are selected to evaluate software company's competence This research suggests the factor effecting on sales performance. And then statistical methods are used for verifying relationship between the factor and sales performance.

• Journal of the Korean Regional Science Association
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• v.39 no.1
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• pp.3-20
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• 2023

This study aims to categorize the 14 major gentrified commercial areas of Seoul and analyze their characteristics based on their sense of place. To achieve this, we conducted hierarchical cluster analysis using text data collected from Naver Blog. We divided the districts into two dimensions: "experience" and "feature" and analyzed their characteristics using LDA (Latent Dirichlet Allocation) of the text data and statistical data collected from Seoul Open Data Square. As a result, we classified the commercial districts of Seoul into 5 categories: 'theater district,' 'traditional cultural district,' 'female-beauty district,' 'exclusive restaurant and medical district,' and 'trend-leading district.' The findings of this study are expected to provide valuable insights for policy-makers to develop more efficient and suitable commercial policies.

• Animal Bioscience
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• v.36 no.8
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• pp.1156-1166
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• 2023

Objective: Pork belly is a cut of meat with high worldwide demand. However, although the belly is comprised of multiple muscles and fat, unlike the loin muscle, research on their genetic parameters has yet to focus on a representative cut. To use swine breeding, it is necessary to estimate heritability against pork belly traits. Moreover, estimating genetic correlations is needed to identify genetic relationship among the traditional carcass and meat quality traits. This study sought to estimate the heritability of the carcass, belly, and their component traits, as well as the genetic correlations among them, to confirm whether these traits can be improved. Methods: A total of 543 Yorkshire pigs (406 castrated males and 137 females) from 49 sires and 244 dam were used in this study. To estimate genetic parameters, a total of 12 traits such as lean meat production ability, meat quality and pork belly traits were chosen. The heritabilities were estimated by using genome-wide efficient mixed model association software. The statistical model was selected so that farm, carcass weight, sex, and slaughter season were fixed effects. In addition, its genetic parameters were calculated via MTG2 software. Results: The heritability estimates for the 7th belly slice along the whole plate and its components were low to moderate (0.07±0.07 to 0.33±0.07). Moreover, the genetic correlations among the carcass and belly traits were moderate to high (0.28±0.20 to 0.99±0.31). Particularly, the rectus abdominis muscle exhibited a high absolute genetic correlation with the belly and meat quality (0.73±52 to 0.93±0.43). Conclusion: A moderate to high correlation coefficient was obtained based on the genetic parameters. The belly could be genetically improved to contain a larger proportion of muscle regardless of lean meat production ability.

• The Korea Journal of Herbology
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• v.37 no.2
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• pp.13-21
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• 2022

Objectives : The evaluation of academic achievement is pivotal for establishing accurate direction and adequate level of medical education. The purpose of this study was to firstly establish innovative item analysis technique of Item Response Theory (IRT) for analyzing multiple-choice test of herbology in the traditional Korean medicine education which has not been available for the difficulty of test theory and statistical calculation. Methods : The answers of 390 students (2012-2018) to the 14 item herbology test in college of Korean medicine were used for the item analysis. As for the multidimensional analysis of item characteristics, difficulty, discrimination, and guessing parameters along with item-total correlation and percentage of correct answer were calculated using Classical Test Theory (CTT) and IRT. Results : The validity parameters of strong and weak items were illustrated in multiple perspectives. There were 4 items with six acceptable index scores, and 5 items with only one acceptable index score. The item discrimination of IRT was found to have no significant correlation with difficulty and discrimination indices of CTT emphasizing attention of professionals of medical education as for the test credibility. Conclusion : The critical suggestions for the development, utilization and revision of test items in the e-learning and evidence-based Teaching era were made based on the results of item analysis using IRT. The current study would firstly provide foundation for upgrading the quality of Korean medicine education using test theory.

• Journal of Korean Neurosurgical Society
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• v.65 no.3
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• pp.457-468
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• 2022

Objective : The clinical management paradigm of skull base chordomas is still challenging. Surgical resection plays an important role of affecting the prognosis. Endonasal endoscopic approach (EEA) has gradually become the preferred surgical approach in most cases, but traditional transcranial surgery cannot be completely replaced. This study presents a comparison of the results of the two surgical strategies and a summary of the treatment algorithms for skull base chordomas. Methods : We retrospectively analyzed the surgical outcomes and follow-up data of 48 patients with skull base chordomas diagnosed pathologically who received transnasal midline approaches (TMA) and transcranial lateral approaches (TLA) from 2010 to 2020. Results : Among the 48 patients, 36 cases were adopted TMA and 12 cases were performed with TLA. In terms of gross total resection (GTR) rate, 27.8% in TMA and 16.7% in TLA and with EEA alone it was increased to 38.9%, while 29.7% in primary surgery. In TMA, the cerebrospinal fluid (CSF) leak remains the most common complication (13 cases, 36.1%), other main complications included death, cranial nerve palsy, hypopituitarism, all the comparisons were no statistical significance. The Karnofsky Performance Scale scores in TMA were all better than those in TLA at different time, and the overall survival (OS) and recurrence free survival/progression free survival was just the reverse. Conclusion : The EEA for skull base chordomas resection has improved the GTR rate, but transcranial approach is still an alternative approach. It is necessary to select an appropriate surgical approach based on the location and the pattern of tumor growth in order to obtain the best surgical outcomes.

• Journal of the Korean Society of Food Culture
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• v.22 no.2
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• pp.201-209
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• 2007

The purpose of this study was to analyze usage frequency and importance of competencies which are required to restaurant industry professionals. For this purpose, opinions of restaurant industry professionals on the competency were surveyed using questionnaires. To develop a questionnaire, a total of 27 competency variables which are required to restaurant industry professionals were drawn through literature review. Questionnaires were distributed to 300 professionals in restaurant industry with different positions using random sampling. Out of 300 questionnaires, 221 questionnaires were used for analysis. Statistical analysis was conducted using SPSS 10.0, including descriptive analysis, ANOVA and t-test. Reliability test and factor analysis were also conducted to evaluate the reliability and validity of the questionnaire. As a result, 'attitude and personality' factor was recognized as the most frequently used and the most important competency factor of restaurant industry professionals. Therefore, the competency such as 'sincerity', 'responsibility', 'sense of honesty', 'positive attitude', 'tolerance and justice', should be more emphasized in restaurant management education. The level of current usage and importance of each competency were different according to age, education level, working experience, position, number of employees, type of restaurant and type of management.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.6B
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• pp.543-550
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• 2009

The traditional simple extrapolation type short term quantitative rainfall forecast can not realize the evolution of rainfall generating weather system. To overcome the drawback of the linear extrapolation type rainfall forecasting model, the history of a weather system from sequential weather radar information and a polynomial regression technique were used to generate forecast fileds of x-directional, y-directional velocities and radar reflectivity which considered the nonlinear behavior related to the evolution of weather systems. Results demonstrated that test statistics of forecasts using the developed model is better than that of 2-CAPPI forecast. However there is still a large room to improve the forecast of spatial and temporal evolution of local storms since the model is not based on a fully physical approach but a statistical approach.