The Fifteenth International Conference on Advanced Engineering Computing and Applications in Sciences

ADVCOMP 2021

October 03, 2021 to October 07, 2021 - Barcelona, Spain

Deadlines

Submission

Jul 27, 2021

Notification

Aug 19, 2021

Registration

Sep 02, 2021

Camera ready

Sep 04, 2021

Deadlines differ for special tracks. Please consult the conference home page for special tracks Call for Papers (if any).

Publication

Published by IARIA XPS Press

Archived in the free access ThinkMind Digital Library

Prints available at Curran Associates, Inc.

Authors of selected papers will be invited to submit extended versions to a IARIA Journal

Indexing Procedure

Affiliated Journals

ADVCOMP 2021 - The Fifteenth International Conference on Advanced Engineering Computing and Applications in Sciences

October 03, 2021 - October 07, 2021

ADVCOMP 2021: Awards
Onsite and Online Options: In order to accommodate a large number of situations, we are offering the option for either physical presence or virtual participation (pdf slides or pre-recorded videos).

The papers listed below have been selected as "Best Papers" based on the reviews of the original submission, the camera-ready version, and the presentation during the conference. For the awarded papers, a digital award will be issued in the name of the authors. The authors of these papers are also receiving invitations to submit an extended article version to one of the IARIA Journals.

 

Awarded Papers (also Invited for IARIA Journals)

Synapse: Facilitating Large-scale Data Management in Research Contexts
Daniel Andresen, Gerrick Teague

A Novel Application of Machine Learning to a New SEM Silicate Mineral Dataset
Benjamin Parfitt, Robert Welch

 

The following papers have been selected on the basis of their contents, specificaly for lending themselves to an interesting extended work. The authors of these papers are receiving invitations to submit an extended article version to one of the IARIA Journals.

Papers Invited for IARIA Journals

AMPRO-HPCC: A Machine-Learning Tool for Predicting Resources on Slurm HPC Clusters
Mohammed Tanash, Daniel Andresen, William Hsu

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