Computers

27 pages, 1178 KiB

Open AccessArticle

Ensemble-Based Biometric Verification: Defending Against Multi-Strategy Deepfake Image Generation

by Hilary Zen, Rohan Wagh, Miguel Wanderley, Gustavo Bicalho, Rachel Park, Megan Sun, Rafael Palacios, Lucas Carvalho, Guilherme Rinaldo and Amar Gupta

Computers 2025, 14(6), 225; https://doi.org/10.3390/computers14060225 - 9 Jun 2025

Abstract

Deepfake images, synthetic images created using digital software, continue to present a serious threat to online platforms. This is especially relevant for biometric verification systems, as deepfakes that attempt to bypass such measures increase the risk of impersonation, identity theft and scams. Although [...] Read more.

Deepfake images, synthetic images created using digital software, continue to present a serious threat to online platforms. This is especially relevant for biometric verification systems, as deepfakes that attempt to bypass such measures increase the risk of impersonation, identity theft and scams. Although research on deepfake image detection has provided many high-performing classifiers, many of these commonly used detection models lack generalizability across different methods of deepfake generation. For companies and governments fighting identify fraud, a lack of generalization is challenging, as malicious actors may use a variety of deepfake image-generation methods available through online wrappers. This work explores if combining multiple classifiers into an ensemble model can improve generalization without losing performance across different generation methods. It also considers current methods of deepfake image generation, with a focus on publicly available and easily accessible methods. We compare our framework against its underlying models to show how companies can better respond to emerging deepfake generation methods. Full article

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31 pages, 9733 KiB

Open AccessArticle

Gamifying Sociological Surveys Through Serious Games—A Data Analysis Approach Applied to Multiple-Choice Question Responses Datasets

by Alexandros Gazis and Eleftheria Katsiri

Computers 2025, 14(6), 224; https://doi.org/10.3390/computers14060224 - 7 Jun 2025

Abstract

E-polis is a serious digital game designed to gamify sociological surveys studying young people’s political opinions. In this platform game, players navigate a digital world, encountering quests posing sociological questions. Players’ answers shape the city-game world, altering building structures based on their choices. [...] Read more.

E-polis is a serious digital game designed to gamify sociological surveys studying young people’s political opinions. In this platform game, players navigate a digital world, encountering quests posing sociological questions. Players’ answers shape the city-game world, altering building structures based on their choices. E-polis is a serious game, not a government simulation, aiming to understand players’ behaviors and opinions; thus, we do not train the players but rather understand them and help them visualize their choices in shaping a city’s future. Also, it is noticed that no correct or incorrect answers apply. Moreover, our game utilizes a novel middleware architecture for development, diverging from typical asset-prefab-scene and script segregation. This article presents the data layer of our game’s middleware, specifically focusing on data analysis based on respondents’ gameplay answers. E-polis represents an innovative approach to gamifying sociological research, providing a unique platform for gathering and analyzing data on political opinions among youth and contributing to the broader field of serious games. Full article

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26 pages, 12177 KiB

Open AccessArticle

An Efficient Hybrid 3D Computer-Aided Cephalometric Analysis for Lateral Cephalometric and Cone-Beam Computed Tomography (CBCT) Systems

by Laurine A. Ashame, Sherin M. Youssef, Mazen Nabil Elagamy and Sahar M. El-Sheikh

Computers 2025, 14(6), 223; https://doi.org/10.3390/computers14060223 - 7 Jun 2025

Abstract

Lateral cephalometric analysis is commonly used in orthodontics for skeletal classification to ensure an accurate and reliable diagnosis for treatment planning. However, most current research depends on analyzing different type of radiographs, which requires more computational time than 3D analysis. Consequently, this study [...] Read more.

Lateral cephalometric analysis is commonly used in orthodontics for skeletal classification to ensure an accurate and reliable diagnosis for treatment planning. However, most current research depends on analyzing different type of radiographs, which requires more computational time than 3D analysis. Consequently, this study addresses fully automatic orthodontics tracing based on the usage of artificial intelligence (AI) applied to 2D and 3D images, by designing a cephalometric system that analyzes the significant landmarks and regions of interest (ROI) needed in orthodontics tracing, especially for the mandible and maxilla teeth. In this research, a computerized system is developed to automate the tasks of orthodontics evaluation during 2D and Cone-Beam Computed Tomography (CBCT or 3D) systems measurements. This work was tested on a dataset that contains images of males and females obtained from dental hospitals with patient-informed consent. The dataset consists of 2D lateral cephalometric, panorama and CBCT radiographs. Many scenarios were applied to test the proposed system in landmark prediction and detection. Moreover, this study integrates the Grad-CAM (Gradient-Weighted Class Activation Mapping) technique to generate heat maps, providing transparent visualization of the regions the model focuses on during its decision-making process. By enhancing the interpretability of deep learning predictions, Grad-CAM strengthens clinical confidence in the system’s outputs, ensuring that ROI detection aligns with orthodontic diagnostic standards. This explainability is crucial in medical AI applications, where understanding model behavior is as important as achieving high accuracy. The experimental results achieved an accuracy exceeding 98.9%. This research evaluates and differentiates between the two-dimensional and the three-dimensional tracing analyses applied to measurements based on the practices of the European Board of Orthodontics. The results demonstrate the proposed methodology’s robustness when applied to cephalometric images. Furthermore, the evaluation of 3D analysis usage provides a clear understanding of the significance of integrated deep-learning techniques in orthodontics. Full article

(This article belongs to the Special Issue Machine Learning Applications in Pattern Recognition)

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13 pages, 817 KiB

Open AccessArticle

Evaluating the Predictive Power of Software Metrics for Fault Localization

by Issar Arab, Kenneth Magel and Mohammed Akour

Computers 2025, 14(6), 222; https://doi.org/10.3390/computers14060222 - 6 Jun 2025

Abstract

Fault localization remains a critical challenge in software engineering, directly impacting debugging efficiency and software quality. This study investigates the predictive power of various software metrics for fault localization by framing the task as a multi-class classification problem and evaluating it using the [...] Read more.

Fault localization remains a critical challenge in software engineering, directly impacting debugging efficiency and software quality. This study investigates the predictive power of various software metrics for fault localization by framing the task as a multi-class classification problem and evaluating it using the Defects4J dataset. We fitted thousands of models and benchmarked different algorithms—including deep learning, Random Forest, XGBoost, and LightGBM—to choose the best-performing model. To enhance model transparency, we applied explainable AI techniques to analyze feature importance. The results revealed that test suite metrics consistently outperform static and dynamic metrics, making them the most effective predictors for identifying faulty classes. These findings underscore the critical role of test quality and coverage in automated fault localization. By combining machine learning with transparent feature analysis, this work delivers practical insights to support more efficient debugging workflows. It lays the groundwork for an iterative process that integrates metric-based predictive models with large language models (LLMs), enabling future systems to automatically generate targeted test cases for the most fault-prone components, which further enhances the automation and precision of software testing. Full article

(This article belongs to the Special Issue Best Practices, Challenges and Opportunities in Software Engineering)

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28 pages, 3100 KiB

Open AccessArticle

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

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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16 pages, 452 KiB

Open AccessArticle

GARMT: Grouping-Based Association Rule Mining to Predict Future Tables in Database Queries

by Peixiong He, Libo Sun, Xian Gao, Yi Zhou and Xiao Qin

Computers 2025, 14(6), 220; https://doi.org/10.3390/computers14060220 - 6 Jun 2025

Abstract

In modern data management systems, structured query language (SQL) databases, as a mature and stable technology, have become the standard for processing structured data. These databases ensure data integrity through strongly typed schema definitions and support complex transaction management and efficient query processing [...] Read more.

In modern data management systems, structured query language (SQL) databases, as a mature and stable technology, have become the standard for processing structured data. These databases ensure data integrity through strongly typed schema definitions and support complex transaction management and efficient query processing capabilities. However, data sparsity—where most fields in large table sets remain unused by most queries—leads to inefficiencies in access optimization. We propose a grouping-based approach (GARMT) that partitions SQL queries into fixed-size groups and applies a modified FP-Growth algorithm (GFP-Growth) to identify frequent table access patterns. Experiments on a real-world dataset show that grouping significantly reduces runtime—by up to 40%—compared to the ungrouped baseline while preserving rule relevance. These results highlight the practical value of query grouping for efficient pattern discovery in sparse database environments. Full article

(This article belongs to the Special Issue Cloud Computing and Big Data Mining)

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18 pages, 1435 KiB

Open AccessArticle

Threats to the Digital Ecosystem: Can Information Security Management Frameworks, Guided by Criminological Literature, Effectively Prevent Cybercrime and Protect Public Data?

by Shahrukh Mushtaq and Mahmood Shah

Computers 2025, 14(6), 219; https://doi.org/10.3390/computers14060219 - 4 Jun 2025

Abstract

As cyber threats escalate in scale and sophistication, the imperative to secure public data through theoretically grounded and practically viable frameworks becomes increasingly urgent. This review investigates whether and how criminology theories have effectively informed the development and implementation of information security management [...] Read more.

As cyber threats escalate in scale and sophistication, the imperative to secure public data through theoretically grounded and practically viable frameworks becomes increasingly urgent. This review investigates whether and how criminology theories have effectively informed the development and implementation of information security management frameworks (ISMFs) to prevent cybercrime and fortify the digital ecosystem’s resilience. Anchored in a comprehensive bibliometric analysis of 617 peer-reviewed records extracted from Scopus and Web of Science, the study employs Multiple Correspondence Analysis (MCA), conceptual co-word mapping, and citation coupling to systematically chart the intellectual landscape bridging criminology and cybersecurity. The review reveals those foundational criminology theories—particularly routine activity theory, rational choice theory, and deterrence theory—have been progressively adapted to cyber contexts, offering novel insights into offender behaviour, target vulnerability, and systemic guardianship. In parallel, the study critically engages with global cybersecurity standards such as National Institute of Standards and Technology (NIST) and ISO, to evaluate how criminological principles are embedded in practice. Using data from the Global Cybersecurity Index (GCI), the paper introduces an innovative visual mapping of the divergence between cybersecurity preparedness and digital development across 170+ countries, revealing strategic gaps and overperformers. This paper ultimately argues for an interdisciplinary convergence between criminology and cybersecurity governance, proposing that the integration of criminological logic into cybersecurity frameworks can enhance risk anticipation, attacker deterrence, and the overall security posture of digital public infrastructures. Full article

(This article belongs to the Special Issue Using New Technologies in Cyber Security Solutions (2nd Edition))

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23 pages, 1252 KiB

Open AccessArticle

Exploring the Potential of the Bicameral Mind Theory in Reinforcement Learning Algorithms

by Munavvarkhon Mukhitdinova and Mariana Petrova

Computers 2025, 14(6), 218; https://doi.org/10.3390/computers14060218 - 3 Jun 2025

Abstract

This study explores the potential of Julian Jaynes’ bicameral mind theory in enhancing reinforcement learning (RL) algorithms and large language models (LLMs) for artificial intelligence (AI) systems. By drawing parallels between the dual-process structure of the bicameral mind, the observation–action cycle in RL, [...] Read more.

This study explores the potential of Julian Jaynes’ bicameral mind theory in enhancing reinforcement learning (RL) algorithms and large language models (LLMs) for artificial intelligence (AI) systems. By drawing parallels between the dual-process structure of the bicameral mind, the observation–action cycle in RL, and the “thinking”/”writing” processes in LLMs, we hypothesize that incorporating principles from this theory could lead to more efficient and adaptive AI. Empirical evidence from OpenAI’s CoinRun and RainMazes models, together with analysis of Claude, Gemini, and ChatGPT functioning, supports our hypothesis, demonstrating the universality of the dual-component structure across different types of AI systems. We propose a conceptual model for integrating bicameral mind principles into AI architectures capable of guiding the development of systems that effectively generalize knowledge across various tasks and environments. Full article

(This article belongs to the Special Issue Emerging Trends in Machine Learning and Artificial Intelligence)

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41 pages, 4206 KiB

Open AccessSystematic Review

A Systematic Literature Review on Load-Balancing Techniques in Fog Computing: Architectures, Strategies, and Emerging Trends

by Danah Aldossary, Ezaz Aldahasi, Taghreed Balharith and Tarek Helmy

Computers 2025, 14(6), 217; https://doi.org/10.3390/computers14060217 - 2 Jun 2025

Abstract

Fog computing has emerged as a promising paradigm to extend cloud services toward the edge of the network, enabling low-latency processing and real-time responsiveness for Internet of Things (IoT) applications. However, the distributed, heterogeneous, and resource-constrained nature of fog environments introduces significant challenges [...] Read more.

Fog computing has emerged as a promising paradigm to extend cloud services toward the edge of the network, enabling low-latency processing and real-time responsiveness for Internet of Things (IoT) applications. However, the distributed, heterogeneous, and resource-constrained nature of fog environments introduces significant challenges in balancing workloads efficiently. This study presents a systematic literature review (SLR) of 113 peer-reviewed articles published between 2020 and 2024, aiming to provide a comprehensive overview of load-balancing strategies in fog computing. This review categorizes fog computing architectures, load-balancing algorithms, scheduling and offloading techniques, fault-tolerance mechanisms, security models, and evaluation metrics. The analysis reveals that three-layer (IoT–Fog–Cloud) architectures remain predominant, with dynamic clustering and virtualization commonly employed to enhance adaptability. Heuristic and hybrid load-balancing approaches are most widely adopted due to their scalability and flexibility. Evaluation frequently centers on latency, energy consumption, and resource utilization, while simulation is primarily conducted using tools such as iFogSim and YAFS. Despite considerable progress, key challenges persist, including workload diversity, security enforcement, and real-time decision-making under dynamic conditions. Emerging trends highlight the growing use of artificial intelligence, software-defined networking, and blockchain to support intelligent, secure, and autonomous load balancing. This review synthesizes current research directions, identifies critical gaps, and offers recommendations for designing efficient and resilient fog-based load-balancing systems. Full article

(This article belongs to the Special Issue Edge and Fog Computing for Internet of Things Systems (2nd Edition))

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29 pages, 1299 KiB

Open AccessArticle

Towards Trustworthy Energy Efficient P2P Networks: A New Method for Validating Computing Results in Decentralized Networks

by Fernando Rodríguez-Sela and Borja Bordel

Computers 2025, 14(6), 216; https://doi.org/10.3390/computers14060216 - 2 Jun 2025

Abstract

Decentralized P2P networks have emerged as robust instruments to execute computing tasks, with enhanced security and transparency. Solutions such as Blockchain have proved to be successful in a large catalog of critical applications such as cryptocurrency, intellectual property, etc. However, although executions are [...] Read more.

Decentralized P2P networks have emerged as robust instruments to execute computing tasks, with enhanced security and transparency. Solutions such as Blockchain have proved to be successful in a large catalog of critical applications such as cryptocurrency, intellectual property, etc. However, although executions are transparent and P2P are resistant against common cyberattacks, they tend to be untrustworthy. P2P nodes typically do not offer any evidence about the quality of their resolution of the delegated computing tasks, so trustworthiness of results is threatened. To mitigate this challenge, in usual P2P networks, many different replicas of the same computing task are delegated to different nodes. The final result is the one most nodes reached. But this approach is very resource consuming, especially in terms of energy, as many unnecessary computing tasks are executed. Therefore, new solutions to achieve trustworthy P2P networks, but with an energy efficiency perspective, are needed. This study addresses this challenge. The purpose of the research is to evaluate the effectiveness of an audit-based and score-based approach is assigned to each node instead of performing identical tasks redundantly on different nodes in the network. The proposed solution employs probabilistic methods to detect the malicious nodes taking into account parameters like number of executed tasks and number of audited ones giving a value to the node, and game theory which consider that all nodes play with the same rules. Qualitative and quantitative experimental methods are used to evaluate its impact. The results reveal a significant reduction in network energy consumption, minimum a 50% comparing to networks in which each task is delivered to different nodes considering the task is delivered to a pair of nodes, supporting the effectiveness of the proposed approach. Full article

(This article belongs to the Section Blockchain Infrastructures and Enabled Applications)

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17 pages, 1481 KiB

Open AccessArticle

Enhancing Injector Performance Through CFD Optimization: Focus on Cavitation Reduction

by Jose Villagomez-Moreno, Aurelio Dominguez-Gonzalez, Carlos Gustavo Manriquez-Padilla, Juan Jose Saucedo-Dorantes and Angel Perez-Cruz

Computers 2025, 14(6), 215; https://doi.org/10.3390/computers14060215 - 2 Jun 2025

Abstract

The use of computer-aided engineering (CAE) tools has become essential in modern design processes, significantly streamlining mechanical design tasks. The integration of optimization algorithms further enhances these processes by facilitating studies on mechanical behavior and accelerating iterative operations. A key focus lies in [...] Read more.

The use of computer-aided engineering (CAE) tools has become essential in modern design processes, significantly streamlining mechanical design tasks. The integration of optimization algorithms further enhances these processes by facilitating studies on mechanical behavior and accelerating iterative operations. A key focus lies in understanding and mitigating the detrimental effects of cavitation on injector surfaces, as it can reduce the injector lifespan and induce material degradation. By combining advanced numerical finite element tools with algorithmic optimization, these adverse effects can be effectively mitigated. The incorporation of computational tools enables efficient numerical analyses and rapid, automated modifications of injector designs, significantly enhancing the ability to explore and refine geometries. The primary goal remains the minimization of cavitation phenomena and the improvement in injector performance, while the collaborative use of specialized software environments ensures a more robust and streamlined design process. Specifically, using the simulated annealing algorithm (SA) helps identify the optimal configuration that minimizes cavitation-induced effects. The proposed approach provides a robust set of tools for engineers and researchers to enhance injector performance and effectively address cavitation-related challenges. The results derived from this integrated framework illustrate the effectiveness of the optimization methodology in facilitating the development of more efficient and reliable injector systems. Full article

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29 pages, 2066 KiB

Open AccessArticle

Improved Big Data Security Using Quantum Chaotic Map of Key Sequence

by Archana Kotangale, Meesala Sudhir Kumar and Amol P. Bhagat

Computers 2025, 14(6), 214; https://doi.org/10.3390/computers14060214 - 1 Jun 2025

Abstract

In the era of ubiquitous big data, ensuring secure storage, transmission, and processing has become a paramount concern. Classical cryptographic methods face increasing vulnerabilities in the face of quantum computing advancements. This research proposes an enhanced big data security framework integrating a quantum [...] Read more.

In the era of ubiquitous big data, ensuring secure storage, transmission, and processing has become a paramount concern. Classical cryptographic methods face increasing vulnerabilities in the face of quantum computing advancements. This research proposes an enhanced big data security framework integrating a quantum chaotic map of key sequence (QCMKS), which synergizes the principles of quantum mechanics and chaos theory to generate highly unpredictable and non-repetitive key sequences. The system incorporates quantum random number generation (QRNG) for true entropy sources, quantum key distribution (QKD) for secure key exchange immune to eavesdropping, and quantum error correction (QEC) to maintain integrity against quantum noise. Additionally, quantum optical elements transformation (QOET) is employed to implement state transformations on photonic qubits, ensuring robustness during transmission across quantum networks. The integration of QCMKS with QRNG, QKD, QEC, and QOET significantly enhances the confidentiality, integrity, and availability of big data systems, laying the groundwork for a quantum-resilient data security paradigm. While the proposed framework demonstrates strong theoretical potential for improving big data security, its practical robustness and performance are subject to current quantum hardware limitations, noise sensitivity, and integration complexities. Full article

(This article belongs to the Section ICT Infrastructures for Cybersecurity)

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36 pages, 2094 KiB

Open AccessArticle

Generating Accessible Webpages from Models

by Karla Ordoñez-Briceño, José R. Hilera, Luis De-Marcos and Rodrigo Saraguro-Bravo

Computers 2025, 14(6), 213; https://doi.org/10.3390/computers14060213 - 31 May 2025

Abstract

Despite significant efforts to promote web accessibility through the adoption of various standards and tools, the web remains inaccessible to many users. One of the main barriers is the limited knowledge of accessibility issues among website designers. This gap in expertise results in [...] Read more.

Despite significant efforts to promote web accessibility through the adoption of various standards and tools, the web remains inaccessible to many users. One of the main barriers is the limited knowledge of accessibility issues among website designers. This gap in expertise results in the development of websites that fail to meet accessibility standards, hindering access for people with diverse abilities and needs. In response to this challenge, this paper presents the ACG WebAcc prototype, which enables the automatic generation of accessible HTML code using a model-driven development (MDD) approach. The tool takes as input a Unified Modeling Language (UML) model, with a specific profile, and incorporates predefined Object Constraint Language (OCL) rules to ensure compliance with accessibility guidelines. By automating this process, ACG WebAcc reduces the need for extensive knowledge of accessibility standards, making it easier for designers to create accessible websites. Full article

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30 pages, 1368 KiB

Open AccessArticle

Pain Level Classification Using Eye-Tracking Metrics and Machine Learning Models

by Oussama El Othmani and Sami Naouali

Computers 2025, 14(6), 212; https://doi.org/10.3390/computers14060212 - 30 May 2025

Abstract

Pain estimation is a critical aspect of healthcare, particularly for patients who are unable to communicate discomfort effectively. The traditional methods, such as self-reporting or observational scales, are subjective and prone to bias. This study proposes a novel system for non-invasive pain estimation [...] Read more.

Pain estimation is a critical aspect of healthcare, particularly for patients who are unable to communicate discomfort effectively. The traditional methods, such as self-reporting or observational scales, are subjective and prone to bias. This study proposes a novel system for non-invasive pain estimation using eye-tracking technology and advanced machine learning models. The methodology begins with preprocessing steps, including resizing, normalization, and data augmentation, to prepare high-quality input face images. DeepLabV3+ is employed for the precise segmentation of the eye and face regions, achieving 95% accuracy. Feature extraction is performed using VGG16, capturing key metrics such as pupil size, blink rate, and saccade velocity. Multiple machine learning models, including Random Forest, SVM, MLP, XGBoost, and NGBoost, are trained on the extracted features. XGBoost achieves the highest classification accuracy of 99.5%, demonstrating its robustness for pain level classification on a scale from 0 to 5. The feature analysis using SHAP values reveals that pupil size and blink rate contribute most to the predictions, with SHAP contribution scores of 0.42 and 0.35, respectively. The loss curves for DeepLabV3+ confirm rapid convergence during training, ensuring reliable segmentation. This work highlights the transformative potential of combining eye-tracking data with machine learning for non-invasive pain estimation, with significant applications in healthcare, human–computer interaction, and assistive technologies. Full article

(This article belongs to the Topic Visual Computing and Understanding: New Developments and Trends)

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31 pages, 1751 KiB

Open AccessArticle

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

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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19 pages, 1594 KiB

Open AccessArticle

Leave as Fast as You Can: Using Generative AI to Automate and Accelerate Hospital Discharge Reports

by Alex Trejo Omeñaca, Esteve Llargués Rocabruna, Jonny Sloan, Michelle Catta-Preta, Jan Ferrer i Picó, Julio Cesar Alfaro Alvarez, Toni Alonso Solis, Eloy Lloveras Gil, Xavier Serrano Vinaixa, Daniela Velasquez Villegas, Ramon Romeu Garcia, Carles Rubies Feijoo, Josep Maria Monguet i Fierro and Beatriu Bayes Genis

Computers 2025, 14(6), 210; https://doi.org/10.3390/computers14060210 - 28 May 2025

Abstract

Clinical documentation, particularly the hospital discharge report (HDR), is essential for ensuring continuity of care, yet its preparation is time-consuming and places a considerable clinical and administrative burden on healthcare professionals. Recent advancements in Generative Artificial Intelligence (GenAI) and the use of prompt [...] Read more.

Clinical documentation, particularly the hospital discharge report (HDR), is essential for ensuring continuity of care, yet its preparation is time-consuming and places a considerable clinical and administrative burden on healthcare professionals. Recent advancements in Generative Artificial Intelligence (GenAI) and the use of prompt engineering in large language models (LLMs) offer opportunities to automate parts of this process, improving efficiency and documentation quality while reducing administrative workload. This study aims to design a digital system based on LLMs capable of automatically generating HDRs using information from clinical course notes and emergency care reports. The system was developed through iterative cycles, integrating various instruction flows and evaluating five different LLMs combined with prompt engineering strategies and agent-based architectures. Throughout the development, more than 60 discharge reports were generated and assessed, leading to continuous system refinement. In the production phase, 40 pneumology discharge reports were produced, receiving positive feedback from physicians, with an average score of 2.9 out of 4, indicating the system’s usefulness, with only minor edits needed in most cases. The ongoing expansion of the system to additional services and its integration within a hospital electronic system highlights the potential of LLMs, when combined with effective prompt engineering and agent-based architectures, to generate high-quality medical content and provide meaningful support to healthcare professionals. Hospital discharge reports (HDRs) are pivotal for continuity of care but consume substantial clinician time. Generative AI systems based on large language models (LLMs) could streamline this process, provided they deliver accurate, multilingual, and workflow-compatible outputs. We pursued a three-stage, design-science approach. Proof-of-concept: five state-of-the-art LLMs were benchmarked with multi-agent prompting to produce sample HDRs and define the optimal agent structure. Prototype: 60 HDRs spanning six specialties were generated and compared with clinician originals using ROUGE with average scores compatible with specialized news summarizing models in Spanish and Catalan (lower scores). A qualitative audit of 27 HDR pairs showed recurrent divergences in medication dose (56%) and social context (52%). Pilot deployment: The AI-HDR service was embedded in the hospital’s electronic health record. In the pilot, 47 HDRs were autogenerated in real-world settings and reviewed by attending physicians. Missing information and factual errors were flagged in 53% and 47% of drafts, respectively, while written assessments diminished the importance of these errors. An LLM-driven, agent-orchestrated pipeline can safely draft real-world HDRs, cutting administrative overhead while achieving clinician-acceptable quality, not without errors that require human supervision. Future work should refine specialty-specific prompts to curb omissions, add temporal consistency checks to prevent outdated data propagation, and validate time savings and clinical impact in multi-center trials. Full article

(This article belongs to the Special Issue Natural Language Processing (NLP) and Large Language Modelling)

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20 pages, 1138 KiB

Open AccessArticle

Adoption Drivers of Intelligent Virtual Assistants in Banking: Rethinking the Artificial Intelligence Banker

by Rui Ramos, Joaquim Casaca and Rui Patrício

Computers 2025, 14(6), 209; https://doi.org/10.3390/computers14060209 - 27 May 2025

Abstract

The adoption of Intelligent Virtual Assistants (IVAs) in the banking sector presents new opportunities to enhance customer service efficiency, reduce operational costs, and modernize service delivery channels. However, the factors driving IVA adoption and usage, particularly in specific regional contexts such as Portugal, [...] Read more.

The adoption of Intelligent Virtual Assistants (IVAs) in the banking sector presents new opportunities to enhance customer service efficiency, reduce operational costs, and modernize service delivery channels. However, the factors driving IVA adoption and usage, particularly in specific regional contexts such as Portugal, remain underexplored. This study examined the determinants of IVA adoption intention and actual usage in the Portuguese banking sector, drawing on the Technology Acceptance Model (TAM) as its theoretical foundation. Data were collected through an online questionnaire distributed to 154 banking customers after they interacted with a commercial bank’s IVA. The analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings revealed that perceived usefulness significantly influences the intention to adopt, which in turn significantly impacts actual usage. In contrast, other variables—including trust, ease of use, anthropomorphism, awareness, service quality, and gendered voice—did not show a significant effect. These results suggest that Portuguese users adopt IVAs based primarily on functional utility, highlighting the importance of outcome-oriented design and communication strategies. This study contributes to the understanding of technology adoption in mature digital markets and offers practical guidance for banks seeking to enhance the perceived value of their virtual assistants. Full article

(This article belongs to the Special Issue AI in Its Ecosystem)

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21 pages, 1337 KiB

Open AccessArticle

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

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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24 pages, 4739 KiB

Open AccessArticle

Secured Audio Framework Based on Chaotic-Steganography Algorithm for Internet of Things Systems

by Mai Helmy and Hanaa Torkey

Computers 2025, 14(6), 207; https://doi.org/10.3390/computers14060207 - 26 May 2025

Abstract

The exponential growth of interconnected devices in the Internet of Things (IoT) has raised significant concerns about data security, especially when transmitting sensitive information over wireless channels. Traditional encryption techniques often fail to meet the energy and processing constraints of resource-limited IoT devices. [...] Read more.

The exponential growth of interconnected devices in the Internet of Things (IoT) has raised significant concerns about data security, especially when transmitting sensitive information over wireless channels. Traditional encryption techniques often fail to meet the energy and processing constraints of resource-limited IoT devices. This paper proposes a novel hybrid security framework that integrates chaotic encryption and steganography to enhance confidentiality, integrity, and resilience in audio communication. Chaotic systems generate unpredictable keys for strong encryption, while steganography conceals the existence of sensitive data within audio signals, adding a covert layer of protection. The proposed approach is evaluated within an Orthogonal Frequency Division Multiplexing (OFDM)-based wireless communication system, widely recognized for its robustness against interference and channel impairments. By combining secure encryption with a practical transmission scheme, this work demonstrates the effectiveness of the proposed hybrid method in realistic IoT environments, achieving high performance in terms of signal integrity, security, and resistance to noise. Simulation results indicate that the OFDM system incorporating chaotic algorithm modes alongside steganography outperforms the chaotic algorithm alone, particularly at higher Eb/No values. Notably, with DCT-OFDM, the chaotic-CFB based on steganography algorithm achieves a performance gain of approximately 30 dB compared to FFT-OFDM and DWT-based systems at Eb/No = 8 dB. These findings suggest that steganography plays a crucial role in enhancing secure transmission, offering greater signal deviation, reduced correlation, a more uniform histogram, and increased resistance to noise, especially in high BER scenarios. This highlights the potential of hybrid cryptographic-steganographic methods in safeguarding sensitive audio information within IoT networks and provides a foundation for future advancements in secure IoT communication systems. Full article

(This article belongs to the Special Issue Edge and Fog Computing for Internet of Things Systems (2nd Edition))

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