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Highlights in Science Engineering and Technology
Article . 2022 . Peer-reviewed
License: CC BY NC
Data sources: Crossref
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Preface: 2022 International Conference on Mathematical Modeling and Machine Learning (MMML 2022)

Authors: Genevieve Clare; Shuai Wang;

Preface: 2022 International Conference on Mathematical Modeling and Machine Learning (MMML 2022)

Abstract

2022 International Conference on Mathematical Modelling and Machine Learning (MMML 2022) was successfully held during 26-27 November, 2022 in Sydney, Australia. The conference intends to invite worldwide famous scientists, experts, scholars, and researchers for academic presentations. MMML 2022 aims to bring together leading experts and scholars in the fields of mathematical modeling and machine learning to share their experience and research results. It also provides an excellent interdisciplinary platform for researchers, practitioners and educators to showcase the latest innovations, trends and concerns in these fields, as well as practical challenges encountered and adopted solutions. This conference cordially invites high-quality research contributions to describe original and unpublished results of experimental or theoretical work in mathematical modeling and machine learning for presentation at conferences. Less than 60 articles were selected from more than 90 submissions after peer-review. Authors participated in MMML 2022 with oral presentations and posters, promoting the communication among researchers and experts from institutes and universities. Organizing Committee of MMML 2022 Sydney, Australia

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    popularity
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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
gold