Literature Database Entry

khanzadeh2024machine-tutorial

Roya Khanzadeh, Jorge Torres Gómez and Stefan Angerbauer, "Machine Learning in the IOBNT: Exploting the Potentail of Nano-Scale Communication and Computing," Tutorial, IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN 2024), Stockholm, Sweden, May 05, 2024.

Abstract

This tutorial introduces the role of Machine Learning (ML) algorithms in the Internet of Bio-Nano Things (IoBNT), which is an emerging communication framework. The tutorial aims to identify the need for ML-enabled communication and computing techniques, specifically advocating for the bio-inspired Molecular Communications (MC) paradigm. It addresses the limitations and challenges of existing model-based approaches in MC systems and emphasizes the potential of ML and Explainable Artificial Intelligence (XAI) in IoBNT networks. It also covers the possible practical realization of ML models on nano-scales. The tutorial aims to bridge the gap between ML-driven methods and existing IoBNT challenges by providing researchers with fundamental knowledge and application examples of ML in this emerging field.

Quick access

BibTeX BibTeX

Contact

Roya Khanzadeh
Jorge Torres Gómez
Stefan Angerbauer

BibTeX reference

@misc{khanzadeh2024machine-tutorial,
    author = {Khanzadeh, Roya and Torres G{\'{o}}mez, Jorge and Angerbauer, Stefan},
    title = {{Machine Learning in the IOBNT: Exploting the Potentail of Nano-Scale Communication and Computing}},
    howpublished = {Tutorial},
    publisher = {IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN 2024)},
    location = {Stockholm, Sweden},
    day = {05},
    month = {05},
    year = {2024},
   }
   
   

Copyright notice

Links to final or draft versions of papers are presented here to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted or distributed for commercial purposes without the explicit permission of the copyright holder.

The following applies to all papers listed above that have IEEE copyrights: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

The following applies to all papers listed above that are in submission to IEEE conference/workshop proceedings or journals: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

The following applies to all papers listed above that have ACM copyrights: ACM COPYRIGHT NOTICE. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept., ACM, Inc., fax +1 (212) 869-0481, or permissions@acm.org.

The following applies to all SpringerLink papers listed above that have Springer Science+Business Media copyrights: The original publication is available at www.springerlink.com.

This page was automatically generated using BibDB and bib2web.

Last modified: 2024-05-20