Special Issue "Advances in Swarm Intelligence, Data Science and Their Applications"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 31 October 2021.

Special Issue Editors

Prof. Dr. Ying Tan
E-Mail Website
Guest Editor
School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China
Interests: swarm intelligence; fireworks algorithms; multi-agent theories
Special Issues and Collections in MDPI journals
Prof. Dr. Yan Pei
E-Mail Website
Guest Editor
School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Japan
Interests: natural computing; fireworks algorithms; optimization theories; data mining; machine learning; pattern recognition
Special Issues and Collections in MDPI journals
Prof. Dr. Gaige Wang
E-Mail Website
Guest Editor
Department of Computer Science and Technology, Ocean University of China, 266100 Qingdao, China
Interests: swarm intelligence; natural computing
Special Issues and Collections in MDPI journals
Prof. Dr. Fernando Buarque
E-Mail Website
Guest Editor
University of Pernambuco, Brazil
Interests: machine learning; natural computing; optimization theories; fish school algorithms, pattern recognition, hybrid algorithms

Special Issue Information

Dear Colleagues,

This Special Issue will cover the most recent discovery and development centered around two major topics: swarm intelligence and data science.

Swarm intelligence systems typically study the complex collective behavior that arises from decentralized simple agents with local and/or global interaction. The inspiration for swarm intelligence algorithms usually comes from natural behavior or phenomena, such as ant colonies, bird flocks, fireworks, etc. Typical subdomains of swarm intelligence are swarm-based optimization techniques and multi-agent cooperative systems. It has been proven that swarm intelligence is an effective way to tackle complex problems that arise in various domains such as power systems, robotics, information systems, image processing, computation chemistry, and so on. The importance of swarm intelligence in today’s society is gradually being brought to a whole new level.

On the other hand, data science has gained more and more momentum in the era of big data and artificial intelligence. It utilizes theories and techniques from machine learning, statistics, and information theory to help us extract valuable knowledge, patterns, and insights from data that are usually very large and complex. Some typical applications of data science are fraud detection, recommender systems, bioinformatics, stock market prediction, and so on. Our Special Issue is mainly concerned with advances in the field of data mining, machine learning, pattern recognition, automatic control, and their respective applications.

Prof. Dr. Ying Tan
Prof. Dr. Yan Pei
Prof. Dr. Gaige Wang
Prof. Dr. Fernando Buarque
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 papers will be 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. Electronics is an international peer-reviewed open access semimonthly 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 1800 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.

Keywords

  • Swarm intelligence
  • Big data
  • Natural computing
  • Fireworks algorithms
  • Multi-agent theories
  • Optimization theories
  • Data mining
  • Machine learning
  • Pattern recognition
  • Automatic control

Published Papers (1 paper)

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Research

Article
Predicting Fundraising Performance in Medical Crowdfunding Campaigns Using Machine Learning
Electronics 2021, 10(2), 143; https://doi.org/10.3390/electronics10020143 - 11 Jan 2021
Viewed by 741
Abstract
The coronavirus disease (COVID-19) pandemic has flooded public health organizations around the world, highlighting the significance and responsibility of medical crowdfunding in filling a series of gaps and shortcomings in the publicly funded health system and providing a new fundraising solution for people [...] Read more.
The coronavirus disease (COVID-19) pandemic has flooded public health organizations around the world, highlighting the significance and responsibility of medical crowdfunding in filling a series of gaps and shortcomings in the publicly funded health system and providing a new fundraising solution for people that addresses health-related needs. However, the fact remains that medical fundraising from crowdfunding sources is relatively low and only a few studies have been conducted regarding this issue. Therefore, the performance predictions and multi-model comparisons of medical crowdfunding have important guiding significance to improve the fundraising rate and promote the sustainable development of medical crowdfunding. Based on the data of 11,771 medical crowdfunding campaigns from a leading donation-based platform called Weibo Philanthropy, machine-learning algorithms were applied. The results demonstrate the potential of ensemble-based machine-learning algorithms in the prediction of medical crowdfunding project fundraising amounts and leave some insights that can be taken into consideration by new researchers and help to produce new management practices. Full article
(This article belongs to the Special Issue Advances in Swarm Intelligence, Data Science and Their Applications)
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