Special Issue "Innovative Algorithms Trend to Artificial Intelligence and Internet of Things"

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

Deadline for manuscript submissions: 31 July 2021.

Special Issue Editors

Prof. Dr. Yu-Huei Cheng
E-Mail Website
Guest Editor
Department of Information and Communication Engineering, Chaoyang University of Technology, 413310 Taichung, Taiwan
Interests: artificial intelligence; automatic control; bioinformatics; biomedical engineering; computational intelligence; embedded systems; electric and hybrid vehicles; internet of things; machine learning; mobile medical; power electronics; renewable energy
Special Issues and Collections in MDPI journals
Prof. Dr. Che-Nan Kuo
E-Mail Website
Guest Editor
Department of Artificial Intelligence, CTBC Business School, 709 Tainan, Taiwan
Interests: artificial intelligence; interconnection networks; discrete mathematics; computation theory; graph theory; and algorithm analysis

Special Issue Information

The popularity of Internet of Things (IoTs) technologies has led to the accumulation of a large amount of data, promoting the need for artificial intelligence (AI) algorithms for data analysis. People are increasingly accustomed to the convenience and personalized services provided by IoT devices. The seamless connection of integrating AI and IoT technologies (we called it as AIoT) has become a current and future trend. This Special Issue explores the integration of IoT and AI technologies to develop innovative AIoT algorithms and provide services that meet and even exceed user expectations. We invite academic researchers and industry professionals from a broad range of disciplines to submit to this Special Issue.

Prospective authors are invited to submit original papers to this Special Issue. Topics of interest include, but are not limited to the following:

  • IoTs technologies and algorithms
  • AI technologies and algorithms
  • AIoT innovative algorithms
  • AIoT simulation and applications
  • Other related aspects

Prof. Yu-Huei Cheng
Guest Editor

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. 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 1400 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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Research

Open AccessArticle
Crowd Evacuation Guidance Based on Combined Action Reinforcement Learning
Algorithms 2021, 14(1), 26; https://0-doi-org.brum.beds.ac.uk/10.3390/a14010026 - 18 Jan 2021
Viewed by 609
Abstract
Existing crowd evacuation guidance systems require the manual design of models and input parameters, incurring a significant workload and a potential for errors. This paper proposed an end-to-end intelligent evacuation guidance method based on deep reinforcement learning, and designed an interactive simulation environment [...] Read more.
Existing crowd evacuation guidance systems require the manual design of models and input parameters, incurring a significant workload and a potential for errors. This paper proposed an end-to-end intelligent evacuation guidance method based on deep reinforcement learning, and designed an interactive simulation environment based on the social force model. The agent could automatically learn a scene model and path planning strategy with only scene images as input, and directly output dynamic signage information. Aiming to solve the “dimension disaster” phenomenon of the deep Q network (DQN) algorithm in crowd evacuation, this paper proposed a combined action-space DQN (CA-DQN) algorithm that grouped Q network output layer nodes according to action dimensions, which significantly reduced the network complexity and improved system practicality in complex scenes. In this paper, the evacuation guidance system is defined as a reinforcement learning agent and implemented by the CA-DQN method, which provides a novel approach for the evacuation guidance problem. The experiments demonstrate that the proposed method is superior to the static guidance method, and on par with the manually designed model method. Full article
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