International Journal of Applied Science - Research and Review Open Access

  • ISSN: 2394-9988
  • Journal h-index: 10
  • Journal CiteScore: 2.27
  • Journal Impact Factor: 1.33
  • Average acceptance to publication time (5-7 days)
  • Average article processing time (30-45 days) Less than 5 volumes 30 days
    8 - 9 volumes 40 days
    10 and more volumes 45 days

Edwin Lughofer

Edwin Lughofer
Researcher at the Fuzzy-Logic Laboratorium Linz-Hagenberg/Department of Knowledge-Based Mathematical Systems
Johannes Kepler University Linz, Austria

Biography

Edwin Lughofer received his PhD-degree from the Johannes Kepler University Linz (JKU) in 2005. He is currently Key Researcher with the Fuzzy Logic Laboratorium Linz / Department of Knowledge-Based Mathematical Systems (JKU) in the Softwarepark Hagenberg www.flll.jku.at/staff/edwin/. He has participated in several basic and applied research projects on European and national level, with a focus on topics of Industry 4.0 and FoF (Factories of the Future). He has published around 150 journal and conference papers in the fields of evolving fuzzy systems, machine learning and vision, data stream mining, active learning, classification and clustering, fault detection and diagnosis, including a monograph on Evolving Fuzzy Systems (Springer) and an edited book on Learning in Non-stationary Environments (Springer). He is associate editor of the international journals IEEE Transactions on Fuzzy Systems, Evolving Systems, Information Fusion, Soft Computing and Complex and Intelligent Systems, the general chair of the IEEE Conference on EAIS 2014 in Linz, the publication chair of IEEE EAIS 2015, 2016 and 2017, and the Area chair of the FUZZ-IEEE 2015 conference in Istanbul. In 2006 he received the best paper award at the International Symposium on Evolving Fuzzy Systems, and in 2013 the best paper award at the IFAC conference in Manufacturing Modeling, Management and Control (800 participants).

Research Interest

Evolving (Intelligent) Systems, Fuzzy Systems, Soft Computing, Machine Learning, Active Learning, Decision Making, System Identification, Fault Detection and Identification, Sensor Fusion, Computer Vision, Quality Control.