23/09/2021 09:42:00

Accepted Special Sessions

Special Session 1

Special Session on Machine Learning and Computer Vision in Industry 4.0

Enrique Dominguez, University of Málaga
Jose Garcia Rodriguez, University of Alicante
Ramon Moreno Jiménez, Grupo Antolin

In the coming years, the use of machine learning and computer vision in industry is a trend that is changing not only large corporations, but also small and medium-sized businesses. Thanks to these technologies, the innovation in the industrial sector is giving rise to the named “smart factories”, allowing them to obtain multiple advantages.

This special session tries to provide a common platform for academics, developers, and industry-related researchers to discuss, share experiences and explore the new technological advances. The objective is to integrate an international scientific community working on industrial applications of machine learning and computer vision for fruitful discussions and ideas on the evolution of these technologies.


Special Session 2

Crossing the border between Soft Computing and Machine Learning

Giuseppe Psaila, University of Bergamo
Paolo Fosci, University of Bergamo

Soft Computing and Machine Learning have provided a large variety of tehcniques to process
large volumes of data in a way that traditional
crisp computing is not able to provide.
In fact, approximate matching, flexible models and
machine learning are able to achieve incredible results in many application scenarios.

However, what is exactly soft computing? and what is exactly machine learning?
Is it possible to identify a border between them?
Can soft computing provide the methods to explain machine learning?

The special session solicits papers that study (and possibly identify) the border between soft computing and machine learning (giving clear definitions of what soft computing is and what machine learning is), as well as papers that try to
jointly applly soft computing and machine learning, to “cross the broder” and adopt them in a sinergic way.
Papers that foster the discussion on the topic are wlecome.
Position papers are welcome.

Topics of interest.
The topics of interest for the special session encompass (but are not limited to) the following ones.

– Characterizing the border between softo computing and machine learning.
– Machine-learning techniques based on fuzzy sets.
– Differences between Machine learning and Deep learning.
– Neural Network: architectures, models and meaning of trained models.
– Differences among neural-network techniques.
– The rationale behind neural-network architectures.
– Fuzzy sets and soft computing used to describe models produced by machine-learning techniques.
– Imprecision and uncertainty in databases and datasets.
– Possibilistic approaches to machine learning.
– Comparisong of probnabilistic approaches and possibilistic approaches in soft computing and/or machine learning.
– Soft querying and soft information retrieval.
– Soft computing for explainable word embedding.


Special Session 3

Time Series Forecasting in Industrial and Environmental Applications (TSF)

Federico Divina, Pablo de Olavide University
Mario Giacobini, University of Torino
José Luis Vázquez Noguera, Universidad Nacional de Asunción
Miguel García Torres, Pablo de Olavide University of Seville
José F. Torres, Pablo de Olavide University of Seville

Time series can be found in almost all disciplines nowadays. Thus, time series forecasting is becoming a consolidated discipline that provides meaningful information in a wide variety of application areas, turning their efficient analysis into the utmost relevance for the scientific community.

This session pays attention to the extraction of useful knowledge from time series in the context of industrial and environmental applications. The analysis of very large time series, given its relevance in the emergent context of big data, is also encouraged. Topics of interest for the special session, always in the context of industrial and environmental applications, include but are not limited to:

1. Machine learning applied to time series forecasting.
2. Deep learning applied to time series forecasting.
3. New approaches for big data time series forecasting.
4. Hybrid systems for time series analysis.
5. Ensemble approaches for time series analysis.