Chapter 1: what a fuzzy set is and what it is not?.- Chapter 2: Fuzzy random variables á la Kruse & Meyer and á la Puri & Ralescu: key differences and coincidences.- Chapter 3: Statistical Inference for Incomplete Ranking Data: A Comparison of two Likelihood-Based Estimators.- Chapter 4: Interval Type–2 Defuzzification Using Uncertainty Weights.- Chapter 5: Exploring time-resolved data for patterns and validating single clusters.- Chapter 6: Interpreting Cluster Structure in Waveform Data with Visual Assessment and Dunn’s Index.- Chapter 7: A shared encoder DNN for integrated recognition and segmentation of traffic scenes.- Chapter 8: Fuzzy ontology support for knowledge mobilisation

This book is a collection of several contributions which show the state of the art in specific areas of Computational Intelligence. This carefully edited book honors the 65th birthday of Rudolf Kruse. The main focus of these contributions lies on treating vague data as well as uncertain and imprecise information with automated procedures, which use techniques from statistics, control theory, clustering, neural networks etc. to extract useful and employable knowledge. 

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