IKE Lab@HVL, Norway
Intelligent Knowledge Engineering
26 Dec 2019

Special Session on FUZZ-IEEE 2020, Fuzzy and Uncertain Intelligent Knowledge Engineering Systems

Fuzzy and Uncertain Intelligent Knowledge Engineering Systems

For FUZZ-IEEE 2020

Glasgow, Scotland, United Kingdom – July 19-24, 2020


Session Organizers:

Jerry Chun-Wei Lin, Western Norway University of Applied Sciences, Norway, jerrylin@ieee.org 

Uday Kiran, NICT and The University of Tokyo, Tokyo, Japan, uday_rage@tkl.iis.u-tokyo.ac.jp

Gautam Srivastava, Brandon University, Canada, SRIVASTAVAG@brandonu.ca


Contact Person: Jerry Chun-Wei Lin, jerrylin@ieee.org 


Session description:

Intelligent knowledge engineering systems have become an emerging research topic in recent years since it applies discovered information and knowledge into making further effective decisions. Compared to the traditional data analytic techniques, the representation of linguistic terms based on fuzzy-set theory provides more understandable, alternative ways for decision-making. Concurrently, as the amount of collected data from IoT environment and mobile devices rapidly grows, data uncertainty is also considered as an important factor in knowledge discovery involved in intelligent knowledge engineering systems. Designing an efficient and effective intelligent knowledge engineering system is an emerging topic and issue in recent decades, especially focusing on considering the uncertain and fuzzy factors for data analytics, prediction and pattern mining. The main contents of intelligent knowledge engineering system are mining, retrieving, and analysing the novel, interesting, useful and surprising patterns from data for further decision-making. Popular techniques from the field of artificial intelligence such as machine learning and optimization techniques are also often used to make predictable decisions in a limited amount of time. Many frameworks and platforms such as Hadoop or Spark are also adapted to efficiently handle large databases and streams, commonly found in this big data era. This special session of FUZZ-IEEE 2020 focuses on issues regarding intelligent knowledge engineering under the uncertain and fuzzy-set concepts, including analytics and knowledge and pattern mining from data collected in real-world applications (fuzzy and uncertainty), that can be used to provide decision and strategy making abilities to intelligent systems. We welcome the innovative, creative, original, cutting-edge and state-of-the-art theoretical and applied contributions on this and relevant issues. Topics of interest include but are not limited to:

  • Intelligent systems based on fuzzy-set theory
  • Fuzzy data analytics and mining
  • Uncertain data mining and decision-making
  • Fuzzy knowledge prediction
  • Theoretical methodology for fuzzy intelligent systems
  • Fuzzy machine learning techniques
  • Fuzzy big data mining and prediction
  • Knowledge engineering for fuzzy decision making
  • Security and privacy issues based on fuzzy intelligent systems
  • Fuzzy image recognition and prediction
  • AI for fuzzy-set systems
  • Optimization for fuzzy knowledge engineering
  • Fuzzy granularities
  • Fuzzy bigdata mining based on the Hadoop or Spark platforms
  • Fuzzy knowledge integration and fusion
  • Applications based on fuzzy-set theory
  • Fuzzy visualization models


Important Dates

  • Paper Submission: 15th January, 2020
  • Notification of Acceptance: 15th March, 2020
  • Camera Ready Paper: 15th April 2020
  • Conference Dates: 19th-24th July, 2020

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