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Prediction Model of Survival Analysis for Customer Relationship Management

첫 페이지 보기
  • 발행기관
    보안공학연구지원센터(IJSEIA) 바로가기
  • 간행물
    International Journal of Software Engineering and Its Applications SCOPUS 바로가기
  • 통권
    Vol.10 No.9 (2016.09)바로가기
  • 페이지
    pp.9-18
  • 저자
    C.M.Velu, J. Yamini Devi, Vijay Krishna Dhulipalla, Debnath Bhattacharyya
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A284103

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원문정보

초록

영어
The survival analysis examines life-time of an item or human being or an animal. For example, 1) employee satisfaction may lie in promotion in a particular company. 2) Similarly, medical researchers are keenly interested in survival of patients by giving an excellent treatment for dangerous diseases. 3) For engineering equipment’s, reliability, availability of a component or an item plays major role in one of the following replacement policy: i)Individual replacement of an item ii)Group replacement of items iii)both (i) and (ii), to be adopted for the smooth functioning of the system to avoid shut down in a manufacturing company. 4) Some specific examples in medicine are after giving chemotherapy for a particular cancer, the patient lives many years beyond of medical history. As an example, if we know a patient survives 60 months and is then censored, use is made of the fact that the patient lived during the first 60 months. After the time of censoring, the censored value is dropped from any survival calculations. Considering our example, we don’t know how much beyond 60 months the patient survived, so this data is not used in calculating the survival function beyond that point. In this way survival analysis makes use of censored data. In both survival tables and plots, censored events are noted. In this paper, we wish to build prediction model for the survival of a particular item or human being. We use Kaplan-Meir Method (KMM) to study the same.

목차

Abstract
 1. Introduction
 2. Survival Analysis
 3. Survival Concepts
 4. Data Secrecy
 5. Significance
 6. Computational Methods
 7. Performance Analysis
 8. Results
 9. Future Scope of the Paper
 10. Conclusion
 References

키워드

Survival analysis Replacement policy life-time Prediction mortality

저자

  • C.M.Velu [ Department of Computer Science and Engineering, KL University, Vaddeswaram, AP, 522502, India ]
  • J. Yamini Devi [ Department of Computer Science and Engineering, KL University, Vaddeswaram, AP, 522502, India ]
  • Vijay Krishna Dhulipalla [ Department of Management Studies, VFSTR University, Vadlamudi-522213, Guntur, India ]
  • Debnath Bhattacharyya [ Department of Computer Science and Engineering, Vignan’s Institute of Information Technology, Visakhapatnam-530049, India ]

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    보안공학연구지원센터(IJSEIA) [Science & Engineering Research Support Center, Republic of Korea(IJSEIA)]
  • 설립연도
    2006
  • 분야
    공학>컴퓨터학
  • 소개
    1. 보안공학에 대한 각종 조사 및 연구 2. 보안공학에 대한 응용기술 연구 및 발표 3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최 4. 보안공학 기술의 상호 협조 및 정보교환 5. 보안공학에 관한 표준화 사업 및 규격의 제정 6. 보안공학에 관한 산학연 협동의 증진 7. 국제적 학술 교류 및 기술 협력 8. 보안공학에 관한 논문지 발간 9. 기타 본 회 목적 달성에 필요한 사업

간행물

  • 간행물명
    International Journal of Software Engineering and Its Applications
  • 간기
    월간
  • pISSN
    1738-9984
  • 수록기간
    2008~2016
  • 등재여부
    SCOPUS
  • 십진분류
    KDC 505 DDC 605

이 권호 내 다른 논문 / International Journal of Software Engineering and Its Applications Vol.10 No.9

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