Achmad Nizar Hidayanto – författare

The four-volume proceedings LNCS 13108, 13109, 13110, and 13111 constitutes the proceedings of the 28th International Conference on Neural Information Processing, ICONIP 2021, which was held during December 8-12, 2021. The conference was planned to take place in Bali, Indonesia but changed to an online format due to the COVID-19 pandemic.

The total of 226 full papers presented in these proceedings was carefully reviewed and selected from 1093 submissions. The papers were organized in topical sections as follows:

Part I: Theory and algorithms;

Part II: Theory and algorithms; human centred computing; AI and cybersecurity;

Part III: Cognitive neurosciences; reliable, robust, and secure machine learning algorithms; theory and applications of natural computing paradigms; advances in deep and shallow machine learning algorithms for biomedical data and imaging; applications;  

Part IV: Applications.

The four-volume proceedings LNCS 13108, 13109, 13110, and 13111 constitutes the proceedings of the 28th International Conference on Neural Information Processing, ICONIP 2021, which was held during December 8-12, 2021. The conference was planned to take place in Bali, Indonesia but changed to an online format due to the COVID-19 pandemic.

The total of 226 full papers presented in these proceedings was carefully reviewed and selected from 1093 submissions. The papers were organized in topical sections as follows:

Part I: Theory and algorithms;

Part II: Theory and algorithms; human centred computing; AI and cybersecurity;

Part III: Cognitive neurosciences; reliable, robust, and secure machine learning algorithms; theory and applications of natural computing paradigms; advances in deep and shallow machine learning algorithms for biomedical data and imaging; applications;  

Part IV: Applications.

The four-volume proceedings LNCS 13108, 13109, 13110, and 13111 constitutes the proceedings of the 28th International Conference on Neural Information Processing, ICONIP 2021, which was held during December 8-12, 2021. The conference was planned to take place in Bali, Indonesia but changed to an online format due to the COVID-19 pandemic.

The total of 226 full papers presented in these proceedings was carefully reviewed and selected from 1093 submissions. The papers were organized in topical sections as follows:

Part I: Theory and algorithms;

Part II: Theory and algorithms; human centred computing; AI and cybersecurity;

Part III: Cognitive neurosciences; reliable, robust, and secure machine learning algorithms; theory and applications of natural computing paradigms; advances in deep and shallow machine learning algorithms for biomedical data and imaging; applications;  

Part IV: Applications.

The four-volume proceedings LNCS 13108, 13109, 13110, and 13111 constitutes the proceedings of the 28th International Conference on Neural Information Processing, ICONIP 2021, which was held during December 8-12, 2021. The conference was planned to take place in Bali, Indonesia but changed to an online format due to the COVID-19 pandemic.

The total of 226 full papers presented in these proceedings was carefully reviewed and selected from 1093 submissions. The papers were organized in topical sections as follows:

Part I: Theory and algorithms;

Part II: Theory and algorithms; human centred computing; AI and cybersecurity;

Part III: Cognitive neurosciences; reliable, robust, and secure machine learning algorithms; theory and applications of natural computing paradigms; advances in deep and shallow machine learning algorithms for biomedical data and imaging; applications;  

Part IV: Applications.

The 176 short and workshop papers presented in this volume were carefully reviewed and selected for publication out of 1093 submissions. The papers are organized in topical sections as follows: theory and algorithms; AI and cybersecurity; cognitive neurosciences; human centred computing; advances in deep and shallow machine learning algorithms for biomedical data and imaging; reliable, robust, and secure machine learning algorithms; theory and applications of natural computing paradigms; applications.

* The conference was held virtually due to the COVID-19 pandemic.