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Applied Sciences
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Applications of Emerging Digital Technologies: Beyond AI & IoT
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Applications of Emerging Digital Technologies: Beyond AI & IoT

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 September 2021) | Viewed by 38129

Special Issue Editors

Prof. Dr. Enrico Vezzetti

E-Mail Website
Guest Editor
Department of Management and Production Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy
Interests: product lifecycle management; product design and development; human–machine interaction; human–machine interface; human–computer interaction; computer-aided design; extended reality; augmented reality; virtual reality; artificial intelligence; behavioral analysis; digital therapies; minimally invasive surgery
Special Issues, Collections and Topics in MDPI journals
Prof. Maria Grazia Violante

E-Mail Website
Guest Editor
Department of Management and Production Engineering (DIGEP), Politecnico di Torino, Torino, Italy
Interests: virtual reality; user engagment; eser satisfaction; e-learning
Special Issues, Collections and Topics in MDPI journals
Prof. Sandro Moos

E-Mail Website
Guest Editor
Department of Management and Production Engineering, Politecnico di Torino, 10129, Torino, Italy
Interests: computer-aided design; computer-aided design applied to medical applications

Special Issue Information

Dear Colleagues,

Emerging technologies, including Internet of Things, Artificial Intelligence, Machine Learning, 3D printing, nanotechnology, robotics, virtual reality, augmented reality, and various other new emerging technologies, are progressively recasting action and interaction with society in domains as varied as education, culture, healthcare, business, agriculture, tourism, transport, and so on.

The opportunities afforded by emerging technologies can add value across all sectors of the society. In agriculture, one of the world’s oldest industries, the combination of satellite technologies, drones, and better use of data will support farmers in watering crops, planting, and fertilizing. For businesses, these technologies have the potential to help to develop new products, access new markets, and improve the use of data to target customers about their experience and deliver safer working environments. In manufacturing, 3D printing, collaboration with cloud-based tools on component design and production, and implementation of sensors connected using Internet of Things (IoT) technology help to ensure high-quality products, monitor production processes, and anticipate demand and inform new product development. In dangerous or inaccessible workplaces, such as resourses and mining, drones and sensors can collect real-time data, support planning and management of mining operations, and improve the safety of mine workers. In tourism, virtual reality and 360 degree mobile technologies show engaging travel experiences to attract tourists, and the use of data with machine learning enables to predict the preferences of travel of the consumer, generating personalised offers. A whole range of services, such as online banking, shopping, and entertainment, that people use every day is more available and more accessible thanks these technologies. In Education, what and how teachers teach and what and how students learn can be improved by the use of emerging technologies. In Health, a better management of patient flows, reduction of waiting time, increasing access to specialized services via digital channels, and health information online are some of the benefits of the use of these technologies. Emerging technologies help to improve the management of transport systems and traffic flows in cities and make cars, trains, and buses more efficient and safer thanks to the increasing automation of vehicles and implementation of improved safety features to detect driver alertness.

Given the current surge of emerging technologies, it is important to synthesize the current knowledge about the interactions and impacts between society and emerging technologies. The aim of this Special Issue is to advance scholarly understanding of how these new technologies can be used theoretically, empirically, methodologically, and practically and to understand how society and emerging technologies interact and impact with each other.

Prof. Enrico Vezzetti
Prof. Maria Grazia Violante
Prof. Sandro Moos

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 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

  • Emerging technologies
  • Internet of Things
  • Artificial Intelligence
  • Machine Learning
  • Smart cities
  • 3D printing
  • Nanotechnology
  • Robotics
  • Virtual reality
  • Augmented reality
  • Education
  • Health
  • Services
  • Transport
  • Manufacturing
  • Agriculture
  • Tourism
  • Culture…

Published Papers (5 papers)

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Research

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29 pages, 5743 KiB  
Article
The Moderating Role of Personal Innovativeness and Users Experience in Accepting the Smart Meter Technology
by Gamal Alkawsi, Nor’ashikin Ali and Yahia Baashar
Appl. Sci. 2021, 11(8), 3297; https://doi.org/10.3390/app11083297 - 07 Apr 2021
Cited by 34 | Viewed by 4551
Abstract
The rapid development of smart technologies and data analytics empowers most industries to evolve their systems and introduce innovative applications. Consequently, smart metering technology, an internet of things-based application service, is diffusing rapidly in the energy sector. Regardless of its associated benefits, smart [...] Read more.
The rapid development of smart technologies and data analytics empowers most industries to evolve their systems and introduce innovative applications. Consequently, smart metering technology, an internet of things-based application service, is diffusing rapidly in the energy sector. Regardless of its associated benefits, smart meters continue to struggle from consumers’ acceptance. To promote smart meters’ successful deployment, research is needed to better understand consumers’ acceptance of smart metering. Motivated by these concerns, a smart meter acceptance model is developed to evaluate the moderation role of experience and personal innovativeness factors among residential consumers. A cross-sectional research design was used in this study. Data were collected using a self-administrated questionnaire from 318 smart meters consumers who have had experience in using it. Hypothetical relationships were assessed and validated using partial least squares structural equation modelling. The empirical findings exert the moderating role of experience and personal innovativeness of smart meter acceptance that achieved an acceptable fit with the data, and specifically, five out of nine hypotheses were supported. Full article
(This article belongs to the Special Issue Applications of Emerging Digital Technologies: Beyond AI & IoT)
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14 pages, 1753 KiB  
Article
Key Factors in the Implementation of the Internet of Things in the Hotel Sector
by Alfonso Infante-Moro, Juan C. Infante-Moro and Julia Gallardo-Pérez
Appl. Sci. 2021, 11(7), 2924; https://doi.org/10.3390/app11072924 - 25 Mar 2021
Cited by 26 | Viewed by 3631
Abstract
Many factors can influence decision-making, and if you wish to know which are the most influential factors in a decision, they must be classified by their degrees of influence. This study seeks to determine the most influential factors in the decision of hotels [...] Read more.
Many factors can influence decision-making, and if you wish to know which are the most influential factors in a decision, they must be classified by their degrees of influence. This study seeks to determine the most influential factors in the decision of hotels to accept and implement the Internet of Things in their services through a literary review and a causal study carried out on experts in technology and hotels. The methodology involves the use of fuzzy cognitive maps and the FCMappers tool. The results obtained show that the following factors are among the most influential (in order of relevance): the perceived reliability of the technology, the relative advantage it gives, the level of top management support, compatibility, customer pressure, information systems provider support, security, business partner pressure, characteristics of the leader or manager, government pressure or incentives, pressure from competitors, technological organizational readiness, complexity, size of the company, and the perceived cost. Full article
(This article belongs to the Special Issue Applications of Emerging Digital Technologies: Beyond AI & IoT)
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18 pages, 1816 KiB  
Article
Improvement of Tourists Satisfaction According to Their Non-Verbal Preferences Using Computational Intelligence
by Claudia C. Tusell-Rey, Ricardo Tejeida-Padilla, Oscar Camacho-Nieto, Yenny Villuendas-Rey and Cornelio Yáñez-Márquez
Appl. Sci. 2021, 11(6), 2491; https://doi.org/10.3390/app11062491 - 11 Mar 2021
Cited by 4 | Viewed by 2776
Abstract
In the tourism industry it is common that the information obtained from customers can be varied, dispersed, and with high volumes of data. In this context, the automatic analysis of information has been proposed through electronic customer relationship management, which refers to marketing [...] Read more.
In the tourism industry it is common that the information obtained from customers can be varied, dispersed, and with high volumes of data. In this context, the automatic analysis of information has been proposed through electronic customer relationship management, which refers to marketing activities, tools and techniques, delivered with the use of electronic channels for the specific purpose of locating, building and improving long- term relationships with customers, to enhance their individual potential. In this paper, we refer to the analysis of information in three aspects: customer satisfaction, the study of customer behavior and the forecast of tourist demand. Specifically, we have created a novel dataset comprising the non-verbal preference assessment of tourists who are clients of the Sol Cayo Guillermo hotel belonging to the Melia hotel chain, in Jardines del Rey, Cuba. Then, by applying Computational Intelligence algorithms to this dataset, we achieve segment customers according to their non-verbal preferences, in order to increase their satisfaction, and therefore the client profitability. In order to achieve a good performance in the realization of this task, we have proposed two modifications of the Naïve Associative Classifier, whose results are compared with the most relevant computational algorithms of the state of the art. The experimentally obtained values of balanced accuracy and averaged F1 measure show that, by clearly improving the results of the state-of-the-art algorithms, our proposal is adequate to successfully use electronic customer relationship management in the tourist services provided by hotel chains. Full article
(This article belongs to the Special Issue Applications of Emerging Digital Technologies: Beyond AI & IoT)
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14 pages, 3228 KiB  
Article
Questionnaires or Inner Feelings: Who Measures the Engagement Better?
by Francesca Nonis, Elena Carlotta Olivetti, Federica Marcolin, Maria Grazia Violante, Enrico Vezzetti and Sandro Moos
Appl. Sci. 2020, 10(2), 609; https://doi.org/10.3390/app10020609 - 15 Jan 2020
Cited by 6 | Viewed by 2381
Abstract
This work proposes an innovative method for evaluating users’ engagement, combining the User Engagement Scale (UES) questionnaire and a facial expression recognition (FER) system, active research topics of increasing interest in the human–computer interaction domain (HCI). The subject of the study is a [...] Read more.
This work proposes an innovative method for evaluating users’ engagement, combining the User Engagement Scale (UES) questionnaire and a facial expression recognition (FER) system, active research topics of increasing interest in the human–computer interaction domain (HCI). The subject of the study is a 3D simulator that reproduces a virtual FabLab in which users can approach and learn 3D modeling software and 3D printing. During the interaction with the virtual environment, a structured-light camera acquires the face of the participant in real-time, to catch its spontaneous reactions and compare them with the answers to the UES closed-ended questions. FER methods allow overcoming some intrinsic limits in the adoption of questioning methods, such as the non-sincerity of the interviewees and the lack of correspondence with facial expressions and body language. A convolutional neural network (CNN) has been trained on the Bosphorus database (DB) to perform expression recognition and the classification of the video frames in three classes of engagement (deactivation, average activation, and activation) according to the model of emotion developed by Russell. The results show that the two methodologies can be integrated to evaluate user engagement, to combine weighted answers and spontaneous reactions and to increase knowledge for the design of the new product or service. Full article
(This article belongs to the Special Issue Applications of Emerging Digital Technologies: Beyond AI & IoT)
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Review

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17 pages, 1413 KiB  
Review
X-Ray Bone Fracture Classification Using Deep Learning: A Baseline for Designing a Reliable Approach
by Leonardo Tanzi, Enrico Vezzetti, Rodrigo Moreno and Sandro Moos
Appl. Sci. 2020, 10(4), 1507; https://doi.org/10.3390/app10041507 - 22 Feb 2020
Cited by 31 | Viewed by 20998
Abstract
In recent years, bone fracture detection and classification has been a widely discussed topic and many researchers have proposed different methods to tackle this problem. Despite this, a universal approach able to classify all the fractures in the human body has not yet [...] Read more.
In recent years, bone fracture detection and classification has been a widely discussed topic and many researchers have proposed different methods to tackle this problem. Despite this, a universal approach able to classify all the fractures in the human body has not yet been defined. We aim to analyze and evaluate a selection of papers, chosen according to their representative approach, where the authors applied different deep learning techniques to classify bone fractures, in order to select the strengths of each of them and try to delineate a generalized strategy. Each study is summarized and evaluated using a radar graph with six values: area under the curve (AUC), test accuracy, sensitivity, specificity, dataset size and labelling reliability. Plus, we defined the key points which should be taken into account when trying to accomplish this purpose and we compared each study with our baseline. In recent years, deep learning and, in particular, the convolution neural network (CNN), has achieved results comparable to those of humans in bone fracture classification. Adopting a correct generalization, we are reasonably sure that a computer-aided diagnosis (CAD) system, correctly designed to assist doctors, would save a considerable amount of time and would limit the number of wrong diagnoses. Full article
(This article belongs to the Special Issue Applications of Emerging Digital Technologies: Beyond AI & IoT)
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