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Journals
Algorithms
Special Issues
Feature Papers in Evolutionary Algorithms and Machine Learning
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Special Issue "Feature Papers in Evolutionary Algorithms and Machine Learning"

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 17093

Special Issue Editors

Prof. Dr. Akemi Galvez Tomida

E-Mail Website
Guest Editor
1. Department of Applied Mathematics and Computational Sciences, University of Cantabria, C.P. 39005 Santander, Spain
2. Department of Information Science, Faculty of Sciences, Toho University, 2-2-1 Miyama, Funabashi 274-8510, Japan
Interests: artificial Intelligence; soft computing for optimization; evolutionary computation; computational intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Amir Mosavi

E-Mail Website
Guest Editor
Institute of Automation, Obuda University, 1034 Budapest, Hungary
Interests: machine learning; deep learning; ensemble models; hybrid models; applied mathematics; soft computing; deep reinforcement learning; machine learning for big data; mathematical IT; hydropower modeling; prediction models; time series prediction; business intelligence; climate models; machine learning for remote sensing; hazard models; extreme events; atmospheric model; forecasting models; predictive analytics; meta-heuristic techniques
Special Issues, Collections and Topics in MDPI journals
Dr. Andres Iglesias

E-Mail Website
Guest Editor
1. Department of Applied Mathematics and Computational Sciences, University of Cantabria, C.P. 39005 Santander, Spain
2. Department of Information Science, Faculty of Sciences, Toho University, 2-2-1 Miyama, Funabashi 274-8510, Japan
Interests: swarm intelligence and swarm robotics; bio-inspired optimisation; computer graphics; geometric modelling
Special Issues, Collections and Topics in MDPI journals
Dr. Pedram Ghamisi

E-Mail Website
Guest Editor
1. Machine Learning Group, Helmholtz Institute Freiberg for Resource Technology, Helmholtz-Zentrum Dresden-Rossendorf, 09599 Freiberg, Germany
2. Institute of Advanced Research in Artificial Intelligence (IARAI), 1030 Vienna, Austria
Interests: machine and deep learning; image and signal processing; hyperspectral image analysis; multisensor data fusion
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Uwe Reuter

E-Mail Website
Guest Editor
Faculty Computing Centre, Dresden University of Technology, 01062 Dresden, Germany
Interests: mathematical optimization; fuzzy number; neuro-fuzzy; fuzzy logic
Prof. Dr. Annamária R. Várkonyi-Kóczy

E-Mail Website
Guest Editor
1. Institute of Automation, Óbuda University, Budapest 1034, Hungary
2. Department of Mathematics and Informatics, J. Selye University, 945 01 Komarno, Slovakia
Interests: machine learning; soft computing techniques; big data analysis; IoT; predictive analytics; hybrid techniques in intelligent measurement; signal and image processing; modeling and diagnostics; fault diagnostics; optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a new Special Issue to promote our new section Evolutionary Algorithms and Machine Learning, which will mainly focus on either selected areas of research or special techniques.

Manuscripts for this Special Issue will be accepted by the editorial office, Editor-in-Chief, and editorial board members by invitation only. All the papers in this Special Issue will be published free of charge.

Prof. Dr. Akemi Galvez Tomida
Dr. Amir Mosavi
Dr. Andres Iglesias
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Article
COVID-19 Outbreak Prediction with Machine Learning
by
Sina F. Ardabili
,
Amir Mosavi
,
Pedram Ghamisi
,
Filip Ferdinand
,
Annamaria R. Varkonyi-Koczy
,
Uwe Reuter
,
Timon Rabczuk
and
Peter M. Atkinson
Algorithms 2020, 13(10), 249; https://doi.org/10.3390/a13100249 - 01 Oct 2020
Cited by 204 | Viewed by 16310
Abstract
Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, [...] Read more.
Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and these models are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models need to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to susceptible–infected–recovered (SIR) and susceptible-exposed-infectious-removed (SEIR) models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP; and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior across nations, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. This paper further suggests that a genuine novelty in outbreak prediction can be realized by integrating machine learning and SEIR models. Full article
(This article belongs to the Special Issue Feature Papers in Evolutionary Algorithms and Machine Learning)
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