VAA-CP-EB

Many problems today in the fields of biomedicine and engineering involve huge amounts of data, a large number of variables and a high complexity of the underlying processes, with many factors influencing their behavior, facing common challenges in diagnosis, prognosis, estimation, anomaly detection, accurate and explainable modeling, timeseries and image analysis or knowledge discovery, just to mention a few.

Machine learning (ML) algorithms allow to model complex processes out from massive data, being able to surpass humans in well-defined tasks. However, they are prone to error under changes in the context or in the problem definition. Also, they are often “black box” models that make their integration with expert’s domain knowledge difficult. Humans, in turn, although less precise, can work with poorly posed problems, perform well on a wide range of tasks, and are able to find connections and improve responses through an iterative, exploratory process. Aiming to embrace both approaches, Visual Analytics (VA) has emerged in last years as a powerful paradigm based in the integration of ML and human reasoning by means of data visualization and interaction for complex problem solving.

This special session welcomes any research work that can contribute to this paradigm, including any applications, algorithms, methods or techniques suitable to support or be part of VA solutions to problems in engineering and biomedicine. Some example topics include (but are not limited to):

– ML/AI-powered data visualization
– eXplainable Artificial Intelligence (XAI)
– visualization and/or interaction methods for data analysis
– visual analytics of dynamical systems and timeseries
– visual analytics in process and biomedical data analysis for:
– knowledge discovery
– condition monitoring
– anomaly detection
– prognosis and prediction
– etc.

Chairman: Ignacio Díaz Blanco (idiaz@uniovi.es), Jose María Enguita Gonzalez (jmenguita@uniovi.es); GSDPI research group (http://isa.uniovi.es/GSDPI/) University of Oviedo

Important dates

Please visit AIAI 2023 important dates to be informed about the submission deadlines.

Submission instructions

Submission details can be found at AIAI conference submission page.

All papers should be submitted either in a doc/docx or in a pdf form and will be peer reviewed by at least 2 academic referees. Contributing authors must follow the AIAI2023’s paper format guidelines as far as the IFIP AICT file format.

Papers will be peer reviewed by at least two (-2-) members of the workshop’s program committee.

Accepted papers will be published in the Proceedings of AIAI VOLUME 2, under the Springer IFIP AICT Series.

You can submit you VAA-CP-EB paper here http://www.easyacademia.org/aiai2023.