Advances in Extreme Learning Machines

Dear Colleagues,

For many years, research around extreme learning machines (ELM) has been establishing it as a meaningful and peculiar approach that can be used as a steppingstone for creating application-oriented models, as well as complex, elaborate machine learning models. Recent work has also attempted to bridge these current ideas with that of biological learning mechanisms. As such, the original name of ELM today not only refers to the original technique proposed by Guangbin Huang but also serves as an umbrella term for numerous related approaches directly using the core concepts of the original technique. The focus of this Special Issue is both on novel theoretical improvements and approaches directly using ELM, and on applications of such techniques to data and network security and privacy issues for future networks, such as in the Internet of Things, 5G/6G, and software-defined networks contexts. Specifically, we invite contributions related to this non-exhaustive list of topics:

• Hierarchical/layered ELM;
• Theoretical/biological foundations of ELM;
• ELM and ELM-driven approaches in data and network security and privacy;
• ELM (and related) for 5G/6G architectures and scenario;
• ELM (and related) for software-defined networks and systems.

This Special Issue will mainly consist of extended papers selected from those presented at the 11th and 12th International Conferences on Extreme Learning Machines. Please visit the conference website for a detailed description: https://elm.risklab.fi

We look forward to receiving novel and disruptive research that addresses the aforementioned topics as well as related ones.

Dr. Yoan Miche
Guest Editor

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• extreme learning machines
• hierarchical ELM
• layered ELM
• deep ELM
• ELM for data privacy and security
• ELM for 5G/6G
• ELM for SDN