• Title/Summary/Keyword: label data

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The Effect of Warning Labels on Cigarette Packages: Textual vs. Textual-Visual and Self-Efficacy

  • Halim, Rizal Edy;Muttaqin, Faisal
    • The Journal of Asian Finance, Economics and Business
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    • v.1 no.2
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    • pp.25-30
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    • 2014
  • The small number of fusion research that addresses fear-appeal and self-efficacy in the effectiveness of warning labels cause the label is not clear, then the purpose of the study is the first to examine the effect of the combination of the message. With the experimental method, a total of 240 participants were smokers consist of Indonesian university students in the age range 18-23 years. Data obtained then processed with ANOVA statistical methods and Test T. The results are consistent with previous studies that the visual warning labels more effective than the textual warning labels alone. Results also suggest that a warning label should be both visual and textual messages combined with self-efficacy. Limitations, this study does not differentiate among smokers and non-smokers, in addition to the images are used only one type of disease, so that the research needs to be done to distinguish these factors.

Understanding of Nutrition Labelling Use and Related Factors among Korean Adults

  • Oh, Chorong;Kim, Hak-Seon
    • Culinary science and hospitality research
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    • v.24 no.2
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    • pp.16-22
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    • 2018
  • This study was conducted to investigate that the nutrition labeling use is associated with demographic and psychosocial factors according to each nutrition information on the nutrition labeling in Korean adults. The study subjects (N=1,140) were individuals who were aged 20 years and more and answered on the question of nutrition label use and who participated in the Korean National Health Examination and Nutrition Survey (KNHANES) in 2010.As age older, there was more interest in information such as sugar, protein, fat, cholesterol than calories. In contrast, as age younger, there was more interest intrans-fat, sodium as well as calories. As higher education level, there were more aware of trans-fat, sodium and calories. From the result that the most interested nutrition items were significantly different by democratic factors, we could understand interested nutrient information on the nutrition labels could change according to individual specific education. Therefore, this can also provide basic data for systematic education program by nutrition label use.

New Splitting Criteria for Classification Trees

  • Lee, Yung-Seop
    • Communications for Statistical Applications and Methods
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    • v.8 no.3
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    • pp.885-894
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    • 2001
  • Decision tree methods is the one of data mining techniques. Classification trees are used to predict a class label. When a tree grows, the conventional splitting criteria use the weighted average of the left and the right child nodes for measuring the node impurity. In this paper, new splitting criteria for classification trees are proposed which improve the interpretablity of trees comparing to the conventional methods. The criteria search only for interesting subsets of the data, as opposed to modeling all of the data equally well. As a result, the tree is very unbalanced but extremely interpretable.

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Antecedents of Manufacturer's Private Label Program Engagement : A Focus on Strategic Market Management Perspective (제조업체 Private Labels 도입의 선행요인 : 전략적 시장관리 관점을 중심으로)

  • Lim, Chae-Un;Yi, Ho-Taek
    • Journal of Distribution Research
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    • v.17 no.1
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    • pp.65-86
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    • 2012
  • The $20^{th}$ century was the era of manufacturer brands which built higher brand equity for consumers. Consumers moved from generic products of inconsistent quality produced by local factories in the $19^{th}$ century to branded products from global manufacturers and manufacturer brands reached consumers through distributors and retailers. Retailers were relatively small compared to their largest suppliers. However, sometime in the 1970s, things began to slowly change as retailers started to develop their own national chains and began international expansion, and consolidation of the retail industry from mom-and-pop stores to global players was well under way (Kumar and Steenkamp 2007, p.2) In South Korea, since the middle of the 1990s, the bulking up of retailers that started then has changed the balance of power between manufacturers and retailers. Retailer private labels, generally referred to as own labels, store brands, distributors own private-label, home brand or own label brand have also been performing strongly in every single local market (Bushman 1993; De Wulf et al. 2005). Private labels now account for one out of every five items sold every day in U.S. supermarkets, drug chains, and mass merchandisers (Kumar and Steenkamp 2007), and the market share in Western Europe is even larger (Euromonitor 2007). In the UK, grocery market share of private labels grew from 39% of sales in 2008 to 41% in 2010 (Marian 2010). Planet Retail (2007, p.1) recently concluded that "[PLs] are set for accelerated growth, with the majority of the world's leading grocers increasing their own label penetration." Private labels have gained wide attention both in the academic literature and popular business press and there is a glowing academic research to the perspective of manufacturers and retailers. Empirical research on private labels has mainly studies the factors explaining private labels market shares across product categories and/or retail chains (Dahr and Hoch 1997; Hoch and Banerji, 1993), factors influencing the private labels proneness of consumers (Baltas and Doyle 1998; Burton et al. 1998; Richardson et al. 1996) and factors how to react brand manufacturers towards PLs (Dunne and Narasimhan 1999; Hoch 1996; Quelch and Harding 1996; Verhoef et al. 2000). Nevertheless, empirical research on factors influencing the production in terms of a manufacturer-retailer is rather anecdotal than theory-based. The objective of this paper is to bridge the gap in these two types of research and explore the factors which influence on manufacturer's private label production based on two competing theories: S-C-P (Structure - Conduct - Performance) paradigm and resource-based theory. In order to do so, the authors used in-depth interview with marketing managers, reviewed retail press and research and presents the conceptual framework that integrates the major determinants of private labels production. From a manufacturer's perspective, supplying private labels often starts on a strategic basis. When a manufacturer engages in private labels, the manufacturer does not have to spend on advertising, retailer promotions or maintain a dedicated sales force. Moreover, if a manufacturer has weak marketing capabilities, the manufacturer can make use of retailer's marketing capability to produce private labels and lessen its marketing cost and increases its profit margin. Figure 1. is the theoretical framework based on a strategic market management perspective, integrated concept of both S-C-P paradigm and resource-based theory. The model includes one mediate variable, marketing capabilities, and the other moderate variable, competitive intensity. Manufacturer's national brand reputation, firm's marketing investment, and product portfolio, which are hypothesized to positively affected manufacturer's marketing capabilities. Then, marketing capabilities has negatively effected on private label production. Moderating effects of competitive intensity are hypothesized on the relationship between marketing capabilities and private label production. To verify the proposed research model and hypotheses, data were collected from 192 manufacturers (212 responses) who are producing private labels in South Korea. Cronbach's alpha test, explanatory / comfirmatory factor analysis, and correlation analysis were employed to validate hypotheses. The following results were drawing using structural equation modeling and all hypotheses are supported. Findings indicate that manufacturer's private label production is strongly related to its marketing capabilities. Consumer marketing capabilities, in turn, is directly connected with the 3 strategic factors (e.g., marketing investment, manufacturer's national brand reputation, and product portfolio). It is moderated by competitive intensity between marketing capabilities and private label production. In conclusion, this research may be the first study to investigate the reasons manufacturers engage in private labels based on two competing theoretic views, S-C-P paradigm and resource-based theory. The private label phenomenon has received growing attention by marketing scholars. In many industries, private labels represent formidable competition to manufacturer brands and manufacturers have a dilemma with selling to as well as competing with their retailers. The current study suggests key factors when manufacturers consider engaging in private label production.

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Response Modeling with Semi-Supervised Support Vector Regression (준지도 지지 벡터 회귀 모델을 이용한 반응 모델링)

  • Kim, Dong-Il
    • Journal of the Korea Society of Computer and Information
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    • v.19 no.9
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    • pp.125-139
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    • 2014
  • In this paper, I propose a response modeling with a Semi-Supervised Support Vector Regression (SS-SVR) algorithm. In order to increase the accuracy and profit of response modeling, unlabeled data in the customer dataset are used with the labeled data during training. The proposed SS-SVR algorithm is designed to be a batch learning to reduce the training complexity. The label distributions of unlabeled data are estimated in order to consider the uncertainty of labeling. Then, multiple training data are generated from the unlabeled data and their estimated label distributions with oversampling to construct the training dataset with the labeled data. Finally, a data selection algorithm, Expected Margin based Pattern Selection (EMPS), is employed to reduce the training complexity. The experimental results conducted on a real-world marketing dataset showed that the proposed response modeling method trained efficiently, and improved the accuracy and the expected profit.

Prediction of ship power based on variation in deep feed-forward neural network

  • Lee, June-Beom;Roh, Myung-Il;Kim, Ki-Su
    • International Journal of Naval Architecture and Ocean Engineering
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    • v.13 no.1
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    • pp.641-649
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    • 2021
  • Fuel oil consumption (FOC) must be minimized to determine the economic route of a ship; hence, the ship power must be predicted prior to route planning. For this purpose, a numerical method using test results of a model has been widely used. However, predicting ship power using this method is challenging owing to the uncertainty of the model test. An onboard test should be conducted to solve this problem; however, it requires considerable resources and time. Therefore, in this study, a deep feed-forward neural network (DFN) is used to predict ship power using deep learning methods that involve data pattern recognition. To use data in the DFN, the input data and a label (output of prediction) should be configured. In this study, the input data are configured using ocean environmental data (wave height, wave period, wave direction, wind speed, wind direction, and sea surface temperature) and the ship's operational data (draft, speed, and heading). The ship power is selected as the label. In addition, various treatments have been used to improve the prediction accuracy. First, ocean environmental data related to wind and waves are preprocessed using values relative to the ship's velocity. Second, the structure of the DFN is changed based on the characteristics of the input data. Third, the prediction accuracy is analyzed using a combination comprising five hyperparameters (number of hidden layers, number of hidden nodes, learning rate, dropout, and gradient optimizer). Finally, k-means clustering is performed to analyze the effect of the sea state and ship operational status by categorizing it into several models. The performances of various prediction models are compared and analyzed using the DFN in this study.

Laundering and Care of Knitwear Products (니트의류제품의 세탁 및 관리현황조사)

  • Ko, Soon-Young;Kim, Cha-Hyun;Park, Jin-Hee;Lee, Hyun-Ju;Lee, Jee-Hyun;Lee, Kyu-Hye
    • Journal of the Korean Society of Clothing and Textiles
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    • v.31 no.9_10
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    • pp.1364-1372
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    • 2007
  • The purpose of this research is to provide the fundamental data about knitwear laundering and care. A questionnaire was designed and survey data was colleted from male and female consumers. Descriptive statistics, Chi-squire and t-test were conducted for statistical analysis. Results indicated that female consumers were more susceptible with 'handle with care' or 'fragile label' than male consumers. Females preferred hand washing whereas males preferred dry cleaning. Marital status of respondents did not affect awareness of care labels. Consumers considered more about stability of product shape than stain removal. Although most respondents followed proper ways to knitwear care and laundering, they felt that technical knowledge or deep understanding about washing is needed. In order to increase consumer satisfaction with knitwear products, the importance of care labels should be acknowledged.

High Representation based GAN defense for Adversarial Attack

  • Sutanto, Richard Evan;Lee, Suk Ho
    • International journal of advanced smart convergence
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    • v.8 no.1
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    • pp.141-146
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    • 2019
  • These days, there are many applications using neural networks as parts of their system. On the other hand, adversarial examples have become an important issue concerining the security of neural networks. A classifier in neural networks can be fooled and make it miss-classified by adversarial examples. There are many research to encounter adversarial examples by using denoising methods. Some of them using GAN (Generative Adversarial Network) in order to remove adversarial noise from input images. By producing an image from generator network that is close enough to the original clean image, the adversarial examples effects can be reduced. However, there is a chance when adversarial noise can survive the approximation process because it is not like a normal noise. In this chance, we propose a research that utilizes high-level representation in the classifier by combining GAN network with a trained U-Net network. This approach focuses on minimizing the loss function on high representation terms, in order to minimize the difference between the high representation level of the clean data and the approximated output of the noisy data in the training dataset. Furthermore, the generated output is checked whether it shows minimum error compared to true label or not. U-Net network is trained with true label to make sure the generated output gives minimum error in the end. At last, the remaining adversarial noise that still exist after low-level approximation can be removed with the U-Net, because of the minimization on high representation terms.

A Reconstruction of Classification for Iris Species Using Euclidean Distance Based on a Machine Learning (머신러닝 기반 유클리드 거리를 이용한 붓꽃 품종 분류 재구성)

  • Nam, Soo-Tai;Shin, Seong-Yoon;Jin, Chan-Yong
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.24 no.2
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    • pp.225-230
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    • 2020
  • Machine learning is an algorithm which learns a computer based on the data so that the computer can identify the trend of the data and predict the output of new input data. Machine learning can be classified into supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is a way of learning a machine with given label of data. In other words, a method of inferring a function of the system through a pair of data and a label is used to predict a result using a function inferred about new input data. If the predicted value is continuous, regression analysis is used. If the predicted value is discrete, it is used as a classification. A result of analysis, no. 8 (5, 3.4, setosa), 27 (5, 3.4, setosa), 41 (5, 3.5, setosa), 44 (5, 3.5, setosa) and 40 (5.1, 3.4, setosa) in Table 3 were classified as the most similar Iris flower. Therefore, theoretical practical are suggested.

3D Medical Image Data Augmentation for CT Image Segmentation (CT 이미지 세그멘테이션을 위한 3D 의료 영상 데이터 증강 기법)

  • Seonghyeon Ko;Huigyu Yang;Moonseong Kim;Hyunseung Choo
    • Journal of Internet Computing and Services
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    • v.24 no.4
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    • pp.85-92
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    • 2023
  • Deep learning applications are increasingly being leveraged for disease detection tasks in medical imaging modalities such as X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI). Most data-centric deep learning challenges necessitate the use of supervised learning methodologies to attain high accuracy and to facilitate performance evaluation through comparison with the ground truth. Supervised learning mandates a substantial amount of image and label sets, however, procuring an adequate volume of medical imaging data for training is a formidable task. Various data augmentation strategies can mitigate the underfitting issue inherent in supervised learning-based models that are trained on limited medical image and label sets. This research investigates the enhancement of a deep learning-based rib fracture segmentation model and the efficacy of data augmentation techniques such as left-right flipping, rotation, and scaling. Augmented dataset with L/R flipping and rotations(30°, 60°) increased model performance, however, dataset with rotation(90°) and ⨯0.5 rescaling decreased model performance. This indicates the usage of appropriate data augmentation methods depending on datasets and tasks.