• Journal of Dental Anesthesia and Pain Medicine
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• 제21권6호
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• pp.565-574
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• 2021

Background: Trigeminal neuralgia (TN) is a severe form of pain that affects the daily activities of a patient. Transcutaneous electrical nerve stimulation (TENS) therapy is an emerging option for the treatment of acute and chronic pain. The aim of this study was to evaluate the effect of TENS therapy as an adjunct to drug therapy for the treatment of TN. Methods: A total of 52 patients diagnosed with TN according to the International Classification of Headache Disorders (version 3) were included. Each patient was randomized to either the TENS or placebo TENS groups. Intervention was given in continuous mode and 100-Hz frequency for 20 mins biweekly for 6 weeks. Parameters were measured at baseline, TENS completion and 3 months, 6 months, and 1 year of follow up. The parameters observed were mean carbamazepine dose, mean visual analog scale (VAS) score, mean present pain intensity (PPI) score, and functional outcome. Non-parametric analyses, one-way ANOVA and the Kruskal-Wallis test were applied for intragroup comparisons, while the Mann-Whitney U test and independent t-test were used for intergroup comparisons of variables. The chi-square test was applied to analyze categorical data. Results: Compared to the placebo TENS group, the mean dose of carbamazepine in the TENS group was significantly reduced at TENS completion, as well as at 6 months and 1 year follow up. Changes in mean VAS score, mean PPI score, and functional outcome did not show significant differences between the groups (P>0.05). Conclusion: TENS therapy does not lead to any changes in pain levels but it may reduce the mean dose of carbamazepine when used as an adjunct treatment in patients with TN.

• 환경영향평가
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• 제4권1호
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• pp.59-64
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• 1995

Environmental Impact Assesment(EIA) is composed of various procedures, such as screening, scoping, inventory survey, prediction, assessment, mitigation measure, alternative assessment, and post management. Remote sensing introduced lately begins to be applied ecosystem and land use in inventory survey and assessment of EIA. This study explains on land use classification, buffering analysis of residential area, and overlaying analysis of odor predictive data with residential area for application to EIA with remote sensing data. Residential area extracted from land use classification of remote sensing provides effectively buffering analysis of residential area in selection of landfill site with GIS. It could assess also residential effect to an offensive odor by overlaying analysis. Application methods in EIA should be enlarged to assess effectively.

• Journal of Information Processing Systems
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• 제10권4호
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• pp.555-567
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• 2014

Biometric performance improvement is a challenging task. In this paper, a hierarchical strategy fusion based on multimodal biometric system is presented. This strategy relies on a combination of several biometric traits using a multi-level biometric fusion hierarchy. The multi-level biometric fusion includes a pre-classification fusion with optimal feature selection and a post-classification fusion that is based on the similarity of the maximum of matching scores. The proposed solution enhances biometric recognition performances based on suitable feature selection and reduction, such as principal component analysis (PCA) and linear discriminant analysis (LDA), as much as not all of the feature vectors components support the performance improvement degree.

• Journal of Forest and Environmental Science
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• 제37권1호
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• pp.44-51
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• 2021

Rapid urbanization and population growth have caused substantial land use land cover (LULC) change in the Kathmandu valley. The lack of temporal and geographical data regarding LULC in the middle mountain region like Kathmandu has been challenging to assess the changes that have occurred. The purpose of this study is to investigate the changes in LULC in Chandragiri Municipality between 1996 and 2017 using geographical information system (GIS) and remote sensing. Using Landsat imageries of 1996 and 2017, this study analyzed the LULC change over 21 years. The images were classified using the Maximum Likelihood classification method and post classified using the change detection technique in GIS. The result shows that severe land cover changes have occurred in the Forest (11.63%), Built-up areas (3.68%), Agriculture (-11.26%), Shrubland (-0.15%), and Bareland (-3.91%) in the region from 1996 to 2017. This paper highlights the use of GIS and remote sensing in understanding the changes in LULC in the south-west part of Kathmandu valley.

• International Journal of Computer Science & Network Security
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• 제22권11호
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• pp.265-271
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• 2022

Social media is a form of communication based on the internet to share information through content and images. Their choice of profile images and type of image they post can be closely connected to their personality. The user posted images are designated as personality traits. The objective of this study is to predict five factor model personality dimensions from profile images by using deep learning and neural networks. Developed a deep learning framework-based neural network for personality prediction. The personality types of the Big Five Factor model can be quantified from user profile images. To measure the effectiveness, proposed two models using convolution Neural Networks to classify each personality of the user. Done performance analysis among two different models for efficiently predict personality traits from profile image. It was found that VGG-69 CNN models are best performing models for producing the classification accuracy of 91% to predict user personality traits.

• Asian-Australasian Journal of Animal Sciences
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• 제33권7호
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• pp.1202-1208
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• 2020

Objective: The aim of this study was to identify a distribution pattern of meat quality grade (MQG) as a function of carcass yield index (CYI) and the gender of Hanwoo (bull, cow, and steer) to determine the optimum point between both yield and quality. We also attempted to identify how pre- and post-deboning variables affect the gender-specific beef quality of Hanwoo. Methods: A total of 31 deboning variables, consisting of 7 pre-deboning and 24 post-deboning variables from bulls (n = 139), cows (n = 69), and steers (n = 153), were obtained from the National Institute of Animal Science (NIAS) in South Korea. The database was reconstructed to be suitable for a statistical significance test between the CYI and the MQG as well as classification of meat quality. Discriminant function analysis was used for classifying MQG using the deboning parameters of Hanwoo by gender. Results: The means of CYI according to 1+, 1, 2, and 3 of MQG were 68.64±2.02, 68.85±1.94, 68.62±5.88, and 70.99±3.32, respectively. High carcass yield correlated with low-quality grade, while high-quality meat most frequently was obtained from steers. The classification ability of pre-deboning parameters was higher than that of post-deboning parameters. Moisture and the shear force were the common significant parameters in all discriminant functions having a classification accuracy of 80.6%, 71%, and 56.9% for the bull, cow, and steer, respectively. Conclusion: This study provides basic information for predicting the meat quality by gender using pre-deboning variables consistent with the actual grading index.

• Journal of Electrical Engineering and Technology
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• 제12권6호
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• pp.2106-2117
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• 2017

An effective statistical feature extraction approach of data sampling of fault in the combined transmission system is presented in this paper. The proposed algorithm leads to high accuracy at minimum cost to predict fault location and fault type classification. This algorithm requires impedance measurement data from one end of the transmission line. Modal decomposition is used to extract positive sequence impedance. Then, the fault signal is decomposed by using discrete wavelet transform. Statistical sampling is used to extract appropriate fault features as benchmark of decomposed signal to train classifier. Support Vector Machine (SVM) is used to illustrate the performance of statistical sampling performance. The overall time of sampling is not exceeding 1 1/4 cycles, taking into account the interval time. The proposed method takes two steps of sampling. The first step takes 3/4 cycle of during-fault and the second step takes 1/4 cycle of post fault impedance. The interval time between the two steps is assumed to be 1/4 cycle. Extensive studies using MATLAB software show accurate fault location estimation and fault type classification of the proposed method. The classifier result is presented and compared with well-established travelling wave methods and the performance of the algorithms are analyzed and discussed.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1996년도 추계학술대회발표논문집; 고려대학교, 서울; 26 Oct. 1996
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• pp.199-202
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• 1996

Although numerous studies demonstrate that one technique outperforms the others for a given data set, there is often no way to tell a priori which of these techniques will be most effective to solve a specific problem. Alternatively, it has been suggested that a better approach to classification problem might be to integrate several different forecasting techniques by combining their results. The issues of interest are how to integrate different modeling techniques to increase the prediction performance. This paper proposes the post-model integration method, which means integration is performed after individual techniques produce their own outputs, by finding the best combination of the results of each method. To get the optimal or near optimal combination of different prediction techniques. Genetic Algorithms (GAs) are applied, which are particularly suitable for multi-parameter optimization problems with an objective function subject to numerous hard and soft constraints. This study applied three individual classification techniques (Discriminant analysis, Logit and Neural Networks) as base models to the corporate failure prediction context. Results of composite prediction were compared to the individual models. Preliminary results suggests that the use of integrated methods will offer improved performance in business classification problems.

• 한국경영과학회지
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• 제22권3호
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• pp.163-173
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• 1997

Although numerous studies demonstrate that one technique outperforms the others for a given data set, it is hard to tell a priori which of these techniques will be the most effective to solve a specific problem. It has been suggested that the better approach to classification problem might be to integrate several different forecasting techniques by combining their results. The issues of interest are how to integrate different modeling techniques to increase the predictive performance. This paper proposes the post-model integration method, which tries to find the best combination of the results provided by individual techniques. To get the optimal or near optimal combination of different prediction techniques, Genetic Algorithms (GAs) are applied, which are particularly suitable for multi-parameter optimization problems with an object function subject to numerous hard and soft constraints. This study applies three individual classification techniques (Discriminant analysis, Logit model and Neural Networks) as base models for the corporate failure prediction. The results of composite predictions are compared with the individual models. Preliminary results suggests that the use of integrated methods improve the performance of business classification.

• 한국주거학회:학술대회논문집
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• 한국주거학회 2008년 추계학술발표대회 논문집
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• pp.327-331
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• 2008

In Korea, welfare needs brought on by the rapidly growing of elderly population. Also, the senior group with spare time, wealth and health is increasing. Currently, the people have known about needs welfare system for improving quality of life during senescence personally and socially. In addition to the senior friendly industry, having distinction with simple approach medical services, will be great expanding after 2010. And there will be a new welfare services plan supporting long term care insurance for senior citizens. But we don't have prepared systematically organized senior friendly industry and products yet. So, the purpose of the study is to analyze systematically the classification characteristics of the senior friendly products according to the difficulty of daily living for senior citizens. Above all, the study finds out the senior friendly products characteristics according to the consumer's various situations about health and housing space. Therefore the result of the study reveals that the simulation guidelines for choosing the products depend on the customer's needs are essential and useful to improve senior friendly industry. And the post evaluation data of senior friendly products would be put to practical use in the industry.