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Artificial Intelligence and Emerging Technologies
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Artificial Intelligence and Emerging Technologies

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 (20 November 2022) | Viewed by 55887

Special Issue Editors

Prof. Dr. Vassilis Charissis

E-Mail Website
Guest Editor
School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK
Interests: human-computer interaction; virtual/augmented reality; artificial intelligence; simulation systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dimitris Drikakis

E-Mail Website
Guest Editor
Institute for Advanced Modeling and Simulation, University of Nicosia, Nicosia CY-2417, Cyprus
Interests: computational fluid dynamics; turbulence; shock-waves; multi-component mixing; micronano-scale flows; machine learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Talib Dbouk

E-Mail Website
Guest Editor
Medical School, University of Nicosia, 46 Makedonitissas Avenue, Nicosia CY-2417, Cyprus
Interests: optimization; computational physics; bio-physics; complex fluids; artificial intelligence and data science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The current pandemic has reduced and, in some cases, diminished human activities and interactions, at a personal, city, country, and continental level. At the same time, it highlighted significant flaws in the current usage of technologies and shortcomings that have crippled the infrastructure of major organisations and countries. Similar issues might re-occur in the future, due to other pandemics and/or natural disasters. This Special Issue will present current research and applications in emerging fields that combine Artificial Intelligence with Virtual and Augmented Reality, Big Data, IoT, Physics-Based Machine Learning, major event prediction models and simulation scenarios, amongst other computational and emerging technologies, which could contribute to preventing small or large scale calamities and maintaining the everyday way of life.

Prof. Dr. Vassilis Charissis
Prof. Dr. Dimitris Drikakis
Dr. Talib Dbouk
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

  • Digital Twins (smart cities, engineering, medicine)
  • Computational Modelling and Simulation
  • Sensors, monitoring and simulation
  • Artificial Intelligence and Physics-based Machine Learning
  • Major events’ prediction models and simulation scenarios
  • Big data for urban informatics
  • e-Health applications (Web, VR, AR)
  • e-Commerce and e-Business (Web, VR, AR)
  • e-Learning (Web, VR, AR)
  • Sensing and IoT for urban environments (smart cities)
  • Social computing and networks
  • Ubiquitous Computing
  • AI and wearable computing
  • Vehicular networks
  • Autonomous Vehicles (AVs, UAVs)
  • Human mobility modelling
  • Multi-Agent Systems and Artificial Intelligence
  • Mobile crowdsourcing for urban analytics
  • Collective Intelligence
  • Intelligent Web Applications

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Published Papers (7 papers)

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Research

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12 pages, 1041 KiB  
Article
Deep Learning-Based Context-Aware Recommender System Considering Contextual Features
by Soo-Yeon Jeong and Young-Kuk Kim
Appl. Sci. 2022, 12(1), 45; https://doi.org/10.3390/app12010045 - 21 Dec 2021
Cited by 17 | Viewed by 5778
Abstract
A context-aware recommender system can make recommendations to users by considering contextual information such as time and place, not only the scores assigned to items by users. However, as a user preferences matrix is expanded in a multidimensional matrix, data sparsity is maximized. [...] Read more.
A context-aware recommender system can make recommendations to users by considering contextual information such as time and place, not only the scores assigned to items by users. However, as a user preferences matrix is expanded in a multidimensional matrix, data sparsity is maximized. In this paper, we propose a deep learning-based context-aware recommender system that considers the contextual features. Based on existing deep learning models, we combine a neural network and autoencoder to extract characteristics and predict scores in the process of restoring input data. The newly proposed model is able to easily reflect various type of contextual information and predicts user preferences by considering the feature of user, item and context. The experimental results confirm that the proposed method is mostly superior to the existing method in all datasets. Also, for the dataset with data sparsity problem, it was confirmed that the performance of the proposed method is higher than that of existing methods. The proposed method has higher precision by 0.01–0.05 than other recommender systems in a dataset with many context dimensions. And it showed good performance with a high precision of 0.03 to 0.09 in a small dimensional dataset. Full article
(This article belongs to the Special Issue Artificial Intelligence and Emerging Technologies)
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13 pages, 10588 KiB  
Article
3D Foot Reconstruction Based on Mobile Phone Photographing
by Lulu Niu, Gang Xiong, Xiuqin Shang, Chao Guo, Xi Chen and Huaiyu Wu
Appl. Sci. 2021, 11(9), 4040; https://doi.org/10.3390/app11094040 - 29 Apr 2021
Cited by 10 | Viewed by 3111
Abstract
Foot measurement is necessary for personalized customization. Nowadays, people usually obtain their foot size by using a ruler or foot scanner. However, there are some disadvantages to this, namely, large measurement error and variance when using rulers, and high price and poor convenience [...] Read more.
Foot measurement is necessary for personalized customization. Nowadays, people usually obtain their foot size by using a ruler or foot scanner. However, there are some disadvantages to this, namely, large measurement error and variance when using rulers, and high price and poor convenience when using a foot scanner. To tackle these problems, we obtain foot parameters by 3D foot reconstruction based on mobile phone photography. Firstly, foot images are taken by a mobile phone. Secondly, the SFM (Structure-from-Motion) algorithm is used to acquire the corresponding parameters and then to calculate the camera position to construct the sparse model. Thirdly, the PMVS (Patch-based Multi View System) is adopted to build a dense model. Finally, the Meshlab is used to process and measure the foot model. The result shows that the experimental error of the 3D foot reconstruction method is around 1 mm, which is tolerable for applications such as shoe tree customization. The experiment proves that the method can construct the 3D foot model efficiently and easily. This technology has broad application prospects in the fields of shoe size recommendation, high-end customized shoes and medical correction. Full article
(This article belongs to the Special Issue Artificial Intelligence and Emerging Technologies)
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10 pages, 4902 KiB  
Article
Seepage Mechanism of Tight Sandstone Reservoir Based on Digital Core Simulation Method
by Huaiyu Wu, Xisong Dong, Yang Xu, Gang Xiong, Zhen Shen and Yong Wang
Appl. Sci. 2021, 11(9), 3741; https://doi.org/10.3390/app11093741 - 21 Apr 2021
Cited by 5 | Viewed by 1744
Abstract
Recently, tight sandstone oil has played an increasingly important role in the energy strategies of countries around the world. However, the understanding of a microscopic mechanism is still not clear enough, which has been affecting the improvement of the recovery of tight sandstone [...] Read more.
Recently, tight sandstone oil has played an increasingly important role in the energy strategies of countries around the world. However, the understanding of a microscopic mechanism is still not clear enough, which has been affecting the improvement of the recovery of tight sandstone oil. In this article, a digital core model was established to simulate the pore network of a physical core with CT scan and difference equations were verified by Fourier counting. Then, a combination of orthogonal tests and cubic digital cores was used to experimentally investigate various parameters including pressure, length, permeability, viscosity, and time. By combining the physical experiments with the digital core methods, it can be observed that the state of the micro-crack affects the conductivity of the core, which may be the decisive reason for changing the pressure gradient. The orthogonal test showed that the sensitivity of the parameters was pressure, length, permeability, time, and viscosity in order. The results of the numerical simulations showed that this method can reveal the seepage mechanism of a tight sandstone reservoir, greatly shortening the experimental time and improving flexibility. Full article
(This article belongs to the Special Issue Artificial Intelligence and Emerging Technologies)
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28 pages, 9031 KiB  
Article
Employing Emerging Technologies to Develop and Evaluate In-Vehicle Intelligent Systems for Driver Support: Infotainment AR HUD Case Study
by Vassilis Charissis, Jannat Falah, Ramesh Lagoo, Salsabeel F. M. Alfalah, Soheeb Khan, Shu Wang, Samar Altarteer, Kweku Bram Larbi and Dimitris Drikakis
Appl. Sci. 2021, 11(4), 1397; https://doi.org/10.3390/app11041397 - 4 Feb 2021
Cited by 38 | Viewed by 5986
Abstract
The plurality of current infotainment devices within the in-vehicle space produces an unprecedented volume of incoming data that overwhelm the typical driver, leading to higher collision probability. This work presents an investigation to an alternative option which aims to manage the incoming information [...] Read more.
The plurality of current infotainment devices within the in-vehicle space produces an unprecedented volume of incoming data that overwhelm the typical driver, leading to higher collision probability. This work presents an investigation to an alternative option which aims to manage the incoming information while offering an uncluttered and timely manner of presenting and interacting with the incoming data safely. The latter is achieved through the use of an augmented reality (AR) head-up display (HUD) system, which projects the information within the driver’s field of view. An uncluttered gesture recognition interface provides the interaction with the AR visuals. For the assessment of the system’s effectiveness, we developed a full-scale virtual reality driving simulator which immerses the drivers in challenging, collision-prone, scenarios. The scenarios unfold within a digital twin model of the surrounding motorways of the city of Glasgow. The proposed system was evaluated in contrast to a typical head-down display (HDD) interface system by 30 users, showing promising results that are discussed in detail. Full article
(This article belongs to the Special Issue Artificial Intelligence and Emerging Technologies)
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14 pages, 3222 KiB  
Article
Building Urban Public Traffic Dynamic Network Based on CPSS: An Integrated Approach of Big Data and AI
by Gang Xiong, Zhishuai Li, Huaiyu Wu, Shichao Chen, Xisong Dong, Fenghua Zhu and Yisheng Lv
Appl. Sci. 2021, 11(3), 1109; https://doi.org/10.3390/app11031109 - 26 Jan 2021
Cited by 9 | Viewed by 2653
Abstract
The extensive proliferation of urban transit cards and smartphones has witnessed the feasibility of the collection of citywide travel behaviors and the estimation of traffic status in real-time. In this paper, an urban public traffic dynamic network based on the cyber-physical-social system (CPSS-UPTDN) [...] Read more.
The extensive proliferation of urban transit cards and smartphones has witnessed the feasibility of the collection of citywide travel behaviors and the estimation of traffic status in real-time. In this paper, an urban public traffic dynamic network based on the cyber-physical-social system (CPSS-UPTDN) is proposed as a universal framework for advanced public transportation systems, which can optimize the urban public transportation based on big data and AI methods. Firstly, we introduce three modules and two loops which composes of the novel framework. Then, the key technologies in CPSS-UPTDN are studied, especially collecting and analyzing traffic information by big data and AI methods, and a particular implementation of CPSS-UPTDN is discussed, namely the artificial system, computational experiments, and parallel execution (ACP) method. Finally, a case study is performed. The data sources include both traffic congestion data from physical space and cellular data from social space, which can improve the prediction performance for traffic status. Furthermore, the service quality of urban public transportation can be promoted by optimizing the bus dispatching based on the parallel execution in our framework. Full article
(This article belongs to the Special Issue Artificial Intelligence and Emerging Technologies)
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Review

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25 pages, 2408 KiB  
Review
Artificial Intelligence Evolution in Smart Buildings for Energy Efficiency
by Hooman Farzaneh, Ladan Malehmirchegini, Adrian Bejan, Taofeek Afolabi, Alphonce Mulumba and Precious P. Daka
Appl. Sci. 2021, 11(2), 763; https://doi.org/10.3390/app11020763 - 14 Jan 2021
Cited by 124 | Viewed by 30346
Abstract
The emerging concept of smart buildings, which requires the incorporation of sensors and big data (BD) and utilizes artificial intelligence (AI), promises to usher in a new age of urban energy efficiency. By using AI technologies in smart buildings, energy consumption can be [...] Read more.
The emerging concept of smart buildings, which requires the incorporation of sensors and big data (BD) and utilizes artificial intelligence (AI), promises to usher in a new age of urban energy efficiency. By using AI technologies in smart buildings, energy consumption can be reduced through better control, improved reliability, and automation. This paper is an in-depth review of recent studies on the application of artificial intelligence (AI) technologies in smart buildings through the concept of a building management system (BMS) and demand response programs (DRPs). In addition to elaborating on the principles and applications of the AI-based modeling approaches widely used in building energy use prediction, an evaluation framework is introduced and used for assessing the recent research conducted in this field and across the major AI domains, including energy, comfort, design, and maintenance. Finally, the paper includes a discussion on the open challenges and future directions of research on the application of AI in smart buildings. Full article
(This article belongs to the Special Issue Artificial Intelligence and Emerging Technologies)
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20 pages, 3596 KiB  
Review
Security Aspects for Rpl-Based Protocols: A Systematic Review in IoT
by Karen Avila, Daladier Jabba and Javier Gomez
Appl. Sci. 2020, 10(18), 6472; https://doi.org/10.3390/app10186472 - 17 Sep 2020
Cited by 29 | Viewed by 4243
Abstract
The Internet of things (IoT) is a concept that has gained traction over the last decade. IoT networks have evolved around the wireless sensor network (WSN), and the following research looks at relevant IoT concepts and the different security issues that occur specifically [...] Read more.
The Internet of things (IoT) is a concept that has gained traction over the last decade. IoT networks have evolved around the wireless sensor network (WSN), and the following research looks at relevant IoT concepts and the different security issues that occur specifically at the network layer. This analysis is performed using a structured literature review (SLR). This form of bibliographic review has been a trend in recent years. Its strength is the performance of a bibliometric analysis that allows studying both trends in the line of research that you want to address and the relevant authors. This SLR reviews 53 proposals between 2011 and 2020, whose contribution is to mitigate attacks in the RPL (Routing Protocol for Low-Power and Lossy Networks) protocol. The revised proposals emerged after selecting keywords and databases in which to apply the search. Initially, approximately 380 research works appeared, for which it was necessary to continue using filters to refine the proposals to be included. After reading titles and abstracts, 53 papers were finally selected. In addition to analyzing the attacks mitigated in the RPL protocol, it is intended to identify the trend by which these attacks are reduced, as a result of the review, nine attacks have been found: rank, blackhole, selective forwarding, wormhole, DODAG (Destination-Oriented Directed Acyclic Graph) version number, DAO (Destination Advertisement Object) inconsistency, DIO (DODAG Information Object) suppression, Sybil, and sinkhole. Each of the 53 proposals analyzed in this review has an associated mitigation strategy, these strategies have been categorized into four groups, based on authentication or cryptography, based on network monitoring, based on secure parent node selection and other. According to the results, the authors’ primary mitigation strategy is based on network monitoring, with 30%. This review also identifies the principal authors and countries that need the development of this line of research. Full article
(This article belongs to the Special Issue Artificial Intelligence and Emerging Technologies)
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