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Applied Sciences
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Artificial Intelligence and Ambient Intelligence: Innovative Paths
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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 (30 July 2022) | Viewed by 11073

Special Issue Editors

Dr. Dalila Durães

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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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jason J. Jung

E-Mail Website
Guest Editor
Department of Computer Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
Interests: artificial intelligence; big data; social networks; story analytics; story engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Paulo Novais

E-Mail Website
Guest Editor
Algoritmi Center, Campus of Gualtar, 4710-057 Braga, Portugal
Interests: ambient intelligence; ambient assisted living; conflict resolution; behavioral analysis; intelligent tutors
Special Issues, Collections and Topics 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 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. 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 2400 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 (5 papers)

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Research

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13 pages, 1435 KiB  
Article
Transformer-Based Hybrid Forecasting Model for Multivariate Renewable Energy
by Guilherme Afonso Galindo Padilha, JeongRyun Ko, Jason J. Jung and Paulo Salgado Gomes de Mattos Neto
Appl. Sci. 2022, 12(21), 10985; https://doi.org/10.3390/app122110985 - 30 Oct 2022
Cited by 3 | Viewed by 1882
Abstract
In recent years, the use of renewable energy has grown significantly in electricity generation. However, the output of such facilities can be uncertain, affecting their reliability. The forecast of renewable energy production is necessary to guarantee the system’s stability. Several authors have already [...] Read more.
In recent years, the use of renewable energy has grown significantly in electricity generation. However, the output of such facilities can be uncertain, affecting their reliability. The forecast of renewable energy production is necessary to guarantee the system’s stability. Several authors have already developed deep learning techniques and hybrid systems to make predictions as accurate as possible. However, the accurate forecasting of renewable energy still is a challenging task. This work proposes a new hybrid system for renewable energy forecasting that combines the traditional linear model (Seasonal Autoregressive Integrated Moving Average—SARIMA) with a state-of-the-art Machine Learning (ML) model, Transformer neural network, using exogenous data. The proposal, named H-Transformer, is compared with other hybrid systems and single ML models, such as Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Networks (RNN), using five data sets of wind speed and solar energy. The proposed H-Transformer attained the best result compared to all single models in all datasets and evaluation metrics. Finally, the hybrid H-Transformer obtained the best result in most cases when compared to other hybrid approaches, showing that the proposal can be a useful tool in renewable energy forecasting. Full article
(This article belongs to the Special Issue Artificial Intelligence and Ambient Intelligence: Innovative Paths)
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13 pages, 564 KiB  
Article
AWMC: Abnormal-Weather Monitoring and Curation Service Based on Dynamic Graph Embedding
by Yuxuan Gu, Jiakai Gu, Gen Li, Heeseung Yun, Jason J. Jung, Sojung An and David Camacho
Appl. Sci. 2022, 12(20), 10444; https://doi.org/10.3390/app122010444 - 17 Oct 2022
Viewed by 1822
Abstract
This paper presents a system, namely, the abnormal-weather monitoring and curation service (AWMC), which provides people with a better understanding of abnormal weather conditions. The service can analyze a set of multivariate weather datasets (i.e., 7 meteorological datasets from 18 cities in Korea) [...] Read more.
This paper presents a system, namely, the abnormal-weather monitoring and curation service (AWMC), which provides people with a better understanding of abnormal weather conditions. The service can analyze a set of multivariate weather datasets (i.e., 7 meteorological datasets from 18 cities in Korea) and show (i) which dates are mostly abnormal in a certain city, and (ii) which cities are mostly abnormal on a certain date. In particular, the dynamic graph-embedding-based anomaly detection method was employed to measure anomaly scores. We implemented the service and conducted evaluations. Regarding the results of monitoring abnormal weather, AWMC shows that the average precision was approximately 90.9%, recall was 93.2%, and F1 score was 92.1% for all the cities. Full article
(This article belongs to the Special Issue Artificial Intelligence and Ambient Intelligence: Innovative Paths)
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21 pages, 1896 KiB  
Article
A Study on Deriving Improvements through User Recognition Analysis of Artificial Intelligence Speakers
by Seong-Jeong Yoon and Min-Yong Kim
Appl. Sci. 2022, 12(19), 9651; https://doi.org/10.3390/app12199651 - 26 Sep 2022
Viewed by 1359
Abstract
Recently, artificial intelligence speakers have been used a lot in homes and offices. However, users say that it is an automated speaker, not an artificial intelligence speaker. Regression analysis was performed by applying the Value-Based Acceptance Model (VAM) to see if there are [...] Read more.
Recently, artificial intelligence speakers have been used a lot in homes and offices. However, users say that it is an automated speaker, not an artificial intelligence speaker. Regression analysis was performed by applying the Value-Based Acceptance Model (VAM) to see if there are any improvements to the negative perceptions of users mentioned above. As a result of the regression analysis, improvements were needed for convenience and security threats, and it did not reach the level of anthropomorphism such as with humans. In addition, it is concluded that the factors that positively affect the perceived value are usefulness and enjoyment and that they are somewhat satisfied with the burden of technical difficulties, cost, and reliability of the information. In conclusion, artificial intelligence should continuously collect various data and provide information or suggest choices and alternatives through the process of analysis, learning, and inference. However, as a result of this study, it is concluded that it is similar to an automated machine that simply finds the data among many data connected to the Internet, plays music, and connects to a site where you can shop and process it non-face-to-face. The rationale for being similar to an automated machine is that it has not reached the level of anthropomorphism. Full article
(This article belongs to the Special Issue Artificial Intelligence and Ambient Intelligence: Innovative Paths)
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13 pages, 559 KiB  
Article
Traffic Incident Detection Based on Dynamic Graph Embedding in Vehicular Edge Computing
by Gen Li, Tri-Hai Nguyen and Jason J. Jung
Appl. Sci. 2021, 11(13), 5861; https://doi.org/10.3390/app11135861 - 24 Jun 2021
Cited by 13 | Viewed by 2240
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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Review

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34 pages, 609 KiB  
Review
On-Device Object Detection for More Efficient and Privacy-Compliant Visual Perception in Context-Aware Systems
by Ivan Rodriguez-Conde, Celso Campos and Florentino Fdez-Riverola
Appl. Sci. 2021, 11(19), 9173; https://doi.org/10.3390/app11199173 - 02 Oct 2021
Cited by 4 | Viewed by 2569
Abstract
Ambient Intelligence (AmI) encompasses technological infrastructures capable of sensing data from environments and extracting high-level knowledge to detect or recognize users’ features and actions, as well as entities or events in their surroundings. Visual perception, particularly object detection, has become one of the [...] Read more.
Ambient Intelligence (AmI) encompasses technological infrastructures capable of sensing data from environments and extracting high-level knowledge to detect or recognize users’ features and actions, as well as entities or events in their surroundings. Visual perception, particularly object detection, has become one of the most relevant enabling factors for this context-aware user-centered intelligence, being the cornerstone of relevant but complex tasks, such as object tracking or human action recognition. In this context, convolutional neural networks have proven to achieve state-of-the-art accuracy levels. However, they typically result in large and highly complex models that typically demand computation offloading onto remote cloud platforms. Such an approach has security- and latency-related limitations and may not be appropriate for some AmI use cases where the system response time must be as short as possible, and data privacy must be guaranteed. In the last few years, the on-device paradigm has emerged in response to those limitations, yielding more compact and efficient neural networks able to address inference directly on client machines, thus providing users with a smoother and better-tailored experience, with no need of sharing their data with an outsourced service. Framed in that novel paradigm, this work presents a review of the recent advances made along those lines in object detection, providing a comprehensive study of the most relevant lightweight CNN-based detection frameworks, discussing the most paradigmatic AmI domains where such an approach has been successfully applied, the different challenges arisen, the key strategies and techniques adopted to create visual solutions for image-based object classification and localization, as well as the most relevant factors to bear in mind when assessing or comparing those techniques, such as the evaluation metrics or the hardware setups used. Full article
(This article belongs to the Special Issue Artificial Intelligence and Ambient Intelligence: Innovative Paths)
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