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DSBFI 2020: ISSAT International Conference on Data Science in Business, Finance and Industry

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투고 마감일:
2020-04-15 연장
통보일:
2020-04-30
개최일:
2020-07-01
개최지:
Da Nang, Vietnam
개최 횟수:
2
조회: 13808   팔로우: 0   참가: 0
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DSBFI
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회반 지수 (CP-I)

38.0 / 100
전체 5,694개 중 5,414위 · 상위 96%

경영·사회과학 분야 73개 중 65위 데이터 마이닝·데이터베이스 분야 336개 중 327위

학술적 인정 (35%) 데이터 없음 — 중립 기준값 50점으로 계산 —
투고 선별성 (20%) 데이터 없음 — 중립 기준값 50점으로 계산 —
개최 횟수 (20%)
30
커뮤니티 관심도 (10%)
8
공개 자료 충실도 (15%)
25

사용한 입력: 확인되는 개최 횟수: 2 · 지난 24개월 동안 이 페이지를 연 연구자: 2명

공개 자료에서 빠진 항목: 역대 게재율 (+4.5) · 역대 회차 (+3.0) · 최우수 논문 기록 (+2.3)
주최자는 학회를 인증 신청한 뒤 이 페이지에서 바로 추가할 수 있습니다. 점수는 매일 밤 다시 계산됩니다. 이 점수를 올리는 방법

신뢰도 45% — 점수 중 중립 기준값이 아니라 실제 관측된 데이터에 근거한 비율. 이 점수는 어떻게 계산되나 · 전체 순위 보기 · 알고리즘 버전 1.1 · 산출일 2026-10-09

논문 모집

DSBFI 2020 (ISSAT International Conference on Data Science in Business, Finance and Industry) is an academic conference held in Da Nang, Vietnam on 2020-07-01. The paper submission deadline is 2020-04-15 (extended). Acceptance notifications are sent on 2020-04-30.

Aims and Scope The Second ISSAT International Conference on Data Science in Business, Finance and Industry (DSBFI 2020) conference provides an international forum for presentation of new results as well as exchanges and disseminates innovative and practical development experiences. The conference covers all aspects of data science in business, finance and industry including algorithms, modeling and applications. Large amount of data created by various mobile platforms, social media interactions, e-commerce transactions, and IoT provide an opportunity for business, finance and industry to improve the quality of their products and services by effective use of data science. DSBFI draws researchers, application developers, and practitioners from a wide range of data science related areas such as statistics, business analytics, computer science, engineering, and applied science. By promoting novel, high-quality research findings and innovative solutions to challenging data science problems, the conference seeks to advance the state-of-the-art in data science. Topics of Interest Topics of interest for submission include, but are not limited to: Advanced Statistical Methods in Data Science Algorithms, Models and Theory of Data Mining Machine Learning and Statistical Methods for Data Mining Predictive Modeling and Analytics Machine Learning in Business, Finance and Industry Data Virtualization and Quality Data Mining Applications in Healthcare, Finance and Industry Application of Big Data in Industry Conversational Recommender Systems Quantitative Modeling in Big Data Data Warehouse for Business Intelligence Computer Vision - Algorithms and Applications Artificial Intelligence (AI) and Autonomous Machines Big Data Mining and Analytics Statistical Techniques and Tools for Data Science Healthcare Systems and Management Information and Data Processing in Business Spatial Data Analysis Search and Knowledge Discovery Data Intelligence and Security Security, Trust and Risk in Big Data Mobile Systems and Development for Handheld Devices Business and Operation Analytics Service Innovation and Management Supply Chain Management Systems Modeling and Simulation Technology and Knowledge Management Applications of data science in business, finance, social sciences, physical sciences, life sciences, web, marketing, precision medicine, education, health informatics, and industry Best Paper Awards / Journal Publications Two outstanding papers presented at the conference will be chosen for the ISSAT Best Paper and Best Student Paper Awards. Student must be first author and presenter at the conference to quality.
최종 수정: Dou Sun ()

관련 학회

관련 저널

CCF정식 명칭영향력 지수출판사ISSN
Computers in Industry9.1Elsevier0166-3615
AIEEE Transactions on Multimedia9.7IEEE1520-9210
CKnowledge-Based Systems7.2Elsevier0950-7051
BSoftware & Systems Modeling3.2Springer1619-1366
AIEEE Transactions on Computers3.8IEEE0018-9340
CFuture Generation Computer Systems5.9Elsevier0167-739X
CNeurocomputing6.5Elsevier0925-2312
CPattern Recognition Letters3.9Elsevier0167-8655
BPattern Recognition7.6Elsevier0031-3203
IEEE Access3.6IEEE2169-3536

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