Selection Of Attributes Research Articles

컨조인트 분석을 활용한 홍삼커피 선택속성의 상대적 중요도 분석

컨조인트 분석을 활용한 홍삼커피 선택속성의 상대적 중요도 분석

Stated-Preference Survey Design and Testing in Health Applications.

Following the conceptualization of a well-formulated and relevant research question, selection of an appropriate stated-preference method, and related methodological issues, researchers are tasked with developing a survey instrument. A major goal of designing a stated-preference survey for health applications is to elicit high-quality data that reflect thoughtful responses from well-informed respondents. Achieving this goal requires researchers to design engaging surveys that maximize response rates, minimize hypothetical bias, and collect all the necessary information needed to answer the research question. Designing such a survey requires researchers to make numerous interrelated decisions that build upon the decision context, selection of attributes, and experimental design. Such decisions include considering the setting(s) and study population in which the survey will be administered, the format and mode of administration, and types of contextual information to collect. Development of a survey is an interactive process in which feedback from respondents should be collected and documented through qualitative pre-test interviews and pilot testing. This paper describes important issues to consider across all major steps required to design and test a stated-choice survey to elicit patient preferences for health preference research.

A Multilevel Spatial Model to Investigate Voting Behaviour in the 2019 UK General Election

This paper presents a modelling framework which can detect the simultaneous presence of two different types of spatial process. The first is the variation from a global mean resulting from a geographical unit’s ‘vertical’ position within a nested hierarchical structure such as the county and region where it is situated. The second is the variation at the smaller scale of individual units due to the ‘horizontal’ influence of nearby locations. The former is captured using a multi-level modelling structure while the latter is accounted for by an autoregressive component at the lowest level of the hierarchy. Such a model not only estimates spatially-varying parameters according to geographical scale, but also the relative contribution of each process to the overall spatial variation. As a demonstration, the study considers the association of a selection of socio-economic attributes with voting behaviour in the 2019 UK general election. It finds evidence of the presence of both types of spatial effects, and describes how they suggest different associations between census profile and voting behaviour in different parts of England and Wales.

Risk factors for persistent versus episodic mother-to-infant bonding difficulties in postpartum women in a nationwide Japanese longitudinal study

BackgroundThe quality of mother-to-infant bonding (MIB) is a crucial determinant of nurturing behaviors and infant development, with bonding difficulties (MIBD) posing a substantial threat. While it is essential to identify MIBD risk factors, previous studies have generally examined MIBD at one time point, leaving the contributors to persistent MIBD uncertain. This study aims to discern longitudinal risk factors for persistent versus episodic MIBD. MethodsWe evaluated 1833 postpartum Japanese women who delivered in the past twelve months, utilizing the Mother-to-Infant Bonding Scale (MIBS) and other sociodemographic, health, pregnancy, childbirth, and child-rearing related data (T1). Follow-up data were obtained six months later (T2). MIBD was defined as a MIBS score of five or more, with “persistent” and “episodic” MIBD classified based on its occurrence at both or either one of the time points, respectively. Logistic generalized estimating equations and inverse probability weighting were used to identify risk factors and address selective attribution bias. ResultsOf the subjects, 15.8 % reported episodic and 11.3 % reported persistent MIBD. Shared risk factors for both conditions included postpartum depression and low levels of family support (OR = 1.501–6.343). However, pre-pregnancy underweight status (OR = 1.698) was a unique risk factor for episodic MIBD, while first-time motherhood, no or discontinuation of breastfeeding, and later postpartum months (OR = 1.540–3.179) were distinctive risk factors for persistent MIBD. ConclusionWe identified both shared and unique risk factors for episodic and persistent MIBD. Particular attention should be afforded to persistent MIBD and early and proactive interventions to mitigate identified risk factors are recommended.

Comparison and selection of criteria and features in morphological analysis

The article deals with the issues of using the method of pairwise comparisons in the selection of attributes and criteria in morphological analysis. The paper specifies the possibilities of solving the problems of collecting, analysing, evaluating and selecting rational options. The fundamental impossibility of building accurate models leads to the necessity of decision making under conditions of uncertainty. The analytic hierarchy process is used to solve the problems. The formulation and method of solving the problem are given. Using an example, the application of the proposed approach is shown. In conclusion and conclusions are drawn and the advantages of the stated algorithm are considered. The use of the proposed approach allows increasing the validity of the decisions made in morphological analysis and thus increasing the efficiency of research.

Investigating the best attributes in selection of e-tailer for purchasing footwear: a conjoint approach

Investigating the best attributes in selection of e-tailer for purchasing footwear: a conjoint approach

Patient preferences for the treatment of chronic cough: a discrete choice experiment

BackgroundChronic cough is common, negatively affects quality of life and has limited treatment options. Inhibition of purinergic signalling is a promising therapeutic approach but is associated with taste-related adverse effects....

Investigating the determinants and effects of prestige sensitivity in fashion retailing

Targeting the prestige fashion segment enables fashion retailers to continue growing while facing a challenging environment. To attract prestige-sensitive consumers, it is important to understand their buying motivations and responses to retail actions. Thus, this paper examines the motivational predictors and effects of prestige sensitivity in fashion retailing, using the symbolic self-completion theory as a framework. It also explores the moderating role of fashion innovativeness. The data collected from shoppers of apparel products (N = 289) in Croatia were analyzed using structural equation modelling (SEM). The results show that recognition enhancement, sexual attraction, and recreation shopping motivations, which help enhance individuals’ self-identity in society, drive prestige sensitivity. At the same time, fashion innovativeness significantly moderates the relationship between recreation motive and prestige sensitivity. Prestige-seeking consumers positively respond to mannequin displays, spend more time shopping, and focus more on symbolic apparel attributes while shopping. This study contributes to the self-completion theory applied in fashion retailing by developing and testing the model that links prestige sensitivity with shopping motivations, fashion innovativeness, the selection of apparel attributes, response to visual merchandising, and time expenditure. The findings provide recommendations for marketers on how to develop fashion products/prestige brands in line with the expectations of prestige-seeking shoppers, target this market segment more efficiently, and increase the effectiveness of marketing efforts.

Coarse-to-fine Image Aesthetics Assessment With Dynamic Attribute Selection

Coarse-to-fine Image Aesthetics Assessment With Dynamic Attribute Selection

A study on the consumer behavior and attitude toward low-sodium convenience store foods.

This study aims to explore the potential of convenience stores as platforms for healthy food consumption, including low-sodium options, in response to the increasing trend of meal behaviors at convenience stores and the growing demand for healthy eating. In the study, 627 Korean participants aged 10 to 39 were involved. A self-reported questionnaire survey was used and questions were regarding purchase patterns, consumption behaviors, perceptions and selection attributes of convenience store foods, and consumer perception factors for low-sodium options. Data analysis was conducted using SPSS 26.0 (SPSS, Version 26.0 for Windows, SPSS Inc., Chicago, IL, USA). The study uncovered significant disparities in the consumption behavior and perception of convenience store foods, as well as variations in the importance and satisfaction levels with convenience store food attributes, including consumer perception factors for low-sodium options, based on sex and age. Furthermore, it was observed that awareness of the need for low-sodium options significantly influenced purchase intentions. This study analyzed consumer attitude toward low-sodium convenience store foods to assess the potentiality for promoting healthy eating in convenience stores. These findings indicate the important role that convenience stores can play as platforms for healthy food sales.

수정된 IPA를 활용한 K-급식 선택속성과 서비스 개선에 관한 연구

In this study, K-meal service was classified into industries and educational institutions, and we tried to analyze the difference in recognizing the importance-performance of K-meal selection attributes according to consumer types. this study used a total of 240 copies for empirical analysis targeting consumers and college students aged 20 or older living in Seoul and the metropolitan area who have experience using K-meals. first, as a result of analyzing the importance-performance of K-meal selection attributes it was found that the taste, price and quality of food and the special and diverse menu selection attributes were considered the most important. second, K-meal selection attributes using IPA are the most important for consumers, and the taste, quality and price of food, special and diverse menus, and hygiene in restaurants were found to be attributes. third, the importance-performance of K-meal selection attributes according to industries and educational institutions showed the taste of food and various menus as the most highly recognized attributes.

The Effect of Tourist Destination Selection Attributes on Satisfaction and Revisit Intention of the MZ Generation

The Effect of Tourist Destination Selection Attributes on Satisfaction and Revisit Intention of the MZ Generation

Detection for melanoma skin cancer through ACCF, BPPF, and CLF techniques with machine learning approach

Intense sun exposure is a major risk factor for the development of melanoma, an abnormal proliferation of skin cells. Yet, this more prevalent type of skin cancer can also develop in less-exposed areas, such as those that are shaded. Melanoma is the sixth most common type of skin cancer. In recent years, computer-based methods for imaging and analyzing biological systems have made considerable strides. This work investigates the use of advanced machine learning methods, specifically ensemble models with Auto Correlogram Methods, Binary Pyramid Pattern Filter, and Color Layout Filter, to enhance the detection accuracy of Melanoma skin cancer. These results suggest that the Color Layout Filter model of the Attribute Selection Classifier provides the best overall performance. Statistics for ROC, PRC, Kappa, F-Measure, and Matthews Correlation Coefficient were as follows: 90.96% accuracy, 0.91 precision, 0.91 recall, 0.95 ROC, 0.87 PRC, 0.87 Kappa, 0.91 F-Measure, and 0.82 Matthews Correlation Coefficient. In addition, its margins of error are the smallest. The research found that the Attribute Selection Classifier performed well when used in conjunction with the Color Layout Filter to improve image quality.

Robust attribute selection to improve the Parkinson's disease classification: a hybrid approach

Parkinson’s disease (PD) is known to be a neurodegenerative syndrome that progresses chronically. As a result of the damage or death of brain neurons that generate dopamine patients tend to face difficulty when performing simple everyday tasks like walking, writing, or speaking. The main contribution of this work presents a hybrid method for improving predicting PD. This methodology has been obtained by means of testing a number of different combinations of classification algorithms and approaches for selecting attributes. A total of three attributes selection methods (correlation, information gain, and variance threshold) and three classifiers (decision trees (DT), naive bayes (NB), and support vector machine (SVM)) have been adopted. The speech data set provided by University of California-Irvine (UCI) machine learning (ML) repository is adopted to analyze the performance of different combinations. The combination of information gain and DT classifier achieved the best performance rather than other combination methods, reaching a classification accuracy of (97.43%). Finally, an additional comparison of the performance analysis with the results of previous studies was made and it was found that the proposed methodology proved to outperform the results of other studies conducted in this field.

N-Dimensional intuitionistic fuzzy index matrix representation of multidimensional data partitioning methods

In the present research paper, the data partitioning operations are investigated. Their functionalities are discussed. n-Dimensional intuitionistic fuzzy index matrix is used to represent the multidimensional data partitioning. Attributes selection and values selection by predicates is presented.

Artificial intelligence model for prediction of cardiovascular disease: An empirical study

Cardiovascular disease (CVD) is a disease related to the heart and blood vessels. Prediction of CVD is essential for early detection and diagnosis, which is however compounded by the complex interplay between medical history, physical examination outcomes, and imaging results. While the existing automated systems are fraught with the usage of irrelevant and redundant attributes, artificial intelligence (AI) helps in the identification of potential CVD populations by prediction models. This work aims at developing an AI model for predicting CVD using different classifications of machine learning techniques. The CVD dataset was obtained from the UCI repository containing about 76 cardiac attributes for training in various machine learning models, which include a hybrid of artificial neural network-genetic algorithm (ANN-GA), artificial neural network, support vector machine (SVM), K-means, K-nearest neighbor (KNN), and decision tree (DT). The performance of the models was measured in terms of accuracy, means square error, sensitivity, specificity, and precision. The results showed that the hybrid model of ANN-GA performs better with an accuracy of 86.4%, compared to the SVM, K-means, KNN, and DT measured at 84.0%, 59.6%, 79.0%, and 77.8%, respectively. It was observed that the system performs better as the number of datasets increases in the database, with a fewer selection of attributes using genetic algorithm for selection. Thus, the ANN-GA model is recommended for CVD prediction and diagnosis.

Efficient Feature Selection in High Dimensional Data Based on Enhanced Binary Chimp Optimization Algorithms and Machine Learning

Feature selection with the highest performance accuracy is the biggest win for multidimensional data. The Chimpanzee Optimization Algorithm (ChOA) serves as a crucial technique for dealing with multidimensional global optimization issues. However, ChOA often lacks fast convergence and good selection of sensitive attributes leading to poor performance. To address these issues, most significant features were selected using two variants of ChOA called BChimp1 and BChimp2 (BChimp1 and BChimp are available at : https://www.mathworks.com/matlabcentral/fileexchange/133267-binary-chimpoptimization-algorithm-forfeatures-selection. September 22, 202). BChimp1 selects the optimal solution from the four best possible solutions and it applies a stochastic crossover on four moving solutions to deeply speed-up convergence level. BChimp2 uses the sigmoid function to select the significant features. Then, these features were trained using six-well known classifiers. The proposed techniques tend to select the most significant features, speed up the convergence rate and decrease training time for high-dimensional data. 23 standard datasets with six well-known classifiers were employed to assess the performance of BChimp1 and BChimp2. Experimental results validate the efficiency of BChimp1 and BChimp2 in enhancing accuracy by 83.83% and 82.02%, and reducing dimensionality by 42.77% and 72.54%, respectively. However, time-evaluation results of BChimp1 and BChimp2 in all datasets showed fast convergence and surpassed current optimization algorithms such as PSO, GWA, GOA, and GA.

The Consumption Orientation Level and Behavioral Intention of Beauty Shop According to the Pandemic Era Risk Perception and the Measures for Vitalizing the Service Industry

The ultimate purpose of this study is to present the differentiated strategies of the beauty service industry by drawing the vitalization measures for its new operation and development into the high-value added industry in the changed time flow of the pandemic era. Total 756 questionnaires were used for the final analysis. The collected data was analyzed by using the SPSS 22.0 Statistical Program and AMOS 22.0 Statistical Program. First, the risk perception had significant effects on consumption orientation. Second, the direct effects of risk perception on behavioral intention were not statistically significant while there were indirect effects of mediating the consumption orientation. Third, the direct effects of risk perception on selection attributes were not statistically significant while there were indirect effects of mediating the consumption orientation. Fourth, the consumption orientation had significant effects on behavioral intention. Fifth, the consumption orientation had significant effects on selection attributes. Sixth, the behavioral intention had significant effects on selection attributes.

Popular Attributes Selection in Formulating Control Variables of House Preference for Assisting Hedonic Pricing Model in Malaysia

The house is a vital facility with a high financial investment portion of livelihood. Many studies discovered the significant variables influencing willingness to pay (WTP) for house preferences using the hedonic pricing model to reveal the living needs. Numerous literature reviews have been done on the theory’s functional models, methodology, and application. However, less focus has been given to the appropriate control variables applied in the model according to the location. Thus, researchers often needed help identifying the suitable variables to be adopted as the control attributes possibly influencing WTP for the hedonic pricing model to minimize the model’s misspecification or over-specification. Hence, this study aims to review the empirical house preference variables, mainly in Malaysia case studies, and identify the popular attributes based on functional categories and their impact on the WTP. The identification narratively uses 35 articles from five scholarly databases and discovered 87 empirical attributes categorized into four functional variables: 'locational and neighbourhood,' 'building’s structural, ’natural and environmental,' and 'socio-related.’ This reveals the understanding of house preferences so that a proper data acquisition process, methods, and model functional forms can be prudently considered when dealing with the hedonic pricing model in developing countries, specifically Malaysia.

Attribute Selection Hybrid Network Model for risk factors analysis of postpartum depression using Social media

Background and objectivePostpartum Depression (PPD) is a frequently ignored birth-related consequence. Social network analysis can be used to address this issue because social media network serves as a platform for their users to communicate with their friends and share their opinions, photos, and videos, which reflect their moods, feelings, and sentiments. In this work, the depression of delivered mothers is identified using the PPD score and segregated into control and depressed groups. Recently, to detect depression, deep learning methods have played a vital role. However, these methods still do not clarify why some people have been identified as depressed.MethodsWe have developed Attribute Selection Hybrid Network (ASHN) to detect the postpartum depression diagnoses framework. Later analysis of the post of mothers who have been confirmed with the score calculated by the experts of the field using physiological questionnaire score. The model works on the analysis of the attributes of the negative Facebook posts for Depressed user Diagnosis, which is a large general forum. This framework explains the process of analyzing posts containing Sentiment, depressive symptoms, and reflective thinking and suggests psycho-linguistic and stylistic attributes of depression in posts.ResultsThe experimental results show that ASHN works well and is easy to understand. Here, four attribute networks based on psychological studies were used to analyze the different parts of posts by depressed users. The results of the experiments show the extraction of psycho-linguistic markers-based attributes, the recording of assessment metrics including Precision, Recall and F1 score and visualization of those attributes were used title-wise as well as words wise and compared with daily life, depression and postpartum depressed people using Word cloud. Furthermore, a comparison to a reference with Baseline and ASHN model was carried out.ConclusionsAttribute Selection Hybrid Network (ASHN) mimics the importance of attributes in social media posts to predict depressed mothers. Those mothers were anticipated to be depressed by answering a questionnaire designed by domain experts with prior knowledge of depression. This work will help researchers look at social media posts to find useful evidence for other depressive symptoms.