Special Issue "Artificial Intelligence and Ambient Intelligence: Innovative Paths"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (15 August 2021).

Special Issue Editors

Dr. Dalila Durães
E-Mail Website
Guest Editor
Departamento de Informática, Universidade do Minho, Campus of Gualtar, 4710 -057 Braga, Portugal
Interests: artificial intelligence; human–computer interaction; behavior analysis; sentiment analysis; and human action recognition
Prof. Dr. Jason J. Jung
E-Mail Website
Guest Editor
Knowledge Engineering Laboratory, Department of Computer Engineering, Chung-Ang University, 84 Heukseok, Dongjak, Seoul, Korea
Interests: artificial intelligence; big data; social networks; story analytics; story engineering
Special Issues and Collections in MDPI journals
Prof. Dr. Paulo Novais
E-Mail Website
Guest Editor
ALGORITMI Centre, Department of Informatics, School of Engineering, University of Minho, 4710-057 Braga, Portugal
Interests: artificial intelligence; machine learning; ambient intelligence; affective computing
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

I invite you to contribute to a Special Issue of the journal Applied Sciences, "Artificial Intelligence and Ambient Intelligence: Innovative Paths", which aims to present research papers in the interdisciplinary areas of Artificial Intelligence and Ambient Intelligence.

Ambient Intelligence (AmI) is a well-known multidisciplinary approach that aims to improve the way environments and people interact. These environments must be attentive to people's needs and personalize their requirements, predict behaviour and act with sensitivity and attention in a responsible and discreet way.

Another primary concern of AmI is in the domain of human–computer interaction and focuses on offering ways to interact with systems more naturally through friendly interfaces. This field is evolving rapidly, as can be witnessed by the emerging natural language and interaction types based on gestures.

For AmI to be successful, human interaction with the power of computing and embedded systems in the vicinity must be smooth, and happen without people noticing. The only awareness that people should have of AmI is increased safety, comfort, and well-being, appearing naturally and inherently, without its presence being noticed. Thus, integrated solutions for human–computer interaction are needed, offering a more natural way of interacting with users and supporting them effectively. 

Artificial Intelligence in the context of AmI must support the effective use of more "intelligence" in the development of these environments, providing effective and useful support to the user and the essential knowledge needed for decision making.

This Special Issue aims to present research papers in the interdisciplinary areas of Artificial Intelligence and AmI. I thus invite you to submit your innovations and high-quality contributions that demonstrate progress in these areas, in the form of original research papers, mini-reviews, and perspective articles. To this end, we invite innovations and high-quality contributions that demonstrate progress in these areas.

Dr. Dalila Durães
Prof. Dr. Jason J. Jung
Prof. Dr. Paulo Novais
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. Applied Sciences 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 2000 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

  • artificial intelligence for AmI
  • ambient-assisted living
  • ubiquitous computing
  • pervasive computing
  • context-aware computing
  • robotics for AmI
  • computational creativity
  • e-health
  • e-learning and tutoring systems
  • other applications

Published Papers (1 paper)

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Research

Article
Traffic Incident Detection Based on Dynamic Graph Embedding in Vehicular Edge Computing
Appl. Sci. 2021, 11(13), 5861; https://doi.org/10.3390/app11135861 - 24 Jun 2021
Viewed by 352
Abstract
With a large of time series dataset from the Internet of Things in Ambient Intelligence-enabled smart environments, many supervised learning-based anomaly detection methods have been investigated but ignored the correlation among the time series. To address this issue, we present a new idea [...] Read more.
With a large of time series dataset from the Internet of Things in Ambient Intelligence-enabled smart environments, many supervised learning-based anomaly detection methods have been investigated but ignored the correlation among the time series. To address this issue, we present a new idea for anomaly detection based on dynamic graph embedding, in which the dynamic graph comprises the multiple time series and their correlation in each time interval. We propose an entropy for measuring a graph’s information injunction with a correlation matrix to define similarity between graphs. A dynamic graph embedding model based on the graph similarity is proposed to cluster the graphs for anomaly detection. We implement the proposed model in vehicular edge computing for traffic incident detection. The experiments are carried out using traffic data produced by the Simulation of Urban Mobility framework. The experimental findings reveal that the proposed method achieves better results than the baselines by 14.5% and 18.1% on average with respect to F1-score and accuracy, respectively. Full article
(This article belongs to the Special Issue Artificial Intelligence and Ambient Intelligence: Innovative Paths)
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