Extracting information from a massive amount of humans’ natural behaviour and cognition patterns has allowed supporting the machine learning and decisions in many fields, ranging from computer science to engineering. Human-in-the-loop approaches interact with fuzzy reasoning as well as machine learning are gaining popularity as a better approach to training more accurate models, because the human feedback into the learning loop of the machine can help it improve faster. Recent advances in fuzzy systems and machine learning, are giving momentum to human-in-the-loop approaches to enable complex paradigms that operate in connection with human beings.

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Given the remarkable achievement associated with the processing of human physiological signals obtained from neuroimaging modalities and cognitive systems, it has been proposed as a useful and effective framework for the modelling and understanding of human behaviour and cognition patterns as well as to enable a direct communication pathway between the human and machine. This paves the way for developing new human-in-the-loop interacting and interfacing techniques in fuzzy reasoning and machine learning that foster the capabilities for understanding and modelling the training process.

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This special session is aimed at gathering outstanding papers about integrating human informatics and their interacting applications using fuzzy systems and machine learning. This will provide a snapshot of the latest advances in the contribution of human-in-the-loop interactions powered by fuzzy learning frameworks.