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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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29 pages, 2873 KB  
Article
Hybrid FEM-AI Approach for Thermographic Monitoring of Biomedical Electronic Devices
by Danilo Pratticò, Domenico De Carlo, Gaetano Silipo and Filippo Laganà
Computers 2025, 14(9), 344; https://doi.org/10.3390/computers14090344 - 22 Aug 2025
Cited by 48 | Viewed by 3102
Abstract
Prolonged operation of biomedical devices may compromise electronic component integrity due to cyclic thermal stress, thereby impacting both functionality and safety. Regulatory standards require regular inspections, particularly for surgical applications, highlighting the need for efficient and non-invasive diagnostic tools. This study introduces an [...] Read more.
Prolonged operation of biomedical devices may compromise electronic component integrity due to cyclic thermal stress, thereby impacting both functionality and safety. Regulatory standards require regular inspections, particularly for surgical applications, highlighting the need for efficient and non-invasive diagnostic tools. This study introduces an integrated system that combines finite element models, infrared thermographic analysis, and artificial intelligence to monitor thermal stress in printed circuit boards (PCBs) within biomedical devices. A dynamic thermal model, implemented in COMSOL Multiphysics® (version 6.2), identifies regions at high risk of thermal overload. The infrared measurements acquired through a FLIR P660 thermal camera provided experimental validation and a dataset for training a hybrid artificial intelligence system. This model integrates deep learning-based U-Net architecture for thermal anomaly segmentation with machine learning classification of heat diffusion patterns. By combining simulation, the proposed system achieved an F1-score of 0.970 for hotspot segmentation using a U-Net architecture and an F1-score of 0.933 for the classification of heat propagation modes via a Multi-Layer Perceptron. This study contributes to the development of intelligent diagnostic tools for biomedical electronics by integrating physics-based simulation and AI-driven thermographic analysis, supporting automatic classification and localisation of thermal anomalies, real-time fault detection and predictive maintenance strategies. Full article
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20 pages, 1320 KB  
Article
A Method for Few-Shot Modulation Recognition Based on Reinforcement Metric Meta-Learning
by Fan Zhou, Xiao Han, Jinyang Ren, Wei Wang, Yang Wang, Peiying Zhang and Shaolin Liao
Computers 2025, 14(9), 346; https://doi.org/10.3390/computers14090346 - 22 Aug 2025
Cited by 1 | Viewed by 1728
Abstract
In response to the problem where neural network models fail to fully learn signal sample features due to an insufficient number of signal samples, leading to a decrease in the model’s ability to recognize signal modulation methods, a few-shot signal modulation mode recognition [...] Read more.
In response to the problem where neural network models fail to fully learn signal sample features due to an insufficient number of signal samples, leading to a decrease in the model’s ability to recognize signal modulation methods, a few-shot signal modulation mode recognition method based on reinforcement metric meta-learning (RMML) is proposed. This approach, grounded in meta-learning techniques, employs transfer learning to building a feature extraction network that effectively extracts the data features under few-shot conditions. Building on this, by integrating the measurement of features of similar samples and the differences between features of different classes of samples, the metric network’s target loss function is optimized, thereby improving the network’s ability to distinguish between features of different modulation methods. The experimental results demonstrate that this method exhibits a good performance in processing new class signals that have not been previously trained. Under the condition of 5-way 5-shot, when the signal-to-noise ratio (SNR) is 0 dB, this method can achieve an average recognition accuracy of 91.8%, which is 2.8% higher than that of the best-performing baseline method, whereas when the SNR is 18 dB, the model’s average recognition accuracy significantly improves to 98.5%. Full article
(This article belongs to the Special Issue Wireless Sensor Networks in IoT)
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16 pages, 3704 KB  
Article
Optimization of Scene and Material Parameters for the Generation of Synthetic Training Datasets for Machine Learning-Based Object Segmentation
by Malte Nagel, Kolja Hedrich, Nils Melchert, Lennart Hinz and Eduard Reithmeier
Computers 2025, 14(8), 341; https://doi.org/10.3390/computers14080341 - 21 Aug 2025
Viewed by 1368
Abstract
Synthetic training data is often essential for neural-network-based segmentation when real datasets are difficult or impossible to obtain. Conventional synthetic data generation relies on manually selecting scene and material parameters. This can lead to poor performance because the optimal parameters are often non-intuitive [...] Read more.
Synthetic training data is often essential for neural-network-based segmentation when real datasets are difficult or impossible to obtain. Conventional synthetic data generation relies on manually selecting scene and material parameters. This can lead to poor performance because the optimal parameters are often non-intuitive and depend heavily on the specific use case and on the objects to be segmented. This study proposes a novel, automated optimization pipeline to improve the quality of synthetic datasets for specific object segmentation tasks. Synthetic datasets are generated by varying material and scene parameters with the BlenderProc framework. These parameters are optimized with the Optuna framework to maximize the average precision achieved by models trained on this data and validated using a small real dataset. After initial single-parameter studies and subsequent multidimensional optimization, optimal scene and material parameters are identified for each object. The results demonstrate the potential of this optimization pipeline to produce synthetic training datasets that enhance neural network performance for specific segmentation tasks, offering insights into the critical role of scene design and material selection in synthetic data generation. Full article
(This article belongs to the Special Issue Operations Research: Trends and Applications)
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54 pages, 6926 KB  
Review
A Comprehensive Review of Sensor Technologies in IoT: Technical Aspects, Challenges, and Future Directions
by Sadiq H. Abdulhussain, Basheera M. Mahmmod, Almuntadher Alwhelat, Dina Shehada, Zainab I. Shihab, Hala J. Mohammed, Tuqa H. Abdulameer, Muntadher Alsabah, Maryam H. Fadel, Susan K. Ali, Ghadeer H. Abbood, Zianab A. Asker and Abir Hussain
Computers 2025, 14(8), 342; https://doi.org/10.3390/computers14080342 - 21 Aug 2025
Cited by 60 | Viewed by 23729
Abstract
The rapid advancements in wireless technology and digital electronics have led to the widespread adoption of compact, intelligent devices in various aspects of daily life. These advanced systems possess the capability to sense environmental changes, process data, and communicate seamlessly within interconnected networks. [...] Read more.
The rapid advancements in wireless technology and digital electronics have led to the widespread adoption of compact, intelligent devices in various aspects of daily life. These advanced systems possess the capability to sense environmental changes, process data, and communicate seamlessly within interconnected networks. Typically, such devices integrate low-power radio transmitters and multiple smart sensors, hence enabling efficient functionality across wide ranges of applications. Alongside these technological developments, the concept of the IoT has emerged as a transformative paradigm, facilitating the interconnection of uniquely identifiable devices through internet-based networks. This paper aims to provide a comprehensive exploration of sensor technologies, detailing their integral role within IoT frameworks and examining their impact on optimizing efficiency and service delivery in modern wireless communications systems. Also, it presents a thorough review of sensor technologies, current research trends, and the associated challenges in this evolving field, providing a detailed explanation of recent advancements and IoT-integrated sensor systems, with a particular emphasis on the fundamental architecture of sensors and their pivotal role in modern technological applications. It explores the core benefits of sensor technologies and delivers an in-depth classification of their fundamental types. Beyond reviewing existing developments, this study identifies key open research challenges and outlines prospective directions for future exploration, offering valuable insights for both academic researchers and industry professionals. Ultimately, this paper serves as an essential reference for understanding sensor technologies and their potential contributions to IoT-driven solutions. This study offers meaningful contributions to academic and industrial sectors, facilitating advancements in sensor innovation. Full article
(This article belongs to the Special Issue The Internet of Things—Current Trends, Applications, and Future Challenges (2nd Edition))
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23 pages, 2044 KB  
Article
Topic Modeling of Positive and Negative Reviews of Soulslike Video Games
by Tibor Guzsvinecz
Computers 2025, 14(8), 339; https://doi.org/10.3390/computers14080339 - 19 Aug 2025
Cited by 2 | Viewed by 3830
Abstract
Soulslike games are renowned for their challenging gameplay and distinctive design. To examine player reception of this genre, 993,932 user reviews of 21 Soulslike video games were collected from the Steam platform, of which 418,483 were tagged as English and analyzed. Latent Dirichlet [...] Read more.
Soulslike games are renowned for their challenging gameplay and distinctive design. To examine player reception of this genre, 993,932 user reviews of 21 Soulslike video games were collected from the Steam platform, of which 418,483 were tagged as English and analyzed. Latent Dirichlet Allocation (LDA) was applied to identify and compare thematic patterns across positive and negative reviews. The resulting topics were grouped into five categories: aesthetics, gameplay mechanics, feelings, bugs/issues, and miscellaneous. Positive reviews emphasized aesthetics and atmosphere, whereas negative reviews focused on gameplay mechanics and technical issues. Notably, emotional tone differed significantly between review types. Overall, these results may benefit game developers refining design elements, researchers investigating player experience, and critics analyzing the reception of Soulslike games. Furthermore, the study provides a basis for understanding player perspectives in Soulslike games and establishes a foundation for comparative research with newer titles such as Elden Ring. Full article
(This article belongs to the Section Human–Computer Interactions)
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21 pages, 806 KB  
Tutorial
Multi-Layered Framework for LLM Hallucination Mitigation in High-Stakes Applications: A Tutorial
by Sachin Hiriyanna and Wenbing Zhao
Computers 2025, 14(8), 332; https://doi.org/10.3390/computers14080332 - 16 Aug 2025
Cited by 5 | Viewed by 10953
Abstract
Large language models (LLMs) now match or exceed human performance on many open-ended language tasks, yet they continue to produce fluent but incorrect statements, which is a failure mode widely referred to as hallucination. In low-stakes settings this may be tolerable; in regulated [...] Read more.
Large language models (LLMs) now match or exceed human performance on many open-ended language tasks, yet they continue to produce fluent but incorrect statements, which is a failure mode widely referred to as hallucination. In low-stakes settings this may be tolerable; in regulated or safety-critical domains such as financial services, compliance review, and client decision support, it is not. Motivated by these realities, we develop an integrated mitigation framework that layers complementary controls rather than relying on any single technique. The framework combines structured prompt design, retrieval-augmented generation (RAG) with verifiable evidence sources, and targeted fine-tuning aligned with domain truth constraints. Our interest in this problem is practical. Individual mitigation techniques have matured quickly, yet teams deploying LLMs in production routinely report difficulty stitching them together in a coherent, maintainable pipeline. Decisions about when to ground a response in retrieved data, when to escalate uncertainty, how to capture provenance, and how to evaluate fidelity are often made ad hoc. Drawing on experience from financial technology implementations, where even rare hallucinations can carry material cost, regulatory exposure, or loss of customer trust, we aim to provide clearer guidance in the form of an easy-to-follow tutorial. This paper makes four contributions. First, we introduce a three-layer reference architecture that organizes mitigation activities across input governance, evidence-grounded generation, and post-response verification. Second, we describe a lightweight supervisory agent that manages uncertainty signals and triggers escalation (to humans, alternate models, or constrained workflows) when confidence falls below policy thresholds. Third, we analyze common but under-addressed security surfaces relevant to hallucination mitigation, including prompt injection, retrieval poisoning, and policy evasion attacks. Finally, we outline an implementation playbook for production deployment, including evaluation metrics, operational trade-offs, and lessons learned from early financial-services pilots. Full article
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25 pages, 28917 KB  
Article
Synthetic Data-Driven Methods to Accelerate the Deployment of Deep Learning Models: A Case Study on Pest and Disease Detection in Precision Viticulture
by Telmo Adão, Agnieszka Chojka, David Pascoal, Nuno Silva, Raul Morais and Emanuel Peres
Computers 2025, 14(8), 327; https://doi.org/10.3390/computers14080327 - 13 Aug 2025
Cited by 6 | Viewed by 2229
Abstract
The development of reliable visual inference models is often constrained by the burdensome and time-consuming processes involved in collecting and annotating high-quality datasets. This challenge becomes more acute in domains where key phenomena are time-dependent or event-driven, narrowing the opportunity window to capture [...] Read more.
The development of reliable visual inference models is often constrained by the burdensome and time-consuming processes involved in collecting and annotating high-quality datasets. This challenge becomes more acute in domains where key phenomena are time-dependent or event-driven, narrowing the opportunity window to capture representative observations. Yet, accelerating the deployment of deep learning (DL) models is crucial to support timely, data-driven decision-making in operational settings. To tackle such an issue, this paper explores the use of 2D synthetic data grounded in real-world patterns to train initial DL models in contexts where annotated datasets are scarce or can only be acquired within restrictive time windows. Two complementary approaches to synthetic data generation are investigated: rule-based digital image processing and advanced text-to-image generative diffusion models. These methods can operate independently or be combined to enhance flexibility and coverage. A proof-of-concept is presented through a couple case studies in precision viticulture, a domain often constrained by seasonal dependencies and environmental variability. Specifically, the detection of Lobesia botrana in sticky traps and the classification of grapevine foliar symptoms associated with black rot, ESCA, and leaf blight are addressed. The results suggest that the proposed approach potentially accelerates the deployment of preliminary DL models by comprehensively automating the production of context-aware datasets roughly inspired by specific challenge-driven operational settings, thereby mitigating the need for time-consuming and labor-intensive processes, from image acquisition to annotation. Although models trained on such synthetic datasets require further refinement—for example, through active learning—the approach offers a scalable and functional solution that reduces human involvement, even in scenarios of data scarcity, and supports the effective transition of laboratory-developed AI to real-world deployment environments. Full article
(This article belongs to the Special Issue Machine Learning and Statistical Learning with Applications 2025)
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31 pages, 2730 KB  
Article
Cybersecurity Threats in Saudi Healthcare: Exploring Email Communication Practices to Enhance Cybersecurity Among Healthcare Employees in Saudi Arabia
by Ebtesam Shadadi, Rasha Ibrahim and Essam Ghadafi
Computers 2025, 14(8), 324; https://doi.org/10.3390/computers14080324 - 12 Aug 2025
Cited by 1 | Viewed by 5383
Abstract
As cyber threats such as phishing and ransomware continue to escalate, healthcare systems are facing significant challenges in protecting sensitive data and ensuring operational continuity. This study explores how email communication practices influence cybersecurity in Saudi Arabia’s healthcare sector, particularly within the framework [...] Read more.
As cyber threats such as phishing and ransomware continue to escalate, healthcare systems are facing significant challenges in protecting sensitive data and ensuring operational continuity. This study explores how email communication practices influence cybersecurity in Saudi Arabia’s healthcare sector, particularly within the framework of rapid digitalisation under Vision 2030. The research employs a qualitative approach, with semi-structured interviews conducted with 40 healthcare professionals across various hospitals. A phenomenological analysis of the data revealed several key vulnerabilities, including inconsistent cybersecurity training, a reliance on informal messaging apps, and limited awareness of phishing tactics. The inconsistent cybersecurity training across regions emerged as a major weakness affecting overall resilience. These findings, grounded in rich qualitative data, offer a significant standalone contribution to understanding cybersecurity in healthcare settings. The findings highlight the need for mandatory training and awareness programmes and policy reforms to enhance cyber resilience within healthcare settings. Full article
(This article belongs to the Section Human–Computer Interactions)
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45 pages, 3405 KB  
Article
Electric Network Frequency as Environmental Fingerprint for Metaverse Security: A Comprehensive Survey
by Mohsen Hatami, Lhamo Dorje, Xiaohua Li and Yu Chen
Computers 2025, 14(8), 321; https://doi.org/10.3390/computers14080321 - 8 Aug 2025
Cited by 2 | Viewed by 3154
Abstract
The rapid expansion of the Metaverse presents complex security challenges, particularly in verifying virtual objects and avatars within immersive environments. Conventional authentication methods, such as passwords and biometrics, often prove inadequate in these dynamic environments, especially as essential infrastructures, such as smart grids, [...] Read more.
The rapid expansion of the Metaverse presents complex security challenges, particularly in verifying virtual objects and avatars within immersive environments. Conventional authentication methods, such as passwords and biometrics, often prove inadequate in these dynamic environments, especially as essential infrastructures, such as smart grids, integrate with virtual platforms. Cybersecurity threats intensify as advanced attacks introduce fraudulent data, compromising system reliability and safety. Using the Electric Network Frequency (ENF), a naturally varying signal emitted from power grids, provides an innovative environmental fingerprint to authenticate digital twins and Metaverse entities in the smart grid. This paper provides a comprehensive survey of the ENF as an environmental fingerprint for enhancing Metaverse security, reviewing its characteristics, sensing methods, limitations, and applications in threat modeling and the CIA triad (Confidentiality, Integrity, and Availability), and presents a real-world case study to demonstrate its effectiveness in practical settings. By capturing the ENF as having a unique signature that is timestamped, this method strengthens security by directly correlating physical grid behavior and virtual interactions, effectively combating threats such as deepfake manipulations. Building upon recent developments in signal processing, this strategy reinforces the integrity of digital environments, delivering robust protection against evolving cyber–physical risks and facilitating secure, scalable virtual infrastructures. Full article
(This article belongs to the Special Issue Using New Technologies in Cyber Security Solutions (2nd Edition))
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29 pages, 2673 KB  
Review
Integrating Large Language Models into Digital Manufacturing: A Systematic Review and Research Agenda
by Chourouk Ouerghemmi and Myriam Ertz
Computers 2025, 14(8), 318; https://doi.org/10.3390/computers14080318 - 7 Aug 2025
Cited by 13 | Viewed by 8627
Abstract
Industries 4.0 and 5.0 are based on technological advances, notably large language models (LLMs), which are making a significant contribution to the transition to smart factories. Although considerable research has explored this phenomenon, the literature remains fragmented and lacks an integrative framework that [...] Read more.
Industries 4.0 and 5.0 are based on technological advances, notably large language models (LLMs), which are making a significant contribution to the transition to smart factories. Although considerable research has explored this phenomenon, the literature remains fragmented and lacks an integrative framework that highlights the multifaceted implications of using LLMs in the context of digital manufacturing. To address this limitation, we conducted a systematic literature review, analyzing 53 papers selected according to predefined inclusion and exclusion criteria. Our descriptive and thematic analyses, respectively, mapped new trends and identified emerging themes, classified into three axes: (1) manufacturing process optimization, (2) data structuring and innovation, and (3) human–machine interaction and ethical challenges. Our results revealed that LLMs can enhance operational performance and foster innovation while redistributing human roles. Our research offers an in-depth understanding of the implications of LLMs. Finally, we propose a future research agenda to guide future studies. Full article
(This article belongs to the Special Issue AI in Complex Engineering Systems)
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31 pages, 1583 KB  
Article
Ensuring Zero Trust in GDPR-Compliant Deep Federated Learning Architecture
by Zahra Abbas, Sunila Fatima Ahmad, Adeel Anjum, Madiha Haider Syed, Saif Ur Rehman Malik and Semeen Rehman
Computers 2025, 14(8), 317; https://doi.org/10.3390/computers14080317 - 4 Aug 2025
Cited by 5 | Viewed by 3665
Abstract
Deep Federated Learning (DFL) revolutionizes machine learning (ML) by enabling collaborative model training across diverse, decentralized data sources without direct data sharing, emphasizing user privacy and data sovereignty. Despite its potential, DFL’s application in sensitive sectors is hindered by challenges in meeting rigorous [...] Read more.
Deep Federated Learning (DFL) revolutionizes machine learning (ML) by enabling collaborative model training across diverse, decentralized data sources without direct data sharing, emphasizing user privacy and data sovereignty. Despite its potential, DFL’s application in sensitive sectors is hindered by challenges in meeting rigorous standards like the GDPR, with traditional setups struggling to ensure compliance and maintain trust. Addressing these issues, our research introduces an innovative Zero Trust-based DFL architecture designed for GDPR compliant systems, integrating advanced security and privacy mechanisms to ensure safe and transparent cross-node data processing. Our base paper proposed the basic GDPR-Compliant DFL Architecture. Now we validate the previously proposed architecture by formally verifying it using High-Level Petri Nets (HLPNs). This Zero Trust-based framework facilitates secure, decentralized model training without direct data sharing. Furthermore, we have also implemented a case study using the MNIST and CIFAR-10 datasets to evaluate the existing approach with the proposed Zero Trust-based DFL methodology. Our experiments confirmed its effectiveness in enhancing trust, complying with GDPR, and promoting DFL adoption in privacy-sensitive areas, achieving secure, ethical Artificial Intelligence (AI) with transparent and efficient data processing. Full article
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15 pages, 1515 KB  
Article
Ontology-Based Data Pipeline for Semantic Reaction Classification and Research Data Management
by Hendrik Borgelt, Frederick Gabriel Kitel and Norbert Kockmann
Computers 2025, 14(8), 311; https://doi.org/10.3390/computers14080311 - 1 Aug 2025
Cited by 1 | Viewed by 1824
Abstract
Catalysis research is complex and interdisciplinary, involving diverse physical effects and challenging data practices. Research data often captures only selected aspects, such as specific reactants and products, limiting its utility for machine learning and the implementation of FAIR (Findable, Accessible, Interoperable, Reusable) workflows. [...] Read more.
Catalysis research is complex and interdisciplinary, involving diverse physical effects and challenging data practices. Research data often captures only selected aspects, such as specific reactants and products, limiting its utility for machine learning and the implementation of FAIR (Findable, Accessible, Interoperable, Reusable) workflows. To improve this, semantic structuring through ontologies is essential. This work extends the established ontologies by refining logical relations and integrating semantic tools such as the Web Ontology Language or the Shape Constraint Language. It incorporates application programming interfaces from chemical databases, such as the Kyoto Encyclopedia of Genes and Genomes and the National Institute of Health’s PubChem database, and builds upon established ontologies. A key innovation lies in automatically decomposing chemical substances through database entries and chemical identifier representations to identify functional groups, enabling more generalized reaction classification. Using new semantic functionality, functional groups are flexibly addressed, improving the classification of reactions such as saponification and ester cleavage with simultaneous oxidation. A graphical interface (GUI) supports user interaction with the knowledge graph, enabling ontological reasoning and querying. This approach demonstrates improved specificity of the newly established ontology over its predecessors and offers a more user-friendly interface for engaging with structured chemical knowledge. Future work will focus on expanding ontology coverage to support a wider range of reactions in catalysis research. Full article
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30 pages, 7223 KB  
Article
Smart Wildlife Monitoring: Real-Time Hybrid Tracking Using Kalman Filter and Local Binary Similarity Matching on Edge Network
by Md. Auhidur Rahman, Stefano Giordano and Michele Pagano
Computers 2025, 14(8), 307; https://doi.org/10.3390/computers14080307 - 30 Jul 2025
Cited by 8 | Viewed by 4564
Abstract
Real-time wildlife monitoring on edge devices poses significant challenges due to limited power, constrained bandwidth, and unreliable connectivity, especially in remote natural habitats. Conventional object detection systems often transmit redundant data of the same animals detected across multiple consecutive frames as a part [...] Read more.
Real-time wildlife monitoring on edge devices poses significant challenges due to limited power, constrained bandwidth, and unreliable connectivity, especially in remote natural habitats. Conventional object detection systems often transmit redundant data of the same animals detected across multiple consecutive frames as a part of a single event, resulting in increased power consumption and inefficient bandwidth usage. Furthermore, maintaining consistent animal identities in the wild is difficult due to occlusions, variable lighting, and complex environments. In this study, we propose a lightweight hybrid tracking framework built on the YOLOv8m deep neural network, combining motion-based Kalman filtering with Local Binary Pattern (LBP) similarity for appearance-based re-identification using texture and color features. To handle ambiguous cases, we further incorporate Hue-Saturation-Value (HSV) color space similarity. This approach enhances identity consistency across frames while reducing redundant transmissions. The framework is optimized for real-time deployment on edge platforms such as NVIDIA Jetson Orin Nano and Raspberry Pi 5. We evaluate our method against state-of-the-art trackers using event-based metrics such as MOTA, HOTA, and IDF1, with a focus on detected animals occlusion handling, trajectory analysis, and counting during both day and night. Our approach significantly enhances tracking robustness, reduces ID switches, and provides more accurate detection and counting compared to existing methods. When transmitting time-series data and detected frames, it achieves up to 99.87% bandwidth savings and 99.67% power reduction, making it highly suitable for edge-based wildlife monitoring in resource-constrained environments. Full article
(This article belongs to the Special Issue Intelligent Edge: When AI Meets Edge Computing)
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18 pages, 3347 KB  
Article
Assessment of Machine Learning-Driven Retrievals of Arctic Sea Ice Thickness from L-Band Radiometry Remote Sensing
by Ferran Hernández-Macià, Gemma Sanjuan Gomez, Carolina Gabarró and Maria José Escorihuela
Computers 2025, 14(8), 305; https://doi.org/10.3390/computers14080305 - 28 Jul 2025
Cited by 1 | Viewed by 1768
Abstract
This study evaluates machine learning-based methods for retrieving thin Arctic sea ice thickness (SIT) from L-band radiometry, using data from the European Space Agency’s (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite. In addition to the operational ESA product, three alternative approaches are [...] Read more.
This study evaluates machine learning-based methods for retrieving thin Arctic sea ice thickness (SIT) from L-band radiometry, using data from the European Space Agency’s (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite. In addition to the operational ESA product, three alternative approaches are assessed: a Random Forest (RF) algorithm, a Convolutional Neural Network (CNN) that incorporates spatial coherence, and a Long Short-Term Memory (LSTM) neural network designed to capture temporal coherence. Validation against in situ data from the Beaufort Gyre Exploration Project (BGEP) moorings and the ESA SMOSice campaign demonstrates that the RF algorithm achieves robust performance comparable to the ESA product, despite its simplicity and lack of explicit spatial or temporal modeling. The CNN exhibits a tendency to overestimate SIT and shows higher dispersion, suggesting limited added value when spatial coherence is already present in the input data. The LSTM approach does not improve retrieval accuracy, likely due to the mismatch between satellite resolution and the temporal variability of sea ice conditions. These results highlight the importance of L-band sea ice emission modeling over increasing algorithm complexity and suggest that simpler, adaptable methods such as RF offer a promising foundation for future SIT retrieval efforts. The findings are relevant for refining current methods used with SMOS and for developing upcoming satellite missions, such as ESA’s Copernicus Imaging Microwave Radiometer (CIMR). Full article
(This article belongs to the Special Issue Machine Learning and Statistical Learning with Applications 2025)
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24 pages, 569 KB  
Systematic Review
Artificial Intelligence Approach for Waste-Printed Circuit Board Recycling: A Systematic Review
by Muhammad Mohsin, Stefano Rovetta, Francesco Masulli and Alberto Cabri
Computers 2025, 14(8), 304; https://doi.org/10.3390/computers14080304 - 27 Jul 2025
Cited by 9 | Viewed by 3913
Abstract
The rapid advancement of technology has led to a substantial increase in Waste Electrical and Electronic Equipment (WEEE), which poses significant environmental threats and increases pressure on the planet’s limited natural resources. In response, Artificial Intelligence (AI) has emerged as a key enabler [...] Read more.
The rapid advancement of technology has led to a substantial increase in Waste Electrical and Electronic Equipment (WEEE), which poses significant environmental threats and increases pressure on the planet’s limited natural resources. In response, Artificial Intelligence (AI) has emerged as a key enabler of the Circular Economy (CE), particularly in improving the speed and precision of waste sorting through machine learning and computer vision techniques. Despite this progress, to our knowledge, no comprehensive, systematic review has focused specifically on the role of AI in disassembling and recycling Waste-Printed Circuit Boards (WPCBs). This paper addresses this gap by systematically reviewing recent advancements in AI-driven disassembly and sorting approaches with a focus on machine learning and vision-based methodologies. The review is structured around three areas: (1) the availability and use of datasets for AI-based WPCB recycling; (2) state-of-the-art techniques for selective disassembly and component recognition to enable fast WPCB recycling; and (3) key challenges and possible solutions aimed at enhancing the recovery of critical raw materials (CRMs) from WPCBs. Full article
(This article belongs to the Special Issue Advanced Image Processing and Computer Vision (2nd Edition))
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37 pages, 2776 KB  
Article
Design of Identical Strictly and Rearrangeably Nonblocking Folded Clos Networks with Equally Sized Square Crossbars
by Yamin Li
Computers 2025, 14(7), 293; https://doi.org/10.3390/computers14070293 - 20 Jul 2025
Viewed by 2117
Abstract
Clos networks and their folded versions, fat trees, are widely adopted in interconnection network designs for data centers and supercomputers. There are two main types of Clos networks: strictly nonblocking Clos networks and rearrangeably nonblocking Clos networks. Strictly nonblocking Clos networks can connect [...] Read more.
Clos networks and their folded versions, fat trees, are widely adopted in interconnection network designs for data centers and supercomputers. There are two main types of Clos networks: strictly nonblocking Clos networks and rearrangeably nonblocking Clos networks. Strictly nonblocking Clos networks can connect an idle input to an idle output without interfering with existing connections. Rearrangeably nonblocking Clos networks can connect an idle input to an idle output with rearrangements of existing connections. Traditional strictly nonblocking Clos networks have two drawbacks. One drawback is the use of crossbars with different numbers of input and output ports, whereas the currently available switches are square crossbars with the same number of input and output ports. Another drawback is that every connection goes through a fixed number of stages, increasing the length of the communication path. A drawback of traditional fat trees is that the root stage uses differently sized crossbar switches than the other stages. To solve these problems, this paper proposes an Identical Strictly NonBlocking folded Clos (ISNBC) network that uses equally sized square crossbars for all switches. Correspondingly, this paper also proposes an Identical Rearrangeably NonBlocking folded Clos (IRNBC) network. Both ISNBC and IRNBC networks can have any number of stages, can use equally sized square crossbars with no unused switch ports, and can utilize shortcut connections to reduce communication path lengths. Moreover, both ISNBC and IRNBC networks have a lower switch crosspoint cost ratio relative to a single crossbar than their corresponding traditional Clos networks. Specifically, ISNBC networks use 46.43% to 87.71% crosspoints of traditional strictly nonblocking folded Clos networks, and IRNBC networks use 53.85% to 60.00% crosspoints of traditional rearrangeably nonblocking folded Clos networks. Full article
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18 pages, 2423 KB  
Article
A New AI Framework to Support Social-Emotional Skills and Emotion Awareness in Children with Autism Spectrum Disorder
by Andrea La Fauci De Leo, Pooneh Bagheri Zadeh, Kiran Voderhobli and Akbar Sheikh Akbari
Computers 2025, 14(7), 292; https://doi.org/10.3390/computers14070292 - 20 Jul 2025
Cited by 2 | Viewed by 6569
Abstract
This research highlights the importance of Emotion Aware Technologies (EAT) and their implementation in serious games to assist children with Autism Spectrum Disorder (ASD) in developing social-emotional skills. As AI is gaining popularity, such tools can be used in mobile applications as invaluable [...] Read more.
This research highlights the importance of Emotion Aware Technologies (EAT) and their implementation in serious games to assist children with Autism Spectrum Disorder (ASD) in developing social-emotional skills. As AI is gaining popularity, such tools can be used in mobile applications as invaluable teaching tools. In this paper, a new AI framework application is discussed that will help children with ASD develop efficient social-emotional skills. It uses the Jetpack Compose framework and Google Cloud Vision API as emotion-aware technology. The framework is developed with two main features designed to help children reflect on their emotions, internalise them, and train them how to express these emotions. Each activity is based on similar features from literature with enhanced functionalities. A diary feature allows children to take pictures of themselves, and the application categorises their facial expressions, saving the picture in the appropriate space. The three-level minigame consists of a series of prompts depicting a specific emotion that children have to match. The results of the framework offer a good starting point for similar applications to be developed further, especially by training custom models to be used with ML Kit. Full article
(This article belongs to the Special Issue AI in Its Ecosystem)
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21 pages, 2105 KB  
Article
Implementing Virtual Reality for Fire Evacuation Preparedness at Schools
by Rashika Tasnim Keya, Ilona Heldal, Daniel Patel, Pietro Murano and Cecilia Hammar Wijkmark
Computers 2025, 14(7), 286; https://doi.org/10.3390/computers14070286 - 18 Jul 2025
Cited by 3 | Viewed by 3041
Abstract
Emergency preparedness training in organizations frequently involves simple evacuation drills triggered by fire alarms, limiting the opportunities for broader skill development. Digital technologies, particularly virtual reality (VR), offer promising methods to enhance learning for handling incidents and evacuations. However, implementing VR-based training remains [...] Read more.
Emergency preparedness training in organizations frequently involves simple evacuation drills triggered by fire alarms, limiting the opportunities for broader skill development. Digital technologies, particularly virtual reality (VR), offer promising methods to enhance learning for handling incidents and evacuations. However, implementing VR-based training remains challenging due to unclear integration strategies within organizational practices and a lack of empirical evidence of VR’s effectiveness. This paper explores how VR-based training tools can be implemented in schools to enhance emergency preparedness among students, teachers, and staff. Following a design science research process, data were collected from a questionnaire-based study involving 12 participants and an exploratory study with 13 participants. The questionnaire-based study investigates initial attitudes and willingness to adopt VR training, while the exploratory study assesses the VR prototype’s usability, realism, and perceived effectiveness for emergency preparedness training. Despite a limited sample size and technical constraints of the early prototype, findings indicate strong student enthusiasm for gamified and immersive learning experiences. Teachers emphasized the need for technical and instructional support to regularly utilize VR training modules, while firefighters acknowledged the potential of VR tools, but also highlighted the critical importance of regular drills and professional validation. The relevance of the results of utilizing VR in this context is further discussed in terms of how it can be integrated into university curricula and aligned with other accessible digital preparedness tools. Full article
(This article belongs to the Special Issue Extended or Mixed Reality (AR + VR): Technology and Applications (2nd Edition))
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23 pages, 2250 KB  
Article
Machine Learning Techniques for Uncertainty Estimation in Dynamic Aperture Prediction
by Carlo Emilio Montanari, Robert B. Appleby, Davide Di Croce, Massimo Giovannozzi, Tatiana Pieloni, Stefano Redaelli and Frederik F. Van der Veken
Computers 2025, 14(7), 287; https://doi.org/10.3390/computers14070287 - 18 Jul 2025
Viewed by 1329
Abstract
The dynamic aperture is an essential concept in circular particle accelerators, providing the extent of the phase space region where particle motion remains stable over multiple turns. The accurate prediction of the dynamic aperture is key to optimising performance in accelerators such as [...] Read more.
The dynamic aperture is an essential concept in circular particle accelerators, providing the extent of the phase space region where particle motion remains stable over multiple turns. The accurate prediction of the dynamic aperture is key to optimising performance in accelerators such as the CERN Large Hadron Collider and is crucial for designing future accelerators like the CERN Future Circular Hadron Collider. Traditional methods for computing the dynamic aperture are computationally demanding and involve extensive numerical simulations with numerous initial phase space conditions. In our recent work, we have devised surrogate models to predict the dynamic aperture boundary both efficiently and accurately. These models have been further refined by incorporating them into a novel active learning framework. This framework enhances performance through continual retraining and intelligent data generation based on informed sampling driven by error estimation. A critical attribute of this framework is the precise estimation of uncertainty in dynamic aperture predictions. In this study, we investigate various machine learning techniques for uncertainty estimation, including Monte Carlo dropout, bootstrap methods, and aleatory uncertainty quantification. We evaluated these approaches to determine the most effective method for reliable uncertainty estimation in dynamic aperture predictions using machine learning techniques. Full article
(This article belongs to the Special Issue Machine Learning and Statistical Learning with Applications 2025)
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18 pages, 533 KB  
Article
Comparative Analysis of Deep Learning Models for Intrusion Detection in IoT Networks
by Abdullah Waqas, Sultan Daud Khan, Zaib Ullah, Mohib Ullah and Habib Ullah
Computers 2025, 14(7), 283; https://doi.org/10.3390/computers14070283 - 17 Jul 2025
Cited by 6 | Viewed by 2705
Abstract
The Internet of Things (IoT) holds transformative potential in fields such as power grid optimization, defense networks, and healthcare. However, the constrained processing capacities and resource limitations of IoT networks make them especially susceptible to cyber threats. This study addresses the problem of [...] Read more.
The Internet of Things (IoT) holds transformative potential in fields such as power grid optimization, defense networks, and healthcare. However, the constrained processing capacities and resource limitations of IoT networks make them especially susceptible to cyber threats. This study addresses the problem of detecting intrusions in IoT environments by evaluating the performance of deep learning (DL) models under different data and algorithmic conditions. We conducted a comparative analysis of three widely used DL models—Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Bidirectional LSTM (biLSTM)—across four benchmark IoT intrusion detection datasets: BoTIoT, CiCIoT, ToNIoT, and WUSTL-IIoT-2021. Each model was assessed under balanced and imbalanced dataset configurations and evaluated using three loss functions (cross-entropy, focal loss, and dual focal loss). By analyzing model efficacy across these datasets, we highlight the importance of generalizability and adaptability to varied data characteristics that are essential for real-world applications. The results demonstrate that the CNN trained using the cross-entropy loss function consistently outperforms the other models, particularly on balanced datasets. On the other hand, LSTM and biLSTM show strong potential in temporal modeling, but their performance is highly dependent on the characteristics of the dataset. By analyzing the performance of multiple DL models under diverse datasets, this research provides actionable insights for developing secure, interpretable IoT systems that can meet the challenges of designing a secure IoT system. Full article
(This article belongs to the Special Issue Application of Deep Learning to Internet of Things Systems)
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19 pages, 5755 KB  
Article
A Context-Aware Doorway Alignment and Depth Estimation Algorithm for Assistive Wheelchairs
by Shanelle Tennekoon, Nushara Wedasingha, Anuradhi Welhenge, Nimsiri Abhayasinghe and Iain Murray
Computers 2025, 14(7), 284; https://doi.org/10.3390/computers14070284 - 17 Jul 2025
Cited by 1 | Viewed by 1789
Abstract
Navigating through doorways remains a daily challenge for wheelchair users, often leading to frustration, collisions, or dependence on assistance. These challenges highlight a pressing need for intelligent doorway detection algorithm for assistive wheelchairs that go beyond traditional object detection. This study presents the [...] Read more.
Navigating through doorways remains a daily challenge for wheelchair users, often leading to frustration, collisions, or dependence on assistance. These challenges highlight a pressing need for intelligent doorway detection algorithm for assistive wheelchairs that go beyond traditional object detection. This study presents the algorithmic development of a lightweight, vision-based doorway detection and alignment module with contextual awareness. It integrates channel and spatial attention, semantic feature fusion, unsupervised depth estimation, and doorway alignment that offers real-time navigational guidance to the wheelchairs control system. The model achieved a mean average precision of 95.8% and a F1 score of 93%, while maintaining low computational demands suitable for future deployment on embedded systems. By eliminating the need for depth sensors and enabling contextual awareness, this study offers a robust solution to improve indoor mobility and deliver actionable feedback to support safe and independent doorway traversal for wheelchair users. Full article
(This article belongs to the Special Issue AI for Humans and Humans for AI (AI4HnH4AI))
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16 pages, 1251 KB  
Article
Enhanced Detection of Intrusion Detection System in Cloud Networks Using Time-Aware and Deep Learning Techniques
by Nima Terawi, Huthaifa I. Ashqar, Omar Darwish, Anas Alsobeh, Plamen Zahariev and Yahya Tashtoush
Computers 2025, 14(7), 282; https://doi.org/10.3390/computers14070282 - 17 Jul 2025
Cited by 16 | Viewed by 3353
Abstract
This study introduces an enhanced Intrusion Detection System (IDS) framework for Denial-of-Service (DoS) attacks, utilizing network traffic inter-arrival time (IAT) analysis. By examining the timing between packets and other statistical features, we detected patterns of malicious activity, allowing early and effective DoS threat [...] Read more.
This study introduces an enhanced Intrusion Detection System (IDS) framework for Denial-of-Service (DoS) attacks, utilizing network traffic inter-arrival time (IAT) analysis. By examining the timing between packets and other statistical features, we detected patterns of malicious activity, allowing early and effective DoS threat mitigation. We generate real DoS traffic, including normal, Internet Control Message Protocol (ICMP), Smurf attack, and Transmission Control Protocol (TCP) classes, and develop nine predictive algorithms, combining traditional machine learning and advanced deep learning techniques with optimization methods, including the synthetic minority sampling technique (SMOTE) and grid search (GS). Our findings reveal that while traditional machine learning achieved moderate accuracy, it struggled with imbalanced datasets. In contrast, Deep Neural Network (DNN) models showed significant improvements with optimization, with DNN combined with GS (DNN-GS) reaching 89% accuracy. However, we also used Recurrent Neural Networks (RNNs) combined with SMOTE and GS (RNN-SMOTE-GS), which emerged as the best-performing with a precision of 97%, demonstrating the effectiveness of combining SMOTE and GS and highlighting the critical role of advanced optimization techniques in enhancing the detection capabilities of IDS models for the accurate classification of various types of network traffic and attacks. Full article
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31 pages, 17130 KB  
Article
A Space-Time Plume Algorithm to Represent and Compute Dynamic Places
by Brent Dell and May Yuan
Computers 2025, 14(7), 278; https://doi.org/10.3390/computers14070278 - 15 Jul 2025
Viewed by 1366
Abstract
Contrary to what is represented in geospatial databases, places are dynamic and shaped by events. Point clustering analysis commonly assumes events occur in an empty space and therefore ignores geospatial features where events take place. This research introduces relational density, a novel concept [...] Read more.
Contrary to what is represented in geospatial databases, places are dynamic and shaped by events. Point clustering analysis commonly assumes events occur in an empty space and therefore ignores geospatial features where events take place. This research introduces relational density, a novel concept redefining density as relative to the spatial structure of geospatial features rather than an absolute measure. Building on this, we developed Space-Time Plume, a new algorithm for detecting and tracking evolving event clusters as smoke plumes in space and time, representing dynamic places. Unlike conventional density-based methods, Space-Time Plume dynamically adapts spatial reachability based on the underlying spatial structure and other zone-based parameters across multiple temporal intervals to capture hierarchical plume dynamics. The algorithm tracks plume progression, identifies spatiotemporal relationships, and reveals the emergence, evolution, and disappearance of event-driven places. A case study of crime events in Dallas, Texas, USA, demonstrates the algorithm’s performance and its capacity to represent and compute criminogenic places. We further enhance metaball rendering with Perlin noise to visualize plume structures and their spatiotemporal evolution. A comparative analysis with ST-DBSCAN shows Space-Time Plume’s competitive computational efficiency and ability to represent dynamic places with richer geographic insights. Full article
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17 pages, 1514 KB  
Article
Examining the Flow Dynamics of Artificial Intelligence in Real-Time Classroom Applications
by Zoltán Szűts, Tünde Lengyelné Molnár, Réka Racskó, Geoffrey Vaughan, Szabolcs Ceglédi and Dalma Lilla Dominek
Computers 2025, 14(7), 275; https://doi.org/10.3390/computers14070275 - 14 Jul 2025
Cited by 3 | Viewed by 2649
Abstract
The integration of artificial intelligence (AI) into educational environments is fundamentally transforming the learning process, raising new questions regarding student engagement and motivation. This empirical study investigates the relationship between AI-based learning support and the experience of flow, defined as the optimal state [...] Read more.
The integration of artificial intelligence (AI) into educational environments is fundamentally transforming the learning process, raising new questions regarding student engagement and motivation. This empirical study investigates the relationship between AI-based learning support and the experience of flow, defined as the optimal state of deep attention and intrinsic motivation, among university students. Building on Csíkszentmihályi’s flow theory and current models of technology-enhanced learning, we applied a validated, purposefully developed AI questionnaire (AIFLQ) to 142 students from two Hungarian universities: the Ludovika University of Public Service and Eszterházy Károly Catholic University. The participants used generative AI tools (e.g., ChatGPT 4, SUNO) during their academic tasks. Based on the results of the Mann–Whitney U test, significant differences were found between students from the two universities in the immersion and balance factors, as well as in the overall flow score, while the AI-related factor showed no statistically significant differences. The sustainability of the flow experience appears to be linked more to pedagogical methodological factors than to institutional ones, highlighting the importance of instructional support in fostering optimal learning experiences. Demographic variables also influenced the flow experience. In gender comparisons, female students showed significantly higher values for the immersion factor. According to the Kruskal–Wallis test, educational attainment also affected the flow experience, with students holding higher education degrees achieving higher flow scores. Our findings suggest that through the conscious design of AI tools and learning environments, taking into account instructional support and learner characteristics, it is possible to promote the development of optimal learning states. This research provides empirical evidence at the intersection of AI and motivational psychology, contributing to both domestic and international discourse in educational psychology and digital pedagogy. Full article
(This article belongs to the Special Issue Transformative Approaches in Education: Harnessing AI, Augmented Reality, and Virtual Reality for Innovative Teaching and Learning)
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37 pages, 2921 KB  
Article
A Machine-Learning-Based Data Science Framework for Effectively and Efficiently Processing, Managing, and Visualizing Big Sequential Data
by Alfredo Cuzzocrea, Islam Belmerabet, Abderraouf Hafsaoui and Carson K. Leung
Computers 2025, 14(7), 276; https://doi.org/10.3390/computers14070276 - 14 Jul 2025
Cited by 1 | Viewed by 5096
Abstract
In recent years, the open data initiative has led to the willingness of many governments, researchers, and organizations to share their data and make it publicly available. Healthcare, disease, and epidemiological data, such as privacy statistics on patients who have suffered from epidemic [...] Read more.
In recent years, the open data initiative has led to the willingness of many governments, researchers, and organizations to share their data and make it publicly available. Healthcare, disease, and epidemiological data, such as privacy statistics on patients who have suffered from epidemic diseases such as the Coronavirus disease 2019 (COVID-19), are examples of open big data. Therefore, huge volumes of valuable data have been generated and collected at high speed from a wide variety of rich data sources. Analyzing these open big data can be of social benefit. For example, people gain a better understanding of disease by analyzing and mining disease statistics, which can inspire them to participate in disease prevention, detection, control, and combat. Visual representation further improves data understanding and corresponding results for analysis and mining, as a picture is worth a thousand words. In this paper, we present a visual data science solution for the visualization and visual analysis of large sequence data. These ideas are illustrated by the visualization and visual analysis of sequences of real epidemiological data of COVID-19. Through our solution, we enable users to visualize the epidemiological data of COVID-19 over time. It also allows people to visually analyze data and discover relationships between popular features associated with COVID-19 cases. The effectiveness of our visual data science solution in improving the user experience of visualization and visual analysis of large sequence data is demonstrated by the real-life evaluation of these sequenced epidemiological data of COVID-19. Full article
(This article belongs to the Special Issue Computational Science and Its Applications 2024 (ICCSA 2024))
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23 pages, 17084 KB  
Article
Training First Responders Through VR-Based Situated Digital Twins
by Nikolaos Partarakis, Theodoros Evdaimon, Menelaos Katsantonis and Xenophon Zabulis
Computers 2025, 14(7), 274; https://doi.org/10.3390/computers14070274 - 11 Jul 2025
Cited by 4 | Viewed by 4406
Abstract
This study examines first responder training to deliver realistic, adaptable, and scalable solutions aimed at equipping personnel to handle high-risk, rapidly developing scenarios. The proposed method leverages Virtual Reality, Augmented Reality, and digital twins to enable immersive and situationally relevant training for security-critical [...] Read more.
This study examines first responder training to deliver realistic, adaptable, and scalable solutions aimed at equipping personnel to handle high-risk, rapidly developing scenarios. The proposed method leverages Virtual Reality, Augmented Reality, and digital twins to enable immersive and situationally relevant training for security-critical incidents. The method is structured into three distinct phases: definition, digitization, and implementation. The outcome of this approach is the creation of virtual training scenarios that simulate real situations and incident dynamics. The methodology employs photogrammetric reconstruction, simulation of human behavior through locomotion, and virtual security systems, such as surveillance and drone technology. Alongside the methodology, a case study of a large public event is presented to illustrate its feasibility in real-world applications. This study offers a comprehensive and adaptive structure for the design and deployment of digitally augmented training systems. This provides a practical basis for enhancing readiness in a range of operational domains. Full article
(This article belongs to the Special Issue Extended or Mixed Reality (AR + VR): Technology and Applications (2nd Edition))
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25 pages, 9056 KB  
Article
Creating Digital Twins to Celebrate Commemorative Events in the Metaverse
by Vicente Jover and Silvia Sempere
Computers 2025, 14(7), 273; https://doi.org/10.3390/computers14070273 - 10 Jul 2025
Cited by 6 | Viewed by 3052
Abstract
This paper explores the potential and implications arising from the convergence of virtual reality, the metaverse, and digital twins in translating a real-world commemorative event into a virtual environment. It emphasizes how such integration influences digital transformation processes, particularly in reshaping models of [...] Read more.
This paper explores the potential and implications arising from the convergence of virtual reality, the metaverse, and digital twins in translating a real-world commemorative event into a virtual environment. It emphasizes how such integration influences digital transformation processes, particularly in reshaping models of social interaction. Virtual reality is conceptualized as an immersive technology, enabling advanced multisensory experiences within persistent virtual spaces, such as the metaverse. Furthermore, this study delves into the concept of digital twins—high-fidelity virtual representations of physical systems, processes, and objects—highlighting their application in simulation, analysis, forecasting, prevention, and operational enhancement. In the context of virtual events, the convergence of these technologies is examined as a means to create interactive, adaptable, and scalable environments capable of accommodating diverse social groups and facilitating global accessibility. As a practical application, a digital twin of the Ferrándiz and Carbonell buildings—the most iconic architectural ensemble on the Alcoi campus—was developed to host a virtual event commemorating the 50th anniversary of the integration of the Alcoi School of Industrial Technical Engineering into the Universitat Politècnica de València in 1972. The virtual environment was subsequently evaluated by a sample of users, including students and faculty, to assess usability and functionality, and to identify areas for improvement. The digital twin achieved a score of 88.39 out of 100 on the System Usability Scale (SUS). The findings underscore the key opportunities and challenges associated with the adoption of these emerging technologies, particularly regarding their adaptability in reconfiguring digital environments for work, social interaction, and education. Using this case study as a foundation, this paper offers insights into the strategic role of the metaverse in extending environmental perception and its transformative potential for the future digital ecosystem through the implementation of digital twins. Full article
(This article belongs to the Special Issue Extended or Mixed Reality (AR + VR): Technology and Applications (2nd Edition))
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26 pages, 4876 KB  
Article
A Systematic Approach to Evaluate the Use of Chatbots in Educational Contexts: Learning Gains, Engagements and Perceptions
by Wei Qiu, Chit Lin Su, Nurabidah Binti Jamil, Maung Thway, Samuel Soo Hwee Ng, Lei Zhang, Fun Siong Lim and Joel Weijia Lai
Computers 2025, 14(7), 270; https://doi.org/10.3390/computers14070270 - 9 Jul 2025
Cited by 10 | Viewed by 7983
Abstract
As generative artificial intelligence (GenAI) chatbots gain traction in educational settings, a growing number of studies explore their potential for personalized, scalable learning. However, methodological fragmentation has limited the comparability and generalizability of findings across the field. This study proposes a unified, learning [...] Read more.
As generative artificial intelligence (GenAI) chatbots gain traction in educational settings, a growing number of studies explore their potential for personalized, scalable learning. However, methodological fragmentation has limited the comparability and generalizability of findings across the field. This study proposes a unified, learning analytics–driven framework for evaluating the impact of GenAI chatbots on student learning. Grounded in the collection, analysis, and interpretation of diverse learner data, the framework integrates assessment outcomes, conversational interactions, engagement metrics, and student feedback. We demonstrate its application through a multi-week, quasi-experimental study using a Socratic-style chatbot designed with pedagogical intent. Using clustering techniques and statistical analysis, we identified patterns in student–chatbot interaction and linked them to changes in learning outcomes. This framework provides researchers and educators with a replicable structure for evaluating GenAI interventions and advancing coherence in learning analytics–based educational research. Full article
(This article belongs to the Special Issue Smart Learning Environments)
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12 pages, 349 KB  
Article
Agentic AI for Cultural Heritage: Embedding Risk Memory in Semantic Digital Twins
by George Pavlidis
Computers 2025, 14(7), 266; https://doi.org/10.3390/computers14070266 - 7 Jul 2025
Cited by 8 | Viewed by 4082
Abstract
Cultural heritage preservation increasingly relies on data-driven technologies, yet most existing systems lack the cognitive and temporal depth required to support meaningful, transparent, and policy-informed decision-making. This paper proposes a conceptual framework for memory-enabled, semantically grounded AI agents in the cultural domain, showing [...] Read more.
Cultural heritage preservation increasingly relies on data-driven technologies, yet most existing systems lack the cognitive and temporal depth required to support meaningful, transparent, and policy-informed decision-making. This paper proposes a conceptual framework for memory-enabled, semantically grounded AI agents in the cultural domain, showing how the integration of the ICCROM/CCI ABC method for risk assessment into the Panoptes ontology enables the structured encoding of risk cognition over time. This structured risk memory becomes the foundation for agentic reasoning, supporting prioritization, justification, and long-term preservation planning. It is argued that this approach constitutes a principled step toward the development of Cultural Agentic AI: autonomous systems that remember, reason, and act in alignment with cultural values. Proof-of-concept simulations illustrate how memory-enabled agents can trace evolving risk patterns, trigger policy responses, and evaluate mitigation outcomes through structured, explainable reasoning. Full article
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25 pages, 3142 KB  
Article
Mobile Augmented Reality Games Towards Smart Learning City Environments: Learning About Sustainability
by Margarida M. Marques, João Ferreira-Santos, Rita Rodrigues and Lúcia Pombo
Computers 2025, 14(7), 267; https://doi.org/10.3390/computers14070267 - 7 Jul 2025
Cited by 8 | Viewed by 2334
Abstract
This study explores the potential of mobile augmented reality games (MARGs) in promoting sustainability competencies within the context of a smart learning city environment. Anchored in the EduCITY project, which integrates location-based AR-enhanced games into an interactive mobile app, the research investigates how [...] Read more.
This study explores the potential of mobile augmented reality games (MARGs) in promoting sustainability competencies within the context of a smart learning city environment. Anchored in the EduCITY project, which integrates location-based AR-enhanced games into an interactive mobile app, the research investigates how these tools support Education for Sustainable Development (ESD). Employing a mixed-methods approach, data were collected through the GreenComp-based Questionnaire (GCQuest) and anonymous gameplay logs generated by the app. Thematic analysis of 358 responses revealed four key learning domains: ‘cultural awareness’, ‘environmental protection’, ‘sustainability awareness’, and ‘contextual knowledge’. Quantitative performance data from game logs highlighted substantial variation across games, with the highest performance found in those with more frequent AR integration and multiple iterative refinements. Participants engaging with AR-enhanced features (optional) outperformed others. This study provides empirical evidence for the use of MARGs to cultivate sustainability-related knowledge, skills, and attitudes, particularly when grounded in local realities and enhanced through thoughtful design. Beyond the EduCITY project, the study proposes a replicable model for assessing sustainability competencies, with implications for broader integration of AR across educational contexts in ESD. The paper concludes with a critical reflection on methodological limitations and suggests future directions, including adapting the GCQuest for use with younger learners in primary education. Full article
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13 pages, 3210 KB  
Article
Bridging Tradition and Innovation: Transformative Educational Practices in Museums with AI and VR
by Michele Domenico Todino, Eliza Pitri, Argyro Fella, Antonia Michaelidou, Lucia Campitiello, Francesca Placanica, Stefano Di Tore and Maurizio Sibilio
Computers 2025, 14(7), 257; https://doi.org/10.3390/computers14070257 - 30 Jun 2025
Cited by 10 | Viewed by 4213
Abstract
This paper explores the intersection of folk art, museums, and education in the 20th century, with a focus on the concept of art as experience, emphasizing the role of museums as active, inclusive learning spaces. A collaboration between the University of Salerno and [...] Read more.
This paper explores the intersection of folk art, museums, and education in the 20th century, with a focus on the concept of art as experience, emphasizing the role of museums as active, inclusive learning spaces. A collaboration between the University of Salerno and the University of Nicosia has developed virtual museum environments using virtual reality (VR) to enhance engagement with cultural heritage. These projects aim to make museums more accessible and interactive, with future potential in integrating artificial intelligence NPC and VR strategies for personalized visitor experiences of the Nicosia Folk Art Museum. Full article
(This article belongs to the Special Issue Transformative Approaches in Education: Harnessing AI, Augmented Reality, and Virtual Reality for Innovative Teaching and Learning)
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14 pages, 236 KB  
Systematic Review
Artificial Intelligence and the Future of Mental Health in a Digitally Transformed World
by Aggeliki Kelly Fanarioti and Kostas Karpouzis
Computers 2025, 14(7), 259; https://doi.org/10.3390/computers14070259 - 30 Jun 2025
Cited by 6 | Viewed by 7804
Abstract
Artificial Intelligence (AI) is reshaping mental healthcare by enabling new forms of diagnosis, therapy, and patient monitoring. Yet this digital transformation raises complex policy and ethical questions that remain insufficiently addressed. In this paper, we critically examine how AI-driven innovations are being integrated [...] Read more.
Artificial Intelligence (AI) is reshaping mental healthcare by enabling new forms of diagnosis, therapy, and patient monitoring. Yet this digital transformation raises complex policy and ethical questions that remain insufficiently addressed. In this paper, we critically examine how AI-driven innovations are being integrated into mental health systems across different global contexts, with particular attention to governance, regulation, and social justice. The study follows the PRISMA-ScR methodology to ensure transparency and methodological rigor, while also acknowledging its inherent limitations, such as the emphasis on breadth over depth and the exclusion of non-English sources. Drawing on international guidelines, academic literature, and emerging national strategies, it identifies both opportunities, such as improved access and personalized care, and threats, including algorithmic bias, data privacy risks, and diminished human oversight. Special attention is given to underrepresented populations and the risks of digital exclusion. The paper argues for a value-driven approach that centers equity, transparency, and informed consent in the deployment of AI tools. It concludes with actionable policy recommendations to support the ethical implementation of AI in mental health, emphasizing the need for cross-sectoral collaboration and global accountability mechanisms. Full article
(This article belongs to the Special Issue AI in Its Ecosystem)
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32 pages, 3165 KB  
Article
Exploring the Role of Artificial Intelligence in Detecting Advanced Persistent Threats
by Pedro Ramos Brandao
Computers 2025, 14(7), 245; https://doi.org/10.3390/computers14070245 - 23 Jun 2025
Cited by 10 | Viewed by 5053
Abstract
The rapid evolution of cyber threats, particularly Advanced Persistent Threats (APTs), poses significant challenges to the security of information systems. This paper explores the pivotal role of Artificial Intelligence (AI) in enhancing the detection and mitigation of APTs. By leveraging machine learning algorithms [...] Read more.
The rapid evolution of cyber threats, particularly Advanced Persistent Threats (APTs), poses significant challenges to the security of information systems. This paper explores the pivotal role of Artificial Intelligence (AI) in enhancing the detection and mitigation of APTs. By leveraging machine learning algorithms and data analytics, AI systems can identify patterns and anomalies that are indicative of sophisticated cyber-attacks. This study examines various AI-driven methodologies, including anomaly detection, predictive analytics, and automated response systems, highlighting their effectiveness in real-time threat detection and response. Furthermore, we discuss the integration of AI into existing cybersecurity frameworks, emphasizing the importance of collaboration between human analysts and AI systems in combating APTs. The findings suggest that the adoption of AI technologies not only improves the accuracy and speed of threat detection but also enables organizations to proactively defend against evolving cyber threats, probably achieving a 75% reduction in alert volume. Full article
(This article belongs to the Section ICT Infrastructures for Cybersecurity)
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27 pages, 1417 KB  
Article
A BERT-Based Multimodal Framework for Enhanced Fake News Detection Using Text and Image Data Fusion
by Mohammed Al-alshaqi, Danda B. Rawat and Chunmei Liu
Computers 2025, 14(6), 237; https://doi.org/10.3390/computers14060237 - 16 Jun 2025
Cited by 15 | Viewed by 10928
Abstract
The spread of fake news on social media is complicated by the fact that fake information spreads extremely fast in both textual and visual formats. Traditional approaches to the detection of fake news focus mainly on text and image features, thereby missing valuable [...] Read more.
The spread of fake news on social media is complicated by the fact that fake information spreads extremely fast in both textual and visual formats. Traditional approaches to the detection of fake news focus mainly on text and image features, thereby missing valuable information contained within images and texts. In response to this, we propose a multimodal fake news detection method based on BERT, with an extension to text combined with the extracted text from images through Optical Character Recognition (OCR). Here, we consider extending feature analysis with BERT_base_uncased to process inputs for retrieving relevant text from images and determining a confidence score that suggests the probability of the news being authentic. We report extensive experimental results on the ISOT, WELFAKE, TRUTHSEEKER, and ISOT_WELFAKE_TRUTHSEEKER datasets. Our proposed model demonstrates better generalization on the TRUTHSEEKER dataset with an accuracy of 99.97%, achieving substantial improvements over existing methods with an F1-score of 0.98. Experimental results indicate a potential accuracy increment of +3.35% compared to the latest baselines. These results highlight the potential of our approach to serve as a strong resource for automatic fake news detection by effectively integrating both text and visual data streams. Findings suggest that using diverse datasets enhances the resilience of detection systems against misinformation strategies. Full article
(This article belongs to the Special Issue Recent Advances in Social Networks and Social Media)
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27 pages, 3100 KB  
Article
Reducing Delivery Times by Utilising On-Site Wire Arc Additive Manufacturing with Digital-Twin Methods
by Stefanie Sell, Kevin Villani and Marc Stautner
Computers 2025, 14(6), 221; https://doi.org/10.3390/computers14060221 - 6 Jun 2025
Cited by 3 | Viewed by 2890
Abstract
The increasing demand for smaller batch sizes and mass customisation in production poses considerable challenges to logistics and manufacturing efficiency. Conventional methodologies are unable to address the need for expeditious, cost-effective distribution of premium-quality products tailored to individual specifications. Additionally, the reliability and [...] Read more.
The increasing demand for smaller batch sizes and mass customisation in production poses considerable challenges to logistics and manufacturing efficiency. Conventional methodologies are unable to address the need for expeditious, cost-effective distribution of premium-quality products tailored to individual specifications. Additionally, the reliability and resilience of global logistics chains are increasingly under pressure. Additive manufacturing is regarded as a potentially viable solution to these problems, as it enables on-demand, on-site production, with reduced resource usage in production. Nevertheless, there are still significant challenges to be addressed, including the assurance of product quality and the optimisation of production processes with respect to time and resource efficiency. This article examines the potential of integrating digital twin methodologies to establish a fully digital and efficient process chain for on-site additive manufacturing. This study focuses on wire arc additive manufacturing (WAAM), a technology that has been successfully implemented in the on-site production of naval ship propellers and excavator parts. The proposed approach aims to enhance process planning efficiency, reduce material and energy consumption, and minimise the expertise required for operational deployment by leveraging digital twin methodologies. The present paper details the current state of research in this domain and outlines a vision for a fully virtualised process chain, highlighting the transformative potential of digital twin technologies in advancing on-site additive manufacturing. In this context, various aspects and components of a digital twin framework for wire arc additive manufacturing are examined regarding their necessity and applicability. The overarching objective of this paper is to conduct a preliminary investigation for the implementation and further development of a comprehensive DT framework for WAAM. Utilising a real-world sample, current already available process steps are validated and actual missing technical solutions are pointed out. Full article
(This article belongs to the Section Internet of Things (IoT) and Industrial IoT)
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31 pages, 1751 KB  
Article
Enhancing User Experiences in Digital Marketing Through Machine Learning: Cases, Trends, and Challenges
by Alexios Kaponis, Manolis Maragoudakis and Konstantinos Chrysanthos Sofianos
Computers 2025, 14(6), 211; https://doi.org/10.3390/computers14060211 - 29 May 2025
Cited by 9 | Viewed by 10265
Abstract
Online marketing environments are rapidly being transformed by Artificial Intelligence (AI). This represents the implementation of Machine Learning (ML) that has significant potential in content personalization, enhanced usability, and hyper-targeted marketing, and it will reconfigure how businesses reach and serve customers. This systematic [...] Read more.
Online marketing environments are rapidly being transformed by Artificial Intelligence (AI). This represents the implementation of Machine Learning (ML) that has significant potential in content personalization, enhanced usability, and hyper-targeted marketing, and it will reconfigure how businesses reach and serve customers. This systematic examination of machine learning in the Digital Marketing (DM) industry is also closely examined, focusing on its effect on human–computer interaction (HCI). This research methodically elucidates how machine learning can be applied to the automation of strategies for user engagement that increase user experience (UX) and customer retention, and how to optimize recommendations from consumer behavior. The objective of the present study is to critically analyze the functional and ethical considerations of ML integration in DM and to evaluate its implications on data-driven personalization. Through selected case studies, the investigation also provides empirical evidence of the implications of ML applications on UX/customer loyalty as well as associated ethical aspects. These include algorithmic bias, concerns about the privacy of the data, and the need for greater transparency of ML-based decision-making processes. This research also contributes to the field by delivering actionable, data-driven strategies for marketing professionals and offering them frameworks to deal with the evolving responsibilities and tasks that accompany the introduction of ML technologies into DM. Full article
(This article belongs to the Special Issue Harnessing Artificial Intelligence for Social and Semantic Understanding)
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29 pages, 2570 KB  
Article
Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders
by Vahid Babaey and Hamid Reza Faragardi
Computers 2025, 14(6), 205; https://doi.org/10.3390/computers14060205 - 26 May 2025
Cited by 24 | Viewed by 6769
Abstract
The increasing sophistication of web-based services has intensified the risk of zero-day attacks, exposing critical vulnerabilities in user information security. Traditional detection systems often rely on labeled attack data and struggle to identify novel threats without prior knowledge. This paper introduces a novel [...] Read more.
The increasing sophistication of web-based services has intensified the risk of zero-day attacks, exposing critical vulnerabilities in user information security. Traditional detection systems often rely on labeled attack data and struggle to identify novel threats without prior knowledge. This paper introduces a novel one-class ensemble method for detecting zero-day web attacks, combining the strengths of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and stacked autoencoders through latent representation concatenation and compression. Additionally, a structured tokenization strategy based on character-level analysis is employed to enhance input consistency and reduce feature dimensionality. The proposed method was evaluated using the CSIC 2012 dataset, achieving 97.58% accuracy, 97.52% recall, 99.76% specificity, and 99.99% precision, with a false positive rate of just 0.2%. Compared to conventional ensemble techniques like majority voting, our approach demonstrates superior anomaly detection performance by fusing diverse feature representations at the latent level rather than the output level. These results highlight the model’s effectiveness in accurately detecting unknown web attacks with low false positives, addressing major limitations of existing detection frameworks. Full article
(This article belongs to the Special Issue Using New Technologies in Cyber Security Solutions (2nd Edition))
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21 pages, 1337 KB  
Article
Applications of Multi-Criteria Decision Making in Information Systems for Strategic and Operational Decisions
by Mitra Madanchian and Hamed Taherdoost
Computers 2025, 14(6), 208; https://doi.org/10.3390/computers14060208 - 26 May 2025
Cited by 17 | Viewed by 8972
Abstract
Business problems today are complicated and involve considering numerous dimensions to be weighed against each other, leading to opposing goals that must be compromised on to discover the best solution. Multi-Criteria Decision Making or MCDM plays an essential role in this situation here. [...] Read more.
Business problems today are complicated and involve considering numerous dimensions to be weighed against each other, leading to opposing goals that must be compromised on to discover the best solution. Multi-Criteria Decision Making or MCDM plays an essential role in this situation here. MCDM techniques and procedures analyze, score, and select between options that have various conflicting criteria. This systematic review investigates applications of MCDM methods within Management Information Systems (MIS) based on evidence from 40 peer-reviewed articles selected from the Scopus database. Key methods discussed are Analytic Hierarchy Process (AHP), TOPSIS, fuzzy logic-based methods, and Analytic Network Process (ANP). These methods were applied across MIS strategic planning, re-source assignment, risk assessment, and technology selection. The review contributes further by categorizing MCDM application into thematic decision domains, evaluating methodological directions, and mapping the strengths of each method against specific MIS problems. Theoretical guidelines are suggested to align the type of decision with an appropriate MCDM strategy. The study demonstrates how the addition of MCDM enhances MIS capability with data-driven, transparent decision-making power. Implications and directions for future research are presented to guide scholars and practitioners. Full article
(This article belongs to the Special Issue Next Generation Blockchain, Information Security and Soft Computing for Future IoT Networks)
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23 pages, 714 KB  
Systematic Review
A Systematic Review of Mind Maps, STEM Education, Algorithmic and Procedural Learning
by Chrysovalantis Kefalis, Constantine Skordoulis and Athanasios Drigas
Computers 2025, 14(6), 204; https://doi.org/10.3390/computers14060204 - 23 May 2025
Cited by 11 | Viewed by 13321
Abstract
This systematic review investigates the use of mind maps in STEM education, focusing on their application, effectiveness, and contextual factors. The main objectives were to examine whether mind maps are used as learning or assessment tools, the research designs employed, the type of [...] Read more.
This systematic review investigates the use of mind maps in STEM education, focusing on their application, effectiveness, and contextual factors. The main objectives were to examine whether mind maps are used as learning or assessment tools, the research designs employed, the type of interaction (individual vs. collaborative), and the format (digital vs. paper-based). Studies were identified through systematic searches in ERIC, Scopus, and Web of Science, including peer-reviewed journal articles published between 2019 and 2024. The inclusion criteria required empirical research studies using mind maps in STEM contexts with measurable outcomes related to learning or engagement. Studies without empirical data or not focused on STEM education were excluded. Fifty studies met the inclusion criteria. Most employed quasi-experimental designs (n = 29), including 22 with pre–post-test measurements. The mind maps were mainly used as learning tools (n = 40), in individual settings (n = 24), with student-generated (n = 36) and digital formats (n = 21) being most common. The reported outcomes included improved academic performance, conceptual understanding, critical thinking, and motivation and reduced cognitive load. The limitations included inconsistent reporting of the map types and theoretical underpinnings. The findings suggest that mind maps are effective tools for enhancing learning and engagement in STEM education and warrant broader pedagogical integration. Full article
(This article belongs to the Special Issue Transformative Approaches in Education: Harnessing AI, Augmented Reality, and Virtual Reality for Innovative Teaching and Learning)
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34 pages, 13580 KB  
Article
A Novel MaxViT Model for Accelerated and Precise Soybean Leaf and Seed Disease Identification
by Al Shahriar Uddin Khondakar Pranta, Hasib Fardin, Jesika Debnath, Amira Hossain, Anamul Haque Sakib, Md. Redwan Ahmed, Rezaul Haque, Ahmed Wasif Reza and M. Ali Akber Dewan
Computers 2025, 14(5), 197; https://doi.org/10.3390/computers14050197 - 18 May 2025
Cited by 16 | Viewed by 3227
Abstract
Timely diagnosis of soybean diseases is essential to protect yields and limit global economic loss, yet current deep learning approaches suffer from small, imbalanced datasets, single-organ focus, and limited interpretability. We propose MaxViT-XSLD (MaxViT XAI-Seed–Leaf-Diagnostic), a Vision Transformer that integrates multiaxis attention with [...] Read more.
Timely diagnosis of soybean diseases is essential to protect yields and limit global economic loss, yet current deep learning approaches suffer from small, imbalanced datasets, single-organ focus, and limited interpretability. We propose MaxViT-XSLD (MaxViT XAI-Seed–Leaf-Diagnostic), a Vision Transformer that integrates multiaxis attention with MBConv layers to jointly classify soybean leaf and seed diseases while remaining lightweight and explainable. Two benchmark datasets were upscaled through elastic deformation, Gaussian noise, brightness shifts, rotation, and flipping, enlarging ASDID from 10,722 to 16,000 images (eight classes) and the SD set from 5513 to 10,000 images (five classes). Under identical augmentation and hyperparameters, MaxViT-XSLD delivered 99.82% accuracy on ASDID and 99.46% on SD, surpassing competitive ViT, CNN, and lightweight SOTA variants. High PR-AUC and MCC values, confirmed via 10-fold stratified cross-validation and Wilcoxon tests, demonstrate robust generalization across data splits. Explainable AI (XAI) techniques further enhanced interpretability by highlighting biologically relevant features influencing predictions. Its modular design also enables future model compression for edge deployment in resource-constrained settings. Finally, we deploy the model in SoyScan, a real-time web tool that streams predictions and visual explanations to growers and agronomists. These findings establishes a scalable, interpretable system for precision crop health monitoring and lay the groundwork for edge-oriented, multimodal agricultural diagnostics. Full article
(This article belongs to the Special Issue Advanced Image Processing and Computer Vision (2nd Edition))
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17 pages, 1468 KB  
Article
A Case Study of Computational Thinking Analysis Using SOLO Taxonomy in Scientific–Mathematical Learning
by Alejandro De la Hoz Serrano, Andrés Álvarez-Murillo, Eladio José Fernández Torrado, Miguel Ángel González Maestre and Lina Viviana Melo Niño
Computers 2025, 14(5), 192; https://doi.org/10.3390/computers14050192 - 15 May 2025
Cited by 3 | Viewed by 5022
Abstract
Education nowadays requires a certain variety of resources that allow for the acquisition of 21st-century skills, including computational thinking. Educational robotics emerges as a digital resource that supports the development of these skills in both male and female students across different educational stages. [...] Read more.
Education nowadays requires a certain variety of resources that allow for the acquisition of 21st-century skills, including computational thinking. Educational robotics emerges as a digital resource that supports the development of these skills in both male and female students across different educational stages. However, it is necessary to investigate in depth evaluations that analyze the acquisition of Computational Thinking skills in pre-service teachers, especially when scientific and mathematical content learning programs are designed. This study aims to analyze Computational Thinking skills using the SOLO taxonomy, with an approach to science and mathematics learning, through an intervention based on programming and Educational Robotics. A quasi-experimental design was used on a total sample of 116 pre-service teachers. The SOLO taxonomy categorization was used to associate each level of the taxonomy with the computational concepts analyzed through a quantitative questionnaire. The taxonomy levels associated with Computational Thinking skills correspond to uni-structural and multi-structural levels. Males presented better results before the intervention, while subsequently, females presented better levels of Computational Thinking, as well as a greater association with the higher complexity level of learning analyzed. In turn, there was a trend between the levels of the SOLO taxonomy and computational concepts, so that an increase in skill for a concept occurs similarly at both the uni-structural level and the multi-structural level. The SOLO taxonomy is presented as a proper tool for learning assessment since it allows for a more detailed understanding of the quality of students’ learning. Therefore, the SOLO taxonomy serves as a valuable resource in the evaluation of Computational Thinking skills. Full article
(This article belongs to the Special Issue Transformative Approaches in Education: Harnessing AI, Augmented Reality, and Virtual Reality for Innovative Teaching and Learning)
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43 pages, 2755 KB  
Systematic Review
Analyzing Visitor Behavior to Enhance Personalized Experiences in Smart Museums: A Systematic Literature Review
by Rosen Ivanov and Victoria Velkova
Computers 2025, 14(5), 191; https://doi.org/10.3390/computers14050191 - 14 May 2025
Cited by 32 | Viewed by 15265
Abstract
This systematic review provides an analysis of information gathered from 33 chosen publications during the past decade. The analysis reveals the primary methodologies applied and identifies the visitor behaviors that enable personalized content delivery. Statistical and Data Analysis is the predominant methodology in [...] Read more.
This systematic review provides an analysis of information gathered from 33 chosen publications during the past decade. The analysis reveals the primary methodologies applied and identifies the visitor behaviors that enable personalized content delivery. Statistical and Data Analysis is the predominant methodology in the reviewed publications. The methodology is present in 97% of the publications. AI and Machine Learning (63.6%) and Mobile/Interactive Technologies (60.6%) are most frequently paired with this methodology. Behavioral Analytics Platforms and Mobile/Wearable Devices are the most used technologies (42.4%) for delivering personalized content. A total of 39.4% of publications utilize Location Tracking Systems. The most frequent visitor behavior analysis focuses on Interactive Engagement and Movement Patterns, which occur 72.7% of the time, before Learning Patterns and Physical Positioning, which occur 63.6% of the time. The behavioral analysis of Group Dynamics (27.3%) and Emotional Response (18.2%) represents the least common practice when museums personalize their content despite the significance of social interaction analysis among visitors. The leading content personalization methods currently include real-time personalization systems combined with AI-driven systems and location-based technologies. Personalized content delivery systems face challenges including privacy protection and scalability issues paired with expensive implementation costs, which especially affect smaller museums. Researchers should explore how new technologies, such as virtual reality, augmented reality, and advanced biometric systems, can be integrated into future developments. Full article
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24 pages, 58563 KB  
Article
Interpretable Deep Learning for Diabetic Retinopathy: A Comparative Study of CNN, ViT, and Hybrid Architectures
by Weijie Zhang, Veronika Belcheva and Tatiana Ermakova
Computers 2025, 14(5), 187; https://doi.org/10.3390/computers14050187 - 12 May 2025
Cited by 29 | Viewed by 8831
Abstract
Diabetic retinopathy (DR) is a leading cause of vision impairment worldwide, requiring early detection for effective treatment. Deep learning models have been widely used for automated DR classification, with Convolutional Neural Networks (CNNs) being the most established approach. Recently, Vision Transformers (ViTs) have [...] Read more.
Diabetic retinopathy (DR) is a leading cause of vision impairment worldwide, requiring early detection for effective treatment. Deep learning models have been widely used for automated DR classification, with Convolutional Neural Networks (CNNs) being the most established approach. Recently, Vision Transformers (ViTs) have shown promise, but a direct comparison of their performance and interpretability remains limited. Additionally, hybrid models that combine CNN and transformer-based architectures have not been extensively studied. This work systematically evaluates CNNs (ResNet-50), ViTs (Vision Transformer and SwinV2-Tiny), and hybrid models (Convolutional Vision Transformer, LeViT-256, and CvT-13) on DR classification using publicly available retinal image datasets. The models are assessed based on classification accuracy and interpretability, applying Grad-CAM and Attention-Rollout to analyze decision-making patterns. Results indicate that hybrid models outperform both standalone CNNs and ViTs, achieving a better balance between local feature extraction and global context awareness. The best-performing model (CvT-13) achieved a Quadratic Weighted Kappa (QWK) score of 0.84 and an AUC of 0.93 on the test set. Interpretability analysis shows that CNNs focus on fine-grained lesion details, while ViTs exhibit broader but less localized attention. These findings provide valuable insights for optimizing deep learning models in medical imaging, supporting the development of clinically viable AI-driven DR screening systems. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence and Modeling Frameworks in Health Informatics and Related Fields)
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48 pages, 6522 KB  
Systematic Review
The Influence of Artificial Intelligence Tools on Learning Outcomes in Computer Programming: A Systematic Review and Meta-Analysis
by Manal Alanazi, Ben Soh, Halima Samra and Alice Li
Computers 2025, 14(5), 185; https://doi.org/10.3390/computers14050185 - 9 May 2025
Cited by 20 | Viewed by 23071
Abstract
This systematic review and meta-analysis investigates the impact of artificial intelligence (AI) tools, including ChatGPT 3.5 and GitHub Copilot, on learning outcomes in computer programming courses. A total of 35 controlled studies published between 2020 and 2024 were analysed to assess the effectiveness [...] Read more.
This systematic review and meta-analysis investigates the impact of artificial intelligence (AI) tools, including ChatGPT 3.5 and GitHub Copilot, on learning outcomes in computer programming courses. A total of 35 controlled studies published between 2020 and 2024 were analysed to assess the effectiveness of AI-assisted learning. The results indicate that students using AI tools outperformed those without such aids. The meta-analysis findings revealed that AI-assisted learning significantly reduced task completion time (SMD = −0.69, 95% CI [−2.13, −0.74], I2 = 95%, p = 0.34) and improved student performance scores (SMD = 0.86, 95% CI [0.36, 1.37], p = 0.0008, I2 = 54%). However, AI tools did not provide a statistically significant advantage in learning success or ease of understanding (SMD = 0.16, 95% CI [−0.23, 0.55], p = 0.41, I2 = 55%), with sensitivity analysis suggesting result variability. Student perceptions of AI tools were overwhelmingly positive, with a pooled estimate of 1.0 (95% CI [0.92, 1.00], I2 = 0%). While AI tools enhance computer programming proficiency and efficiency, their effectiveness depends on factors such as tool functionality and course design. To maximise benefits and mitigate over-reliance, tailored pedagogical strategies are essential. This study underscores the transformative role of AI in computer programming education and provides evidence-based insights for optimising AI-assisted learning. Full article
(This article belongs to the Section Cloud Continuum and Enabled Applications)
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37 pages, 46669 KB  
Article
ViX-MangoEFormer: An Enhanced Vision Transformer–EfficientFormer and Stacking Ensemble Approach for Mango Leaf Disease Recognition with Explainable Artificial Intelligence
by Abdullah Al Noman, Amira Hossain, Anamul Sakib, Jesika Debnath, Hasib Fardin, Abdullah Al Sakib, Rezaul Haque, Md. Redwan Ahmed, Ahmed Wasif Reza and M. Ali Akber Dewan
Computers 2025, 14(5), 171; https://doi.org/10.3390/computers14050171 - 2 May 2025
Cited by 16 | Viewed by 5181
Abstract
Mango productivity suffers greatly from leaf diseases, leading to economic and food security issues. Current visual inspection methods are slow and subjective. Previous Deep-Learning (DL) solutions have shown promise but suffer from imbalanced datasets, modest generalization, and limited interpretability. To address these challenges, [...] Read more.
Mango productivity suffers greatly from leaf diseases, leading to economic and food security issues. Current visual inspection methods are slow and subjective. Previous Deep-Learning (DL) solutions have shown promise but suffer from imbalanced datasets, modest generalization, and limited interpretability. To address these challenges, this study introduces the ViX-MangoEFormer, which combines convolutional kernels and self-attention to effectively diagnose multiple mango leaf conditions in both balanced and imbalanced image sets. To benchmark against ViX-MangoEFormer, we developed a stacking ensemble model (MangoNet-Stack) that utilizes five transfer learning networks as base learners. All models were trained with Grad-CAM produced pixel-level explanations. In a combined dataset of 25,530 images, ViX-MangoEFormer achieved an F1 score of 99.78% and a Matthews Correlation Coefficient (MCC) of 99.34%. This performance consistently outperformed individual pre-trained models and MangoNet-Stack. Additionally, data augmentation has improved the performance of every architecture compared to its non-augmented version. Cross-domain tests on morphologically similar crop leaves confirmed strong generalization. Our findings validate the effectiveness of transformer attention and XAI in mango leaf disease detection. ViX-MangoEFormer is deployed as a web application that delivers real-time predictions, probability scores, and visual rationales. The system enables growers to respond quickly and enhances large-scale smart crop health monitoring. Full article
(This article belongs to the Special Issue Deep Learning and Explainable Artificial Intelligence)
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20 pages, 3977 KB  
Article
Investigation of Multiple Hybrid Deep Learning Models for Accurate and Optimized Network Slicing
by Ahmed Raoof Nasser and Omar Younis Alani
Computers 2025, 14(5), 174; https://doi.org/10.3390/computers14050174 - 2 May 2025
Cited by 7 | Viewed by 2773
Abstract
In 5G wireless communication, network slicing is considered one of the key network elements, which aims to provide services with high availability, low latency, maximizing data throughput, and ultra-reliability and save network resources. Due to the exponential expansion of cellular networking in the [...] Read more.
In 5G wireless communication, network slicing is considered one of the key network elements, which aims to provide services with high availability, low latency, maximizing data throughput, and ultra-reliability and save network resources. Due to the exponential expansion of cellular networking in the number of users along with the new applications, delivering the desired Quality of Service (QoS) requires an accurate and fast network slicing mechanism. In this paper, hybrid deep learning (DL) approaches are investigated using convolutional neural networks (CNNs), Long Short-Term Memory (LSTM), recurrent neural networks (RNNs), and Gated Recurrent Units (GRUs) to provide an accurate network slicing model. The proposed hybrid approaches are CNN-LSTM, CNN-RNN, and CNN-GRU, where a CNN is initially used for effective feature extraction and then LSTM, an RNN, and GRUs are utilized to achieve an accurate network slice classification. To optimize the model performance in terms of accuracy and model complexity, the hyperparameters of each algorithm are selected using the Bayesian optimization algorithm. The obtained results illustrate that the optimized hybrid CNN-GRU algorithm provides the best performance in terms of slicing accuracy (99.31%) and low model complexity. Full article
(This article belongs to the Special Issue Distributed Computing Paradigms for the Internet of Things: Exploring Cloud, Edge, and Fog Solutions)
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21 pages, 4491 KB  
Article
PyChatAI: Enhancing Python Programming Skills—An Empirical Study of a Smart Learning System
by Manal Alanazi, Ben Soh, Halima Samra and Alice Li
Computers 2025, 14(5), 158; https://doi.org/10.3390/computers14050158 - 23 Apr 2025
Cited by 6 | Viewed by 4489
Abstract
This paper presents strategies for effectively integrating AI tools into programming education and provides recommendations for enhancing student learning outcomes through intelligent educational systems. Learning computer programming is a cognitively demanding task that requires dedication, logical reasoning, and persistence. Many beginners struggle with [...] Read more.
This paper presents strategies for effectively integrating AI tools into programming education and provides recommendations for enhancing student learning outcomes through intelligent educational systems. Learning computer programming is a cognitively demanding task that requires dedication, logical reasoning, and persistence. Many beginners struggle with debugging and often lack effective problem-solving strategies. To address these issues, this study investigates PyChatAI—a bilingual, AI-powered chatbot designed to support novice Python programmers by providing real-time feedback, answering coding-related questions, and fostering independent problem-solving skills. PyChatAI offers continuous, personalised assistance and is particularly beneficial for students who prefer remote or low-pressure learning environments. An empirical evaluation employing a Solomon Four-Group design revealed significant improvements across all programming skill areas, with especially strong gains in theoretical understanding, code writing, and debugging proficiency. Full article
(This article belongs to the Special Issue Smart Learning Environments)
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18 pages, 2949 KB  
Article
Generative Artificial Intelligence as a Catalyst for Change in Higher Education Art Study Programs
by Anna Ansone, Zinta Zālīte-Supe and Linda Daniela
Computers 2025, 14(4), 154; https://doi.org/10.3390/computers14040154 - 20 Apr 2025
Cited by 27 | Viewed by 9581
Abstract
Generative Artificial Intelligence (AI) has emerged as a transformative tool in art education, offering innovative avenues for creativity and learning. However, concerns persist among educators regarding the potential misuse of text-to-image generators as unethical shortcuts. This study explores how bachelor’s-level art students perceive [...] Read more.
Generative Artificial Intelligence (AI) has emerged as a transformative tool in art education, offering innovative avenues for creativity and learning. However, concerns persist among educators regarding the potential misuse of text-to-image generators as unethical shortcuts. This study explores how bachelor’s-level art students perceive and use generative AI in artistic composition. Ten art students participated in a lecture on composition principles and completed a practical composition task using both traditional methods and generative AI tools. Their interactions were observed, followed by the administration of a questionnaire capturing their reflections. Qualitative analysis of the data revealed that students recognize the potential of generative AI for ideation and conceptual development but find its limitations frustrating for executing nuanced artistic tasks. This study highlights the current utility of generative AI as an inspirational and conceptual mentor rather than a precise artistic tool, highlighting the need for structured training and a balanced integration of generative AI with traditional design methods. Future research should focus on larger participant samples, assess the evolving capabilities of generative AI tools, and explore their potential to teach fundamental art concepts effectively while addressing concerns about academic integrity. Enhancing the functionality of these tools could bridge gaps between creativity and pedagogy in art education. Full article
(This article belongs to the Special Issue Smart Learning Environments)
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15 pages, 3742 KB  
Article
An Innovative Approach to Topic Clustering for Social Media and Web Data Using AI
by Ioannis Kapantaidakis, Emmanouil Perakakis, George Mastorakis and Ioannis Kopanakis
Computers 2025, 14(4), 142; https://doi.org/10.3390/computers14040142 - 10 Apr 2025
Cited by 5 | Viewed by 7679
Abstract
The vast amount of social media and web data offers valuable insights for purposes such as brand reputation management, topic research, competitive analysis, product development, and public opinion surveys. However, analysing these data to identify patterns and extract valuable insights is challenging due [...] Read more.
The vast amount of social media and web data offers valuable insights for purposes such as brand reputation management, topic research, competitive analysis, product development, and public opinion surveys. However, analysing these data to identify patterns and extract valuable insights is challenging due to the vast number of posts, which can number in the thousands within a single day. One practical approach is topic clustering, which creates clusters of mentions that refer to a specific topic. Following this process will create several manageable clusters, each containing hundreds or thousands of posts. These clusters offer a more meaningful overview of the discussed topics, eliminating the need to categorise each post manually. Several topic detection algorithms can achieve clustering of posts, such as LDA, NMF, BERTopic, etc. The existing algorithms, however, have several important drawbacks, including language constraints and slow or resource-intensive data processing. Moreover, the labels for the clusters typically consist of a few keywords that may not make sense unless one explores the mentions within the cluster. Recently, with the introduction of AI large language models, such as GPT-4, new techniques can be realised for topic clustering to address the aforementioned issues. Our novel approach (AI Mention Clustering) employs LLMs at its core to produce an algorithm for efficient and accurate topic clustering of web and social data. Our solution was tested on social and web data and compared to the popular existing algorithm of BERTopic, demonstrating superior resource efficiency and absolute accuracy of clustered documents. Furthermore, it produces summaries of the clusters that are easily understood by humans instead of just representative keywords. This approach enhances the productivity of social and web data researchers by providing more meaningful and interpretable results. Full article
(This article belongs to the Special Issue Harnessing Artificial Intelligence for Social and Semantic Understanding)
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36 pages, 1629 KB  
Review
A Systematic Review of Blockchain-Based Initiatives in Comparison to Best Practices Used in Higher Education Institutions
by Diana Laura Silaghi and Daniela Elena Popescu
Computers 2025, 14(4), 141; https://doi.org/10.3390/computers14040141 - 8 Apr 2025
Cited by 33 | Viewed by 13993
Abstract
Blockchain technology, originally introduced through Bitcoin cryptocurrency in 2008, has rapidly expanded beyond its financial roots, offering innovative solutions for secure data management across various sectors, including education. Higher education institutions, faced with challenges in managing academic records, verifying degrees, assessing skills, and [...] Read more.
Blockchain technology, originally introduced through Bitcoin cryptocurrency in 2008, has rapidly expanded beyond its financial roots, offering innovative solutions for secure data management across various sectors, including education. Higher education institutions, faced with challenges in managing academic records, verifying degrees, assessing skills, and safeguarding personal data, have increasingly looked to blockchain for answers. Blockchain’s transparent, immutable, and decentralized nature provides potential solutions to these longstanding problems. This systematic review assesses blockchain-based proposals for academic certificates management, aiming to highlight globally recognized best practices, explore the latest applications, and identify key challenges hindering the widespread adoption of blockchain technology in education. A thorough discussion based on the findings introduces potential solutions to mitigate these challenges and provides insights into possible future research directions that could help overcome these obstacles. Full article
(This article belongs to the Special Issue Harnessing the Blockchain Technology in Unveiling Futuristic Applications)
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