Informatics

24 pages, 1212 KB

Open AccessArticle

Heart Attack Risk Prediction via Stacked Ensemble Metamodeling: A Machine Learning Framework for Real-Time Clinical Decision Support

by Brandon N. Nava-Martinez, Sahid S. Hernandez-Hernandez, Denzel A. Rodriguez-Ramirez, Jose L. Martinez-Rodriguez, Ana B. Rios-Alvarado, Alan Diaz-Manriquez, Jose R. Martinez-Angulo and Tania Y. Guerrero-Melendez

Informatics 2025, 12(4), 110; https://doi.org/10.3390/informatics12040110 - 11 Oct 2025

Abstract

Cardiovascular diseases claim millions of lives each year, yet timely diagnosis remains a significant challenge due to the high number of patients and associated costs. Although various machine learning solutions have been proposed for this problem, most approaches rely on careful data preprocessing [...] Read more.

Cardiovascular diseases claim millions of lives each year, yet timely diagnosis remains a significant challenge due to the high number of patients and associated costs. Although various machine learning solutions have been proposed for this problem, most approaches rely on careful data preprocessing and feature engineering workflows that could benefit from more comprehensive documentation in research publications. To address this issue, this paper presents a machine learning framework for predicting heart attack risk online. Our systematic methodology integrates a unified pipeline featuring advanced data preprocessing, optimized feature selection, and an exhaustive hyperparameter search using cross-validated grid evaluation. We employ a metamodel ensemble strategy, testing and combining six traditional supervised models along with six stacking and voting ensemble models. The proposed system achieves accuracies ranging from 90.2% to 98.9% on three independent clinical datasets, outperforming current state-of-the-art methods. Additionally, it powers a deployable, lightweight web application for real-time decision support. By merging cutting-edge AI with clinical usability, this work offers a scalable solution for early intervention in cardiovascular care. Full article

(This article belongs to the Special Issue Health Data Management in the Age of AI)

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22 pages, 618 KB

Open AccessArticle

Comparison of Ensemble and Meta-Ensemble Models for Early Risk Prediction of Acute Myocardial Infarction

by Daniel Cristóbal Andrade-Girón, Juana Sandivar-Rosas, William Joel Marin-Rodriguez, Marcelo Gumercindo Zúñiga-Rojas, Abrahán Cesar Neri-Ayala and Ernesto Díaz-Ronceros

Informatics 2025, 12(4), 109; https://doi.org/10.3390/informatics12040109 - 11 Oct 2025

Abstract

Cardiovascular disease (CVD) is a major cause of mortality around the world. This underscores the critical need to implement effective predictive tools to inform clinical decision-making. This study aimed to compare the predictive performance of ensemble learning algorithms, including Bagging, Random Forest, Extra [...] Read more.

Cardiovascular disease (CVD) is a major cause of mortality around the world. This underscores the critical need to implement effective predictive tools to inform clinical decision-making. This study aimed to compare the predictive performance of ensemble learning algorithms, including Bagging, Random Forest, Extra Trees, Gradient Boosting, and AdaBoost, when applied to a clinical dataset comprising patients with CVD. The methodology entailed data preprocessing and cross-validation to regulate generalization. The performance of the model was evaluated using a variety of metrics, including accuracy, F1 score, precision, recall, Cohen’s Kappa, and area under the curve (AUC). Among the models evaluated, Bagging demonstrated the best overall performance (accuracy ± SD: 93.36% ± 0.22; F1 score: 0.936; AUC: 0.9686). It also reached the lowest average rank (1.0) in Friedman test and was placed, together with Extra Trees (accuracy ± SD: 90.76% ± 0.18; F1 score: 0.916; AUC: 0.9689), in the superior statistical group (group A) according to Nemenyi post hoc test. The two models demonstrated a high degree of agreement with the actual labels (Kappa: 0.87 and 0.83, respectively), thereby substantiating their reliability in authentic clinical contexts. The findings substantiated the preeminence of aggregation-based ensemble methods in terms of accuracy, stability, and concordance. This underscored the prominence of Bagging and Extra Trees as optimal candidates for cardiovascular diagnostic support systems, where reliability and generalization were paramount. Full article

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30 pages, 712 KB

Open AccessReview

A Review on Scholarly Publication Recommender Systems: Features, Approaches, Evaluation, and Open Research Directions

by Anita Khadka and Saurav Sthapit

Informatics 2025, 12(4), 108; https://doi.org/10.3390/informatics12040108 - 10 Oct 2025

Abstract

The exponential growth of scientific literature has made it increasingly difficult for researchers to identify relevant and timely publications within vast academic digital libraries. Although academic search engines, reference management tools, and recommender systems have evolved, many still rely heavily on metadata and [...] Read more.

The exponential growth of scientific literature has made it increasingly difficult for researchers to identify relevant and timely publications within vast academic digital libraries. Although academic search engines, reference management tools, and recommender systems have evolved, many still rely heavily on metadata and lack mechanisms to incorporate full-text content or time-awareness. This review systematically examines the landscape of scholarly publication recommender systems, employing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology for a comprehensive and transparent selection of relevant studies. We highlight the limitations of current systems and explore the potential of integrating fine-grained citation knowledge—such as citation proximity, context, section, graph, and intention—extracted from full-text documents. These elements have shown promise in enhancing both the contextual relevance and recency of recommendations. Our findings highlight the importance of moving beyond accuracy-focused metrics toward user-centric evaluations that emphasise novelty, diversity, and serendipity. This paper advocates for the development of more holistic and adaptive recommender systems that better align with the evolving needs of researchers. Full article

46 pages, 7346 KB

Open AccessReview

Integrating Speech Recognition into Intelligent Information Systems: From Statistical Models to Deep Learning

by Chaoji Wu, Yi Pan, Haipan Wu and Lei Ning

Informatics 2025, 12(4), 107; https://doi.org/10.3390/informatics12040107 - 4 Oct 2025

Abstract

Automatic speech recognition (ASR) has advanced rapidly, evolving from early template-matching systems to modern deep learning frameworks. This review systematically traces ASR’s technological evolution across four phases: the template-based era, statistical modeling approaches, the deep learning revolution, and the emergence of large-scale models [...] Read more.

Automatic speech recognition (ASR) has advanced rapidly, evolving from early template-matching systems to modern deep learning frameworks. This review systematically traces ASR’s technological evolution across four phases: the template-based era, statistical modeling approaches, the deep learning revolution, and the emergence of large-scale models under diverse learning paradigms. We analyze core technologies such as hidden Markov models (HMMs), Gaussian mixture models (GMMs), recurrent neural networks (RNNs), and recent architectures including Transformer-based models and Wav2Vec 2.0. Beyond algorithmic development, we examine how ASR integrates into intelligent information systems, analyzing real-world applications in healthcare, education, smart homes, enterprise systems, and automotive domains with attention to deployment considerations and system design. We also address persistent challenges—noise robustness, low-resource adaptation, and deployment efficiency—while exploring emerging solutions such as multimodal fusion, privacy-preserving modeling, and lightweight architectures. Finally, we outline future research directions to guide the development of robust, scalable, and intelligent ASR systems for complex, evolving environments. Full article

(This article belongs to the Section Machine Learning)

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31 pages, 2508 KB

Open AccessSystematic Review

From Mammogram Analysis to Clinical Integration with Deep Learning in Breast Cancer Diagnosis

by Beibit Abdikenov, Tomiris Zhaksylyk, Aruzhan Imasheva and Dimash Rakishev

Informatics 2025, 12(4), 106; https://doi.org/10.3390/informatics12040106 - 2 Oct 2025

Abstract

Breast cancer is one of the main causes of cancer-related death for women worldwide, and enhancing patient outcomes still depends on early detection. The most common imaging technique for diagnosing and screening for breast cancer is mammography, which has a high potential for [...] Read more.

Breast cancer is one of the main causes of cancer-related death for women worldwide, and enhancing patient outcomes still depends on early detection. The most common imaging technique for diagnosing and screening for breast cancer is mammography, which has a high potential for early lesion detection. With an emphasis on the incorporation of deep learning (DL) techniques, this review examines the changing role of mammography in early breast cancer detection. We examine recent advancements in DL-based approaches for mammogram analysis, including tasks such as classification, segmentation, and lesion detection. Additionally, we assess the limitations of traditional mammographic methods and highlight how DL can enhance diagnostic accuracy, reduce false positives and negatives, and support clinical decision-making. The review emphasizes the potential of DL to assist radiologists in clinical decision-making, as well as increases in diagnostic accuracy and decreases in false positives and negatives. We also discuss issues like interpretability, generalization across populations, and data scarcity. This review summarizes the available data to highlight the revolutionary potential of DL-enhanced mammography in breast cancer screening and to suggest future research avenues for more reliable, transparent, and clinically useful AI-driven solutions. Full article

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

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15 pages, 1072 KB

Open AccessArticle

Balancing Layout Space and Risk Comprehension in Health Communication: A Comparison of Separated and Integrated Icon Arrays

by Li-Jen Wang and Meng-Cong Zheng

Informatics 2025, 12(4), 105; https://doi.org/10.3390/informatics12040105 - 30 Sep 2025

Abstract

This study investigated how icon array layouts influence comprehension of medical risk information, particularly in relation to users’ cognitive abilities. In a within-subjects experiment (N = 121), participants reviewed clinical scenarios with treatment-related risks and side effect risks displayed in either separated or [...] Read more.

This study investigated how icon array layouts influence comprehension of medical risk information, particularly in relation to users’ cognitive abilities. In a within-subjects experiment (N = 121), participants reviewed clinical scenarios with treatment-related risks and side effect risks displayed in either separated or integrated icon arrays. Comprehension was significantly higher for separated treatment-related risk layouts (p < 0.001), while side effect layout showed no effect. Numeracy and graph literacy significantly predicted comprehension. Crucially, individuals with lower numeracy showed marked gains when viewing separated formats, whereas those with higher numeracy performed well regardless of layout. Despite this, participants preferred hybrid formats—separated treatment-related risk with integrated side effect risks—revealing a critical preference–performance gap. By demonstrating how visual layout interacts with user abilities, this study provides actionable guidance for patient decision aid design. The findings show that comprehension accuracy must take precedence over layout compactness and user preference, with separated layouts recommended for treatment-related risks—especially for individuals with lower numeracy—and greater flexibility allowed for side effect risks when space is limited. Full article

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26 pages, 6045 KB

Open AccessArticle

Democratization of Virtual Production: Usability Analysis of Three Solutions with Different Levels of Complexity: Professional, Educational and Cloud-Based

by Roi Méndez-Fernández, Rocío del Pilar Sosa-Fernández, Fátima Fernández-Ledo and Enrique Castelló-Mayo

Informatics 2025, 12(4), 104; https://doi.org/10.3390/informatics12040104 - 30 Sep 2025

Abstract

The technical and technological advances of recent years in the field of real-time photorealistic rendering have enabled enormous development in virtual production. However, the democratization of this technology faces two main obstacles: the high economic cost of implementation and the high complexity of [...] Read more.

The technical and technological advances of recent years in the field of real-time photorealistic rendering have enabled enormous development in virtual production. However, the democratization of this technology faces two main obstacles: the high economic cost of implementation and the high complexity of the necessary software. This paper studies three virtual production software solutions that represent different stages in the democratization process of the technology, ranging from the professional software InfinitySet, to the more generalist and educational environment Edison, and to the cloud version of this same software, Edison OnCloud. To this end, an analysis of their functionalities and interfaces is conducted, the SUS questionnaire is applied, and the three systems are evaluated from the perspective of Nielsen’s usability principles. These tests demonstrate the complexity of the professional software InfinitySet, making it unapproachable for non-expert users without extensive previous training. On the other hand, both Edison and Edison OnCloud show significant usability improvements through limiting and reducing functionalities, which also results in a reduction in implementation costs, making the use of the technology feasible in non-professional environments, such as in education or for streamers. Full article

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

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30 pages, 2218 KB

Open AccessArticle

OntoCaimer: An Ontology Designed to Support Alzheimer’s Patient Care Systems

by Laura Daniela Lasso-Arcinegas, César Jesús Pardo-Calvache and Mauro Callejas-Cuervo

Informatics 2025, 12(4), 103; https://doi.org/10.3390/informatics12040103 - 25 Sep 2025

Abstract

Caring for Alzheimer’s patients presents significant global challenges due to complex symptoms and the constant demand for care, which are further complicated by fragmented information and a lack of explicit integration between physical and computational worlds in existing support systems. This article details [...] Read more.

Caring for Alzheimer’s patients presents significant global challenges due to complex symptoms and the constant demand for care, which are further complicated by fragmented information and a lack of explicit integration between physical and computational worlds in existing support systems. This article details the construction and validation of OntoCaimer, an ontology designed to support Alzheimer’s patient care systems by acting as a comprehensive knowledge base that integrates disease recommendations with concepts from the physical world (sensors and actuators). Utilizing METHONTOLOGY and REFSENO formalisms, OntoCaimer was built as a modular ontology. Its validation through the FOCA method demonstrated a high quality score

(\hat{μ} = 0.99)

, confirming its robustness and suitability. Case studies showcased its functionality in automating recommendations, such as managing patient locations or environmental conditions, to provide proactive support. The main contribution of this work is OntoCaimer, a novel ontology that formally integrates clinical recommendations for Alzheimer’s care with concepts from cyber-physical systems (sensors and actuators). Its scientific novelty lies in bridging the gap between virtual knowledge and physical action, enabling direct and automated interventions in the patient’s environment. This approach significantly advances patient care systems beyond traditional monitoring and alerts, offering a tangible path to reducing caregiver burden. Full article

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14 pages, 2125 KB

Open AccessArticle

A Self-Configurable IoT-Based Monitoring Approach for Environmental Variables in Rotational Grazing Systems

by Rodrigo Garcia, Mario Macea, Samir Castaño and Pedro Guevara

Informatics 2025, 12(4), 102; https://doi.org/10.3390/informatics12040102 - 24 Sep 2025

Abstract

Shaded resting zones in rotational grazing systems are prone to thermal stress due to limited ventilation and the congregation of animals during peak heat periods. Addressing these challenges requires sensing solutions that are not only accurate but also capable of adapting to dynamic [...] Read more.

Shaded resting zones in rotational grazing systems are prone to thermal stress due to limited ventilation and the congregation of animals during peak heat periods. Addressing these challenges requires sensing solutions that are not only accurate but also capable of adapting to dynamic environmental conditions and energy constraints. In this context, we present the development and simulation-based validation of a self-configurable IoT protocol for adaptive environmental monitoring. The approach integrates embedded machine learning, specifically a Random Forest classifier, to detect critical conditions using synthetic data of temperature, humidity, and CO₂. The model achieved an accuracy of 98%, with a precision of 98%, recall of 85%, and F1-score of 91% in identifying critical states. These results demonstrate the feasibility of embedding adaptive intelligence into IoT-based monitoring solutions. The protocol is conceived as a foundation for integration into physical devices and subsequent evaluation in farm environments such as rotational grazing systems. Full article

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24 pages, 5836 KB

Open AccessArticle

Methodology for Digitalizing Railway Vehicle Maintenance Training Using Augmented Reality

by Hwi-Jin Kwon, Ji-Hun Song, Kyung-Suk Kim and Chul-Su Kim

Informatics 2025, 12(4), 101; https://doi.org/10.3390/informatics12040101 - 23 Sep 2025

Abstract

The axle box of a railway vehicle is a critical component, and its maintenance involves complex procedures that are difficult to convey with traditional, document-based manuals. To address these challenges, augmented reality (AR)-based educational content was developed to digitize maintenance training and enhance [...] Read more.

The axle box of a railway vehicle is a critical component, and its maintenance involves complex procedures that are difficult to convey with traditional, document-based manuals. To address these challenges, augmented reality (AR)-based educational content was developed to digitize maintenance training and enhance its effectiveness. The content’s implementation was guided by a systematic storyboard, which was based on interviews with skilled staff. It also utilized specialized algorithms to improve the accuracy of mechanical measurement work and the efficiency of User Interface (UI) generation. The user experience of the developed content was comprehensively evaluated using a combination of two methods: a formative evaluation through direct observation of work performance and a post-survey administered to 40 participants. As a result of the evaluation, the mean work success rate was 62.5%, demonstrating the content’s high efficiency as a training tool. The overall mean score from the post-survey was 4.11, indicating high user satisfaction and perceived usefulness. A one-way ANOVA was performed and revealed a statistically significant difference in post-survey scores among the four age groups. The developed content was found to be more effective for younger participants. The results confirm the high potential of AR as a digital educational method for complex maintenance work. Full article

(This article belongs to the Special Issue Real-World Applications and Prototyping of Information Systems for Extended Reality (VR, AR, and MR))

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29 pages, 3613 KB

Open AccessArticle

CyberKG: Constructing a Cybersecurity Knowledge Graph Based on SecureBERT_Plus for CTI Reports

by Binyong Li, Qiaoxi Yang, Chuang Deng and Hua Pan

Informatics 2025, 12(3), 100; https://doi.org/10.3390/informatics12030100 - 22 Sep 2025

Abstract

Cyberattacks, especially Advanced Persistent Threats (APTs), have become more complex. These evolving threats challenge traditional defense systems, which struggle to counter long-lasting and covert attacks. Cybersecurity Knowledge Graphs (CKGs), enabled through the integration of multi-source CTI, introduce novel approaches for proactive defense. However, [...] Read more.

Cyberattacks, especially Advanced Persistent Threats (APTs), have become more complex. These evolving threats challenge traditional defense systems, which struggle to counter long-lasting and covert attacks. Cybersecurity Knowledge Graphs (CKGs), enabled through the integration of multi-source CTI, introduce novel approaches for proactive defense. However, building CKGs faces challenges such as unclear terminology, overlapping entity relationships in attack chains, and differences in CTI across sources. To tackle these challenges, we propose the CyberKG framework, which improves entity recognition and relation extraction using a SecureBERT_Plus-BiLSTM-Attention-CRF joint architecture. Semantic features are captured using a domain-adapted SecureBERT_Plus model, while temporal dependencies are modeled through BiLSTM. Attention mechanisms highlight key cross-sentence relationships, while CRF incorporates ATT&CK rule constraints. Hierarchical clustering (HAC), based on contextual embeddings, facilitates dynamic entity disambiguation and semantic fusion. Experimental evaluations on the DNRTI and MalwareDB datasets demonstrate strong performance in extraction accuracy, entity normalization, and the resolution of overlapping relations. The constructed knowledge graph supports APT tracking, attack-chain provenance, proactive defense prediction. Full article

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31 pages, 336 KB

Open AccessArticle

Talking Tech, Teaching with Tech: How Primary Teachers Implement Digital Technologies in Practice

by Lyubka Aleksieva, Veronica Racheva and Roumiana Peytcheva-Forsyth

Informatics 2025, 12(3), 99; https://doi.org/10.3390/informatics12030099 - 22 Sep 2025

Abstract

This paper explores how primary school teachers integrate digital technologies into their classroom practice, with a particular focus on the extent to which their stated intentions align with what actually takes place during lessons. Drawing on data from the Bulgarian SUMMIT project on [...] Read more.

This paper explores how primary school teachers integrate digital technologies into their classroom practice, with a particular focus on the extent to which their stated intentions align with what actually takes place during lessons. Drawing on data from the Bulgarian SUMMIT project on digital transformation in education, the study employed a mixed-methods design combining semi-structured interviews, structured lesson observations, and analysis of teaching materials. The sample included 44 teachers from 26 Bulgarian schools, representing a range of educational contexts. The analysis was guided by the Digital Technology Integration Framework (DTIF), which distinguishes between three modes of technology use—Support, Extend, and Transform—based on the depth of pedagogical change. The findings indicated a strong degree of consistency between teachers’ accounts and observed practices in areas such as the use of digital tools for content visualisation, lesson enrichment, and reinforcement of knowledge. At the same time, the study highlights important gaps between teachers’ aspirations and classroom realities. Although many spoke of wanting to promote independent exploration, creativity, collaboration, and digital citizenship, these ambitions were rarely realised in observed lessons. Pupil autonomy and opportunities for creative digital production were limited, with extended and transformative practices appearing only occasionally. No significant subject-specific differences were identified: teachers across disciplines tended to rely on the same set of familiar tools, while more advanced or innovative uses of technology remained rare. Rather than offering a definitive account of progress, the study raises critical questions about teachers’ digital pedagogical competencies, contextual constraints and the depth of technology integration in everyday classroom practice. While digital tools are increasingly present, their use often remains limited to supporting traditional instruction, with extended and transformative applications still aspirational rather than routine. The findings draw attention to context-specific challenges in the Bulgarian primary education system and the importance of aligning digital innovation with pedagogical intent. This highlights the need for sustained professional development focused on learner-centred digital pedagogies, along with stronger institutional support and equitable access to infrastructure. Full article

42 pages, 2077 KB

Open AccessSystematic Review

From E-Government to AI E-Government: A Systematic Review of Citizen Attitudes

by Ioanna Savveli, Maria Rigou and Stefanos Balaskas

Informatics 2025, 12(3), 98; https://doi.org/10.3390/informatics12030098 - 16 Sep 2025

Abstract

Governments increasingly integrate artificial intelligence (AI) into digital public services, and understanding how citizens perceive and respond to these technologies has become essential. This systematic review analyzes 30 empirical studies published from early January 2019 to mid-April 2025, following PRISMA guidelines, to map [...] Read more.

Governments increasingly integrate artificial intelligence (AI) into digital public services, and understanding how citizens perceive and respond to these technologies has become essential. This systematic review analyzes 30 empirical studies published from early January 2019 to mid-April 2025, following PRISMA guidelines, to map the current landscape of citizen attitudes toward AI-enabled e-government services. Guided by four research questions, the study examines: (1) the forms of AI implementation most commonly investigated, (2) the attitudinal variables used to assess user perception, (3) key factors influencing attitudes, and (4) concerns and challenges reported by users. The findings reveal that chatbots dominate current implementations, with behavioral intentions and satisfaction serving as the main outcome measures. Perceived usefulness, ease of use, trust, and perceived risk emerge as recurring determinants of positive attitudes. However, widespread concerns related to privacy and interface usability highlight persistent barriers. Overall, the review underscores the need for transparent, citizen-centered AI design and ethical safeguards to enhance acceptance and trust. It concludes that future research should address understudied applications, include vulnerable populations, and explore perceptions across diverse public sector domains. Full article

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15 pages, 545 KB

Open AccessArticle

The Impact of the 2023 Wikipedia Redesign on User Experience

by Tyler Wilson, Prajjwal Gandharv and Karl Vachuska

Informatics 2025, 12(3), 97; https://doi.org/10.3390/informatics12030097 - 16 Sep 2025

Abstract

In January 2023, Wikipedia introduced its most significant user interface (UI) redesign in over a decade, aiming to improve readability, accessibility, and navigation across devices. Despite the scale of this change, little empirical work has assessed its actual impact on user behavior. This [...] Read more.

In January 2023, Wikipedia introduced its most significant user interface (UI) redesign in over a decade, aiming to improve readability, accessibility, and navigation across devices. Despite the scale of this change, little empirical work has assessed its actual impact on user behavior. This study employs a natural experiment framework, leveraging Wikipedia’s exogenous, site-wide redesign date and large-scale, publicly available data—including clickstream, pageview, and edit histories—to evaluate user experience before and after the change. Using a quasi-experimental design, we estimate an immediate jump of ~1.06 million monthly internal link clicks at launch, while average hourly pageviews in January rose 1.25% despite a one-time –1.79 million dip at rollout. These results highlight the potential of large-scale UI changes to reshape user interaction without broadly alienating users and demonstrate the value of quasi-experimental methods for Human–Computer Interaction (HCI) research. Our approach offers a replicable framework for evaluating real-world design interventions at scale. Full article

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22 pages, 307 KB

Open AccessArticle

Digital Cultural Heritage in Southeast Asia: Knowledge Structures and Resources in GLAM Institutions

by Kanyarat Kwiecien, Wirapong Chansanam and Kulthida Tuamsuk

Informatics 2025, 12(3), 96; https://doi.org/10.3390/informatics12030096 - 15 Sep 2025

Abstract

This study explores the digital organization of cultural heritage knowledge across national GLAM institutions (galleries, libraries, archives, and museums) in the ten ASEAN countries. By employing a qualitative content analysis approach, this research study investigates the types, structures, and dissemination patterns of information [...] Read more.

This study explores the digital organization of cultural heritage knowledge across national GLAM institutions (galleries, libraries, archives, and museums) in the ten ASEAN countries. By employing a qualitative content analysis approach, this research study investigates the types, structures, and dissemination patterns of information resources available on 40 institutional websites. The findings reveal the diversity and richness of Southeast Asian cultural heritage, including national and local wisdom, history, significant figures, and material culture, collected and curated by these institutions. This study identifies key knowledge domains, content overlaps across GLAM sectors, and limitations in metadata and interoperability. Comparative analysis with international cultural knowledge infrastructures, such as the United Nations Educational Scientific and Cultural Organization (UNESCO)’s framework, Europeana, and the World Digital Library, highlights both shared values and regional distinctions. While GLAMs in the ASEAN have made significant strides in digital preservation and access, the lack of standardized metadata and cross-institutional integration impedes broader discoverability and reuse. This study contributes to the discourse on heritage informatics by providing an empirical foundation for enhancing digital cultural heritage systems in developing regions. The implications point toward the need for interoperable metadata standards, regional collaboration, and capacity building to support sustainable digital heritage ecosystems. This research study offers practical insights for policymakers, digital curators, and information professionals seeking to improve cultural knowledge infrastructures in Southeast Asia and similar contexts. Full article

22 pages, 3012 KB

Open AccessArticle

Deep Learning-Based Forecasting of Boarding Patient Counts to Address Emergency Department Overcrowding

by Orhun Vural, Bunyamin Ozaydin, James Booth, Brittany F. Lindsey and Abdulaziz Ahmed

Informatics 2025, 12(3), 95; https://doi.org/10.3390/informatics12030095 - 15 Sep 2025

Abstract

Emergency department (ED) overcrowding remains a major challenge for hospitals, resulting in worse outcomes, longer waits, elevated hospital operating costs, and greater strain on staff. Boarding count, the number of patients who have been admitted to an inpatient unit but are still in [...] Read more.

Emergency department (ED) overcrowding remains a major challenge for hospitals, resulting in worse outcomes, longer waits, elevated hospital operating costs, and greater strain on staff. Boarding count, the number of patients who have been admitted to an inpatient unit but are still in the ED waiting for transfer, is a key patient flow metric that affects overall ED operations. This study presents a deep learning-based approach to forecasting ED boarding counts using only operational and contextual features—derived from hourly ED tracking, inpatient census, weather, holiday, and local event data—without patient-level clinical information. Different deep learning algorithms were tested, including convolutional and transformer-based time-series models, and the best-performing model, Time Series Transformer Plus (TSTPlus), achieved strong performance at the 6-h prediction horizon, with a mean absolute error of 4.30 and an R² score of 0.79. After identifying TSTPlus as the best-performing model, its performance was further evaluated at additional horizons of 8, 10, and 12 h. The model was also evaluated under extreme operational conditions, demonstrating robust and accurate forecasts. These findings highlight the potential of the proposed forecasting approach to support proactive operational planning and reduce ED overcrowding. Full article

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

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38 pages, 3221 KB

Open AccessArticle

Simulating the Effects of Sensor Failures on Autonomous Vehicles for Safety Evaluation

by Francisco Matos, João Durães and João Cunha

Informatics 2025, 12(3), 94; https://doi.org/10.3390/informatics12030094 - 15 Sep 2025

Abstract

Autonomous vehicles (AVs) are increasingly becoming a reality, enabled by advances in sensing technologies, intelligent control systems, and real-time data processing. For AVs to operate safely and effectively, they must maintain a reliable perception of their surroundings and internal state. However, sensor failures, [...] Read more.

Autonomous vehicles (AVs) are increasingly becoming a reality, enabled by advances in sensing technologies, intelligent control systems, and real-time data processing. For AVs to operate safely and effectively, they must maintain a reliable perception of their surroundings and internal state. However, sensor failures, whether due to noise, malfunction, or degradation, can compromise this perception and lead to incorrect localization or unsafe decisions by the autonomous control system. While modern AV systems often combine data from multiple sensors to mitigate such risks through sensor fusion techniques (e.g., Kalman filtering), the extent to which these systems remain resilient under faulty conditions remains an open question. This work presents a simulation-based fault injection framework to assess the impact of sensor failures on AVs’ behavior. The framework enables structured testing of autonomous driving software under controlled fault conditions, allowing researchers to observe how specific sensor failures affect system performance. To demonstrate its applicability, an experimental campaign was conducted using the CARLA simulator integrated with the Autoware autonomous driving stack. A multi-segment urban driving scenario was executed using a modified version of CARLA’s Scenario Runner to support Autoware-based evaluations. Faults were injected simulating LiDAR, GNSS, and IMU sensor failures in different route scenarios. The fault types considered in this study include silent sensor failures and severe noise. The results obtained by emulating sensor failures in our chosen system under test, Autoware, show that faults in LiDAR and IMU gyroscope have the most critical impact, often leading to erratic motion and collisions. In contrast, faults in GNSS and IMU accelerometers were well tolerated. This demonstrates the ability of the framework to investigate the fault-tolerance of AVs in the presence of critical sensor failures. Full article

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26 pages, 3423 KB

Open AccessArticle

Federated Learning Spam Detection Based on FedProx and Multi-Level Multi-Feature Fusion

by Yunpeng Xiong, Junkuo Cao and Guolian Chen

Informatics 2025, 12(3), 93; https://doi.org/10.3390/informatics12030093 - 12 Sep 2025

Abstract

Traditional spam detection methodologies often neglect user privacy preservation, potentially incurring data leakage risks. Furthermore, current federated learning models for spam detection face several critical challenges: (1) data heterogeneity and instability during server-side parameter aggregation, (2) training instability in single neural network architectures [...] Read more.

Traditional spam detection methodologies often neglect user privacy preservation, potentially incurring data leakage risks. Furthermore, current federated learning models for spam detection face several critical challenges: (1) data heterogeneity and instability during server-side parameter aggregation, (2) training instability in single neural network architectures leading to mode collapse, and (3) constrained expressive capability in multi-module frameworks due to excessive complexity. These issues represent fundamental research pain points in federated learning-based spam detection systems. To address this technical challenge, this study innovatively integrates federated learning frameworks with multi-feature fusion techniques to propose a novel spam detection model, FPW-BC. The FPW-BC model addresses data distribution imbalance through the FedProx aggregation algorithm and enhances stability during server-side parameter aggregation via a horse-racing selection strategy. The model effectively mitigates limitations inherent in both single and multi-module architectures through hierarchical multi-feature fusion. To validate FPW-BC’s performance, comprehensive experiments were conducted on six benchmark datasets with distinct distribution characteristics: CEAS, Enron, Ling, Phishing_email, Spam_email, and Fake_phishing, with comparative analysis against multiple baseline methods. Experimental results demonstrate that FPW-BC achieves exceptional generalization capability for various spam patterns while maintaining user privacy preservation. The model attained 99.40% accuracy on CEAS and 99.78% on Fake_phishing, representing significant dual improvements in both privacy protection and detection efficiency. Full article

(This article belongs to the Topic Recent Advances in Artificial Intelligence for Security and Security for Artificial Intelligence)

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32 pages, 3323 KB

Open AccessArticle

A Data-Driven Informatics Framework for Regional Sustainability: Integrating Twin Mean-Variance Two-Stage DEA with Decision Analytics

by Pasura Aungkulanon, Roberto Montemanni, Atiwat Nanphang and Pongchanun Luangpaiboon

Informatics 2025, 12(3), 92; https://doi.org/10.3390/informatics12030092 - 11 Sep 2025

Abstract

This study introduces a novel informatics framework for assessing regional sustainability by integrating Twin Mean-Variance Two-Stage Data Envelopment Analysis (TMV-TSDEA) with a desirability-based decision analytics system. The model evaluates both the efficiency and stability of economic and environmental performance across regions, supporting evidence-based [...] Read more.

This study introduces a novel informatics framework for assessing regional sustainability by integrating Twin Mean-Variance Two-Stage Data Envelopment Analysis (TMV-TSDEA) with a desirability-based decision analytics system. The model evaluates both the efficiency and stability of economic and environmental performance across regions, supporting evidence-based policymaking and strategic planning. Applied to 16 Thai provinces, the framework incorporates a wide range of indicators—such as investment, population, tourism, industrial output, electricity use, forest coverage, and air quality. The twin mean-variance approach captures not only average efficiency but also the consistency of performance over time or under varying scenarios. A two-stage DEA structure models the transformation from economic inputs to environmental outcomes. To ensure comparability, all variables are normalized using desirability functions based on standardized statistical coding. The TMV-TSDEA framework generates composite performance scores that reveal clear disparities among regions. Provinces like Bangkok and Ayutthaya demonstrate a consistent high performance, while others show underperformance or variability requiring targeted policy action. Designed for integration with smart governance platforms, the framework provides a scalable and reproducible tool for regional benchmarking, resource allocation, and sustainability monitoring. By combining informatics principles with advanced analytics, TMV-TSDEA enhances transparency, supports decision-making, and offers a holistic foundation for sustainable regional development. Full article

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