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Informatics
Volume 12
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Informatics, Volume 12, Issue 1 (March 2025) – 33 articles

Cover Story (view full-size image): Concept drift, a phenomenon that can lead to the degradation of classifier performance over time, is commonly addressed by retraining the classifier without considering the properties of drift. 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. The findings reveal three key conclusions: (i) reaction strategies must be tailored to different types of drift; (ii) severity, recurrence, and frequency provide the highest impact on drift, 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. View this paper
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25 pages, 747 KiB  
Article
Development of a Comprehensive Evaluation Scale for LLM-Powered Counseling Chatbots (CES-LCC) Using the eDelphi Method
by Marco Bolpagni and Silvia Gabrielli
Informatics 2025, 12(1), 33; https://doi.org/10.3390/informatics12010033 - 20 Mar 2025
Viewed by 482
Abstract
Background/Objectives: With advancements in Large Language Models (LLMs), counseling chatbots are becoming essential tools for delivering scalable and accessible mental health support. Traditional evaluation scales, however, fail to adequately capture the sophisticated capabilities of these systems, such as personalized interactions, empathetic responses, [...] Read more.
Background/Objectives: With advancements in Large Language Models (LLMs), counseling chatbots are becoming essential tools for delivering scalable and accessible mental health support. Traditional evaluation scales, however, fail to adequately capture the sophisticated capabilities of these systems, such as personalized interactions, empathetic responses, and memory retention. This study aims to design a robust and comprehensive evaluation scale, the Comprehensive Evaluation Scale for LLM-Powered Counseling Chatbots (CES-LCC), using the eDelphi method to address this gap. Methods: A panel of 16 experts in psychology, artificial intelligence, human-computer interaction, and digital therapeutics participated in two iterative eDelphi rounds. The process focused on refining dimensions and items based on qualitative and quantitative feedback. Initial validation, conducted after assembling the final version of the scale, involved 49 participants using the CES-LCC to evaluate an LLM-powered chatbot delivering Self-Help Plus (SH+), an Acceptance and Commitment Therapy-based intervention for stress management. Results: The final version of the CES-LCC features 27 items grouped into nine dimensions: Understanding Requests, Providing Helpful Information, Clarity and Relevance of Responses, Language Quality, Trust, Emotional Support, Guidance and Direction, Memory, and Overall Satisfaction. Initial real-world validation revealed high internal consistency (Cronbach’s alpha = 0.94), although minor adjustments are required for specific dimensions, such as Clarity and Relevance of Responses. Conclusions: The CES-LCC fills a critical gap in the evaluation of LLM-powered counseling chatbots, offering a standardized tool for assessing their multifaceted capabilities. While preliminary results are promising, further research is needed to validate the scale across diverse populations and settings. Full article
(This article belongs to the Section Human-Computer Interaction)
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25 pages, 1821 KiB  
Article
SSL-SurvFormer: A Self-Supervised Learning and Continuously Monotonic Transformer Network for Missing Values in Survival Analysis
by Quang-Hung Le, Brijesh Patel, Donald Adjeroh, Gianfranco Doretto and Ngan Le
Informatics 2025, 12(1), 32; https://doi.org/10.3390/informatics12010032 - 19 Mar 2025
Viewed by 354
Abstract
Survival analysis is a crucial statistical technique used to estimate the anticipated duration until a specific event occurs. However, current methods often involve discretizing the time scale and struggle with managing absent features within the data. This becomes especially pertinent since events can [...] Read more.
Survival analysis is a crucial statistical technique used to estimate the anticipated duration until a specific event occurs. However, current methods often involve discretizing the time scale and struggle with managing absent features within the data. This becomes especially pertinent since events can transpire at any given point, rendering event analysis a continuous concern. Additionally, the presence of missing attributes within tabular data is widespread. By leveraging recent developments of Transformer and Self-Supervised Learning (SSL), we introduce SSL-SurvFormer. This entails a continuously monotonic Transformer network, empowered by SSL pre-training, that is designed to address the challenges presented by continuous events and absent features in survival prediction. Our proposed continuously monotonic Transformer model facilitates accurate estimation of survival probabilities, thereby bypassing the need for temporal discretization. Additionally, our SSL pre-training strategy incorporates data transformation to adeptly manage missing information. The SSL pre-training encompasses two tasks: mask prediction, which identifies positions of absent features, and reconstruction, which endeavors to recover absent elements based on observed ones. Our empirical evaluations conducted across a variety of datasets, including FLCHAIN, METABRIC, and SUPPORT, consistently highlight the superior performance of SSL-SurvFormer in comparison to existing methods. Additionally, SSL-SurvFormer demonstrates effectiveness in handling missing values, a critical aspect often encountered in real-world datasets. Full article
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38 pages, 986 KiB  
Article
Determinants of ThaiMOOC Engagement: A Longitudinal Perspective on Adoption to Continuance
by Kanitsorn Suriyapaiboonwattana and Kate Hone
Informatics 2025, 12(1), 31; https://doi.org/10.3390/informatics12010031 - 19 Mar 2025
Viewed by 436
Abstract
Massive Open Online Courses (MOOCs) have become increasingly prevalent in higher education, with the COVID-19 pandemic further accelerating their integration, particularly in developing countries. While MOOCs offered a vital solution for educational continuity during the pandemic, factors influencing students’ sustained engagement with them [...] Read more.
Massive Open Online Courses (MOOCs) have become increasingly prevalent in higher education, with the COVID-19 pandemic further accelerating their integration, particularly in developing countries. While MOOCs offered a vital solution for educational continuity during the pandemic, factors influencing students’ sustained engagement with them remain understudied. This longitudinal study examines the factors influencing learners’ sustained engagement with ThaiMOOC, incorporating demographic characteristics, usage log data, and key predictors of adoption and completion. Our research collected primary data from 841 university students who enrolled in ThaiMOOC as a mandatory curriculum component, using online surveys with open-ended questions and post-course usage log analysis. Logistic regression analysis indicates that adoption intention, course content, and perceived effectiveness significantly predict students’ Actual Continued Usage (ACU). Moreover, gender, prior MOOC experience, and specific usage behaviors emerge as influential factors. Content analysis highlights the importance of local language support and the desire for safety during the COVID-19 pandemic. Key elements driving ACU include video design, course content, assessment, and learner-to-learner interaction. Full article
(This article belongs to the Section Human-Computer Interaction)
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37 pages, 3526 KiB  
Article
Human-Centred Design Meets AI-Driven Algorithms: Comparative Analysis of Political Campaign Branding in the Harris–Trump Presidential Campaigns
by Hedda Martina Šola, Fayyaz Hussain Qureshi and Sarwar Khawaja
Informatics 2025, 12(1), 30; https://doi.org/10.3390/informatics12010030 - 18 Mar 2025
Viewed by 970
Abstract
This study compared the efficacy of AI neuroscience tools versus traditional design methods in enhancing viewer engagement with political campaign materials from the Harris–Trump presidential campaigns. Utilising a mixed-methods approach, we integrated quantitative analysis employing AI’s eye-tracking consumer behaviour metrics (Predict, trained on [...] Read more.
This study compared the efficacy of AI neuroscience tools versus traditional design methods in enhancing viewer engagement with political campaign materials from the Harris–Trump presidential campaigns. Utilising a mixed-methods approach, we integrated quantitative analysis employing AI’s eye-tracking consumer behaviour metrics (Predict, trained on 180,000 screenings) with an AI-LLM neuroscience-based marketing assistant (CoPilot), with 67,429 areas of interest (AOIs). The original flyer, from an Al Jazeera article, served as the baseline. Professional graphic designers created three redesigned versions, and one was done using recommendations from CoPilot. Metrics including total attention, engagement, start attention, end attention, and percentage seen were evaluated across 13–14 areas of interest (AOIs) for each design. Results indicated that human-enhanced Design 1 with AI eye-tracking achieved superior overall performance across multiple metrics. While the AI-enhanced Design 3 demonstrated strengths in optimising specific AOIs, it did not consistently outperform human-touched designs, particularly in text-heavy areas. The study underscores the complex interplay between neuroscience AI algorithms and human-centred design in political campaign branding, offering valuable insights for future research in neuromarketing and design communication strategies. Python, Pandas, Matplotlib, Seaborn, Spearman correlation, and the Kruskal–Wallis H-test were employed for data analysis and visualisation. Full article
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15 pages, 659 KiB  
Article
Can AI Technologies Support Clinical Supervision? Assessing the Potential of ChatGPT
by Valeria Cioffi, Ottavio Ragozzino, Lucia Luciana Mosca, Enrico Moretto, Enrica Tortora, Annamaria Acocella, Claudia Montanari, Antonio Ferrara, Stefano Crispino, Elena Gigante, Alexander Lommatzsch, Mariano Pizzimenti, Efisio Temporin, Valentina Barlacchi, Claudio Billi, Giovanni Salonia and Raffaele Sperandeo
Informatics 2025, 12(1), 29; https://doi.org/10.3390/informatics12010029 - 17 Mar 2025
Viewed by 586
Abstract
Clinical supervision is essential for trainees, preventing burnout and ensuring the effectiveness of their interventions. AI technologies offer increasing possibilities for developing clinical practices, with supervision being particularly suited for automation. The aim of this study is to evaluate the feasibility of using [...] Read more.
Clinical supervision is essential for trainees, preventing burnout and ensuring the effectiveness of their interventions. AI technologies offer increasing possibilities for developing clinical practices, with supervision being particularly suited for automation. The aim of this study is to evaluate the feasibility of using ChatGPT-4 as a supervisory tool in psychotherapy training. To achieve this, a clinical case was presented to three distinct groups (untrained AI, pre-trained AI, and qualified human supervisor), and their feedback was evaluated by Gestalt psychotherapy trainees using a Likert scale rating of satisfaction. Statistical analysis, using the statistical package SPSS version 25 and applying principal component analysis (PCA) and one-way analysis of variance (ANOVA), demonstrated significant differences in favor of pre-trained AI feedback. PCA highlighted four components of the questionnaire: relational and emotional (C1), didactic and technical quality (C2), treatment support and development (C3), and professional orientation and adaptability (C4). The ratings of satisfaction obtained from the three kinds of supervisory feedback were compared using ANOVA. The feedback generated by the pre-trained AI (f2) was rated significantly higher than the other two (untrained AI feedback (f1) and human feedback (f3)) in C4; in C1, the superiority of f2 over f1 but not over f3 appears significant. These results suggest that AI, when appropriately calibrated, may be an appreciable tool for complementing the effectiveness of clinical supervision, offering an innovative blended supervision methodology, in particular in the area of career guidance. Full article
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15 pages, 1249 KiB  
Article
A Pilot Study Using Natural Language Processing to Explore Textual Electronic Mental Healthcare Data
by Gayathri Delanerolle, Yassine Bouchareb, Suchith Shetty, Heitor Cavalini and Peter Phiri
Informatics 2025, 12(1), 28; https://doi.org/10.3390/informatics12010028 - 13 Mar 2025
Viewed by 652
Abstract
Mental health illness is the single biggest cause of inability within the UK, contributing up to 22.8% of the whole burden compared to 15.9% for cancer and 16.2% for cardiovascular disease. The more extensive financial costs of mental ailments in Britain have been [...] Read more.
Mental health illness is the single biggest cause of inability within the UK, contributing up to 22.8% of the whole burden compared to 15.9% for cancer and 16.2% for cardiovascular disease. The more extensive financial costs of mental ailments in Britain have been evaluated at British Pound Sterling (GBP) 105.2 billion each year. This burden could be decreased with productive forms and utilization of computerized innovations. Electronical health records (EHRs), for instance, could offer an extraordinary opportunity for research and provide improved and optimized care. Consequently, this technological advance would unburden the mental health system and help provide optimized and efficient care to the patients. Using natural language processing methods to explore unstructured EHR text data from mental health services in the National Health Service (NHS) UK brings opportunities and technical challenges in the use of such data and possible solutions. This descriptive study compared technical methods and approaches to leverage large-scale text data in EHRs of mental health service providers in the NHS. We conclude that the method used is suitable for mental health services. However, broader studies including other hospital sites are still needed to validate the method. Full article
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23 pages, 7992 KiB  
Article
Gamification in Virtual Reality Museums: Effects on Hedonic and Eudaimonic Experiences in Cultural Heritage Learning
by Sumalee Sangamuang, Natchaya Wongwan, Kannikar Intawong, Songpon Khanchai and Kitti Puritat
Informatics 2025, 12(1), 27; https://doi.org/10.3390/informatics12010027 - 3 Mar 2025
Viewed by 1173
Abstract
Virtual museums powered by virtual reality (VR) technology serve as innovative platforms for cultural preservation and education, combining accessibility with immersive user experiences. While gamification has been widely explored in educational and entertainment contexts, its impact on user experiences in virtual cultural heritage [...] Read more.
Virtual museums powered by virtual reality (VR) technology serve as innovative platforms for cultural preservation and education, combining accessibility with immersive user experiences. While gamification has been widely explored in educational and entertainment contexts, its impact on user experiences in virtual cultural heritage museums remains underexplored. Prior research has focused primarily on engagement and enjoyment in gamified virtual environments but has not sufficiently distinguished between hedonic (pleasure-driven) and eudaimonic (meaning-driven) experiences or their impact on learning outcomes. This study aims to address this gap by comparing gamified and non-gamified virtual museum designs to evaluate their effects on hedonic and eudaimonic experiences, knowledge acquisition, and behavioral engagement. Using a quasi-experimental approach with 70 participants, the findings indicate that gamification significantly enhances hedonic experiences, including enjoyment, engagement, and satisfaction, while fostering prolonged interaction and deeper exploration. However, eudaimonic outcomes such as personal growth and reflection did not exhibit statistically significant differences. These results underscore the potential of gamified VR environments to balance entertainment and educational value, offering insights into user-centered design strategies for virtual museum systems that bridge technology, culture, and engagement. Full article
(This article belongs to the Section Human-Computer Interaction)
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16 pages, 4536 KiB  
Article
Evaluating the Role of Visual Fidelity in Digital Vtubers on Mandarin Chinese Character Learning
by Xiaoxiao Cao, Wei Tong, Kenta Ono and Makoto Watanabe
Informatics 2025, 12(1), 26; https://doi.org/10.3390/informatics12010026 - 25 Feb 2025
Viewed by 785
Abstract
Despite the growing presence of digital Virtual YouTubers (Vtubers) in educational settings, there is limited empirical evidence on their effectiveness in language acquisition. In this investigation, we delved into the realm of digital education to assess how the visual fidelity of digital Vtuber [...] Read more.
Despite the growing presence of digital Virtual YouTubers (Vtubers) in educational settings, there is limited empirical evidence on their effectiveness in language acquisition. In this investigation, we delved into the realm of digital education to assess how the visual fidelity of digital Vtuber avatars affects the acquisition of Mandarin Chinese characters by beginners. Through incorporating a diverse array of digital Vtubers, ranging from simple two-dimensional figures to complex three-dimensional models, we explored the relationship between digital Vtuber design and learner engagement and efficacy. This study employed a randomized tutorial distribution, immediate post-tutorial quizzing, and a realism scoring rubric, with statistical analysis conducted through Pearson correlation. The analysis, involving 608 participants, illuminated a clear positive correlation: digital Vtubers with higher levels of realism significantly enhanced learning outcomes, underscoring the importance of visual fidelity in educational content. This research substantiates the educational utility of digital Vtubers and underscores their potential in creating more immersive and effective digital learning environments. The findings advocate for leveraging sophisticated digital Vtubers to foster deeper learner engagement, improve educational achievement, and promote sustainable educational practices, offering insights for the future development of digital learning strategies. Full article
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71 pages, 26964 KiB  
Article
Machine Learning Approaches for Fault Detection in Internal Combustion Engines: A Review and Experimental Investigation
by A. Srinivaas, N. R. Sakthivel and Binoy B. Nair
Informatics 2025, 12(1), 25; https://doi.org/10.3390/informatics12010025 - 21 Feb 2025
Viewed by 1870
Abstract
Fault diagnostics in internal combustion engines (ICEs) is vital for optimal operation and avoiding costly breakdowns. This paper reviews methodologies for ICE fault detection, including model-based and data-driven approaches. The former uses physical models of engine components to diagnose defects, while the latter [...] Read more.
Fault diagnostics in internal combustion engines (ICEs) is vital for optimal operation and avoiding costly breakdowns. This paper reviews methodologies for ICE fault detection, including model-based and data-driven approaches. The former uses physical models of engine components to diagnose defects, while the latter employs statistical analysis of sensor data to identify patterns indicating faults. Various methods for ICE fault identification, such as vibration analysis, thermography, acoustic analysis, and optical approaches, are reviewed. This paper also explores the latest approaches for detecting ICE faults. It highlights the challenges in the diagnostic process and ways to enhance result accuracy and reliability. This paper concludes with a review of the progress in fault identification in ICE components and prospects, highlighted by an experimental investigation using 16 machine learning algorithms with seven feature selection techniques under three load conditions to detect faults in a four-cylinder ICE. Additionally, this study incorporates advanced deep learning techniques, including a deep neural network (DNN), a one-dimensional convolutional neural network (1D-CNN), Transformer and a hybrid Transformer and DNN model which demonstrate superior performance in fault detection compared to traditional machine learning methods. Full article
(This article belongs to the Section Machine Learning)
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18 pages, 1054 KiB  
Article
Digital Media Victimization Among Older Adults in Upper-Southern Thailand
by Pimpisa Pituk, Nirachon Chutipattana, Pussadee Laor, Thitipong Sukdee, Jiraprapa Kittikun, Witchayaporn Jitwiratnukool, Rohmatul Fajriyah and Wanvisa Saisanan Na Ayudhaya
Informatics 2025, 12(1), 24; https://doi.org/10.3390/informatics12010024 - 21 Feb 2025
Viewed by 970
Abstract
Online fraud threatens the well-being of older adults, with disparities in digital literacy and socioeconomic conditions amplifying their vulnerability. This study examined digital literacy and fraud victimization behavior among older adults in urban and rural settings, identifying key factors influencing victimization and its [...] Read more.
Online fraud threatens the well-being of older adults, with disparities in digital literacy and socioeconomic conditions amplifying their vulnerability. This study examined digital literacy and fraud victimization behavior among older adults in urban and rural settings, identifying key factors influencing victimization and its consequences. This cross-sectional analytical study, using multi-stage sampling, included 864 participants from Southern Thailand. The findings revealed that 46.3% of participants had adequate digital literacy, while 75.3% experienced fraud victimization, with higher rates of health impacts in rural areas. Higher age (Adjusted Odds Ratios; AOR: 1.83, p = 0.004), income (AOR: 2.28, p = 0.003), and rural residence (AOR: 3.03, p < 0.001) were significantly associated with an increased likelihood of fraudulent victimization. Conversely, being non-Buddhist (AOR: 0.47, p = 0.001) and having an adequate digital literacy (AOR: 0.50, p < 0.001) were protective factors. Fraud victimization significantly affected older adults’ health, with 29.5% reporting the following adverse outcomes: physical (AOR: 5.55), emotional (AOR: 7.80), social (AOR: 4.97), and overall heightened health risks (AOR: 7.71, p < 0.001). This research highlights the importance of improving digital literacy, fostering community awareness, and implementing tailored fraud-prevention strategies to protect older adults. This study provides a foundation for evidence-based policies aimed at mitigating digital risks and enhancing older adults’ well-being in the digital era. Full article
(This article belongs to the Topic Theories and Applications of Human-Computer Interaction)
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19 pages, 274 KiB  
Article
Digital Competences of Digital Natives: Measuring Skills in the Modern Technology Environment
by Danijela Pongrac, Marta Alić and Brigitta Cafuta
Informatics 2025, 12(1), 23; https://doi.org/10.3390/informatics12010023 - 21 Feb 2025
Viewed by 678
Abstract
The fourth industrial revolution has ushered in a new era in which technology is seamlessly integrated into daily life. The digital transformation has created new media formats that require the development of robust digital skills to navigate this landscape. By utilising the Youth [...] Read more.
The fourth industrial revolution has ushered in a new era in which technology is seamlessly integrated into daily life. The digital transformation has created new media formats that require the development of robust digital skills to navigate this landscape. By utilising the Youth Digital Skills Indicator (yDSI) and integrating it with the Digital Competence Framework for Citizens (DigComp 2.2), this research examines media habits and digital competences among Croatian youth aged 10–24, corresponding to Generations Alpha and Z. A sample of 231 participants across three competence domains—information literacy, security and communication—revealed statistically significant generational differences in the first two areas of digital skills. Furthermore, gender-based analyses, conducted using the Mann–Whitney U-test and Spearman correlations for Likert scale responses, showed no significant differences. These findings deepen our understanding of digital natives, how media habits evolve and influence their digital skills, highlighting the need for more tailored strategies to enhance their competences and bridge generational gaps. Full article
19 pages, 3256 KiB  
Article
Predictive Machine Learning Approaches for Supply and Manufacturing Processes Planning in Mass-Customization Products
by Shereen Alfayoumi, Amal Elgammal and Neamat El-Tazi
Informatics 2025, 12(1), 22; https://doi.org/10.3390/informatics12010022 - 19 Feb 2025
Viewed by 675
Abstract
Planning in mass-customization supply and manufacturing processes is a complex process that requires continuous planning and optimization to minimize time and cost across a wide variety of choices in large production volumes. While soft computing techniques are widely used for optimizing mass-customization products, [...] Read more.
Planning in mass-customization supply and manufacturing processes is a complex process that requires continuous planning and optimization to minimize time and cost across a wide variety of choices in large production volumes. While soft computing techniques are widely used for optimizing mass-customization products, they face scalability issues when handling large datasets and rely heavily on manually defined rules, which are prone to errors. In contrast, machine learning techniques offer an opportunity to overcome these challenges by automating rule generation and improving scalability. However, their full potential has yet to be explored. This article proposes a machine learning-based approach to address this challenge, aiming to optimize both the supply and manufacturing planning phases as a practical solution for industry planning or optimization problems. The proposed approach examines supervised machine learning and deep learning techniques for manufacturing time and cost planning in various scenarios of a large-scale real-life pilot study in the bicycle manufacturing domain. This experimentation included K-Nearest Neighbors with regression and Random Forest from the machine learning family, as well as Neural Networks and Ensembles as deep learning approaches. Additionally, Reinforcement Learning was used in scenarios where real-world data or historical experiences were unavailable. The training performance of the pilot study was evaluated using cross-validation along with two statistical analysis methods: the t-test and the Wilcoxon test. These performance evaluation efforts revealed that machine learning techniques outperform deep learning methods and the reinforcement learning approach, with K-NN combined with regression yielding the best results. The proposed approach was validated by industry experts in bicycle manufacturing. It demonstrated up to a 37% reduction in both time and cost for orders compared to traditional expert estimates. Full article
(This article belongs to the Section Industrial Informatics)
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32 pages, 7876 KiB  
Article
Detection of Victimization Patterns and Risk of Gender Violence Through Machine Learning Algorithms
by Edna Rocio Bernal-Monroy, Erika Dajanna Castañeda-Monroy, Rafael Ricardo Rentería-Ramos, Sixto Enrique Campaña-Bastidas, Jessica Barrera, Tania Maribel Palacios-Yampuezan, Olga Lucía González Gustin, Carlos Fernando Tobar-Torres and Zeneida Rocio Ceballos-Villada
Informatics 2025, 12(1), 21; https://doi.org/10.3390/informatics12010021 - 17 Feb 2025
Viewed by 634
Abstract
This paper explores the application of machine learning techniques and statistical analysis to identify the patterns of victimization and the risk of gender-based violence in San Andrés de Tumaco, Nariño, Colombia. Models were developed to classify women according to their vulnerability and risk [...] Read more.
This paper explores the application of machine learning techniques and statistical analysis to identify the patterns of victimization and the risk of gender-based violence in San Andrés de Tumaco, Nariño, Colombia. Models were developed to classify women according to their vulnerability and risk of suffering various forms of violence, which were integrated into a decision-making tool for local authorities. The algorithms employed include K-means for clustering, artificial neural networks, random forests, decision trees, and multiclass classification algorithms combined with fuzzy classification techniques to handle the incomplete data. Implemented in Python and R, the models were statistically validated to ensure their reliability. Analysis based on health data revealed the key victimization patterns and risks associated with gender-based violence in the region. This study presents a data science model that uses a social determinant approach to assess the characteristics and patterns of violence against women in the Pacific region of Nariño. This research was conducted within the framework of the Orquídeas Program of the Colombian Ministry of Science, Technology, and Innovation. Full article
(This article belongs to the Section Social Informatics and Digital Humanities)
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20 pages, 2610 KiB  
Article
DynGraph-BERT: Combining BERT and GNN Using Dynamic Graphs for Inductive Semi-Supervised Text Classification
by Eliton Luiz Scardin Perin, Mariana Caravanti de Souza, Jonathan de Andrade Silva and Edson Takashi Matsubara
Informatics 2025, 12(1), 20; https://doi.org/10.3390/informatics12010020 - 17 Feb 2025
Viewed by 668
Abstract
The combination of Bidirecional Encoder Representations from Transformers (BERT) and Graph Neural Networks (GNNs) has been extensively explored in the text classification literature, usually employing BERT as a feature extractor combined with heterogeneous static graphs. BERT transfers information via token embeddings, which are [...] Read more.
The combination of Bidirecional Encoder Representations from Transformers (BERT) and Graph Neural Networks (GNNs) has been extensively explored in the text classification literature, usually employing BERT as a feature extractor combined with heterogeneous static graphs. BERT transfers information via token embeddings, which are propagated through GNNs. Text-specific information defines a static heterogeneous graph. Static graphs represent specific relationships and do not have the flexibility to add new knowledge to the graph. To address this issue, we build a tied connection between BERT and GNN exclusively using token embeddings to define the graph and propagate the embeddings, which can force the BERT to redefine the GNN graph topology to improve accuracy. Thus, in this study, we re-examine the design spaces and test the limits of what this pure homogeneous graph using BERT embeddings can achieve. Homogeneous graphs offer structural simplicity and greater generalization capabilities, particularly when integrated with robust representations like those provided by BERT. To improve accuracy, the proposed approach also incorporates text augmentation and label propagation at test time. Experimental results show that the proposed method outperforms state-of-the-art methods across all datasets analyzed, with consistent accuracy improvements as more labeled examples are included. Full article
(This article belongs to the Section Machine Learning)
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16 pages, 1558 KiB  
Article
Anemia Classification System Using Machine Learning
by Jorge Gómez Gómez, Camilo Parra Urueta, Daniel Salas Álvarez, Velssy Hernández Riaño and Gustavo Ramirez-Gonzalez
Informatics 2025, 12(1), 19; https://doi.org/10.3390/informatics12010019 - 11 Feb 2025
Viewed by 1552
Abstract
In this study, a system was developed to predict anemia using blood count data and supervised learning algorithms. Anemia, a common condition characterized by low levels of red blood cells or hemoglobin, affects oxygenation and often causes symptoms, such as fatigue and shortness [...] Read more.
In this study, a system was developed to predict anemia using blood count data and supervised learning algorithms. Anemia, a common condition characterized by low levels of red blood cells or hemoglobin, affects oxygenation and often causes symptoms, such as fatigue and shortness of breath. The diagnosis of anemia often requires laboratory tests, which can be challenging in low-resource areas where anemia is common. We built a supervised learning approach and trained three models (Linear Discriminant Analysis, Decision Trees, and Random Forest) using an anemia dataset from a previous study by Sabatini in 2022. The Random Forest model achieved an accuracy of 99.82%, highlighting its capability to subclassify anemia types (microcytic, normocytic, and macrocytic) with high precision, which is a novel advancement compared to prior studies limited to binary classification (presence/absence of anemia) of the same dataset. Full article
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30 pages, 4558 KiB  
Article
AI-Powered Lung Cancer Detection: Assessing VGG16 and CNN Architectures for CT Scan Image Classification
by Rapeepat Klangbunrueang, Pongsathon Pookduang, Wirapong Chansanam and Tassanee Lunrasri
Informatics 2025, 12(1), 18; https://doi.org/10.3390/informatics12010018 - 11 Feb 2025
Viewed by 1741
Abstract
Lung cancer is a leading cause of mortality worldwide, and early detection is crucial in improving treatment outcomes and reducing death rates. However, diagnosing medical images, such as Computed Tomography scans (CT scans), is complex and requires a high level of expertise. This [...] Read more.
Lung cancer is a leading cause of mortality worldwide, and early detection is crucial in improving treatment outcomes and reducing death rates. However, diagnosing medical images, such as Computed Tomography scans (CT scans), is complex and requires a high level of expertise. This study focuses on developing and evaluating the performance of Convolutional Neural Network (CNN) models, specifically the Visual Geometry Group 16 (VGG16) architecture, to classify lung cancer CT scan images into three categories: Normal, Benign, and Malignant. The dataset used consists of 1097 CT images from 110 patients, categorized according to these severity levels. The research methodology began with data collection and preparation, followed by training and testing the VGG16 model and comparing its performance with other CNN architectures, including Residual Network with 50 layers (ResNet50), Inception Version 3 (InceptionV3), and Mobile Neural Network Version 2 (MobileNetV2). The experimental results indicate that VGG16 achieved the highest classification performance, with a Test Accuracy of 98.18%, surpassing the other models. This accuracy highlights VGG16’s strong potential as a supportive diagnostic tool in medical imaging. However, a limitation of this study is the dataset size, which may reduce model accuracy when applied to new data. Future studies should consider increasing the dataset size, using Data Augmentation techniques, fine-tuning model parameters, and employing advanced models such as 3D CNN or Vision Transformers. Additionally, incorporating Gradient-weighted Class Activation Mapping (Grad-CAM) to interpret model decisions would enhance transparency and reliability. This study confirms the potential of CNNs, particularly VGG16, for classifying lung cancer CT images and provides a foundation for further development in medical applications. Full article
(This article belongs to the Section Medical and Clinical Informatics)
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38 pages, 18446 KiB  
Article
Hybrid Machine Learning for IoT-Enabled Smart Buildings
by Robert-Alexandru Craciun, Simona Iuliana Caramihai, Ștefan Mocanu, Radu Nicolae Pietraru and Mihnea Alexandru Moisescu
Informatics 2025, 12(1), 17; https://doi.org/10.3390/informatics12010017 - 11 Feb 2025
Viewed by 748
Abstract
This paper presents an intrusion detection system (IDS) leveraging a hybrid machine learning approach aimed at enhancing the security of IoT devices at the edge, specifically for those utilizing the TCP/IP protocol. Recognizing the critical security challenges posed by the rapid expansion of [...] Read more.
This paper presents an intrusion detection system (IDS) leveraging a hybrid machine learning approach aimed at enhancing the security of IoT devices at the edge, specifically for those utilizing the TCP/IP protocol. Recognizing the critical security challenges posed by the rapid expansion of IoT networks, this work evaluates the proposed IDS model with a primary focus on optimizing training time without sacrificing detection accuracy. The paper begins with a comprehensive review of existing hybrid machine learning models for IDS, highlighting both their strengths and limitations. It then provides an overview of the technologies and methodologies implemented in this work, including the utilization of “Botnet IoT Traffic Dataset For Smart Buildings”, a newly released public dataset tailored for IoT threat detection. The hybrid IDS model is explained in detail, followed by a discussion of experimental results that assess the model’s performance in real-world conditions. Furthermore, the proposed IDS is evaluated for its effectiveness in enhancing IoT security within smart building environments, demonstrating how it can address unique challenges such as resource constraints and real-time threat detection at the edge. This work aims to contribute to the development of efficient, reliable, and scalable IDS solutions to protect IoT ecosystems from emerging security threats. Full article
(This article belongs to the Section Machine Learning)
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20 pages, 1434 KiB  
Article
Automatic Translation Between Kreol Morisien and English Using the Marian Machine Translation Framework
by Zaheenah Beebee Jameela Boodeea, Sameerchand Pudaruth, Nitish Chooramun and Aneerav Sukhoo
Informatics 2025, 12(1), 16; https://doi.org/10.3390/informatics12010016 - 10 Feb 2025
Viewed by 715
Abstract
Kreol Morisien is a vibrant and expressive language that reflects the multicultural heritage of Mauritius. There are different versions of Kreol languages. While Kreol Morisien is spoken in Mauritius, Kreol Rodrige is spoken only in Rodrigues, and they are distinct languages. Being spoken [...] Read more.
Kreol Morisien is a vibrant and expressive language that reflects the multicultural heritage of Mauritius. There are different versions of Kreol languages. While Kreol Morisien is spoken in Mauritius, Kreol Rodrige is spoken only in Rodrigues, and they are distinct languages. Being spoken by only about 1.5 million speakers in the world, Kreol Morisien falls in the category of under-resourced languages. Initially, Kreol Morisien lacked a formalised writing system, with many people using different spellings for the same words. The first step towards standardisation of writing Kreol Morisien was after the publication of the Kreol Morisien orthography in 2011 and Kreol Morisien grammar in 2012 by the Kreol Morisien Academy. Kreol Morisien obtained a national position in the year 2012 when it was introduced in educational organisations. This was a major breakthrough for Kreol Morisien to be recognised as a national language on the same level as English, French, and other oriental languages. By providing a means for Kreol Morisien speakers to connect with others, a translation system will help to preserve and strengthen the identity of the language and its speakers in an increasingly globalized world. The aim of this paper is to develop a translation system for Kreol Morisien and English. Thus, a dataset consisting of 50,000 parallel Kreol Morisien and English sentences was created, where 48,000 sentence pairs were used to train the models, while 1000 sentences were used for evaluation and another 1000 sentences were used for testing. Several machine translation systems such as statistical machine translation, open-source neural machine translation, a Transformer model with attention mechanism, and Marian machine translation are trained and evaluated. Our best model, using MarianMT, achieved a BLEU score of 0.62 for the translation of English to Kreol Morisien and a BLEU score of 0.58 for the translation of Kreol Morisien into English. To our knowledge, these are the highest BLEU scores that are available in the literature for this language pair. A high-quality translation tool for Kreol Morisien will facilitate its integration into digital platforms. This will make previously inaccessible knowledge more accessible, as the information can now be translated into the mother tongue of most Mauritians with reasonable accuracy. Full article
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20 pages, 1215 KiB  
Systematic Review
Machine Learning and Deep Learning Models for Dengue Diagnosis Prediction: A Systematic Review
by Daniel Cristobal Andrade Girón, William Joel Marín Rodriguez, Flor de María Lioo-Jordan and Jose Luis Ausejo Sánchez
Informatics 2025, 12(1), 15; https://doi.org/10.3390/informatics12010015 - 6 Feb 2025
Viewed by 1642
Abstract
The global crisis triggered by the dengue outbreak has increased mortality and placed significant pressure on healthcare services worldwide. In response to this crisis, there has been a notable increase in research employing machine learning and deep learning algorithms to anticipate diagnosis in [...] Read more.
The global crisis triggered by the dengue outbreak has increased mortality and placed significant pressure on healthcare services worldwide. In response to this crisis, there has been a notable increase in research employing machine learning and deep learning algorithms to anticipate diagnosis in patients with suspected dengue. To conduct a comprehensive systematic review, a detailed analysis was carried out to explore and examine the machine learning methodologies applied in diagnosing this disease. An exhaustive search was conducted across numerous scientific databases, including Scopus, IEEE Xplore, PubMed, ACM, ScienceDirect, Wiley, and Sage, encompassing studies up to May 2024. This extensive search yielded a total of 2723 relevant articles. Following a rigorous evaluation, 32 scientific studies were selected for the final review, meeting the established criteria. A comprehensive analysis of these studies revealed the implementation of 48 distinct machine learning and deep learning algorithms, showcasing the heterogeneity of methodological approaches employed in the research domain. The results indicated that, in terms of performance, the support vector machine (SVM) algorithm was the most efficient, being reported in 25% of the analyzed studies. The Random Forest algorithm was the second most frequently used, appearing in 15.62% of the 32 reviewed articles. The PCA-SVM algorithm (poly-5), a variant of SVM, emerged as the best-performing model, achieving 99.52% accuracy, 99.75% sensitivity, and 99.09% specificity. These findings offer significant insights into the potential of machine learning techniques in the early diagnosis of dengue, underscoring the necessity to persist in exploring and refining these methodologies to enhance clinical care in cases of this disease. Full article
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38 pages, 2036 KiB  
Article
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
Viewed by 3381
Abstract
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
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24 pages, 1478 KiB  
Article
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
Viewed by 898
Abstract
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)
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30 pages, 752 KiB  
Article
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
Viewed by 890
Abstract
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, 1809 KiB  
Article
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
Viewed by 876
Abstract
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  
Article
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
Viewed by 2086
Abstract
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  
Systematic 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
Cited by 1 | Viewed by 3538
Abstract
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  
Article
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
Viewed by 861
Abstract
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  
Article
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
Viewed by 859
Abstract
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  
Article
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
Viewed by 1036
Abstract
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  
Article
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
Viewed by 1145
Abstract
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  
Article
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
Viewed by 1270
Abstract
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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