• 복식
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• 제58권4호
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• pp.58-71
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• 2008

The demand for personalized products and service of apparel product has increased dramatically. In order to acquire a personalized apparel product, consumers may have to sacrifice more expense or time. The purpose of this study was to investigate various personalization strategies in apparel business and to identify antecedents that influence the process. Clothing involvement and two price related variables (clothing expense and willingness to pay more) were included in the study as antecedents. Four personalization strategies were included in the study: design selection, size customization, in-store service and promotion personalization. For an empirical study, a conceptual model was designed and research questionnaire was developed. A measure of personalization of apparel shopping was developed based on existing scale items of prior research and a pilot study. Data from 766 men and women in their twenties to forties were used for statistical analysis. Structural Equation Modeling was used for the data analysis. Results indicated that the conceptual model was a good fit to data. Structural paths indicated that there was significant influence of clothing involvement on design selection and sales promotion personalization strategies. Involved consumers spent more on chothing products and were likely to pay more on personalized products and services. Monthly clothing expense influenced size customization significantly. It also had negative influence on service related personalization strategies. Consumers were willing to pay more when it comes to product related personalization strategies such as design and size but not necessarily to service related strategies. This study was an attempt to provide an in-depth and synthesized approach on consumer attitudes toward personalization of apparel products.

• 한국정보과학회논문지:시스템및이론
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• 제27권5호
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• pp.464-474
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• 2000

완전교환 통신은 행렬전이, 푸리에변환 혹은 분산 테이블 검색과 같은 여러 가지 응용에서 아주 많이 활용되는 통신 방법이다. 본 논문은 웜홀 방식을 채용한 2차원 토러스에서의 개시 지연 시간을 줄이기 위하여 분할 및 합병 (divide-and-conquer) 방식을 사용한 효율적인 완전교환 통신 알고리즘을 제 안한다. 전체망을 2x2 형태의 기본셀로 분할한 뒤 각 기본셀에서는 마스터노드라고 불리는 특정 노드를 지정하여 기본셀내의 여타 노드들의 메시지를 이 마스터노드가 수집한다. 이 마스터노드들이 다른 모든 노드로 보내질 메시지를 수집한 뒤 각 기본셀내의 모든 마스터 노드들만이 가상 망을 형성하여 망의 크기가 N/2 x N/2으로 줄어든 상태로 완전 교환 알고리즘을 수행한다. 마스터노드들간의 완전교환 연산을 수행 한 뒤 이 마스터노드들은 자기가 전담했던 여타 노드들의 메시지를 재분배해 줌으로써 주어진 완전교환 연산을 완료한다. 기존의 여러 가지 알고리즘과의 비교 분석을 제시하였으며 제시한 알고리즘이 약 2배 정도의 개시 지연시간 면에서 우수함을 보인다.

• 한국지능시스템학회논문지
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• 제23권1호
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• pp.7-11
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• 2013

전문가의 지식을 기반으로 한 추천시스템에 대한 다양한 연구가 최근 활발히 진행되고 있다. 지금까지의 전문가 기반 추천 시스템이 공통된 전문가 그룹의 지식을 바탕으로 모두에게 아이템을 추천하였다면, 본 논문에서는 개인의 필요와 전문가에 대한 관점을 반영한 개인화된 전문가 그룹의 지식을 기반으로 한 추천 시스템을 제안한다. 개인화된 전문가 그룹을 찾는 과정이 제안하는 추천 시스템에서 가장 중요한 부분이다. 이를 위해 개인화된 전문가를 효율적으로 찾아내는 지지 벡터 머신(SVM) 기반 기법을 제안한다. 추천 시스템에서 널리 사용되는 k 근접이웃 알고리즘과의 비교를 통하여서 개인화된 전문가를 기반으로 한 협업 필터링 추천 시스템의 효용성을 입증한다.

• 한국컴퓨터산업학회논문지
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• 제2권9호
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• pp.1175-1182
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• 2001

원투원 마케팅(데이터베이스 마케팅 또는 관계 마케팅)은 컴퓨터의 발전과 더불어 기업 및 고객에게 이익을 가져올 것이며, 또한 고객의 세일 및 광고에 변화를 가져올 여러 분야 중의 하나이다. 인터넷 쇼핑몰에서 지능적인 고객 서비스의 일환으로, 본 논문에서는 데이터 마이닝 기법으로 잘 알려진 장바구니 분석을 이용한 개인화 된 광고를 제공하는 기법을 제시하고자 한다. 추천 기법의 핵심적인 이론으로 통계학, 데이터 마이닝, 인공 지능, 규칙 기반 매칭 등이 있다. 개인화 된 추천을 위한 규칙 기반 관점에서, 개인화를 위한 마케팅 규칙은 일반적으로 마케팅 전문가로부터 추출되어 고객의 데이터를 갖고 추정한다. 그러나 마케팅 전문가로부터 규칙을 추출하기란 매우 어려울 뿐만 아니라, 작성된 지식 기반 규칙을 검증하고 유지하기도 어렵다. 본 논문에서는 장바구니 분석 기법을 이용하여, 크로스 세일 마케팅 규칙을 추출한 뒤, 고객이 인터넷 쇼핑몰에 방문했을 때 개인화 된 광고를 제공하는데 초점을 두기로 한다.

• 대한예방한의학회지
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• 제20권1호
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• pp.99-110
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• 2016

In Korea and China, traditional medicine's holistic approaches, based on the views of whole-body and whole-person, have been applied to make the solution of health problem. However, these holistic approaches of traditional korea or chinese medicine have been limited in interpreting their theories in a view of modern scientific aspects of medicine. This limitation seems to be mainly due to the reductionism approaches of modern scientific medicine. Traditionally, science has taken a reductionism approach; dissecting biological systems into their constituent parts and studying them in isolation. However, systems biology based on omics technologies is providing a new thought and method for traditional medicine's research and interpretation. Systems biology uses integrity study as the characteristic and bioinformatic technology as the key method for connecting reductionism and holism. Therefore, it has much in common with the theory of traditional medicine. It was reviewed that how systems biology is applied to traditional medicine in Korea and China. Also it was suggested that more future researches on interpretation between traditional medicine and systems biology must be focused on personalized medicine since systems biology will have a major impact on future personalized therapeutic approaches.

• ETRI Journal
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• 제35권6호
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• pp.1105-1114
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• 2013

Owing to the large number of video programs available, a method for accessing preferred videos efficiently through personalized video summaries and clips is needed. The automatic recognition of user states when viewing a video is essential for extracting meaningful video segments. Although there have been many studies on emotion recognition using various user responses, electroencephalogram (EEG)-based research on preference recognition of videos is at its very early stages. This paper proposes classification models based on linear and nonlinear classifiers using EEG features of band power (BP) values and asymmetry scores for four preference classes. As a result, the quadratic-discriminant-analysis-based model using BP features achieves a classification accuracy of 97.39% (${\pm}0.73%$), and the models based on the other nonlinear classifiers using the BP features achieve an accuracy of over 96%, which is superior to that of previous work only for binary preference classification. The result proves that the proposed approach is sufficient for employment in personalized video segmentation with high accuracy and classification power.

• Journal of Nutrition and Health
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• 제55권1호
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• pp.1-9
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• 2022

In the past few decades, great progress has been made on understanding the interaction between nutrition and health status. But despite this wealth of knowledge, health problems related to nutrition continue to increase. This leads us to postulate that the continuing trend may result from a lack of consideration for intra-individual biological variation on dietary responses. Precision nutrition utilizes personal information such as age, gender, lifestyle, diet intake, environmental exposure, genetic variants, microbiome, and epigenetics to provide better dietary advices and interventions. Recent technological advances in the artificial intelligence, big data analytics, cloud computing, and machine learning, have made it possible to process data on a scale and in ways that were previously impossible. A big data platform is built by collecting numerous parameters such as meal features, medical metadata, lifestyle variation, genome diversity and microbiome composition. Sophisticated techniques based on machine learning algorithm can be used to integrate and interpret multiple factors and provide dietary guidance at a personalized or stratified level. The development of a suitable machine learning algorithm would make it possible to suggest a personalized diet or functional food based on analysis of intra-individual metabolic variation. This novel precision nutrition might become one of the most exciting and promising approaches of improving health conditions, especially in the context of non-communicable disease prevention.

• 대한두경부종양학회지
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• 제39권1호
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• pp.1-9
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• 2023

Technological advancement in human genome analysis and ICT (information & communication technologies) brought 'precision medicine' into our clinical practice. Precision medicine is a novel medical approach that provides personalized treatments tailored to each individual by precisely segmenting patient populations, based on robust data including a person's genetic information, disease information, lifestyle information, etc. Precision medicine has a potential to be applied to treating a range of tumors, in addition to non-small cell lung cancer, in which precision oncology has been actively practiced. In this article, we are reviewing precision medicine in head and neck cancer (HNC) with focus on tumor agnostic biomarkers and treatments such as NTRK, MSI-H/dMMR, TMB-H and BRAF V600E, all of which were recently approved by U.S. Food and Drug Administration (FDA).

• Clinical and Experimental Vaccine Research
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• 제13권2호
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• pp.105-120
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• 2024

Vaccines are one of the most important and effective tools in the prevention of infectious diseases and research about all the aspects of vaccinology are essential to increase the number of available vaccines more and more safe and effective. Despite the unquestionable value of vaccinations, vaccine hesitancy has spread worldwide compromising the success of vaccinations. Currently, the main purpose of vaccination campaigns is the immunization of whole populations with the same vaccine formulations and schedules for all individuals. A personalized vaccinology approach could improve modern vaccinology counteracting vaccine hesitancy and giving great benefits for human health. This ambitious purpose would be possible by facing and deepening the areas of vaccinomics and adversomics, two innovative areas of study investigating the role of a series of variables able to influence the immune response to vaccinations and the development of serious side effects, respectively. We reviewed the recent scientific knowledge about these innovative sciences focusing on genetic and non-genetic basis involved in the individual response to vaccines in terms of both immune response and side effects.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.17-25
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• 2023

The alarming global prevalence of Type 2 Diabetes Mellitus (T2DM) has catalyzed an urgent need for robust, early diagnostic methodologies. This study unveils a pioneering approach to predicting T2DM, employing the Extreme Gradient Boosting (XGBoost) algorithm, renowned for its predictive accuracy and computational efficiency. The investigation harnesses a meticulously curated dataset of 4303 samples, extracted from a comprehensive Chinese research study, scrupulously aligned with the World Health Organization's indicators and standards. The dataset encapsulates a multifaceted spectrum of clinical, demographic, and lifestyle attributes. Through an intricate process of hyperparameter optimization, the XGBoost model exhibited an unparalleled best score, elucidating a distinctive combination of parameters such as a learning rate of 0.1, max depth of 3, 150 estimators, and specific colsample strategies. The model's validation accuracy of 0.957, coupled with a sensitivity of 0.9898 and specificity of 0.8897, underlines its robustness in classifying T2DM. A detailed analysis of the confusion matrix further substantiated the model's diagnostic prowess, with an F1-score of 0.9308, illustrating its balanced performance in true positive and negative classifications. The precision and recall metrics provided nuanced insights into the model's ability to minimize false predictions, thereby enhancing its clinical applicability. The research findings not only underline the remarkable efficacy of XGBoost in T2DM prediction but also contribute to the burgeoning field of machine learning applications in personalized healthcare. By elucidating a novel paradigm that accentuates the synergistic integration of multifaceted clinical parameters, this study fosters a promising avenue for precise early detection, risk stratification, and patient-centric intervention in diabetes care. The research serves as a beacon, inspiring further exploration and innovation in leveraging advanced analytical techniques for transformative impacts on predictive diagnostics and chronic disease management.