• Title/Summary/Keyword: Customized classification

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A Design of Customized Market Analysis Scheme Using SVM and Collaboration Filtering Scheme (SVM과 협업적 필터링 기법을 이용한 소비자 맞춤형 시장 분석 기법 설계)

  • Jeong, Eun-Hee;Lee, Byung-Kwan
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.9 no.6
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    • pp.609-616
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    • 2016
  • This paper is proposed a customized market analysis method using SVM and collaborative filtering. The proposed customized market analysis scheme is consists of DC(Data Classification) module, ICF(Improved Collaborative Filtering) module, and CMA(Customized Market Analysis) module. DC module classifies the characteristics of on-line and off-line shopping mall and traditional markets into price, quality, and quantity using SVM. ICF module calculates the similarity by adding age weight and job weight, and generates network using the similarity of purchased item each users, and makes a recommendation list of neighbor nodes. And CMA module provides the result of customized market analysis using the data classification result of DC module and the recommendation list of ICF module. As a result of comparing the proposed customized recommendation list with the existing user based recommendation list, the case of recommendation list using the existing collaborative filtering scheme, precision is 0.53, recall is 0.56, and F-measure is 0.57. But the case of proposed customized recommendation list, precision is 0.78, recall is 0.85, and F-measure is 0.81. That is, the proposed customized recommendation list shows more precision.

Personal Driving Style based ADAS Customization using Machine Learning for Public Driving Safety

  • Giyoung Hwang;Dongjun Jung;Yunyeong Goh;Jong-Moon Chung
    • Journal of Internet Computing and Services
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    • v.24 no.1
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    • pp.39-47
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    • 2023
  • The development of autonomous driving and Advanced Driver Assistance System (ADAS) technology has grown rapidly in recent years. As most traffic accidents occur due to human error, self-driving vehicles can drastically reduce the number of accidents and crashes that occur on the roads today. Obviously, technical advancements in autonomous driving can lead to improved public driving safety. However, due to the current limitations in technology and lack of public trust in self-driving cars (and drones), the actual use of Autonomous Vehicles (AVs) is still significantly low. According to prior studies, people's acceptance of an AV is mainly determined by trust. It is proven that people still feel much more comfortable in personalized ADAS, designed with the way people drive. Based on such needs, a new attempt for a customized ADAS considering each driver's driving style is proposed in this paper. Each driver's behavior is divided into two categories: assertive and defensive. In this paper, a novel customized ADAS algorithm with high classification accuracy is designed, which divides each driver based on their driving style. Each driver's driving data is collected and simulated using CARLA, which is an open-source autonomous driving simulator. In addition, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) machine learning algorithms are used to optimize the ADAS parameters. The proposed scheme results in a high classification accuracy of time series driving data. Furthermore, among the vast amount of CARLA-based feature data extracted from the drivers, distinguishable driving features are collected selectively using Support Vector Machine (SVM) technology by comparing the amount of influence on the classification of the two categories. Therefore, by extracting distinguishable features and eliminating outliers using SVM, the classification accuracy is significantly improved. Based on this classification, the ADAS sensors can be made more sensitive for the case of assertive drivers, enabling more advanced driving safety support. The proposed technology of this paper is especially important because currently, the state-of-the-art level of autonomous driving is at level 3 (based on the SAE International driving automation standards), which requires advanced functions that can assist drivers using ADAS technology.

Classification System of Fashion Emotion for the Standardization of Data (데이터 표준화를 위한 패션 감성 분류 체계)

  • Park, Nanghee;Choi, Yoonmi
    • Journal of the Korean Society of Clothing and Textiles
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    • v.45 no.6
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    • pp.949-964
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    • 2021
  • Accumulation of high-quality data is crucial for AI learning. The goal of using AI in fashion service is to propose of a creative, personalized solution that is close to the know-how of a human operator. These customized solutions require an understanding of fashion products and emotions. Therefore, it is necessary to accumulate data on the attributes of fashion products and fashion emotion. The first step for accumulating fashion data is to standardize the attribute with coherent system. The purpose of this study is to propose a fashion emotional classification system. For this, images of fashion products were collected, and metadata was obtained by allowing consumers to describe their emotions about fashion images freely. An emotional classification system with a hierarchical structure, was then constructed by performing frequency and CONCOR analyses on metadata. A final classification system was proposed by supplementing attribute values with reference to findings from previous studies and SNS data.

A proper folder recommendation technique using frequent itemsets for efficient e-mail classification (효과적인 이메일 분류를 위한 빈발 항목집합 기반 최적 이메일 폴더 추천 기법)

  • Moon, Jong-Pil;Lee, Won-Suk;Chang, Joong-Hyuk
    • Journal of the Korea Society of Computer and Information
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    • v.16 no.2
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    • pp.33-46
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    • 2011
  • Since an e-mail has been an important mean of communication and information sharing, there have been much effort to classify e-mails efficiently by their contents. An e-mail has various forms in length and style, and words used in an e-mail are usually irregular. In addition, the criteria of an e-mail classification are subjective. As a result, it is quite difficult for the conventional text classification technique to be adapted to an e-mail classification efficiently. An e-mail classification technique in a commercial e-mail program uses a simple text filtering technique in an e-mail client. In the previous studies on automatic classification of an e-mail, the Naive Bayesian technique based on the probability has been used to improve the classification accuracy, and most of them are on an e-mail in English. This paper proposes the personalized recommendation technique of an email in Korean using a data mining technique of frequent patterns. The proposed technique consists of two phases such as the pre-processing of e-mails in an e-mail folder and the generating a profile for the e-mail folder. The generated profile is used for an e-mail to be classified into the most appropriate e-mail folder by the subjective criteria. The e-mail classification system is also implemented, which adapts the proposed technique.

Factors Influencing Empowerment of Customized Home Visiting Health Care Services Beneficiaries (방문건강관리사업 대상자의 자기역량 정도)

  • Park, Jeong Sook;Oh, Yun Jung
    • Journal of Korean Public Health Nursing
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    • v.26 no.3
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    • pp.491-503
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    • 2012
  • Purpose: The purpose of this study was to measure empowerment and to identify factors influencing empowerment. Method: Subjects included 767 clients registered with the customized home visiting health services in Daegu. Data collection was performed from June 3 to July 30, 2011. Descriptive statistics, ${\chi}^2$ test, ANOVA, and stepwise multiple regression were used in this study. Results: The mean score for total empowerment was 3.01(${\pm}0.28$). In subscales of total empowerment, the score for individual empowerment was 2.97(${\pm}0.36$), the score for interpersonal relationship empowerment was 3.09(${\pm}0.34$), and the score for political-social empowerment was 2.96(${\pm}0.48$). Job, education, economic status, living arrangement, and client classification were significant factors related to total empowerment in these clients. Job, education, economic status, types of health insurance, living arrangement, age, and client classification were significant factors related to individual empowerment, interpersonal relationship empowerment and political-social empowerment. 4.4 percent of the variance in total empowerment can be explained by education and living arrangement (Cum $R^2=0.044$, F=13.207, p<.001). Individual empowerment, interpersonal relationship empowerment, and political-social empowerment can be explained by education, job, economic status, and living arrangement. Conclusion: An empowerment intervention that includes general characteristics of clients is essential to improving empowerment of customized home visiting health care services beneficiaries.

Smart Senior Job Search: The Elderly-oriented Services for Job Searching with the Spatial Information (공간정보를 활용한 스마트 고령자일자리 맞춤형 검색서비스)

  • Kim, Miyun;Seo, Dongjo
    • Journal of Korea Multimedia Society
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    • v.19 no.8
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    • pp.1433-1443
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    • 2016
  • In the cases of the major cities, high poverty rate of the elderly, immature pension policies, and insufficient market conditions, policies and services for the employment of the elderly decrease the desire for the job participation. It is time to prevent the problems of the elderly, and induce the reachable seniors to participate in social activities. This research provides the location-based, customized job-search service for the elderly in order to actively support the participation in the economic activities of the elderly. The goal of SSJS(Smart Senior Job Search) is to provide the individual elderly with the customized position. It prints the appropriate positions near user location based on the residential area, job classification, and the physical condition, and provides the mash-up of the selectable job range in the unit distance based on the map. This customized service, which enables the seniors to select the type of the jobs based on their physical, mental and life conditions of the seniors, supports the participation in economic activities of the elderly people, and contribute to the expansion of the social job positions for the elderly and the equalization of the local development.

Digital Customized Automation Technology Trends (디지털 커스터마이징 자동화 기술 동향)

  • Song, Eun-young
    • Fashion & Textile Research Journal
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    • v.23 no.6
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    • pp.790-798
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    • 2021
  • With digital technology innovation, increased data access and mobile network use by consumers, products and services are changing toward pursuing differentiated values for personalization, and personalized markets are rapidly emerging in the fashion industry. This study aims to identify trends in digital customized automation technology by deriving types of digital customizing and analyzing cases by type, and to present directions for the development of digital customizing processes and the use of technology in the future. As a research method, a literature study for a theoretical background, a case study for classification and analysis of types was conducted. The results of the study are as follows. The types of digital customizing can be classified into three types: 'cooperative customization', 'selective composition and combination', 'transparent suggestion', and automation technologies shown in each type include 3D printing, 3D virtual clothing, robot mannequin, human automatic measurement program, AR-based fitting service, big data, and AI-based curation function. With the development of digital automation technology, the fashion industry environment is also changing from existing manufacturing-oriented to consumer-oriented, and the production process is rapidly changing with IT and artificial intelligence-based automation technology. The results of this study hope that digital customized automation technology will meet various needs of personalization and customization and present the future direction of digital fashion technology, where fashion brands will expand based on the spread of digital technology.

Classifying Types of Local Governments for Urban Policies in the Metropolitan Era (대도시권 시대의 도시정책을 위한 기초지자체 유형 구분)

  • Kim, Geunyoung
    • Journal of Urban Science
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    • v.9 no.2
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    • pp.21-30
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    • 2020
  • The purpose of this study is to present a plan to distinguish 229 local governments nationwide by taking into account various characteristics such as population, employment, housing, and industry of the region for customized urban policies in the era of metropolitan areas. The National Statistical Portal (KOSIS) collected and standardized data related to population, housing, industry, and finance by region from 2000 to 2015 for the classification of regional types necessary for customized urban policies, and this was used to classify them into regional types that considered population, employment, housing and industry. The summary of the analysis results is as follows. First, as a result of the regional type classification, 10 key employment sites (4.4%), 5 employment centers (2.2%), 38 residential centers (16.6%), 20 growth areas (8.7%), 26 industrial cities (11.4%), 35 low-fertile farming and fishing villages (15.3%) and 95 stagnant areas (41.5%). Second, the Seoul metropolitan area is the most diverse type of metropolitan area in the country, with most of its core employment sites inside Seoul, residential centers inside and outside Seoul, and growth areas in the southeastern part of the country (Busan, Ulsan, and Gyeongsangnam-do) are mixed with industrial and growth areas centered around Busan, Ulsan and surrounding areas, while the rest of the local governments are found to be low-fertile farming villages or stagnant areas. Daegu (Daegu, Gyeongbuk) is an industrial city in Daegu, and the rest of the local governments are either low-density farming and fishing villages or stagnant areas. The Honam region (Gwangju and Jeolla) was found to be a low-mill farming and fishing village or stagnant area except for Gwangju, while the Chungcheong region (Daejeon, Sejong, and Chungcheong) was seen as a growth area with areas adjacent to Daejeon, Sejong, and the Seoul metropolitan area, and some industrial cities were included. Finally, the Gangwon area was mostly classified as low-density farming and fishing villages and stagnant areas.

Gender Classification of Speakers Using SVM

  • Han, Sun-Hee;Cho, Kyu-Cheol
    • Journal of the Korea Society of Computer and Information
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    • v.27 no.10
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    • pp.59-66
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    • 2022
  • This research conducted a study classifying gender of speakers by analyzing feature vectors extracted from the voice data. The study provides convenience in automatically recognizing gender of customers without manual classification process when they request any service via voice such as phone call. Furthermore, it is significant that this study can analyze frequently requested services for each gender after gender classification using a learning model and offer customized recommendation services according to the analysis. Based on the voice data of males and females excluding blank spaces, the study extracts feature vectors from each data using MFCC(Mel Frequency Cepstral Coefficient) and utilizes SVM(Support Vector Machine) models to conduct machine learning. As a result of gender classification of voice data using a learning model, the gender recognition rate was 94%.

The Estimation of the Population by Using the Estimated Appropriate Rate Based on Customized Classification of Agriculture, Livestock and Food Industry (농축산식품산업 특수분류 기반 추정적격률을 이용한 모집단 추정 )

  • Wee Seong Seung;Lee MinCheol;Kim Jin Min;Shin Yong Tae
    • KIPS Transactions on Software and Data Engineering
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    • v.12 no.3
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    • pp.117-124
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    • 2023
  • Through reorganization in 2008, The ministry of Agriculture, Food and Rural Affairs integrated management of the food industry by transferred functions which was scattered in the Ministry of Health and Welfare, and established comprehensive policies covering the primary, secondary, and tertiary industries. In the agricultural industry sector, new business concepts such as smart farm and food tech have recently emerged alongside the fourth industrial revolution. In order for the Ministry of Agriculture, Food, and Rural Affairs to develop appropriate policies for the fourth industrial revolution, it is necessary to accurately estimate the size of agricultural and livestock-related businesses. In 2017, the Ministry of Agriculture, Food, and Rural Affairs initiated research for the agriculture, livestock and food industry's special classification, which was approved by the National Statistical Office in 2020. The estimation of the agriculture, livestock and food industry's size based on special classification is crucial because it has a substantial impact on the formulation and significance of policies. In this paper, the appropriate rate was derived from samples extracted from the special classification and the Korean standard industrial classification. Proposed are a method for estimating the population of the agricultural and livestock food industry, as well as a method for calculating the appropriate rate that more accurately reflects the population than the method currently in use.