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Journal Description

Informatics

Informatics is an international, peer-reviewed, open access journal on information and communication technologies, human–computer interaction, and social informatics, and is published quarterly online by MDPI.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, ESCI (Web of Science), dblp, and other databases.
Journal Rank: JCR - Q2 (Computer Science, Interdisciplinary Applications) / CiteScore - Q1 (Communication)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 38.1 days after submission; acceptance to publication is undertaken in 6.1 days (median values for papers published in this journal in the second half of 2024).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.

Impact Factor: 3.4 (2023); 5-Year Impact Factor: 3.1 (2023)

Imprint Information Journal Flyer Open Access ISSN: 2227-9709

Latest Articles

38 pages, 841 KiB

Open AccessArticle

Advancing Cybersecurity with Honeypots and Deception Strategies

by Zlatan Morić, Vedran Dakić and Damir Regvart

Informatics 2025, 12(1), 14; https://doi.org/10.3390/informatics12010014 - 31 Jan 2025

Cybersecurity threats are becoming more intricate, requiring preemptive actions to safeguard digital assets. This paper examines the function of honeypots as critical instruments for threat detection, analysis, and mitigation. A novel methodology for comparative analysis of honeypots is presented, offering a systematic framework [...] Read more.

Cybersecurity threats are becoming more intricate, requiring preemptive actions to safeguard digital assets. This paper examines the function of honeypots as critical instruments for threat detection, analysis, and mitigation. A novel methodology for comparative analysis of honeypots is presented, offering a systematic framework to assess their efficacy. Seven honeypot solutions, namely Dionaea, Cowrie, Honeyd, Kippo, Amun, Glastopf, and Thug, are analyzed, encompassing various categories, including SSH and HTTP honeypots. The solutions are assessed via simulated network attacks and comparative analyses based on established criteria, including detection range, reliability, scalability, and data integrity. Dionaea and Cowrie exhibited remarkable versatility and precision, whereas Honeyd revealed scalability benefits despite encountering data quality issues. The research emphasizes the smooth incorporation of honeypots with current security protocols, including firewalls and incident response strategies, while offering comprehensive insights into attackers’ tactics, techniques, and procedures (TTPs). Emerging trends are examined, such as incorporating machine learning for adaptive detection and creating cloud-based honeypots. Recommendations for optimizing honeypot deployment include strategic placement, comprehensive monitoring, and ongoing updates. This research provides a detailed framework for selecting and implementing honeypots customized to organizational requirements. Full article

24 pages, 1479 KiB

Open AccessArticle

Analysis of Descriptors of Concept Drift and Their Impacts

by Albert Costa, Rafael Giusti and Eulanda M. dos Santos

Informatics 2025, 12(1), 13; https://doi.org/10.3390/informatics12010013 - 31 Jan 2025

Concept drift, a phenomenon that can lead to degradation of classifier performance over time, is commonly addressed in the literature through detection and reaction strategies. However, these strategies often rely on complete classifier retraining without considering the properties of the drift, which can [...] Read more.

Concept drift, a phenomenon that can lead to degradation of classifier performance over time, is commonly addressed in the literature through detection and reaction strategies. However, these strategies often rely on complete classifier retraining without considering the properties of the drift, which can prove inadequate in many scenarios. Limited attention has been given to understanding the nature of drift and its characterization, which are crucial for designing effective reaction strategies. Drift descriptors provide a means to explain how new concepts replace existing ones, offering valuable insights into the nature of drift. In this context, this work examines the impact of four descriptors—severity, recurrence, frequency, and speed—on concept drift through extensive theoretical and experimental analysis. Experiments were conducted on five datasets with 32 descriptor variations, eight drift detectors, and a non-detection context, resulting in 1440 combinations. The findings reveal three key conclusions: (i) reaction strategies must be tailored to different types of drift; (ii) severity, recurrence, and frequency descriptors have the highest impact, whereas speed has minimal influence; and (iii) there is a need to incorporate mechanisms for describing concept drift into the strategies designed to address it. Full article

(This article belongs to the Section Machine Learning)

30 pages, 752 KiB

Open AccessArticle

How to Improve Usability in Open-Source Software Projects? An Analysis of Evaluation Techniques Through a Multiple Case Study

by Lucrecia Llerena, John W. Castro, Rosa Llerena and Nancy Rodríguez

Informatics 2025, 12(1), 12; https://doi.org/10.3390/informatics12010012 - 27 Jan 2025

Open-source software has experienced steady growth, driving increased research. However, open-source communities still need standardized processes to ensure software quality, and their characteristics make it challenging to adopt many usability techniques from human–computer interaction directly. Our study aims to adapt and evaluate the [...] Read more.

Open-source software has experienced steady growth, driving increased research. However, open-source communities still need standardized processes to ensure software quality, and their characteristics make it challenging to adopt many usability techniques from human–computer interaction directly. Our study aims to adapt and evaluate the feasibility of three usability evaluation techniques—cognitive walkthrough, formal usability testing, and thinking aloud—across three open-source projects (Code::Blocks, Freeplane, and Scribus) from the development team’s perspective. We participated as volunteers in these projects, employing a multiple case study method. We found that usability techniques were not systematically adopted, and procedures specific to open-source projects were lacking. We also identified adverse conditions, such as limited user participation, that hindered adoption. We proposed adaptations to each technique and formalized procedures to address these challenges and apply them in open-source contexts. Additionally, we developed a framework for integrating usability evaluation into OSS projects. To address this, we detailed our framework’s phases, tasks, and artifacts to ensure reusability and adaptability across OSS contexts, providing practical steps for implementation and future validations. In conclusion, usability techniques must be adapted for open-source software, considering the projects’ unique characteristics and philosophy. Although obstacles exist, such as user participation, applying adapted usability techniques in open-source projects is feasible. Full article

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15 pages, 975 KiB

Open AccessArticle

Enhancing Foreign Speakers’ Satisfaction in Learning Indonesian Language with a Gamified Multiplatform Approach

by Rifqi Imaduddin Irfan and Yulyani Arifin

Informatics 2025, 12(1), 11; https://doi.org/10.3390/informatics12010011 - 24 Jan 2025

This study examines how gamification can improve the satisfaction level of foreign learners studying the Indonesian language using the innovative multiplatform application named Belajar Indo. In consideration of the Indonesian government’s heightened focus on language proficiency for foreign workers and students, this study [...] Read more.

This study examines how gamification can improve the satisfaction level of foreign learners studying the Indonesian language using the innovative multiplatform application named Belajar Indo. In consideration of the Indonesian government’s heightened focus on language proficiency for foreign workers and students, this study examines the challenges encountered in Bahasa Indonesia for Foreign Speakers (BIPA) programs. Integrating gamified elements into a Progressive Web App (PWA) presents an engaging alternative to traditional learning resources, which frequently fall short in terms of interactivity and accessibility. Data gathered from surveys, online observation, and user engagement metrics indicate that gamification greatly improves learner motivation, resulting in a success rate of 93.51% and a Net Promoter Score (NPS) of 8.11. The findings demonstrate that gamification enhances language acquisition while simultaneously cultivating a greater enthusiasm for learning Indonesian, proving advantageous for both beginners and advanced learners. This study advances the domain of educational technology by presenting a model that integrates language acquisition with digital innovation, highlighting gamified learning as an effective instrument for foreign language education. Future recommendations involve fine-tuning user interface components to improve usability and maintain elevated engagement levels. Full article

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

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25 pages, 6148 KiB

Open AccessArticle

Toolkit for Inclusion of User Experience Design Guidelines in the Development of Assistants Based on Generative Artificial Intelligence

by Carlos Alberto Peláez, Andrés Solano, Johann A. Ospina, Juan C. Espinosa, Ana S. Montaño, Paola A. Castillo, Juan Sebastián Duque, David A. Castro, Juan M. Nuñez Velasco and Fernando De la Prieta

Informatics 2025, 12(1), 10; https://doi.org/10.3390/informatics12010010 - 24 Jan 2025

This study addresses the need to integrate ethical, human-centered principles into user experience (UX) design for generative AI (GenAI)-based assistants. Acknowledging the ethical and societal challenges posed by the democratization of GenAI, this study developed a set of six UX design guidelines and [...] Read more.

This study addresses the need to integrate ethical, human-centered principles into user experience (UX) design for generative AI (GenAI)-based assistants. Acknowledging the ethical and societal challenges posed by the democratization of GenAI, this study developed a set of six UX design guidelines and 37 recommendations to guide development teams in creating GenAI assistants. A card-based toolkit was designed to encapsulate these guidelines, applying color theory and Gestalt principles to enhance usability and understanding. The design science research methodology (DSRM) was followed, and the toolkit was validated through a hands-on workshop with software and UX professionals, assessing usability, user experience, and utility. The quantitative results indicated the high internal consistency and effectiveness of the toolkit, while the qualitative analysis highlighted its capacity to foster collaboration and address GenAI-specific challenges. This study concludes that the toolkit improves usability and utility in UX design for GenAI-based assistants, though it identifies areas for future enhancement and the need for further validation across varied contexts. Full article

(This article belongs to the Topic Theories and Applications of Human-Computer Interaction)

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23 pages, 533 KiB

Open AccessSystematic Review

Evaluating the Reliability of ChatGPT for Health-Related Questions: A Systematic Review

by Mohammad Beheshti, Imad Eddine Toubal, Khuder Alaboud, Mohammed Almalaysha, Olabode B. Ogundele, Hamza Turabieh, Nader Abdalnabi, Suzanne A. Boren, Grant J. Scott and Butros M. Dahu

Informatics 2025, 12(1), 9; https://doi.org/10.3390/informatics12010009 - 17 Jan 2025

The rapid advancement of large language models like ChatGPT has significantly impacted natural language processing, expanding its applications across various fields, including healthcare. However, there remains a significant gap in understanding the consistency and reliability of ChatGPT’s performance across different medical domains. We [...] Read more.

The rapid advancement of large language models like ChatGPT has significantly impacted natural language processing, expanding its applications across various fields, including healthcare. However, there remains a significant gap in understanding the consistency and reliability of ChatGPT’s performance across different medical domains. We conducted this systematic review according to an LLM-assisted PRISMA setup. The high-recall search term “ChatGPT” yielded 1101 articles from 2023 onwards. Through a dual-phase screening process, initially automated via ChatGPT and subsequently manually by human reviewers, 128 studies were included. The studies covered a range of medical specialties, focusing on diagnosis, disease management, and patient education. The assessment metrics varied, but most studies compared ChatGPT’s accuracy against evaluations by clinicians or reliable references. In several areas, ChatGPT demonstrated high accuracy, underscoring its effectiveness. However, performance varied, and some contexts revealed lower accuracy. The mixed outcomes across different medical domains emphasize the challenges and opportunities of integrating AI like ChatGPT into healthcare. The high accuracy in certain areas suggests that ChatGPT has substantial utility, yet the inconsistent performance across all applications indicates a need for ongoing evaluation and refinement. This review highlights ChatGPT’s potential to improve healthcare delivery alongside the necessity for continued research to ensure its reliability. Full article

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17 pages, 4714 KiB

Open AccessArticle

The Evolution of Digital Security by Design Using Temporal Network Analysis

by Lowri Williams, Hamza Khan and Pete Burnap

Informatics 2025, 12(1), 8; https://doi.org/10.3390/informatics12010008 - 17 Jan 2025

Digital Security by Design (DSbD) is an initiative supported by the UK government aimed at transforming digital technology to deliver necessary digital resilience and prosperity across the UK. As emerging challenges in the field of digital security evolve, it becomes essential to explore [...] Read more.

Digital Security by Design (DSbD) is an initiative supported by the UK government aimed at transforming digital technology to deliver necessary digital resilience and prosperity across the UK. As emerging challenges in the field of digital security evolve, it becomes essential to explore how entities involved in DSbD interact and change over time. Understanding these dynamic relationships can provide crucial insights for the development and improvement of security practices. This paper presents a data-driven analysis of the evolving landscape of DSbD from 2019 to 2024, gathering insights from textual documents referencing DSbD. Using a combination of text mining techniques and network analysis, a large corpus of textual documents was examined to identify key entities, including organisations, individuals, and the relationships between them. A network was then visualised to analyse the structural connections between these entities, revealing how key concepts and actors have evolved. The results and discussion demonstrate that the network analysis offers a unique advantage in tracking and visualising these evolving relationships, providing insights into shifts in focus, emerging trends, and changes in technological adoption over time. For example, a notable finding from the analysis is the substantial increase in node relationships associated with Artificial Intelligence (AI). We hypothesise that this surge reflects the growing integration of AI into digital security strategies, driven by the need for more adaptive and autonomous solutions to tackle evolving cyber threats, as well as the rapid introduction of new AI tools to the market and their swift adoption across various industries. By mapping such connections, such results are useful, helping practitioners and researchers recognise new security demands and adjust strategies to better respond to the evolving landscape of DSbD. Full article

(This article belongs to the Section Big Data Mining and Analytics)

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23 pages, 3671 KiB

Open AccessArticle

Improved YOLOv10 for Visually Impaired: Balancing Model Accuracy and Efficiency in the Case of Public Transportation

by Rio Arifando, Shinji Eto, Tibyani Tibyani and Chikamune Wada

Informatics 2025, 12(1), 7; https://doi.org/10.3390/informatics12010007 - 16 Jan 2025

Advancements in automation and artificial intelligence have significantly impacted accessibility for individuals with visual impairments, particularly in the realm of bus public transportation. Effective bus detection and bus point-of-view (POV) classification are crucial for enhancing the independence of visually impaired individuals. This study [...] Read more.

Advancements in automation and artificial intelligence have significantly impacted accessibility for individuals with visual impairments, particularly in the realm of bus public transportation. Effective bus detection and bus point-of-view (POV) classification are crucial for enhancing the independence of visually impaired individuals. This study introduces the Improved-YOLOv10, a novel model designed to tackle challenges in bus identification and pov classification by integrating Coordinate Attention (CA) and Adaptive Kernel Convolution (AKConv) into the YOLOv10 framework. The Improved YOLOv10 advances the YOLOv10 architecture through the incorporation of CA, which enhances long-range dependency modeling and spatial awareness, and AKConv, which dynamically adjusts convolutional kernels for superior feature extraction. These enhancements aim to improve both detection accuracy and efficiency, essential for real-time applications in assistive technologies. Evaluation results demonstrate that the Improved-YOLOv10 offers significant improvements in detection performance, including better Accuracy, Precision and Recall compared to YOLOv10. The model also exhibits reduced computational complexity and storage requirements, highlighting its efficiency. While the classification results show some trade-offs, with slightly decreased overall F1 score, the complexity of Giga Floating Point Operations (GFLOPs), Parameters, and Weight/MB in the Improved-YOLOv10 remains advantageous for classification tasks. The model’s architectural improvements contribute to its robustness and efficiency, making it a suitable choice for real-time applications and assistive technologies. Full article

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23 pages, 1371 KiB

Open AccessArticle

Post, Predict, and Rank: Exploring the Relationship Between Social Media Strategy and Higher Education Institution Rankings

by Bruna Rocha and Álvaro Figueira

Informatics 2025, 12(1), 6; https://doi.org/10.3390/informatics12010006 - 9 Jan 2025

In today’s competitive higher education sector, institutions increasingly rely on international rankings to secure financial resources, attract top-tier talent, and elevate their global reputation. Simultaneously, these universities have expanded their presence on social media, utilizing sophisticated posting strategies to disseminate information and boost [...] Read more.

In today’s competitive higher education sector, institutions increasingly rely on international rankings to secure financial resources, attract top-tier talent, and elevate their global reputation. Simultaneously, these universities have expanded their presence on social media, utilizing sophisticated posting strategies to disseminate information and boost recognition and engagement. This study examines the relationship between higher education institutions’ (HEIs’) rankings and their social media posting strategies. We gathered and analyzed publications from 18 HEIs featured in a consolidated ranking system, examining various features of their social media posts. To better understand these strategies, we categorized the posts into five predefined topics—engagement, research, image, society, and education. This categorization, combined with Long Short-Term Memory (LSTM) and a Random Forest (RF) algorithm, was utilized to predict social media output in the last five days of each month, achieving successful results. This paper further explores how variations in these social media strategies correlate with the rankings of HEIs. Our findings suggest a nuanced interaction between social media engagement and the perceived prestige of HEIs. Full article

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22 pages, 3073 KiB

Open AccessArticle

Encouraging Sustainable Choices Through Socially Engaged Persuasive Recycling Initiatives: A Participatory Action Design Research Study

by Emilly Marques da Silva, Daniel Schneider, Claudio Miceli and António Correia

Informatics 2025, 12(1), 5; https://doi.org/10.3390/informatics12010005 - 8 Jan 2025

Human-Computer Interaction (HCI) research has illuminated how technology can influence users’ awareness of their environmental impact and the potential for mitigating these impacts. From hot water saving to food waste reduction, researchers have systematically and widely tried to find pathways to speed up [...] Read more.

Human-Computer Interaction (HCI) research has illuminated how technology can influence users’ awareness of their environmental impact and the potential for mitigating these impacts. From hot water saving to food waste reduction, researchers have systematically and widely tried to find pathways to speed up achieving sustainable development goals through persuasive technology interventions. However, motivating users to adopt sustainable behaviors through interactive technologies presents significant psychological, cultural, and technical challenges in creating engaging and long-lasting experiences. Aligned with this perspective, there is a dearth of research and design solutions addressing the use of persuasive technology to promote sustainable recycling behavior. Guided by a participatory design approach, this investigation focuses on the design opportunities for leveraging persuasive and human-centered Internet of Things (IoT) applications to enhance user engagement in recycling activities. The assumption is that one pathway to achieve this goal is to adopt persuasive strategies that may be incorporated into the design of sustainable applications. The insights gained from this process can then be applied to various sustainable HCI scenarios and therefore contribute to HCI’s limited understanding in this area by providing a series of design-oriented research recommendations for informing the development of persuasive and socially engaged recycling platforms. In particular, we advocate for the inclusion of educational content, real-time interactive feedback, and intuitive interfaces to actively engage users in recycling activities. Moreover, recognizing the cultural context in which the technology is socially situated becomes imperative for the effective implementation of smart devices to foster sustainable recycling practices. To this end, we present a case study that seeks to involve children and adolescents in pro-recycling activities within the school environment. Full article

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19 pages, 1785 KiB

Open AccessArticle

Supervised Machine Learning for Real-Time Intrusion Attack Detection in Connected and Autonomous Vehicles: A Security Paradigm Shift

by Ahmad Aloqaily, Emad E. Abdallah, Hiba AbuZaid, Alaa E. Abdallah and Malak Al-hassan

Informatics 2025, 12(1), 4; https://doi.org/10.3390/informatics12010004 - 6 Jan 2025

Recent improvements in self-driving and connected cars promise to enhance traffic safety by reducing risks and accidents. However, security concerns limit their acceptance. These vehicles, interconnected with infrastructure and other cars, are vulnerable to cyberattacks, which could lead to severe costs, including physical [...] Read more.

Recent improvements in self-driving and connected cars promise to enhance traffic safety by reducing risks and accidents. However, security concerns limit their acceptance. These vehicles, interconnected with infrastructure and other cars, are vulnerable to cyberattacks, which could lead to severe costs, including physical injury or death. In this article, we propose a framework for an intrusion detection system to protect internal vehicle communications from potential attacks and ensure secure sent/transferred data. In the proposed system, real auto-network datasets with Spoofing, DoS, and Fuzzy attacks are used. To accurately distinguish between benign and malicious messages, this study employed seven distinct supervised machine-learning algorithms for data classification. The selected algorithms encompassed Decision Trees, Random Forests, Naive Bayes, Logistic Regression, XG Boost, LightGBM, and Multi-layer Perceptrons. The proposed detection system performed well on large real-car hacking datasets. We achieved high accuracy in identifying diverse electronic intrusions across the complex internal networks of connected and autonomous vehicles. Random Forest and LightGBM outperformed the other algorithms examined. Random Forest outperformed the other algorithms in the merged dataset trial, with 99.9% accuracy and the lowest computing cost. The LightGBM algorithm, on the other hand, performed admirably in the domain of binary classification, obtaining the same remarkable 99.9% accuracy with no computing overhead. Full article

(This article belongs to the Special Issue The Smart Cities Continuum via Machine Learning and Artificial Intelligence)

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20 pages, 712 KiB

Open AccessArticle

Investigating Antecedents to Older Adults’ Uptake of Health Information Systems: A Quantitative Case Study of Electronic Personal Health Records

by Reza Torkman, Amir Hossein Ghapanchi and Reza Ghanbarzadeh

Informatics 2025, 12(1), 3; https://doi.org/10.3390/informatics12010003 - 3 Jan 2025

Introduction and Aim: older adults are among the primary beneficiaries of health information systems such as electronic Personal Health Records (PHRs), yet they are notably underrepresented in existing research. This study addresses this gap by focusing on this vulnerable group, aiming to enhance [...] Read more.

Introduction and Aim: older adults are among the primary beneficiaries of health information systems such as electronic Personal Health Records (PHRs), yet they are notably underrepresented in existing research. This study addresses this gap by focusing on this vulnerable group, aiming to enhance their engagement with PHR systems to improve health management. This research seeks to identify the key determinants that influence the intention of older adults to utilize PHR systems, thereby supporting the development of more user-friendly health information technologies for this demographic. Methods: to better understand the functionality of PHR systems and improve user competence, hands-on workshops were conducted for 135 older adults. The workshops aimed to enhance their capability to manage chronic conditions using these systems. Post-training, participants’ intentions to use PHRs were assessed via a survey, applying the Health Belief Model (HBM) as the analytical framework. Data analysis was performed using Partial Least Squares (PLS) path modeling to underscore the robustness of the methodological approach. Findings: the data analysis of the participants’ responses uncovered that seniors’ usage intention toward PHRs is a function of the PHR’s perceived usefulness (p < 0.01, OR = 2.5, 95% CI 1.5–4.1), perceived barriers (p < 0.05, OR = 1.3, 95% CI 0.8–2.1), perceived confidence (p < 0.05, OR = 1.6, 95% CI 1.0–2.5), and cues to action (p < 0.05, OR = 1.8, 95% CI 1.1–2.9). Of these, perceived usefulness was found to be the strongest predictor. The results also indicate that perceived susceptibility and perceived severity did not significantly impact seniors’ intention to use PHRs. Conclusions: according to the findings and based on the HBM literature, if individuals perceive more benefits and fewer barriers to the use of PHRs, greater self-efficacy, and better cues to action, they are more likely to adopt the system. Full article

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17 pages, 2798 KiB

Open AccessArticle

Deep Learning-Based Analysis of Ocular Anterior Segment Diseases from Patient-Self-Captured Smartphone Images

by Byoungyoung Gu, Mark Christopher, Su-Ho Lim and Sally L. Baxter

Informatics 2025, 12(1), 2; https://doi.org/10.3390/informatics12010002 - 31 Dec 2024

The goal of this study is to evaluate the Eye Home Clinic app (ver 1.0), which uses deep learning models to assess the quality of self-captured anterior segment images and detect anterior segment diseases using only the patient’s smartphone. Images undergo quality assessment [...] Read more.

The goal of this study is to evaluate the Eye Home Clinic app (ver 1.0), which uses deep learning models to assess the quality of self-captured anterior segment images and detect anterior segment diseases using only the patient’s smartphone. Images undergo quality assessment based on the ‘DL-Image Eligibility’ model, and usable images are analyzed by the ‘DL-Diagnosis’ model to detect one of several anterior segment diseases. A dataset of 1006 images was used for training, and a dataset of 520 images was used for validation. The ‘DL-Image Eligibility’ model achieved an AUC of 0.87, with an accuracy of 0.75. The ‘DL-Diagnosis’ model had higher specificity (0.97) but lower sensitivity (0.29), with an AUC of 0.62. While the app shows potential for anterior segment telemedicine, improvements are needed in the DL model’s sensitivity for detecting abnormalities. Oversampling techniques, transfer learning, and dataset expansion should be considered to enhance the performance in future research. Based on data from users in over 100 countries, significant differences in photo quality among user groups were also identified. iOS users, younger users (21–40 years), and users reporting eye symptoms submitted more usable images. This study underscores the importance of user education and technological advancements to optimize smartphone-based ocular diagnostics. Full article

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13 pages, 2646 KiB

Open AccessArticle

Audio Watermarking System in Real-Time Applications

by Carlos Jair Santin-Cruz and Gordana Jovanovic Dolecek

Informatics 2025, 12(1), 1; https://doi.org/10.3390/informatics12010001 - 25 Dec 2024

Watermarking is widely employed to protect audio files. Previous research has focused on developing systems that balance performance criteria, including robustness, imperceptibility, and capacity. Most existing systems are designed to work with pre-recorded audio signals, where the characteristics of the host signal are [...] Read more.

Watermarking is widely employed to protect audio files. Previous research has focused on developing systems that balance performance criteria, including robustness, imperceptibility, and capacity. Most existing systems are designed to work with pre-recorded audio signals, where the characteristics of the host signal are known in advance. In such cases, processing time is not a critical factor, as these systems generally do not account for real-time signal acquisition or report tests for real-time signal acquisition nor report the elapsed time between signal acquisition and watermarking output, known as latency. However, the increasing prevalence of audio sharing through real-time streams or video calls is a pressing issue requiring low-latency systems. This work introduces a low-latency watermarking system that utilizes a spread spectrum technique, a method that spreads the signal energy across a wide frequency band while embedding the watermark additively in the time domain to minimize latency. The system’s performance was evaluated by simulating real-time audio streams using two distinct methods. The results demonstrate that the proposed system achieves minimal latency during embedding, addressing the urgent need for such systems. Full article

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17 pages, 902 KiB

Open AccessArticle

Context-Aware Electronic Health Record—Internet of Things and Blockchain Approach

by Tiago Guimarães, Ricardo Duarte, Francini Hak and Manuel Santos

Informatics 2024, 11(4), 98; https://doi.org/10.3390/informatics11040098 - 18 Dec 2024

Hospital inpatient care relies on constant monitoring and reliable real-time data. Continuous improvement, adaptability, and state-of-the-art technologies are critical for ongoing efficiency, productivity, and readiness growth. When appropriately used, technologies, such as blockchain and IoT-enabled devices, can change the practice of medicine and [...] Read more.

Hospital inpatient care relies on constant monitoring and reliable real-time data. Continuous improvement, adaptability, and state-of-the-art technologies are critical for ongoing efficiency, productivity, and readiness growth. When appropriately used, technologies, such as blockchain and IoT-enabled devices, can change the practice of medicine and ensure that it is performed based on correct assumptions and reliable data. The proposed electronic health record (EHR) can obtain context information from beacons, change the user interface of medical devices according to their location, and provide a more user-friendly interface for medical devices. The data generated, which are associated with the location of the beacons and devices, were stored in Hyperledger Fabric, a permissioned distributed ledger technology. Overall, by prompting and adjusting the user interface to context- and location-specific information while ensuring the immutability and value of the data, this solution targets a decrease in medical errors and an increase in the efficiency in healthcare inpatient care by improving user experience and ease of access to data for health professionals. Moreover, given auditing, accountability, and governance needs, it must ensure when, if, and by whom the data are accessed. Full article

(This article belongs to the Section Medical and Clinical Informatics)

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20 pages, 2115 KiB

Open AccessArticle

The Influence of Stakeholder Involvement in the Adoption of Digital Technologies in the UK Construction Industry

by Boroto Hwabamungu and Paul Shepherd

Informatics 2024, 11(4), 97; https://doi.org/10.3390/informatics11040097 - 9 Dec 2024

This study explored stakeholder involvement practice in digitalisation of the construction industry in the UK, and the influence thereof in the adoption of digital technologies. A qualitative interpretive method was followed using a case study approach to collect data. Thematic analysis of twenty-four [...] Read more.

This study explored stakeholder involvement practice in digitalisation of the construction industry in the UK, and the influence thereof in the adoption of digital technologies. A qualitative interpretive method was followed using a case study approach to collect data. Thematic analysis of twenty-four semi-structured interviews and sixty survey responses, which were conducted with different digital technologies adoption actors in the construction industry, allowed the identification of six final themes depicting the influence of stakeholder involvement in the adoption of digital technologies. The findings indicate that stakeholder involvement influence is a function of its embeddedness in an organisation digitalisation approach. Stakeholder involvement embeddedness in the approach, or lack thereof, dictates how the stakeholder landscape is planned and managed, and how communication between and with stakeholder groups occurs. This is the foundation of digitalisation value creation among stakeholders. The approach is prone to digitalisation limitations and intrinsic determinants of adoption, both of which can be positively impacted through better stakeholder involvement practices. Stakeholder involvement practices are therefore catalytic to the subsequent behaviour change for digital technologies adoption and the extent to which digital technologies become adopted. This paper contextualises stakeholder involvement in the adoption of digital technologies in the construction industry, highlighting the catalytic influence of stakeholder involvement embeddedness in the complex digitalisation activity system and its interplay with industry-specific practices and other digital technology adoption determinants. Full article

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55 pages, 8405 KiB

Open AccessReview

Transforming Service Quality in Healthcare: A Comprehensive Review of Healthcare 4.0 and Its Impact on Healthcare Service Quality

by Karam Al-Assaf, Zied Bahroun and Vian Ahmed

Informatics 2024, 11(4), 96; https://doi.org/10.3390/informatics11040096 - 2 Dec 2024

This systematic review investigates the transformative impact of Healthcare 4.0 (HC4.0) technologies on healthcare service quality (HCSQ), focusing on their potential to enhance healthcare delivery while addressing critical challenges. This study reviewed 168 peer-reviewed articles from the Scopus database, published between 2005 and [...] Read more.

This systematic review investigates the transformative impact of Healthcare 4.0 (HC4.0) technologies on healthcare service quality (HCSQ), focusing on their potential to enhance healthcare delivery while addressing critical challenges. This study reviewed 168 peer-reviewed articles from the Scopus database, published between 2005 and 2023. The selection process used clearly defined inclusion and exclusion criteria to identify studies focusing on advanced technologies such as artificial intelligence (AI), the Internet of Things (IoT), and big data analytics. Rayyan software facilitated systematic organization and duplicate removal, while manual evaluation ensured relevance and quality. The findings highlight HC4.0’s potential to improve service delivery, patient outcomes, and operational efficiencies but also reveal challenges, including interoperability, ethical concerns, and access disparities for underserved populations. The results were synthesized descriptively, uncovering key patterns and thematic insights while acknowledging heterogeneity across studies. Limitations include the absence of a formal risk-of-bias assessment and the diversity of methodologies, which precluded quantitative synthesis. This review emphasizes the need for future research on integration frameworks, ethical guidelines, and equitable access policies to realize HC4.0’s transformative potential. No external funding was received, and no formal protocol was registered. Full article

(This article belongs to the Special Issue Health Informatics: Feature Review Papers)

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25 pages, 7680 KiB

Open AccessArticle

Identification of Cancer Stem Cell (CSC)-Associated Genes, Prognostic Value, and Candidate Drugs as Modulators of CSC-Associated Signaling in Carcinomas Through a Multiomics Data Analysis Approach

by Pallabi Mondal, Poulami Singh, Krishna Mahanti and Sankar Bhattacharyya

Informatics 2024, 11(4), 95; https://doi.org/10.3390/informatics11040095 - 29 Nov 2024

Background: Cancer stem cells (CSCs) are a small subpopulation of cancer cells that have the potential for self-renewal and a strong proliferative capacity, and sustain tumorigenesis capabilities. This ability of CSCs to escape immune responses makes the CSCs a primary source of functionally [...] Read more.

Background: Cancer stem cells (CSCs) are a small subpopulation of cancer cells that have the potential for self-renewal and a strong proliferative capacity, and sustain tumorigenesis capabilities. This ability of CSCs to escape immune responses makes the CSCs a primary source of functionally altered, immune-resistant, chemoresistant, aggressive tumor cells. These characteristics determine the potential advantage of targeting CSCs for the treatment of solid tumors. Method: First, we downloaded different gene expression datasets of CSCs from the NCBI-GEO (National Center for Biotechnology Information–Gene Expression Omnibus) database and identified common genes by using a suitable Venn tool. Subsequently, we explored the prognostic significance of the particular genes in particular cancers and analyzed the expression of these genes at the protein level in human solid tumors by using KM plotter (Kaplan-Meier plotter) and an HPA (The Human Protein Atlas) database, respectively. Finally, using a comparative toxicogenomic database, we selected several important drugs or chemicals. Result: From this study, we identified APOC1 as a common upregulated gene in breast cancer and SLC44A5 and CAV2 as common up- and downregulated genes in lung cancer. In ovarian cancer, PRRG4 is a commonly upregulated gene, and ADCY7, AKAP12, TPM2, and FLNC are commonly downregulated genes. These genes also show prognostic significance in respective cancers. Several drugs that are capable of targeting the expression or signaling network of designated genes of CSC were also identified, which may contribute in CSC-targeted cancer therapy. Conclusion: Our study suggests a need for more in-depth experimental investigations to determine the actual functional activity and the mechanism of action of these CSC-associated genes. Full article

(This article belongs to the Section Health Informatics)

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14 pages, 732 KiB

Open AccessEditor’s ChoiceArticle

Does Fun Matter? Using Chatbots for Customer Services

by Tai Ming Wut, Elaine Ah-heung Chan and Helen Shun-mun Wong

Informatics 2024, 11(4), 94; https://doi.org/10.3390/informatics11040094 - 27 Nov 2024

Chatbots are widely used in customer services contexts today. People using chatbots have their pragmatic reasons, like checking delivery status and refund policies. The purpose of the paper is to investigate what are those factors that affect user experience and a chatbot’s service [...] Read more.

Chatbots are widely used in customer services contexts today. People using chatbots have their pragmatic reasons, like checking delivery status and refund policies. The purpose of the paper is to investigate what are those factors that affect user experience and a chatbot’s service quality which influence user satisfaction and electronic word-of-mouth. A survey was conducted in July 2024 to collect responses in Hong Kong about users’ perceptions of chatbots. Contrary to previous literature, entertainment and warmth perception were not associated with user experience and service quality. Social presence was associated with user experience, but not service quality. Competence was relevant to user experience and service quality, which reveals important implications for digital marketers and brands of adopting chatbots to enhance their service quality. Full article

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18 pages, 3100 KiB

Open AccessArticle

A Gap Analysis Framework for an Open Data Portal Assessment Based on Data Provision and Consumption Activities

by Sahaporn Sripramong, Chutiporn Anutariya, Patipat Tumsangthong, Theerawat Wutthitasarn and Marut Buranarach

Informatics 2024, 11(4), 93; https://doi.org/10.3390/informatics11040093 - 27 Nov 2024

An Open Government Data (OGD) portal assessment is necessary to track and monitor the progress of the OGD initiative and to drive improvement. Although OGD benchmarks typically focus on assessing and ranking OGD portals, few have been developed specifically for internal process improvement [...] Read more.

An Open Government Data (OGD) portal assessment is necessary to track and monitor the progress of the OGD initiative and to drive improvement. Although OGD benchmarks typically focus on assessing and ranking OGD portals, few have been developed specifically for internal process improvement within the portal. This paper proposes a gap analysis framework to support the Plan–Do–Check–Act (PDCA) cycle to guide OGD portal improvement. The framework adopted the Importance–Performance Analysis (IPA) to identify gaps in an OGD portal. The analysis measured the performance and importance of an OGD portal based on data provision and consumption activities. Several factors related to data provision and consumption activities are examined, including dataset creation, updates, views, searches, high-value datasets, resource formats, and user data requests. Gap analysis assessment results can help to identify the current situations of different areas on the portal and their gaps in achieving the objectives. A case study of the Data.go.th portal was conducted to exemplify and validate the framework’s adoption. The analysis results of the case study revealed existing patterns of relationships between data provision and consumption activities that can guide the improvement of similar OGD portals. Full article

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