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Informatics
Volume 10
Issue 1
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Informatics, Volume 10, Issue 1 (March 2023) – 32 articles

Cover Story (view full-size image): Over the past few years, both researchers and industry practitioners have been advocating for digital twin (DT) adoption in the construction industry. Notwithstanding the advancement of DT, only minimal consideration has been given to the barriers hindering the technology’s adoption. Currently, no study has comprehensively reviewed the available literature on these barriers. This study conducts a comprehensive literature review on the barriers and presents a classification framework to enhance the DT adoption roadmap. The results show that only a few countries are spearheading DT adoption in the construction industry. This study discusses the main categories of the framework (stakeholder-oriented, industry-related, construction-enterprise-related, and technology-related barriers). View this paper
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24 pages, 1423 KiB  
Article
Affective Design Analysis of Explainable Artificial Intelligence (XAI): A User-Centric Perspective
by Ezekiel Bernardo and Rosemary Seva
Informatics 2023, 10(1), 32; https://doi.org/10.3390/informatics10010032 - 16 Mar 2023
Cited by 5 | Viewed by 6302
Abstract
Explainable Artificial Intelligence (XAI) has successfully solved the black box paradox of Artificial Intelligence (AI). By providing human-level insights on AI, it allowed users to understand its inner workings even with limited knowledge of the machine learning algorithms it uses. As a result, [...] Read more.
Explainable Artificial Intelligence (XAI) has successfully solved the black box paradox of Artificial Intelligence (AI). By providing human-level insights on AI, it allowed users to understand its inner workings even with limited knowledge of the machine learning algorithms it uses. As a result, the field grew, and development flourished. However, concerns have been expressed that the techniques are limited in terms of to whom they are applicable and how their effect can be leveraged. Currently, most XAI techniques have been designed by developers. Though needed and valuable, XAI is more critical for an end-user, considering transparency cleaves on trust and adoption. This study aims to understand and conceptualize an end-user-centric XAI to fill in the lack of end-user understanding. Considering recent findings of related studies, this study focuses on design conceptualization and affective analysis. Data from 202 participants were collected from an online survey to identify the vital XAI design components and testbed experimentation to explore the affective and trust change per design configuration. The results show that affective is a viable trust calibration route for XAI. In terms of design, explanation form, communication style, and presence of supplementary information are the components users look for in an effective XAI. Lastly, anxiety about AI, incidental emotion, perceived AI reliability, and experience using the system are significant moderators of the trust calibration process for an end-user. Full article
(This article belongs to the Special Issue Feature Papers in Human-Computer Interaction)
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22 pages, 2011 KiB  
Article
Impact of E-Learning Activities on English as a Second Language Proficiency among Engineering Cohorts of Malaysian Higher Education: A 7-Month Longitudinal Study
by Dipima Buragohain, Grisana Punpeng, Sureenate Jaratjarungkiat and Sushank Chaudhary
Informatics 2023, 10(1), 31; https://doi.org/10.3390/informatics10010031 - 15 Mar 2023
Cited by 5 | Viewed by 7552
Abstract
Recent technology implementation in learning has inspired language educators to employ various e-learning techniques, strategies, and applications in their pedagogical practices while aiming at improving specific learning efficiencies of students. The current study attempts to blend e-learning activities, including blogging, video making, online [...] Read more.
Recent technology implementation in learning has inspired language educators to employ various e-learning techniques, strategies, and applications in their pedagogical practices while aiming at improving specific learning efficiencies of students. The current study attempts to blend e-learning activities, including blogging, video making, online exercises, and digital storyboarding, with English language teaching and explores its impact on engineering cohorts at a public university in Malaysia. The longitudinal research study used three digital applications—Voyant Tools, Lumos Text Complexity Analyzer, and Advanced Text Analyzer—to analyze the data collected through a variety of digital assignments and activities from two English language courses during the researched academic semesters. Contributing to the available literature on the significance of integrating technology innovation with language learning, the study found that implementing e-learning activities can provide substantial insights into improving the learners’ different linguistic competencies, including writing competency, reading comprehension, and vocabulary enhancement. Moreover, the implementation of such innovative technology can motivate students to engage in more peer interactivity, learning engagement, and self-directed learning. Full article
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14 pages, 603 KiB  
Article
Whitelist or Leave Our Website! Advances in the Understanding of User Response to Anti-Ad-Blockers
by Ignacio Redondo and Gloria Aznar
Informatics 2023, 10(1), 30; https://doi.org/10.3390/informatics10010030 - 12 Mar 2023
Cited by 4 | Viewed by 2531
Abstract
Website publishers cannot monetize the ad impressions that are prevented by ad-blockers. Publishers can then employ anti-ad-blockers that force users to choose between either accepting ad impressions by whitelisting the website in the ad-blocker, or leaving the website without accessing the content. This [...] Read more.
Website publishers cannot monetize the ad impressions that are prevented by ad-blockers. Publishers can then employ anti-ad-blockers that force users to choose between either accepting ad impressions by whitelisting the website in the ad-blocker, or leaving the website without accessing the content. This study delineates the mechanisms of how willingness to whitelist/leave the website are affected by the request’s sensitivity to recipients as well as the users’ psychological reactance and evaluation of the website advertising. We tested the proposed relationships using an online panel sample of 500 ad-blocker users, who were asked about their willingness to whitelist/leave their favorite online newspaper after receiving a hypothetical anti-ad-blocker request—four alternative requests with different sensitivity levels were created and randomly assigned to the participants. The results confirmed that (a) the request’s sensitivity can improve the recipient’s compliance, (b) users’ psychological reactance plays an important role in explaining the overall phenomenon, and (c) a favorable evaluation of the website advertising can improve willingness to whitelist. These findings help to better understand user response to anti-ad-blockers and may also help publishers increase their whitelist ratios. Full article
(This article belongs to the Section Human-Computer Interaction)
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11 pages, 240 KiB  
Article
Strategies for Enhancing Assessment Information Integrity in Mobile Learning
by Godwin Kaisara and Kelvin Joseph Bwalya
Informatics 2023, 10(1), 29; https://doi.org/10.3390/informatics10010029 - 10 Mar 2023
Cited by 3 | Viewed by 2363
Abstract
Mobile learning is a global trend, which has become more widespread in the post-COVID-19 pandemic era. However, with the adoption of mobile learning comes new assessment approaches to evaluate the understanding of the acquired information and knowledge. Nevertheless, there is scant knowledge of [...] Read more.
Mobile learning is a global trend, which has become more widespread in the post-COVID-19 pandemic era. However, with the adoption of mobile learning comes new assessment approaches to evaluate the understanding of the acquired information and knowledge. Nevertheless, there is scant knowledge of how to enhance assessment information integrity in mobile learning assessments. Due to the importance of assessments in evaluating knowledge, integrity is the sine qua non of online assessments. This research focuses on the strategies universities could use to improve assessment information integrity. This research adopts a qualitative design, employing interviews with academics as well as teaching and learning support staff for data collection. The findings reveal five strategies that academics and support staff recommend to enhance assessment information integrity in mobile learning. The theoretical and practical implications are discussed, as well as future research directions. Full article
20 pages, 2273 KiB  
Article
Enhancing Small Medical Dataset Classification Performance Using GAN
by Mohammad Alauthman, Ahmad Al-qerem, Bilal Sowan, Ayoub Alsarhan, Mohammed Eshtay, Amjad Aldweesh and Nauman Aslam
Informatics 2023, 10(1), 28; https://doi.org/10.3390/informatics10010028 - 8 Mar 2023
Cited by 13 | Viewed by 4864
Abstract
Developing an effective classification model in the medical field is challenging due to limited datasets. To address this issue, this study proposes using a generative adversarial network (GAN) as a data-augmentation technique. The research aims to enhance the classifier’s generalization performance, stability, and [...] Read more.
Developing an effective classification model in the medical field is challenging due to limited datasets. To address this issue, this study proposes using a generative adversarial network (GAN) as a data-augmentation technique. The research aims to enhance the classifier’s generalization performance, stability, and precision through the generation of synthetic data that closely resemble real data. We employed feature selection and applied five classification algorithms to thirteen benchmark medical datasets, augmented using the least-square GAN (LS-GAN). Evaluation of the generated samples using different ratios of augmented data showed that the support vector machine model outperforms other methods with larger samples. The proposed data augmentation approach using a GAN presents a promising solution for enhancing the performance of classification models in the healthcare field. Full article
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17 pages, 762 KiB  
Article
Modeling the Influence of Fake Accounts on User Behavior and Information Diffusion in Online Social Networks
by Sara G. Fahmy, Khaled M. Abdelgaber, Omar H. Karam and Doaa S. Elzanfaly
Informatics 2023, 10(1), 27; https://doi.org/10.3390/informatics10010027 - 3 Mar 2023
Cited by 3 | Viewed by 3712
Abstract
The mechanisms of information diffusion in Online Social Networks (OSNs) have been studied extensively from various perspectives with some focus on identifying and modeling the role of heterogeneous nodes. However, none of these studies have considered the influence of fake accounts on human [...] Read more.
The mechanisms of information diffusion in Online Social Networks (OSNs) have been studied extensively from various perspectives with some focus on identifying and modeling the role of heterogeneous nodes. However, none of these studies have considered the influence of fake accounts on human accounts and how this will affect the rumor diffusion process. This paper aims to present a new information diffusion model that characterizes the role of bots in the rumor diffusion process in OSNs. The proposed SIhIbR model extends the classical SIR model by introducing two types of infected users with different infection rates: the users who are infected by human (Ih) accounts with a normal infection rate and the users who are infected by bot accounts (Ib) with a different diffusion rate that reflects the intent and steadiness of this type of account to spread the rumors. The influence of fake accounts on human accounts diffusion rate has been measured using the social impact theory, as it better reflects the deliberate behavior of bot accounts to spread a rumor to a large portion of the network by considering both the strength and the bias of the source node. The experiment results show that the accuracy of the SIhIbR model outperforms the SIR model when simulating the rumor diffusion process in the existence of fake accounts. It has been concluded that fake accounts accelerate the rumor diffusion process as they impact many people in a short time. Full article
(This article belongs to the Special Issue Applications of Complex Networks: Advances and Challenges)
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24 pages, 16713 KiB  
Article
The Influence of Light and Color in Digital Paintings of Environmental Issues on Emotions and Cognitions
by Witthaya Hosap, Chaowanan Khundam, Patibut Preeyawongsakul, Varunyu Vorachart and Frédéric Noël
Informatics 2023, 10(1), 26; https://doi.org/10.3390/informatics10010026 - 3 Mar 2023
Cited by 1 | Viewed by 4745
Abstract
This study aimed to examine the use of light and color in digital paintings and their effect on audiences’ perceptions of environmental issues. Five digital paintings depicting environmental issues have been designed. Digital painting techniques created black-and-white, monochrome, and color images. Each image [...] Read more.
This study aimed to examine the use of light and color in digital paintings and their effect on audiences’ perceptions of environmental issues. Five digital paintings depicting environmental issues have been designed. Digital painting techniques created black-and-white, monochrome, and color images. Each image used utopian and dystopian visualization concepts to communicate hope and despair. In the experiment, 225 volunteers representing students in colleges were separated into three independent groups: the first group was offered black-and-white images, the second group was offered monochromatic images, and the third group was offered color images. After viewing each image, participants were asked to complete questionnaires about their emotions and cognitions regarding environmental issues, including identifying hope and despair and the artist’s perspective at the end. The analysis showed no differences in emotions and cognitions among participants. However, monochromatic images were the most emotionally expressive. The results indicated that the surrounding atmosphere of the images created despair, whereas objects inspired hope. Artists should emphasize the composition of the atmosphere and the objects in the image to convey the concepts of utopia and dystopia to raise awareness of environmental issues. Full article
(This article belongs to the Section Social Informatics and Digital Humanities)
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20 pages, 4273 KiB  
Article
Vertical Integration Dynamics to Innovate in Technology Business
by Pedro Nogueira, Leandro Pereira, Ana Simões, Alvaro Dias and Renato Lopes da Costa
Informatics 2023, 10(1), 25; https://doi.org/10.3390/informatics10010025 - 22 Feb 2023
Cited by 1 | Viewed by 3548
Abstract
Companies try to acquire the finest advantages and techniques in a technologically advanced and end-to-end market to have a stronger foothold there. Although empirical research on this topic links IT to a decline in vertical integration, corporations are increasingly using this corporate strategy. [...] Read more.
Companies try to acquire the finest advantages and techniques in a technologically advanced and end-to-end market to have a stronger foothold there. Although empirical research on this topic links IT to a decline in vertical integration, corporations are increasingly using this corporate strategy. The goal of this study is to show how over the past 22 years, scientific literature has changed with regard to how information technology (IT) affects vertical integration, one of the main types of corporate strategies. The findings demonstrated that vertical integration has been evolving in a balanced manner in a technological environment. Three categories—information technology, innovation, and processes—help explain this association and were discovered through cluster analysis. The direction of operational integration, the degree of industry concentration, demand unpredictability, and innovation should all be considered while making integration decisions. Full article
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20 pages, 10109 KiB  
Article
Fan Fault Diagnosis Using Acoustic Emission and Deep Learning Methods
by Giuseppe Ciaburro, Sankar Padmanabhan, Yassine Maleh and Virginia Puyana-Romero
Informatics 2023, 10(1), 24; https://doi.org/10.3390/informatics10010024 - 15 Feb 2023
Cited by 12 | Viewed by 3625
Abstract
The modern conception of industrial production recognizes the increasingly crucial role of maintenance. Currently, maintenance is thought of as a service that aims to maintain the efficiency of equipment and systems while also taking quality, energy efficiency, and safety requirements into consideration. In [...] Read more.
The modern conception of industrial production recognizes the increasingly crucial role of maintenance. Currently, maintenance is thought of as a service that aims to maintain the efficiency of equipment and systems while also taking quality, energy efficiency, and safety requirements into consideration. In this study, a new methodology for automating the fan maintenance procedures was developed. An approach based on the recording of the acoustic emission and the failure diagnosis using deep learning was evaluated for the detection of dust deposits on the blades of an axial fan. Two operating conditions have been foreseen: No-Fault, and Fault. In the No-Fault condition, the fan blades are perfectly clean while in the Fault condition, deposits of material have been artificially created. Utilizing a pre-trained network (SqueezeNet) built on the ImageNet dataset, the acquired data were used to build an algorithm based on convolutional neural networks (CNN). The transfer learning applied to the images of the spectrograms extracted from the recordings of the acoustic emission of the fan, in the two operating conditions, returned excellent results (accuracy = 0.95), confirming the excellent performance of the methodology. Full article
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13 pages, 443 KiB  
Article
Analysis of Soft Skills and Job Level with Data Science: A Case for Graduates of a Private University
by Sofía Ramos-Pulido, Neil Hernández-Gress and Gabriela Torres-Delgado
Informatics 2023, 10(1), 23; https://doi.org/10.3390/informatics10010023 - 13 Feb 2023
Cited by 2 | Viewed by 3258
Abstract
This study shows the significant features predicting graduates’ job levels, particularly high-level positions. Moreover, it shows that data science methodologies can accurately predict graduate outcomes. The dataset used to analyze graduate outcomes was derived from a private educational institution survey. The original dataset [...] Read more.
This study shows the significant features predicting graduates’ job levels, particularly high-level positions. Moreover, it shows that data science methodologies can accurately predict graduate outcomes. The dataset used to analyze graduate outcomes was derived from a private educational institution survey. The original dataset contains information on 17,898 graduates and approximately 148 features. Three machine learning algorithms, namely, decision trees, random forest, and gradient boosting, were used for data analysis. These three machine learning models were compared with ordinal regression. The results indicate that gradient boosting is the best predictive model, which is 6% higher than the ordinal regression accuracy. The SHapley Additive exPlanations (SHAP), a novel methodology to extract the significant features of different machine learning algorithms, was then used to extract the most important features of the gradient boosting model. Current salary is the most important feature in predicting job levels. Interestingly, graduates who realized the importance of communication skills and teamwork to be good leaders also had higher job positions. Finally, general relevant features to predict job levels include the number of people directly in charge, company size, seniority, and satisfaction with income. Full article
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24 pages, 397 KiB  
Article
Quality of E-Tax System and Tax Compliance Intention: The Mediating Role of User Satisfaction
by Prianto Budi Saptono, Sabina Hodžić, Ismail Khozen, Gustofan Mahmud, Intan Pratiwi, Dwi Purwanto, Muhamad Akbar Aditama, Nisa’ul Haq and Siti Khodijah
Informatics 2023, 10(1), 22; https://doi.org/10.3390/informatics10010022 - 8 Feb 2023
Cited by 12 | Viewed by 9870
Abstract
The effectiveness of the e-tax system in encouraging tax compliance has been largely unexplored. Thus, the current study aims to examine the interrelationship between technological predictors in explaining tax compliance intention among certified tax professionals. Based on the literature on information system success [...] Read more.
The effectiveness of the e-tax system in encouraging tax compliance has been largely unexplored. Thus, the current study aims to examine the interrelationship between technological predictors in explaining tax compliance intention among certified tax professionals. Based on the literature on information system success and tax compliance intention, this paper proposed an expanded conceptual framework that incorporates convenience and perception of reduced compliance costs as predictors and satisfaction as a mediator. The data were collected from 650 tax professionals who used e-Filing and 492 who used e-Form through an online survey and analyzed using hierarchical multiple regression. The empirical results suggest that participants’ perceived service quality of e-Filing services and perceptions of reduced compliance costs positively influence users’ willingness to comply with tax regulations. The latter predictor is also, and only, significant among e-Form users. The empirical results also provide statistical evidence for the mediating role of satisfaction in the relationship between all predictors and tax compliance intention. This study encourages tax policymakers and e-tax filing providers to improve their services to increase user satisfaction and tax compliance. Full article
21 pages, 3260 KiB  
Article
An IoT-Fog-Cloud Integrated Framework for Real-Time Remote Cardiovascular Disease Diagnosis
by Abhilash Pati, Manoranjan Parhi, Mohammad Alnabhan, Binod Kumar Pattanayak, Ahmad Khader Habboush and Mohammad K. Al Nawayseh
Informatics 2023, 10(1), 21; https://doi.org/10.3390/informatics10010021 - 6 Feb 2023
Cited by 25 | Viewed by 3705
Abstract
Recently, it has proven difficult to make an immediate remote diagnosis of any coronary illness, including heart disease, diabetes, etc. The drawbacks of cloud computing infrastructures, such as excessive latency, bandwidth, energy consumption, security, and privacy concerns, have lately been addressed by Fog [...] Read more.
Recently, it has proven difficult to make an immediate remote diagnosis of any coronary illness, including heart disease, diabetes, etc. The drawbacks of cloud computing infrastructures, such as excessive latency, bandwidth, energy consumption, security, and privacy concerns, have lately been addressed by Fog computing with IoT applications. In this study, an IoT-Fog-Cloud integrated system, called a Fog-empowered framework for real-time analysis in heart patients using ENsemble Deep learning (FRIEND), has been introduced that can instantaneously facilitate remote diagnosis of heart patients. The proposed system was trained on the combined dataset of Long-Beach, Cleveland, Switzerland, and Hungarian heart disease datasets. We first tested the model with eight basic ML approaches, including the decision tree, logistic regression, random forest, naive Bayes, k-nearest neighbors, support vector machine, AdaBoost, and XGBoost approaches, and then applied ensemble methods including bagging classifiers, weighted averaging, and soft and hard voting to achieve enhanced outcomes and a deep neural network, a deep learning approach, with the ensemble methods. These models were validated using 16 performance and 9 network parameters to justify this work. The accuracy, PPV, TPR, TNR, and F1 scores of the experiments reached 94.27%, 97.59%, 96.09%, 75.44%, and 96.83%, respectively, which were comparatively higher when the deep neural network was assembled with bagging and hard-voting classifiers. The user-friendliness and the inclusion of Fog computing principles, instantaneous remote cardiac patient diagnosis, low latency, and low energy consumption, etc., are advantages confirmed according to the achieved experimental results. Full article
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15 pages, 1432 KiB  
Article
Impact of Applying Information and Communication Technology Tools in Physical Education Classes
by Attila Varga and László Révész
Informatics 2023, 10(1), 20; https://doi.org/10.3390/informatics10010020 - 4 Feb 2023
Cited by 8 | Viewed by 3556
Abstract
The authors of the present study explored how ICT devices used in P.E. lessons determine psychomotor performance, perceived motivational climate, and motivation. The students were allowed to use ICT devices (smartphone, webpages, Facebook) during a four-week intervention. In the course of the research [...] Read more.
The authors of the present study explored how ICT devices used in P.E. lessons determine psychomotor performance, perceived motivational climate, and motivation. The students were allowed to use ICT devices (smartphone, webpages, Facebook) during a four-week intervention. In the course of the research project aimed to assess the impact of the application of ICT devices on performance and motivation, the participants were divided into two test groups and one control group. The sample consisted of secondary school students including 21 males and 64 females with the Mage = 16.72 years. The results showed that in groups where ICT devices were used, performance (p = 0.04) and task orientation (p = 0.00) significantly improved. Meanwhile, in the group in which ICT devices were not used, the intervention resulted in improved performance (p = 0.00) and by the end of the project, this trend was coupled with increased Ego orientation (p = 0.00) and higher rate of amotivation (p = 0.04). It can be concluded that the use of ICT tools has a positive impact on performance and motivation. Full article
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23 pages, 1535 KiB  
Article
The Nexus between Business Analytics Capabilities and Knowledge Orientation in Driving Business Model Innovation: The Moderating Role of Industry Type
by Mohammad Daradkeh
Informatics 2023, 10(1), 19; https://doi.org/10.3390/informatics10010019 - 31 Jan 2023
Cited by 16 | Viewed by 3540
Abstract
The importance of business analytics (BA) in driving knowledge generation and business innovation has been widely discussed in both the academic and business communities. However, empirical research on the relationship between knowledge orientation and business analytics capabilities in driving business model innovation remains [...] Read more.
The importance of business analytics (BA) in driving knowledge generation and business innovation has been widely discussed in both the academic and business communities. However, empirical research on the relationship between knowledge orientation and business analytics capabilities in driving business model innovation remains scarce. Drawing on the knowledge-based view and dynamic capabilities theory, this study develops a model to investigate the interplay between knowledge orientation and BA capabilities in driving business model innovation. It also explores the moderating role of industry type on this relationship. To test the model, data were collected from a cross-sectional sample of 207 firms (high-tech and non-high-tech industries). Descriptive and structural equation modeling (SEM) were used to test the hypotheses. The findings showed that knowledge orientation and BA capabilities are significantly and positively related to business model innovation. Knowledge commitment, shared vision, and open-mindedness are significantly and positively related to BA perception and recognition capabilities and BA integration capabilities. BA capabilities mediated the relationship between knowledge orientation and business model innovation. The path mechanism of knowledge orientation → BA capabilities → business model innovation shows that industry type has a moderating effect on knowledge orientation and BA capabilities, as well as BA capabilities and business model innovation. This study provides empirically proven insights and practical guidance on the dynamics and mechanisms of BA and organizational knowledge capabilities and their impact on business model innovation. Full article
(This article belongs to the Special Issue Feature Papers in Informatics in 2022)
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22 pages, 796 KiB  
Article
Discovering Entities Similarities in Biological Networks Using a Hybrid Immune Algorithm
by Rocco A. Scollo, Antonio G. Spampinato, Georgia Fargetta, Vincenzo Cutello and Mario Pavone
Informatics 2023, 10(1), 18; https://doi.org/10.3390/informatics10010018 - 31 Jan 2023
Cited by 1 | Viewed by 2153
Abstract
Disease phenotypes are generally caused by the failure of gene modules which often have similar biological roles. Through the study of biological networks, it is possible to identify the intrinsic structure of molecular interactions in order to identify the so-called “disease modules [...] Read more.
Disease phenotypes are generally caused by the failure of gene modules which often have similar biological roles. Through the study of biological networks, it is possible to identify the intrinsic structure of molecular interactions in order to identify the so-called “disease modules”. Community detection is an interesting and valuable approach to discovering the structure of the community in a complex network, revealing the internal organization of the nodes, and has become a leading research topic in the analysis of complex networks. This work investigates the link between biological modules and network communities in test-case biological networks that are commonly used as a reference point and which include Protein–Protein Interaction Networks, Metabolic Networks and Transcriptional Regulation Networks. In order to identify small and structurally well-defined communities in the biological context, a hybrid immune metaheuristic algorithm Hybrid-IA is proposed and compared with several metaheuristics, hyper-heuristics, and the well-known greedy algorithm Louvain, with respect to modularity maximization. Considering the limitation of modularity optimization, which can fail to identify smaller communities, the reliability of Hybrid-IA was also analyzed with respect to three well-known sensitivity analysis measures (NMI, ARI and NVI) that assess how similar the detected communities are to real ones. By inspecting all outcomes and the performed comparisons, we will see that on one hand Hybrid-IA finds slightly lower modularity values than Louvain, but outperforms all other metaheuristics, while on the other hand, it can detect communities more similar to the real ones when compared to those detected by Louvain. Full article
(This article belongs to the Special Issue Applications of Complex Networks: Advances and Challenges)
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15 pages, 2051 KiB  
Article
The Prediction of Road-Accident Risk through Data Mining: A Case Study from Setubal, Portugal
by David Dias, José Silvestre Silva and Alexandre Bernardino
Informatics 2023, 10(1), 17; https://doi.org/10.3390/informatics10010017 - 30 Jan 2023
Cited by 4 | Viewed by 5080
Abstract
This work proposes a tool to predict the risk of road accidents. The developed system consists of three steps: data selection and collection, preprocessing, and the use of mining algorithms. The data were imported from the Portuguese National Guard database, and they related [...] Read more.
This work proposes a tool to predict the risk of road accidents. The developed system consists of three steps: data selection and collection, preprocessing, and the use of mining algorithms. The data were imported from the Portuguese National Guard database, and they related to accidents that occurred from 2019 to 2021. The results allowed us to conclude that the highest concentration of accidents occurs during the time interval from 17:00 to 20:00, and that rain is the meteorological factor with the greatest effect on the probability of an accident occurring. Additionally, we concluded that Friday is the day of the week on which more accidents occur than on other days. These results are of importance to the decision makers responsible for planning the most effective allocation of resources for traffic surveillance. Full article
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12 pages, 291 KiB  
Article
Effectiveness of Telemedicine in Diabetes Management: A Retrospective Study in an Urban Medically Underserved Population Area (UMUPA)
by Lisa Ariellah Ward, Gulzar H. Shah, Jeffery A. Jones, Linda Kimsey and Hani Samawi
Informatics 2023, 10(1), 16; https://doi.org/10.3390/informatics10010016 - 29 Jan 2023
Cited by 3 | Viewed by 3881
Abstract
This paper examines the efficacy of telemedicine (TM) technology compared to traditional face-to-face (F2F) visits as an alternative healthcare delivery service for managing diabetes in populations residing in urban medically underserved areas (UMUPAs). Retrospective electronic patient health records (ePHR) with type 2 diabetes [...] Read more.
This paper examines the efficacy of telemedicine (TM) technology compared to traditional face-to-face (F2F) visits as an alternative healthcare delivery service for managing diabetes in populations residing in urban medically underserved areas (UMUPAs). Retrospective electronic patient health records (ePHR) with type 2 diabetes mellitus (T2DM) were examined from 1 January 2019 to 30 June 2021. Multiple linear regression models indicated that T2DM patients with uncontrolled diabetes utilizing TM were similar to traditional visits in lowering hemoglobin (HbA1c) levels. The healthcare service type significantly predicted HbA1c % values, as the regression coefficient for TM (vs. F2F) showed a significant negative association (B = −0.339, p < 0.001), suggesting that patients using TM were likely to have 0.34 lower HbA1c % values on average when compared with F2F visits. The regression coefficient for female (vs. male) gender showed a positive association (B = 0.190, p < 0.034), with HbA1c % levels showing that female patients had 0.19 higher HbA1c levels than males. Age (B = −0.026, p < 0.001) was a significant predictor of HbA1c % levels, with 0.026 lower HbA1c % levels for each year’s increase in age. Black adults (B = 0.888, p < 0.001), on average, were more likely to have 0.888 higher HbA1c % levels when compared with White adults. Full article
(This article belongs to the Special Issue Feature Papers in Medical and Clinical Informatics)
23 pages, 16721 KiB  
Article
Towards Moving Objects Behavior Analysis: Region Speed Limit Rate Measure
by Francisco Javier Moreno Arboleda, Georgia Garani and Simon Zea Gallego
Informatics 2023, 10(1), 15; https://doi.org/10.3390/informatics10010015 - 29 Jan 2023
Viewed by 1626
Abstract
In this paper, a measure is proposed that, based on the trajectories of moving objects, computes the speed limit rate in each of the cells in which a region is segmented (the space where the objects move). The time is also segmented into intervals. [...] Read more.
In this paper, a measure is proposed that, based on the trajectories of moving objects, computes the speed limit rate in each of the cells in which a region is segmented (the space where the objects move). The time is also segmented into intervals. In this way, the behavior of moving objects can be analyzed with regard to their speed in a cell for a given time interval. An implementation of the corresponding algorithm for this measure and several experiments were conducted with the trajectories of taxis in Porto (Portugal). The results showed that the speed limit rate measure can be helpful for detecting patterns of movement, e.g., in a day (morning hours vs. night hours) or on different days of the week (weekdays vs. weekends). This measure might also serve as a rough estimate for congestion in a (sub)region. This may be useful for traffic analysis, including traffic prediction. Full article
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25 pages, 1856 KiB  
Review
Barriers to the Adoption of Digital Twin in the Construction Industry: A Literature Review
by De-Graft Joe Opoku, Srinath Perera, Robert Osei-Kyei, Maria Rashidi, Keivan Bamdad and Tosin Famakinwa
Informatics 2023, 10(1), 14; https://doi.org/10.3390/informatics10010014 - 28 Jan 2023
Cited by 33 | Viewed by 8551
Abstract
Digital twin (DT) has gained significant recognition among researchers due to its potential across industries. With the prime goal of solving numerous challenges confronting the construction industry (CI), DT in recent years has witnessed several applications in the CI. Hence, researchers have been [...] Read more.
Digital twin (DT) has gained significant recognition among researchers due to its potential across industries. With the prime goal of solving numerous challenges confronting the construction industry (CI), DT in recent years has witnessed several applications in the CI. Hence, researchers have been advocating for DT adoption to tackle the challenges of the CI. Notwithstanding, a distinguishable set of barriers that oppose the adoption of DT in the CI has not been determined. Therefore, this paper identifies the barriers and incorporates them into a classified framework to enhance the roadmap for adopting DT in the CI. This research conducts an extensive review of the literature and analyses the barriers whilst integrating the science mapping technique. Using Scopus, ScienceDirect, and Web of Science databases, 154 related bibliographic records were identified and analysed using science mapping, while 40 carefully selected relevant publications were systematically reviewed. From the review, the top five barriers identified include low level of knowledge, low level of technology acceptance, lack of clear DT value propositions, project complexities, and static nature of building data. The results show that the UK, China, the USA, and Germany are the countries spearheading the DT adoption in the CI, while only a small number of institutions from Australia, the UK, Algeria, and Greece have established institutional collaborations for DT research. A conceptual framework was developed on the basis of 30 identified barriers to support the DT adoption roadmap. The main categories of the framework comprise stakeholder-oriented, industry-related, construction-enterprise-related, and technology-related barriers. The identified barriers and the framework will guide and broaden the knowledge of DT, which is critical for successful adoption in the construction industry. Full article
(This article belongs to the Special Issue Building Smart Cities and Infrastructures for a Sustainable Future)
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17 pages, 324 KiB  
Review
Digital Weather Information in an Embodied World
by Alan E. Stewart and Matthew J. Bolton
Informatics 2023, 10(1), 13; https://doi.org/10.3390/informatics10010013 - 24 Jan 2023
Cited by 2 | Viewed by 2866
Abstract
We review the emergence of digital weather information, the history of human embodied knowing about weather, and two perspectives on cognition, one of which is symbolic (amodal, abstract, and arbitrary) and the other being embodied (embodied, extended, embedded, and enacted) to address the [...] Read more.
We review the emergence of digital weather information, the history of human embodied knowing about weather, and two perspectives on cognition, one of which is symbolic (amodal, abstract, and arbitrary) and the other being embodied (embodied, extended, embedded, and enacted) to address the question: Beyond the general weather information they provide, to what extent can digital devices be used in an embodied way to extend a person’s pick-up of weather information? This is an interesting question to examine because human weather information and knowledge has a long past in our evolutionary history. Our human ancestors had to pick-up immediate information from the environment (including the weather) to survive. Digital weather information and knowing has a comparatively short past and a promising future. After reviewing these relevant topics, we concluded that, with the possible exception of weather radar apps, nothing currently exists in the form of digital products than can extend the immediate sensory reach of people to alert them about just-about-to-occur weather—at least not in the embodied forms of information. We believe that people who are weather salient (i.e., have a strong psychological attunement to the weather) may be in the best position going forward to integrate digital weather knowing with that which is embodied. Full article
(This article belongs to the Special Issue Mapping across Space and Time: A Perspective of Multisource Geospatial Data Matching and Integration)
11 pages, 1176 KiB  
Article
Synergistic Effect of Medical Information Systems Integration: To What Extent Will It Affect the Accuracy Level in the Reports and Decision-Making Systems?
by Ali Azadi and Francisco José García-Peñalvo
Informatics 2023, 10(1), 12; https://doi.org/10.3390/informatics10010012 - 19 Jan 2023
Cited by 3 | Viewed by 2803
Abstract
Nowadays, according to the intention of many hospitals and medical centers to computerize their processes and medical treatments, including data forms and medical images, which are generating a considerable amount of data, IT specialists and data scientists who are oriented to eHealth and [...] Read more.
Nowadays, according to the intention of many hospitals and medical centers to computerize their processes and medical treatments, including data forms and medical images, which are generating a considerable amount of data, IT specialists and data scientists who are oriented to eHealth and related issues know the importance of data integration and its benefits. This study indicates the significance of data integration, especially in medical information systems. It means that the medical subsystems in the HIS (hospital information system) must be integrated, and it is also necessary to unify with the MIS (management information system). In this paper, the accuracy level of the extracted reports from the information system (to evaluate the staff’s performance) will be measured in two ways: (1) At first, the performance of the clinic reception staff will be evaluated. In this way, the personnel attendance system is an independent and separate software, and the mentioned evaluation has been performed by its report. (2) The following year, in the same location, the same evaluation has been performed based on the data extracted from the personnel attendance subsystem, which has been added to the medical information system as an integrated information system. After comparing the accuracy level of both ways, this paper concludes that when the personnel attendance subsystem as a part of the MIS has been unified with the HIS, the reports and, consequently, management decisions will be more accurate; therefore, the managers and decision-makers will perceive the importance of data integration more than in the past. Full article
(This article belongs to the Section Medical and Clinical Informatics)
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5 pages, 193 KiB  
Editorial
Acknowledgment to the Reviewers of Informatics in 2022
by Informatics Editorial Office
Informatics 2023, 10(1), 11; https://doi.org/10.3390/informatics10010011 - 19 Jan 2023
Viewed by 1207
Abstract
High-quality academic publishing is built on rigorous peer review [...] Full article
19 pages, 2070 KiB  
Article
SMPT: A Semi-Supervised Multi-Model Prediction Technique for Food Ingredient Named Entity Recognition (FINER) Dataset Construction
by Kokoy Siti Komariah, Ariana Tulus Purnomo, Ardianto Satriawan, Muhammad Ogin Hasanuddin, Casi Setianingsih and Bong-Kee Sin
Informatics 2023, 10(1), 10; https://doi.org/10.3390/informatics10010010 - 13 Jan 2023
Cited by 5 | Viewed by 3603
Abstract
To pursue a healthy lifestyle, people are increasingly concerned about their food ingredients. Recently, it has become a common practice to use an online recipe to select the ingredients that match an individual’s meal plan and healthy diet preference. The information from online [...] Read more.
To pursue a healthy lifestyle, people are increasingly concerned about their food ingredients. Recently, it has become a common practice to use an online recipe to select the ingredients that match an individual’s meal plan and healthy diet preference. The information from online recipes can be extracted and used to develop various food-related applications. Named entity recognition (NER) is often used to extract such information. However, the problem in building an NER system lies in the massive amount of data needed to train the classifier, especially on a specific domain, such as food. There are food NER datasets available, but they are still quite limited. Thus, we proposed an iterative self-training approach called semi-supervised multi-model prediction technique (SMPT) to construct a food ingredient NER dataset. SMPT is a deep ensemble learning model that employs the concept of self-training and uses multiple pre-trained language models in the iterative data labeling process, with a voting mechanism used as the final decision to determine the entity’s label. Utilizing the SMPT, we have created a new annotated dataset of ingredient entities obtained from the Allrecipes website named FINER. Finally, this study aims to use the FINER dataset as an alternative resource to support food computing research and development. Full article
(This article belongs to the Section Machine Learning)
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26 pages, 37349 KiB  
Article
Blockchain Propels Tourism Industry—An Attempt to Explore Topics and Information in Smart Tourism Management through Text Mining and Machine Learning
by Vikram Puri, Subhra Mondal, Subhankar Das and Vasiliki G. Vrana
Informatics 2023, 10(1), 9; https://doi.org/10.3390/informatics10010009 - 12 Jan 2023
Cited by 31 | Viewed by 7605
Abstract
Blockchain and immersive technology are the pioneers in bringing digitalization to tourism, and researchers worldwide are exploring many facets of these techniques. This paper analyzes the various aspects of blockchain technology and its potential use in tourism. We explore high-frequency keywords, perform network [...] Read more.
Blockchain and immersive technology are the pioneers in bringing digitalization to tourism, and researchers worldwide are exploring many facets of these techniques. This paper analyzes the various aspects of blockchain technology and its potential use in tourism. We explore high-frequency keywords, perform network analysis of relevant publications to analyze patterns, and introduce machine learning techniques to facilitate systematic reviews. We focused on 94 publications from Web Science that dealt with blockchain implementation in tourism from 2017 to 2022. We used Vosviewer for network analysis and artificial intelligence models with the help of machine learning tools to predict the relevance of the work. Many reviewed articles mainly deal with blockchain in tourism and related terms such as smart tourism and crypto tourism. This study is the first attempt to use text analysis to improve the topic modeling of blockchain in tourism. It comprehensively analyzes the technology’s potential use in the hospitality, accommodation, and booking industry. In this context, the paper provides significant value to researchers by giving an insight into the trends and keyword patterns. Tourism still has many unexplored areas; journal articles should also feature special studies on this topic. Full article
(This article belongs to the Section Machine Learning)
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21 pages, 4956 KiB  
Article
Motif-Based Graph Representation Learning with Application to Chemical Molecules
by Yifei Wang, Shiyang Chen, Guobin Chen, Ethan Shurberg, Hang Liu and Pengyu Hong
Informatics 2023, 10(1), 8; https://doi.org/10.3390/informatics10010008 - 11 Jan 2023
Cited by 5 | Viewed by 3806
Abstract
This work considers the task of representation learning on the attributed relational graph (ARG). Both the nodes and edges in an ARG are associated with attributes/features allowing ARGs to encode rich structural information widely observed in real applications. Existing graph neural networks offer [...] Read more.
This work considers the task of representation learning on the attributed relational graph (ARG). Both the nodes and edges in an ARG are associated with attributes/features allowing ARGs to encode rich structural information widely observed in real applications. Existing graph neural networks offer limited ability to capture complex interactions within local structural contexts, which hinders them from taking advantage of the expression power of ARGs. We propose motif convolution module (MCM), a new motif-based graph representation learning technique to better utilize local structural information. The ability to handle continuous edge and node features is one of MCM’s advantages over existing motif-based models. MCM builds a motif vocabulary in an unsupervised way and deploys a novel motif convolution operation to extract the local structural context of individual nodes, which is then used to learn higher level node representations via multilayer perceptron and/or message passing in graph neural networks. When compared with other graph learning approaches to classifying synthetic graphs, our approach is substantially better at capturing structural context. We also demonstrate the performance and explainability advantages of our approach by applying it to several molecular benchmarks. Full article
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16 pages, 1069 KiB  
Article
Addressing Complexity in the Pandemic Context: How Systems Thinking Can Facilitate Understanding of Design Aspects for Preventive Technologies
by My Villius Zetterholm and Päivi Jokela
Informatics 2023, 10(1), 7; https://doi.org/10.3390/informatics10010007 - 11 Jan 2023
Cited by 1 | Viewed by 2644
Abstract
The COVID-19 pandemic constitutes a wicked problem that is defined by rapidly evolving and dynamic conditions, where the physical world changes (e.g., pathogens mutate) and, in parallel, our understanding and knowledge rapidly progress. Various preventive measures have been developed or proposed to manage [...] Read more.
The COVID-19 pandemic constitutes a wicked problem that is defined by rapidly evolving and dynamic conditions, where the physical world changes (e.g., pathogens mutate) and, in parallel, our understanding and knowledge rapidly progress. Various preventive measures have been developed or proposed to manage the situation, including digital preventive technologies to support contact tracing or physical distancing. The complexity of the pandemic and the rapidly evolving nature of the situation pose challenges for the design of effective preventive technologies. The aim of this conceptual paper is to apply a systems thinking model, DSRP (distinctions, systems, relations, perspectives) to explain the underlying assumptions, patterns, and connections of the pandemic domain, as well as to identify potential leverage points for design of preventive technologies. Two different design approaches, contact tracing and nudging for distance, are compared, focusing on how their design and preventive logic are related to system complexity. The analysis explains why a contact tracing technology involves more complexity, which can challenge both implementation and user understanding. A system utilizing nudges can operate using a more distinct system boundary, which can benefit understanding and implementation. However, frequent nudges might pose challenges for user experience. This further implies that these technologies have different contextual requirements and are useful at different levels in society. The main contribution of this work is to show how systems thinking can organize our understanding and guide the design of preventive technologies in the context of epidemics and pandemics. Full article
(This article belongs to the Section Health Informatics)
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16 pages, 1900 KiB  
Article
An Approach to Assess the Impact of Tutorials in Video Games
by Dario Benvenuti, Lauren S. Ferro, Andrea Marrella and Tiziana Catarci
Informatics 2023, 10(1), 6; https://doi.org/10.3390/informatics10010006 - 11 Jan 2023
Viewed by 3768
Abstract
Video games are an established medium that provides interactive entertainment beyond pure enjoyment in many contexts. Game designers create dedicated tutorials to teach players the game mechanisms and rules, such as the conventions for interaction, control schemes, core game mechanics, etc. While effective [...] Read more.
Video games are an established medium that provides interactive entertainment beyond pure enjoyment in many contexts. Game designers create dedicated tutorials to teach players the game mechanisms and rules, such as the conventions for interaction, control schemes, core game mechanics, etc. While effective tutorial design is considered a crucial aspect to support this learning process, the existing literature approaches focus on designing ad hoc tutorials for specific game genres rather than investigating the impact of different tutorial styles on game learnability and player engagement. In this paper, we tackle this challenge by presenting a general-purpose approach aimed at supporting game designers in the identification of the most suitable tutorial style for a specific genre of video games. The approach is evaluated in the context of a simple first-person shooter (FPS) mainstream video game built by the authors through a controlled comparative user experiment involving 46 players. Full article
(This article belongs to the Section Human-Computer Interaction)
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16 pages, 1029 KiB  
Article
Cryptoblend: An AI-Powered Tool for Aggregation and Summarization of Cryptocurrency News
by Andrea Pozzi, Enrico Barbierato and Daniele Toti
Informatics 2023, 10(1), 5; https://doi.org/10.3390/informatics10010005 - 6 Jan 2023
Cited by 7 | Viewed by 3352
Abstract
In the last decade, the techniques of news aggregation and summarization have been increasingly gaining relevance for providing users on the web with condensed and unbiased information. Indeed, the recent development of successful machine learning algorithms, such as those based on the transformers [...] Read more.
In the last decade, the techniques of news aggregation and summarization have been increasingly gaining relevance for providing users on the web with condensed and unbiased information. Indeed, the recent development of successful machine learning algorithms, such as those based on the transformers architecture, have made it possible to create effective tools for capturing and elaborating news from the Internet. In this regard, this work proposes, for the first time in the literature to the best of the authors’ knowledge, a methodology for the application of such techniques in news related to cryptocurrencies and the blockchain, whose quick reading can be deemed as extremely useful to operators in the financial sector. Specifically, cutting-edge solutions in the field of natural language processing were employed to cluster news by topic and summarize the corresponding articles published by different newspapers. The results achieved on 22,282 news articles show the effectiveness of the proposed methodology in most of the cases, with 86.8% of the examined summaries being considered as coherent and 95.7% of the corresponding articles correctly aggregated. This methodology was implemented in a freely accessible web application. Full article
(This article belongs to the Special Issue New Advances in Semantic Recognition and Analysis)
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24 pages, 2851 KiB  
Article
Models and Methods of Designing Data-Centric Microservice Architectures of Digital Enterprises
by Sergey Deryabin, Igor Temkin, Ulvi Rzazade and Egor Kondratev
Informatics 2023, 10(1), 4; https://doi.org/10.3390/informatics10010004 - 5 Jan 2023
Cited by 2 | Viewed by 3713
Abstract
The article is devoted to methods and models of designing systems for the digital transformation of industrial enterprises within the framework of the Industry 4.0 concept. The purpose of this work is to formalize a new notation for graphical modeling of the architecture [...] Read more.
The article is devoted to methods and models of designing systems for the digital transformation of industrial enterprises within the framework of the Industry 4.0 concept. The purpose of this work is to formalize a new notation for graphical modeling of the architecture of complex large-scale systems with data-centric microservice architectures and to present a variant of the reference model of such an architecture for creating an autonomously functioning industrial enterprise. The paper provides a list and justification for the use of functional components of a data-centric microservice architecture based on the analysis of modern approaches to building systems and the authors’ own results obtained during the implementation of a number of projects. The problems of using traditional graphical modeling notations to represent a data-centric microservice architecture are considered. Examples of designing a model of such an architecture for a mining enterprise are given. Full article
(This article belongs to the Topic Software Engineering and Applications)
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22 pages, 1873 KiB  
Article
The Flash Loan Attack Analysis (FAA) Framework—A Case Study of the Warp Finance Exploitation
by Warodom Werapun, Tanakorn Karode, Tanwa Arpornthip, Jakapan Suaboot, Esther Sangiamkul and Pawita Boonrat
Informatics 2023, 10(1), 3; https://doi.org/10.3390/informatics10010003 - 30 Dec 2022
Cited by 3 | Viewed by 7065
Abstract
Decentralized finance (DeFi) has exploded in popularity with a billion-dollar market cap. While uncollateralized lending, known as a flash loan, emerged from DeFi, it has become a primary tool used by attackers to drain investment tokens from DeFi networks. The existing countermeasures seem [...] Read more.
Decentralized finance (DeFi) has exploded in popularity with a billion-dollar market cap. While uncollateralized lending, known as a flash loan, emerged from DeFi, it has become a primary tool used by attackers to drain investment tokens from DeFi networks. The existing countermeasures seem practical, but no comprehensive quantitative analysis framework was available to test them. This paper proposes the Flash loan Attack Analysis (FAA) framework, which aids security practitioners in understanding the DeFi system’s effects on preventative methods when various factors change. The quantitative predictions can help security professionals in identifying hidden dangers and more efficiently adopting countermeasure strategies. The simulation predicts that the existing strategy, fair reserves, can fully protect the platform in a typical market environment; however, in a highly volatile market where the token price drops by 60% in a single hour, it will be broken, causing more than $8 million in damage. Full article
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