Informatics

Open AccessArticle

Proposal of the Indonesian Framework for Telecommunications Infrastructure Based on Network and Socioeconomic Indicators

by

Anna Christina Situmorang

,

Muhammad Suryanegara

,

Dadang Gunawan

and

Filbert H. Juwono

Informatics 2023, 10(2), 44; https://doi.org/10.3390/informatics10020044 - 12 May 2023

Abstract

In Indonesia, there is still a disparity in telecommunications access, with most rural areas experiencing “no signal” or “blank spots.” In contrast, urban areas enjoy modern and societally-beneficial technologies. A comprehensive framework is needed to address the disparity in telecommunications access between “rich” [...] Read more.

In Indonesia, there is still a disparity in telecommunications access, with most rural areas experiencing “no signal” or “blank spots.” In contrast, urban areas enjoy modern and societally-beneficial technologies. A comprehensive framework is needed to address the disparity in telecommunications access between “rich” and “poor” groups in urban and rural/remote areas, respectively. This paper proposes a framework, built by the mathematical model, that can be used as a reference for the Indonesian government in constructing the nation’s telecommunications infrastructure. The framework categorizes Indonesian administrative regions into four grids: Grid #1: “fostered” districts; Grid #2: “developing” districts; Grid #3: “developed” districts; and Grid #4: “independent-advanced” districts. To determine where each district falls in these grids, we propose a novel statistical approach using 17 indicators involving a telecommunications network and socioeconomic factors. The proposed framework results in a grid visualization of 7232 districts in Indonesia. Finally, as this paper is replete with academic research approaches and mathematical model perspectives, it is expected that the results may be a valuable input to the development of the country’s telecommunications policy. Full article

(This article belongs to the Special Issue Building Smart Cities and Infrastructures for a Sustainable Future)

► Show Figures

Figure 1

Open AccessArticle

Risk Factors Influencing Fatal Powered Two-Wheeler At-Fault and Not-at-Fault Crashes: An Application of Spatio-Temporal Hotspot and Association Rule Mining Techniques

by

Reuben Tamakloe

Informatics 2023, 10(2), 43; https://doi.org/10.3390/informatics10020043 - 12 May 2023

Abstract

Studies have explored the factors influencing the safety of PTWs; however, very little has been carried out to comprehensively investigate the factors influencing fatal PTW crashes while considering the fault status of the rider in crash hotspot areas. This study employs spatio-temporal hotspot [...] Read more.

Studies have explored the factors influencing the safety of PTWs; however, very little has been carried out to comprehensively investigate the factors influencing fatal PTW crashes while considering the fault status of the rider in crash hotspot areas. This study employs spatio-temporal hotspot analysis and association rule mining techniques to discover hidden associations between crash risk factors that lead to fatal PTW crashes considering the fault status of the rider at statistically significant PTW crash hotspots in South Korea from 2012 to 2017. The results indicate the presence of consecutively fatal PTW crash hotspots concentrated within Korea’s densely populated capital, Seoul, and new hotspots near its periphery. According to the results, violations such as over-speeding and red-light running were critical contributory factors influencing PTW crashes at hotspots during summer and at intersections. Interestingly, while reckless riding was the main traffic violation leading to PTW rider at-fault crashes at hotspots, violations such as improper safety distance and red-light running were strongly associated with PTW rider not-at-fault crashes at hotspots. In addition, while PTW rider at-fault crashes are likely to occur during summer, PTW rider not-at-fault crashes mostly occur during spring. The findings could be used for developing targeted policies for improving PTW safety at hotspots. Full article

(This article belongs to the Special Issue Feature Papers in Big Data)

► Show Figures

Figure 1

Open AccessArticle

Genealogical Data Mining from Historical Archives: The Case of the Jewish Community in Pisa

by

,

,

,

,

and

Informatics 2023, 10(2), 42; https://doi.org/10.3390/informatics10020042 - 11 May 2023

Abstract

The Jewish community archive in Pisa owns a vast collection of documents and manuscripts that date back centuries. These documents contain valuable genealogical information, including birth, marriage, and death records. This paper aims to describe the preliminary results of the Archivio Storico della [...] Read more.

The Jewish community archive in Pisa owns a vast collection of documents and manuscripts that date back centuries. These documents contain valuable genealogical information, including birth, marriage, and death records. This paper aims to describe the preliminary results of the Archivio Storico della Comunita Ebraica di Pisa (ASCEPI) project, with a focus on the extraction of data from the Nati, Morti e Ballottati (NMB) Registry document in the archive. The NMB Registry contains about 1900 records of births, deaths, and balloted individuals within the Jewish community in Pisa. The study uses a semiautomatic pipeline of digitization, transcription, and Natural Language Processing (NLP) techniques to extract personal data such as names, surnames, birth and death dates, and parental names from each record. The extracted data are then used to build a knowledge base and a genealogical tree for a representative family, Supino. This study demonstrates the potential of using NLP and rule-based techniques to extract valuable information from historical documents and to construct genealogical trees. Full article

(This article belongs to the Special Issue ICT for Genealogical Data)

► Show Figures

Figure 1

Open AccessReview

Exploring the Boundaries of Success: A Literature Review and Research Agenda on Resource, Complementary, and Ecological Boundaries in Digital Platform Business Model Innovation

by

Mohammad Daradkeh

Informatics 2023, 10(2), 41; https://doi.org/10.3390/informatics10020041 - 11 May 2023

Abstract

Digital platform business model innovation is a rapidly evolving field, yet the literature on resource, complementary, and ecological boundaries remains limited, leaving a significant gap in our understanding of the factors that shape the success of these platforms. This paper explores the mechanisms [...] Read more.

Digital platform business model innovation is a rapidly evolving field, yet the literature on resource, complementary, and ecological boundaries remains limited, leaving a significant gap in our understanding of the factors that shape the success of these platforms. This paper explores the mechanisms by which digital platforms enable business model innovation, a topic of significant theoretical and practical importance that has yet to be fully examined. Through a review of the existing literature and an examination of the connotations of digital platforms, the design of platform boundaries, and the deployment of boundary resources, the study finds that (1) the uncertainty of complementors and complementary products drives business model innovation in digital platforms; (2) the design of resource, complementary, and ecological system boundaries is crucial to digital platform business models and manages complementor and complementary product uncertainty while promoting value co-creation; and (3) boundary resources establish, manage, and sustain cross-border relationships that impact value creation and capture. Based on these findings, four research propositions are proposed to guide future research on digital platform business model innovation and provide insights for effectively innovating business models and influencing value creation and capture. Full article

► Show Figures

Figure 1

Open AccessArticle

LARF: Two-Level Attention-Based Random Forests with a Mixture of Contamination Models

by

Andrei Konstantinov

,

Lev Utkin

and

Vladimir Muliukha

Informatics 2023, 10(2), 40; https://doi.org/10.3390/informatics10020040 - 28 Apr 2023

Abstract

This paper provides new models of the attention-based random forests called LARF (leaf attention-based random forest). The first idea behind the models is to introduce a two-level attention, where one of the levels is the “leaf” attention, and the attention mechanism is applied [...] Read more.

This paper provides new models of the attention-based random forests called LARF (leaf attention-based random forest). The first idea behind the models is to introduce a two-level attention, where one of the levels is the “leaf” attention, and the attention mechanism is applied to every leaf of trees. The second level is the tree attention depending on the “leaf” attention. The second idea is to replace the softmax operation in the attention with the weighted sum of the softmax operations with different parameters. It is implemented by applying a mixture of Huber’s contamination models and can be regarded as an analog of the multi-head attention, with “heads” defined by selecting a value of the softmax parameter. Attention parameters are simply trained by solving the quadratic optimization problem. To simplify the tuning process of the models, it is proposed to convert the tuning contamination parameters into trainable parameters and to compute them by solving the quadratic optimization problem. Many numerical experiments with real datasets are performed for studying LARFs. The code of the proposed algorithms is available. Full article

(This article belongs to the Section Machine Learning)

► Show Figures

Figure 1

Open AccessArticle

The Impact of YouTube on Loneliness and Mental Health

by

Luke Balcombe

and

Diego De Leo

Informatics 2023, 10(2), 39; https://doi.org/10.3390/informatics10020039 - 20 Apr 2023

Abstract

There are positives and negatives of using YouTube in terms of loneliness and mental health. YouTube’s streaming content is an amazing resource, however, there may be bias or errors in its recommendation algorithms. Parasocial relationships can also complicate the impact of YouTube use. [...] Read more.

There are positives and negatives of using YouTube in terms of loneliness and mental health. YouTube’s streaming content is an amazing resource, however, there may be bias or errors in its recommendation algorithms. Parasocial relationships can also complicate the impact of YouTube use. Intervention may be necessary when problematic and risky content is associated with unhealthy behaviors and negative impacts on mental health. Children and adolescents are particularly vulnerable. Although YouTube might assist in connecting with peers, there are privacy, safety, and quality issues to consider. This paper is an integrative review of the positive and negative impacts of YouTube with the aim to inform the design and development of a technology-based intervention to improve mental health. The impact of YouTube use on loneliness and mental health was explored by synthesizing a purposive selection (n = 32) of the empirical and theoretical literature. Next, we explored human–computer interaction issues and proposed a concept whereby an independent-of-YouTube algorithmic recommendation system steers users toward verified positive mental health content or promotions. Full article

(This article belongs to the Special Issue Feature Papers in Human-Computer Interaction)

Open AccessArticle

Understanding the Spread of Fake News: An Approach from the Perspective of Young People

by

Alejandro Valencia-Arias

,

Diana María Arango-Botero

,

Sebastián Cardona-Acevedo

,

Sharon Soledad Paredes Delgado

and

Ada Gallegos

Informatics 2023, 10(2), 38; https://doi.org/10.3390/informatics10020038 - 11 Apr 2023

Abstract

The COVID-19 pandemic and the boom of fake news cluttering the internet have revealed the power of social media today. However, young people are not yet aware of their role in the digital age, even though they are the main users of social [...] Read more.

The COVID-19 pandemic and the boom of fake news cluttering the internet have revealed the power of social media today. However, young people are not yet aware of their role in the digital age, even though they are the main users of social media. As a result, the belief that older adults are responsible for information is being re-evaluated. In light of this, the present study was aimed at identifying the factors associated with the spread of fake news among young people in Medellín (Colombia). A total of 404 self-administered questionnaires were processed in a sample of people between the ages of 18 and 34 and analyzed using statistical techniques, such as exploratory factor analysis and structural equation modeling. The results suggest that the instantaneous sharing of fake news is linked to people’s desire to raise awareness among their inner circle, particularly when the messages shared are consistent with their perceptions and beliefs, or to the lack of time to properly verify their accuracy. Finally, passive corrective actions were found to have a less significant impact in the Colombian context than in the context of the original model, which may be explained by cultural factors. Full article

(This article belongs to the Collection Uncertainty in Digital Humanities)

► Show Figures

Figure 1

Open AccessArticle

Towards Independent Students’ Activities, Online Environment and Learning Performance: An Investigation through Synthetic Data and Artificial Neural Networks

by

Malinka Ivanova

and

Tsvetelina Petrova

Informatics 2023, 10(2), 37; https://doi.org/10.3390/informatics10020037 - 10 Apr 2023

Abstract

During the pandemic, universities were forced to convert their educational process online. Students had to adapt to new educational conditions and the proposed online environment. Now, we are back to the traditional blended learning environment and wish to understand the students’ attitudes and [...] Read more.

During the pandemic, universities were forced to convert their educational process online. Students had to adapt to new educational conditions and the proposed online environment. Now, we are back to the traditional blended learning environment and wish to understand the students’ attitudes and perceptions of online learning, knowing that they are able to compare blended and online modes. The aim of this paper is to present the performed predictive analysis regarding the students’ online learning performance taking into account their opinion. The predictive models are created through a supervised machine learning algorithm based on Artificial Neural Networks and are characterized with high accuracy. The analysis is based on generated synthetic datasets, ensuring a high level of students’ privacy preservation. Full article

► Show Figures

Figure 1

Open AccessArticle

Evaluating the Impact of Gamification on the Online Shop of a Game Server: A Comparison between the Portuguese and North American Contexts

by

Diogo Santos

,

Elsa Cardoso

and

Isabel Machado Alexandre

Informatics 2023, 10(2), 36; https://doi.org/10.3390/informatics10020036 - 10 Apr 2023

Abstract

Online commerce has been growing rapidly in an increasingly digital world, and gamification, the practice of designing games in a context outside the industry itself, can be an effective strategy to stimulate consumer engagement and conversion rate. This paper describes the design process [...] Read more.

Online commerce has been growing rapidly in an increasingly digital world, and gamification, the practice of designing games in a context outside the industry itself, can be an effective strategy to stimulate consumer engagement and conversion rate. This paper describes the design process involved in introducing gamification into an online shop that is supported by two game servers of the same kind, namely one in the United States of America (US) and another in Portugal (PT). Through the various phases of the design thinking process, a gamified system was implemented to meet the needs of various types of users frequently found in the shops. The gamification elements used were intended to increase user engagement with the shops so that they would become more aware of existing products and the introduction of new products, promoting purchase through intangible challenges and rewards. The impacts on server revenues and user satisfaction (N = 138) were evaluated one month after introducing the gamification techniques. The results show that gamification has a positive effect on users, with a significant increase in consumer interaction in both shops. However, from a business point of view, the results show only an increase in revenue for the US shop, while the Portuguese shop shows no significant differences compared to previous months. Of the two user groups analyzed, only those who frequent the US shop show receptivity toward intangible rewards, with tangible rewards (discounts) being a greater motivating factor for both groups. Full article

(This article belongs to the Section Human-Computer Interaction)

► Show Figures

Figure 1

Open AccessArticle

On the Need for Healthcare Informatics Training among Medical Doctors in Jordan: A Pilot Study

by

,

,

and

Informatics 2023, 10(2), 35; https://doi.org/10.3390/informatics10020035 - 07 Apr 2023

Abstract

Jordanian healthcare institutes have launched several programs since 2009 to establish health information systems (HISs). Nowadays, the generic expectation is that the use of HIS resources is performed on daily basis among healthcare staff. However, there can be still a noticeable barrier due [...] Read more.

Jordanian healthcare institutes have launched several programs since 2009 to establish health information systems (HISs). Nowadays, the generic expectation is that the use of HIS resources is performed on daily basis among healthcare staff. However, there can be still a noticeable barrier due to a lack of knowledge if medical doctors do not receive proper training on existing HISs. Moreover, the lack of studies on this area hinders the clarity about the received versus the required training skills among medical doctors. To support this research initiative, survey data have been collected from specialized medical doctors who are currently affiliated with five Jordanian universities to assess their need for HIS training. The results also aim to explore the extent of medical doctors’ use of HIS resources in Jordan. Moreover, they examine whether medical doctors require additional training on using HIS resources or not, as well as the main areas of required training programs. Specifically, this paper highlights the main topics that can be suitable subjects for enhanced training programs. The results show that most respondents use HISs in their daily clinical practices. However, most of them have not taken professional training on such systems. Hence, most of the respondents reported the need for additional training programs on several aspects of HIS resources. Moreover, based on the survey results, the most significant areas that require training are biomedical data analysis, artificial intelligence in medicine, health care management, and recent advances in electronic health records, respectively. Therefore, specialized medical doctors in Jordan need training on extracting useful and potential features of HISs. Education and training professionals in healthcare are recommended to establish training programs in Jordanian healthcare centers, which can further improve the quality of healthcare. Full article

(This article belongs to the Section Health Informatics)

► Show Figures

Figure 1

Open AccessArticle

Generating Paraphrase Using Simulated Annealing for Citation Sentences

by

,

,

,

and

Dwi Hendratmo Widyantoro

Informatics 2023, 10(2), 34; https://doi.org/10.3390/informatics10020034 - 30 Mar 2023

Abstract

The paraphrase generator for citation sentences is used to produce several sentence alternatives to avoid plagiarism. Furthermore, the generation results need to pay attention to semantic similarity and lexical divergence standards. This study proposed the StoPGEN model as an algorithm for generating citation [...] Read more.

The paraphrase generator for citation sentences is used to produce several sentence alternatives to avoid plagiarism. Furthermore, the generation results need to pay attention to semantic similarity and lexical divergence standards. This study proposed the StoPGEN model as an algorithm for generating citation paraphrase sentences with stochastic output. The generation process is guided by an objective function using a simulated annealing algorithm to maintain the properties of semantic similarity and lexical divergence. The objective function is created by combining the two factors that maintain these properties. This study combined METEOR and PINC Scores in a linear weighting function that can be adjusted for its value tendency in one of the matrix functions. The dataset of citation sentences that had been labeled with paraphrases was used to test StoPGEN and other models for comparison. The StoPGEN model, with the citation sentences dataset, produced a BLEU score of 55.37, outperforming the bidirectional LSTM method with a value of 28.93. StoPGEN was also tested using Quora data by changing the language source in the architecture section resulting in a BLEU score of 22.37, outperforming UPSA 18.21. In addition, the qualitative evaluation results of the citation sentence generation based on respondents obtained an acceptance value of 50.80. Full article

(This article belongs to the Section Machine Learning)

► Show Figures

Figure 1

Open AccessArticle

Development and Internal Validation of an Interpretable Machine Learning Model to Predict Readmissions in a United States Healthcare System

by

Amanda L. Luo

,

Akshay Ravi

,

Simone Arvisais-Anhalt

,

Anoop N. Muniyappa

,

Xinran Liu

and

Shan Wang

Informatics 2023, 10(2), 33; https://doi.org/10.3390/informatics10020033 - 27 Mar 2023

Abstract

(1) One in four hospital readmissions is potentially preventable. Machine learning (ML) models have been developed to predict hospital readmissions and risk-stratify patients, but thus far they have been limited in clinical applicability, timeliness, and generalizability. (2) Methods: Using deidentified clinical data from [...] Read more.

(1) One in four hospital readmissions is potentially preventable. Machine learning (ML) models have been developed to predict hospital readmissions and risk-stratify patients, but thus far they have been limited in clinical applicability, timeliness, and generalizability. (2) Methods: Using deidentified clinical data from the University of California, San Francisco (UCSF) between January 2016 and November 2021, we developed and compared four supervised ML models (logistic regression, random forest, gradient boosting, and XGBoost) to predict 30-day readmissions for adults admitted to a UCSF hospital. (3) Results: Of 147,358 inpatient encounters, 20,747 (13.9%) patients were readmitted within 30 days of discharge. The final model selected was XGBoost, which had an area under the receiver operating characteristic curve of 0.783 and an area under the precision-recall curve of 0.434. The most important features by Shapley Additive Explanations were days since last admission, discharge department, and inpatient length of stay. (4) Conclusions: We developed and internally validated a supervised ML model to predict 30-day readmissions in a US-based healthcare system. This model has several advantages including state-of-the-art performance metrics, the use of clinical data, the use of features available within 24 h of discharge, and generalizability to multiple disease states. Full article

(This article belongs to the Special Issue Feature Papers in Medical and Clinical Informatics)

► Show Figures

Figure 1

Open AccessArticle

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

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)

► Show Figures

Figure 1

Open AccessArticle

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

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

► Show Figures

Figure 1

Open AccessArticle

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

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)

► Show Figures

Figure 1

Open AccessArticle

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

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

Open AccessArticle

Enhancing Small Medical Dataset Classification Performance Using GAN

by

,

,

,

,

,

and

Informatics 2023, 10(1), 28; https://doi.org/10.3390/informatics10010028 - 08 Mar 2023

Cited by 1

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

► Show Figures

Figure 1

Open AccessArticle

Modeling the Influence of Fake Accounts on User Behavior and Information Diffusion in Online Social Networks

by

,

,

and

Informatics 2023, 10(1), 27; https://doi.org/10.3390/informatics10010027 - 03 Mar 2023

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

S I_{h} I_{b} R

model extends the classical SIR model by introducing two types of infected users with different infection rates: the users who are infected by human

(I_{h})

accounts with a normal infection rate and the users who are infected by bot accounts

(I_{b})

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

S I_{h} I_{b} R

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)

► Show Figures

Figure 1

Open AccessArticle

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 - 03 Mar 2023

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 Digital Humanities)

► Show Figures

Figure 1

Journal Description

Informatics

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI