The era of big data is here and now. The amount of data produced every day grows tremendously in most real-life domains, including medical imaging, genomics, text categorization, computational biology, and many others. Hence, data-driven machine learning-powered approaches are consistently gaining research attention, and they are applied in multiple fields, with intelligent & collaborative systems not being an exception. In this workshop, we strive to present current advances on novel ideas and practical aspects concerning intelligent & collaborative systems which benefit from machine learning, and deep learning in particular. Also, we hope to identify & highlight challenges which are being faced by research and industrial communities in the field.

This workshop covers the latest advances in machine- and deep learning-powered intelligent systems that lead to gain competitive advantages in business and academia scenarios. The ultimate aim is to stimulate research that will lead to the creation of robust intelligent & collaborative systems applicable in a variety of fields (ranging from medical image analysis to smart delivery systems). Industry and academic researchers, professionals and practitioners are invited to exchange their experiences and present their ideas in this field.

We are currently experiencing unprecedented time with COVID-19, but we do hope that things will get better in the following months. Presently, we are thinking to held INCoS-2022 both physically and virtually - the updates will appear at http://voyager.ce.fit.ac.jp/conf/incos/2022/ as they emerge.

The topics of interest include, but are not limited to:

1. Deep learning and Neural Networks: applications, techniques, and tools

2. Soft computing techniques for design of intelligent & collaborative systems

3. Computer vision & image processing in intelligent & collaborative systems

4. Bio-inspired algorithms for intelligent & collaborative systems

5. Hybrid algorithms for intelligent & collaborative systems

6. Heuristic and meta-heuristic algorithms in intelligent & collaborative systems

7. Machine learning in intelligent & collaborative systems

8. Advanced data analysis in intelligent & collaborative systems

9. Approaches, techniques, and challenges in parallelising machine learning-powered intelligent systems

10. Practical applications of intelligent & collaborative systems

11. Automated design and auto-tuning of deep learning and machine learning systems

12. Smart delivery systems (incl. autonomous vehicles)

13. Medical image analysis in intelligent decision-support systems

14. Learning systems: approaches, techniques, and tools

15. Multi- and hyperspectral imaging, analysis and processing

Submission guidelines

Authors should submit a paper with maximum 10 pages in length, including all figures, tables, and references. However, authors can add up to 2 extra pages with the appropriate fee payment. Papers must be prepared using the Lecture Notes Style of Springer Proceedings.

Submission system (select workshop title "Machine learning in intelligent and collaborative systems"): https://edas.info/N28918

Accepted papers will be given guidelines in preparing and submitting the final manuscript(s) together with the notification of acceptance.

Proceedings

All accepted papers of the conference and of workshops will be included in the conference proceedings of Lecture Notes in Networks and Systems (LNNS) by Springer.

Proceedings will be sent by Springer for indexing in EI and SCOPUS, ISI Proceedings, MetaPress, Springerlink.

Important dates

• Submission Deadline: May 10, 2022 May 31, 2022 (select workshop title "Machine learning in intelligent and collaborative systems" in the EDAS system - see http://voyager.ce.fit.ac.jp/conf/incos/2022/authorkit.php)

• Authors Notification: June 10, 2022

• Author Registration: June 25, 2022

• Final Manuscript: June 25, 2022

• Conference Dates: September 7 - September 9, 2022

Registration

Conference registration: http://voyager.ce.fit.ac.jp/conf/incos/2022/registration.php

Workshop organizer & contact

Jakub Nalepa, Silesian University of Technology, Poland (jnalepa@ieee.org)