Information

31 pages, 3129 KB

Open AccessReview

A Review on Gas Pipeline Leak Detection: Acoustic-Based, OGI-Based, and Multimodal Fusion Methods

by Yankun Gong, Chao Bao, Zhengxi He, Yifan Jian, Xiaoye Wang, Haineng Huang and Xintai Song

Information 2025, 16(9), 731; https://doi.org/10.3390/info16090731 (registering DOI) - 25 Aug 2025

Abstract

Pipelines play a vital role in material transportation within industrial settings. This review synthesizes detection technologies for early-stage small gas leaks from pipelines in the industrial sector, with a focus on acoustic-based methods, optical gas imaging (OGI), and multimodal fusion approaches. It encompasses [...] Read more.

Pipelines play a vital role in material transportation within industrial settings. This review synthesizes detection technologies for early-stage small gas leaks from pipelines in the industrial sector, with a focus on acoustic-based methods, optical gas imaging (OGI), and multimodal fusion approaches. It encompasses detection principles, inherent challenges, mitigation strategies, and the state of the art (SOTA). Small leaks refer to low flow leakage originating from defects with apertures at millimeter or submillimeter scales, posing significant detection difficulties. Acoustic detection leverages the acoustic wave signals generated by gas leaks for non-contact monitoring, offering advantages such as rapid response and broad coverage. However, its susceptibility to environmental noise interference often triggers false alarms. This limitation can be mitigated through time-frequency analysis, multi-sensor fusion, and deep-learning algorithms—effectively enhancing leak signals, suppressing background noise, and thereby improving the system’s detection robustness and accuracy. OGI utilizes infrared imaging technology to visualize leakage gas and is applicable to the detection of various polar gases. Its primary limitations include low image resolution, low contrast, and interference from complex backgrounds. Mitigation techniques involve background subtraction, optical flow estimation, fully convolutional neural networks (FCNNs), and vision transformers (ViTs), which enhance image contrast and extract multi-scale features to boost detection precision. Multimodal fusion technology integrates data from diverse sensors, such as acoustic and optical devices. Key challenges lie in achieving spatiotemporal synchronization across multiple sensors and effectively fusing heterogeneous data streams. Current methodologies primarily utilize decision-level fusion and feature-level fusion techniques. Decision-level fusion offers high flexibility and ease of implementation but lacks inter-feature interaction; it is less effective than feature-level fusion when correlations exist between heterogeneous features. Feature-level fusion amalgamates data from different modalities during the feature extraction phase, generating a unified cross-modal representation that effectively resolves inter-modal heterogeneity. In conclusion, we posit that multimodal fusion holds significant potential for further enhancing detection accuracy beyond the capabilities of existing single-modality technologies and is poised to become a major focus of future research in this domain. Full article

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30 pages, 815 KB

Open AccessReview

Next-Generation Machine Learning in Healthcare Fraud Detection: Current Trends, Challenges, and Future Research Directions

by Kamran Razzaq and Mahmood Shah

Information 2025, 16(9), 730; https://doi.org/10.3390/info16090730 (registering DOI) - 25 Aug 2025

Abstract

The growing complexity and size of healthcare systems have rendered fraud detection increasingly challenging; however, the current literature lacks a holistic view of the latest machine learning (ML) techniques with practical implementation concerns. The present study addresses this gap by highlighting the importance [...] Read more.

The growing complexity and size of healthcare systems have rendered fraud detection increasingly challenging; however, the current literature lacks a holistic view of the latest machine learning (ML) techniques with practical implementation concerns. The present study addresses this gap by highlighting the importance of machine learning (ML) in preventing and mitigating healthcare fraud, evaluating recent advancements, investigating implementation barriers, and exploring future research dimensions. To further address the limited research on the evaluation of machine learning (ML) and hybrid approaches, this study considers a broad spectrum of ML techniques, including supervised ML, unsupervised ML, deep learning, and hybrid ML approaches such as SMOTE-ENN, explainable AI, federated learning, and ensemble learning. The study also explored their potential use in enhancing fraud detection in imbalanced and multidimensional datasets. A significant finding of the study was the identification of commonly employed datasets, such as Medicare, the List of Excluded Individuals and Entities (LEIE), and Kaggle datasets, which serve as a baseline for evaluating machine learning (ML) models. The study’s findings comprehensively identify the challenges of employing machine learning (ML) in healthcare systems, including data quality, system scalability, regulatory compliance, and resource constraints. The study provides actionable insights, such as model interpretability to enable regulatory compliance and federated learning for confidential data sharing, which is particularly relevant for policymakers, healthcare providers, and insurance companies that intend to deploy a robust, scalable, and secure fraud detection infrastructure. The study presents a comprehensive framework for enhancing real-time healthcare fraud detection through self-learning, interpretable, and safe machine learning (ML) infrastructures, integrating theoretical advancements with practical application needs. Full article

(This article belongs to the Special Issue Emerging Applications of Machine Learning in Healthcare, Industry, and Beyond)

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22 pages, 3691 KB

Open AccessArticle

Graph Convolutional Network with Agent Attention for Recognizing Digital Ink Chinese Characters Written by International Students

by Huafen Xu and Xiwen Zhang

Information 2025, 16(9), 729; https://doi.org/10.3390/info16090729 (registering DOI) - 25 Aug 2025

Abstract

Digital ink Chinese characters (DICCs) written by international students often contain various errors and irregularities, making the recognition of these characters a highly challenging pattern recognition problem. This paper designs a graph convolutional network with agent attention (GCNAA) for recognizing DICCs written by [...] Read more.

Digital ink Chinese characters (DICCs) written by international students often contain various errors and irregularities, making the recognition of these characters a highly challenging pattern recognition problem. This paper designs a graph convolutional network with agent attention (GCNAA) for recognizing DICCs written by international students. Each sampling point is treated as a vertex in a graph, with connections between adjacent sampling points within the same stroke serving as edges to create a Chinese character graph structure. The GCNAA is used to process the data of the Chinese character graph structure, implemented by stacking Block modules. In each Block module, the graph agent attention module not only models the global context between graph nodes but also reduces computational complexity, shortens training time, and accelerates inference speed. The graph convolution block module models the local adjacency structure of the graph by aggregating local geometric information from neighboring nodes, while graph pooling is employed to learn multi-resolution features. Finally, the Softmax function is used to generate prediction results. Experiments conducted on public datasets such as CASIA-OLWHDB1.0-1.2, SCUT-COUCH2009 GB1&GB2, and HIT-OR3C-ONLINE demonstrate that the GCNAA performs well even on large-category datasets, showing strong generalization ability and robustness. The recognition accuracy for DICCs written by international students reaches 98.7%. Accurate and efficient handwritten Chinese character recognition technology can provide a solid technical foundation for computer-assisted Chinese character writing for international students, thereby promoting the development of international Chinese character education. Full article

(This article belongs to the Section Artificial Intelligence)

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12 pages, 243 KB

Open AccessArticle

On Information-Theoretic Scaling Laws for Wireless Networks

by Liang-Liang Xie

Information 2025, 16(9), 728; https://doi.org/10.3390/info16090728 (registering DOI) - 25 Aug 2025

Abstract

In the development of large wireless networks, scaling law studies can provide fundamental insights. For example, is it possible to build an arbitrarily large wireless network without a wired infrastructure while maintaining a constant communication rate for each user? This is equivalent to [...] Read more.

In the development of large wireless networks, scaling law studies can provide fundamental insights. For example, is it possible to build an arbitrarily large wireless network without a wired infrastructure while maintaining a constant communication rate for each user? This is equivalent to asking if a linear scaling law is achievable for wireless networks. Whether too ambitious a goal or not, this question has attracted intensive research but still remains open. Among many proposals, the hierarchical scheme is impressive in exploiting the MIMO gain with a bootstrapping strategy. In this paper, a careful analysis of the hierarchical scheme exposes the potential influence of the pre-constant in deriving scaling laws. It is found that a modified hierarchical scheme can achieve a throughput up to an arbitrary factor higher than the original one, although it is still short of linear scaling. This study demonstrates the essential importance of the throughput formula itself, rather than the scaling laws consequently derived. Full article

(This article belongs to the Section Wireless Technologies)

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23 pages, 1749 KB

Open AccessReview

A Survey of IPv6 Address Scanning Technologies

by Yang Ma, Liquan Chen and Zhanfeng Wang

Information 2025, 16(9), 727; https://doi.org/10.3390/info16090727 (registering DOI) - 25 Aug 2025

Abstract

Due to the huge network address space of IPv6, the overhead of IPv6 space detection is very large. Thus, how to improve the detection efficiency is a hot research topic. Firstly, the basic process of IPv6 network space detection is illustrated, and then [...] Read more.

Due to the huge network address space of IPv6, the overhead of IPv6 space detection is very large. Thus, how to improve the detection efficiency is a hot research topic. Firstly, the basic process of IPv6 network space detection is illustrated, and then the detection algorithms are categorized into two primary categories: optimization scanning-based detection algorithms and address generation-based detection algorithms. According to the timelines proposed by the algorithms, the ideas of these algorithms are discussed separately. Finally, a comprehensive comparison and analysis of these detection algorithms are conducted from multiple dimensions. Future research directions are discussed, including: collection of active IPv6 addresses and allocation patterns, discovery of IPv6 address detection patterns based on machine learning, improvement of the detection response rate, and moral issues caused by network detection. Full article

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14 pages, 1113 KB

Open AccessArticle

Image Captioning Using Topic Faster R-CNN-LSTM Networks

by Jui-Feng Yeh, Kuei-Mei Lin and Chun-Chieh Chen

Information 2025, 16(9), 726; https://doi.org/10.3390/info16090726 - 25 Aug 2025

Abstract

Image captioning is an important task in cross-modal research in numerous applications. Image captioning aims to capture the semantic content of an image and express it in a linguistically and contextually appropriate sentence. However, existing models mostly trend to focus on a topic [...] Read more.

Image captioning is an important task in cross-modal research in numerous applications. Image captioning aims to capture the semantic content of an image and express it in a linguistically and contextually appropriate sentence. However, existing models mostly trend to focus on a topic generated by the most conspicuous foreground objects. Thus, other topics in the image are often ignored. To address these limitations, we propose a model that can generate richer semantic content and more diverse captions. The proposed model can capture not only main topics using coarse-grained objects but also finds fine-grained visual information from background or minor foreground objects. Our image captioning system combines the ResNet, LSTM, and topic feature models. The ResNet model extracts fine-grained image features and enriches the description of objects. The LSTM model provides a longer context for semantics, increasing the fluency and semantic completeness of the generated sentences. The topic model determines multiple topics based on the image and text content. The topics provide directions for the model to generate different sentences. We evaluate our model on the MSCOCO dataset. The results show that compared with other models, our model achieves a certain improvement in higher-order BLEU scores and a significant improvement in CIDEr score. Full article

(This article belongs to the Special Issue Information Processing in Multimedia Applications)

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28 pages, 2551 KB

Open AccessArticle

Artificial Intelligence in Education (AIEd): Publication Patterns, Keywords, and Research Focuses

by Weijing Zhu, Luxi Wei and Yinghong Qin

Information 2025, 16(9), 725; https://doi.org/10.3390/info16090725 - 25 Aug 2025

Abstract

Since the advent of generative AI, research on AI in Education (AIEd) has experienced explosive growth. This study systematically explores publication dynamics, keyword evolution, and research focuses in AIEd by analyzing 2952 papers from the Web of Science (1990–2024). Using bibliometric methods, 2800 [...] Read more.

Since the advent of generative AI, research on AI in Education (AIEd) has experienced explosive growth. This study systematically explores publication dynamics, keyword evolution, and research focuses in AIEd by analyzing 2952 papers from the Web of Science (1990–2024). Using bibliometric methods, 2800 English publications were screened, with analyses conducted via VOSviewer v1.6.20 and Python v3.11.5. Findings show a surge in publications post-2020, reaching 612 in 2023 and 1216 by November 2024. The US and China are leading contributors, with the University of London and the University of California system as core institutions. Keywords evolved from “AI” and “machine learning” (2018–2020) to “ChatGPT” and “ethics” (post-2022), reflecting dual focuses on technological applications and ethical considerations. Notably, 68% of highly cited papers address ethical controversies, while higher education and medical education emerge as primary application domains, involving personalized learning and intelligent tutoring systems. Cross-disciplinary research is evident, with education studies comprising the largest category. The study reveals AIEd’s shift toward socio-technical integration, highlighting generative AI’s transformative role yet identifying gaps in ethical governance and K-12 research. These insights inform policymakers, journals, and institutions, advocating for enhanced interdisciplinary collaboration and long-term impact research to balance innovation with educational ethics. Full article

(This article belongs to the Special Issue AI Technology-Enhanced Learning and Teaching)

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25 pages, 804 KB

Open AccessArticle

The Role of Mutual Information Estimator Choice in Feature Selection: An Empirical Study on mRMR

by Nikolaos Papaioannou, Georgios Myllis, Alkiviadis Tsimpiris and Vasiliki Vrana

Information 2025, 16(9), 724; https://doi.org/10.3390/info16090724 - 25 Aug 2025

Abstract

Maximum Relevance Minimum Redundancy (mRMR) is a widely used feature selection method that is applied in a wide range of applications in various fields. mRMR adds to the optimal subset the features that have high relevance to the target variable while having minimum [...] Read more.

Maximum Relevance Minimum Redundancy (mRMR) is a widely used feature selection method that is applied in a wide range of applications in various fields. mRMR adds to the optimal subset the features that have high relevance to the target variable while having minimum redundancy with each other. Mutual information is a key component of mRMR as it measures the degree of dependence between two variables. However, the real value of mutual information is not known and needs to be estimated. The aim of this study is to examine whether the choice of mutual information estimator affects the performance of mRMR. To this end, three variations of mRMR are compared. The first one uses Parzen window estimation to assess mutual information between continuous variables. The second is based on equidistant partitioning using the cells method, while the third incorporates a bias-corrected version of the same estimator. All methods are tested with and without a regularization term in the mRMR denominator, introduced to improve numerical stability. The evaluation is conducted on synthetic datasets where the target variable is defined as a combination of continuous features, simulating both linear and nonlinear dependencies. To demonstrate the applicability of the proposed methods, we also include a case study in real-world classification tasks. The study carried out showed that the choice of mutual information estimator can affect the performance of mRMR and it must be carefully selected depending on the dataset and the parameters of the examined problem. The application of the corrected mutual information estimator improves the performance of mRMR in the examined setup. Full article

(This article belongs to the Section Information and Communications Technology)

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36 pages, 590 KB

Open AccessReview

Machine Translation in the Era of Large Language Models:A Survey of Historical and Emerging Problems

by Duygu Ataman, Alexandra Birch, Nizar Habash, Marcello Federico, Philipp Koehn and Kyunghyun Cho

Information 2025, 16(9), 723; https://doi.org/10.3390/info16090723 - 25 Aug 2025

Abstract

Historically regarded as one of the most challenging tasks presented to achieve complete artificial intelligence (AI), machine translation (MT) research has seen continuous devotion over the past decade, resulting in cutting-edge architectures for the modeling of sequential information. While the majority of statistical [...] Read more.

Historically regarded as one of the most challenging tasks presented to achieve complete artificial intelligence (AI), machine translation (MT) research has seen continuous devotion over the past decade, resulting in cutting-edge architectures for the modeling of sequential information. While the majority of statistical models traditionally relied on the idea of learning from parallel translation examples, recent research exploring self-supervised and multi-task learning methods extended the capabilities of MT models, eventually allowing the creation of general-purpose large language models (LLMs). In addition to versatility in providing translations useful across languages and domains, LLMs can in principle perform any natural language processing (NLP) task given sufficient amount of task-specific examples. While LLMs now reach a point where they can both replace and augment traditional MT models, the extent of their advantages and the ways in which they leverage translation capabilities across multilingual NLP tasks remains a wide area for exploration. In this literature survey, we present an introduction to the current position of MT research with a historical look at different modeling approaches to MT, how these might be advantageous for the solution of particular problems, and which problems are solved or remain open in regard to recent developments. We also discuss the connection of MT models leading to the development of prominent LLM architectures, how they continue to support LLM performance across different tasks by providing a means for cross-lingual knowledge transfer, and the redefinition of the task with the possibilities that LLM technology brings. Full article

(This article belongs to the Special Issue Human and Machine Translation: Recent Trends and Foundations)

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17 pages, 1852 KB

Open AccessArticle

A Hybrid Classical-Quantum Neural Network Model for DDoS Attack Detection in Software-Defined Vehicular Networks

by Varun P. Sarvade, Shrirang Ambaji Kulkarni and C. Vidya Raj

Information 2025, 16(9), 722; https://doi.org/10.3390/info16090722 (registering DOI) - 25 Aug 2025

Abstract

A typical Software-Defined Vehicular Network (SDVN) is open to various cyberattacks because of its centralized controller-based framework. A cyberattack, such as a Distributed Denial of Service (DDoS) attack, can easily overload the central SDVN controller. Thus, we require a functional DDoS attack recognition [...] Read more.

A typical Software-Defined Vehicular Network (SDVN) is open to various cyberattacks because of its centralized controller-based framework. A cyberattack, such as a Distributed Denial of Service (DDoS) attack, can easily overload the central SDVN controller. Thus, we require a functional DDoS attack recognition system that can differentiate malicious traffic from normal data traffic. The proposed architecture comprises hybrid Classical-Quantum Machine Learning (QML) methods for detecting DDoS threats. In this work, we have considered three different QML methods, such as Classical-Quantum Neural Networks (C-QNN), Classical-Quantum Boltzmann Machines (C-QBM), and Classical-Quantum K-Means Clustering (C-QKM). Emulations were conducted using a custom-built vehicular network with random movements and varying speeds between 0 and 100 kmph. Also, the performance of these QML methods was analyzed for two different datasets. The results obtained show that the hybrid Classical-Quantum Neural Network (C-QNN) method exhibited better performance in comparison with the other two models. The proposed hybrid C-QNN model achieved an accuracy of 99% and 90% for the UNB-CIC-DDoS dataset and Kaggle DDoS dataset, respectively. The hybrid C-QNN model combines PennyLane’s quantum circuits with traditional methods, whereas the Classical-Quantum Boltzmann Machine (C-QBM) leverages quantum probability distributions for identifying anomalies. Full article

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10 pages, 655 KB

Open AccessReview

AI-Enhanced Cyber Science Education: Innovations and Impacts

by William Triplett

Information 2025, 16(9), 721; https://doi.org/10.3390/info16090721 - 22 Aug 2025

Abstract

Personalized, scalable, and data-driven learning is now possible in cyber science education because of artificial intelligence (AI). This article examines how AI technologies, such as intelligent tutoring, adaptive learning, virtual labs, and AI assessments, are being included in cyber science curricula. Using examples [...] Read more.

Personalized, scalable, and data-driven learning is now possible in cyber science education because of artificial intelligence (AI). This article examines how AI technologies, such as intelligent tutoring, adaptive learning, virtual labs, and AI assessments, are being included in cyber science curricula. Using examples and research studies published between 2020 and 2025 that have undergone peer review, this paper combines qualitative analysis and framework analysis to discover any similarities in how these policies were put into place and their effects. According to the findings, using AI in instruction boosts student interest, increases the number of courses finished, improves skills, and ensures clear instruction in areas such as cybersecurity, digital forensics, and incident response. Ethical issues related to privacy, bias in algorithms, and access issues are also covered in this paper. This study gives a useful approach that helps teachers, curriculum designers, and institution heads use AI in cyber education properly. Full article

(This article belongs to the Special Issue AI Technology-Enhanced Learning and Teaching)

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18 pages, 1149 KB

Open AccessArticle

Advanced Cryptography Using Nanoantennas in Wireless Communication

by Francisco Alves, João Paulo N. Torres, P. Mendonça dos Santos and Ricardo A. Marques Lameirinhas

Information 2025, 16(9), 720; https://doi.org/10.3390/info16090720 - 22 Aug 2025

Abstract

This work presents an end-to-end encryption–decryption framework for securing electromagnetic signals processed through a nanoantenna. The system integrates amplitude normalization, uniform quantization, and Reed–Solomon forward error correction with key establishment via ECDH and bitwise XOR encryption. Two signal types were evaluated: a synthetic [...] Read more.

This work presents an end-to-end encryption–decryption framework for securing electromagnetic signals processed through a nanoantenna. The system integrates amplitude normalization, uniform quantization, and Reed–Solomon forward error correction with key establishment via ECDH and bitwise XOR encryption. Two signal types were evaluated: a synthetic Gaussian pulse and a synthetic voice waveform, representing low- and high-entropy data, respectively. For the Gaussian signal, reconstruction achieved an RMSE = 11.42, MAE = 0.86, PSNR = 26.97 dB, and Pearson’s correlation coefficient = 0.8887. The voice signal exhibited elevated error metrics, with an RMSE = 15.13, MAE = 2.52, PSNR = 24.54 dB, and Pearson correlation = 0.8062, yet maintained adequate fidelity. Entropy analysis indicated minimal changes between the original signal and the reconstructed signal. Furthermore, avalanche testing confirmed strong key sensitivity, with single-bit changes in the key altering approximately 50% of the ciphertext bits. The findings indicate that the proposed pipeline ensures high reconstruction quality with lightweight encryption, rendering it suitable for environments with limited computational resources. Full article

(This article belongs to the Section Information and Communications Technology)

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22 pages, 8391 KB

Open AccessArticle

Combine Virtual Reality and Machine-Learning to Identify the Presence of Dyslexia: A Cross-Linguistic Approach

by Michele Materazzini, Gianluca Morciano, José Manuel Alcalde-Llergo, Enrique Yeguas-Bolívar, Giuseppe Calabrò, Andrea Zingoni and Juri Taborri

Information 2025, 16(9), 719; https://doi.org/10.3390/info16090719 - 22 Aug 2025

Abstract

This study explores the use of virtual reality (VR) and artificial intelligence (AI) to predict the presence of dyslexia in Italian and Spanish university students. In particular, the research investigates whether VR-derived data from Silent Reading (SR) tests and self-esteem assessments can differentiate [...] Read more.

This study explores the use of virtual reality (VR) and artificial intelligence (AI) to predict the presence of dyslexia in Italian and Spanish university students. In particular, the research investigates whether VR-derived data from Silent Reading (SR) tests and self-esteem assessments can differentiate between students that are affected by dyslexia and students that are not, employing machine learning (ML) algorithms. Participants completed VR-based tasks measuring reading performance and self-esteem. A preliminary statistical analysis (t-tests and Mann–Whitney tests) on these data was performed, to compare the obtained scores between individuals with and without dyslexia, revealing significant differences in completion time for the SR test, but not in accuracy, nor in self-esteem. Then, supervised ML models were trained and tested, demonstrating an ability to classify the presence/absence of dyslexia with an accuracy of 87.5% for Italian, 66.6% for Spanish, and 75.0% for the pooled group. These findings suggest that VR and ML can effectively be used as supporting tools for assessing dyslexia, particularly by capturing differences in task completion speed, but language-specific factors may influence classification accuracy. Full article

(This article belongs to the Special Issue Machine Learning and Artificial Intelligence with Applications)

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26 pages, 2328 KB

Open AccessArticle

Physiological State Recognition via HRV and Fractal Analysis Using AI and Unsupervised Clustering

by Galya Georgieva-Tsaneva, Krasimir Cheshmedzhiev, Yoan-Aleksandar Tsanev and Miroslav Dechev

Information 2025, 16(9), 718; https://doi.org/10.3390/info16090718 - 22 Aug 2025

Abstract

Early detection of physiological dysregulation is critical for timely intervention and effective health management. Traditional monitoring systems often rely on labeled data and predefined thresholds, limiting their adaptability and generalization to unseen conditions. To address this, we propose a framework for label-free classification [...] Read more.

Early detection of physiological dysregulation is critical for timely intervention and effective health management. Traditional monitoring systems often rely on labeled data and predefined thresholds, limiting their adaptability and generalization to unseen conditions. To address this, we propose a framework for label-free classification of physiological states using Heart Rate Variability (HRV), combined with unsupervised machine learning techniques. This approach is particularly valuable when annotated datasets are scarce or unavailable—as is often the case in real-world wearable and IoT-based health monitoring. In this study, data were collected from participants under controlled conditions representing rest, stress, and physical exertion. Core HRV parameters such as the SDNN (Standard Deviation of all Normal-to-Normal intervals), RMSSD (Root Mean Square of the Successive Differences), DFA (Detrended Fluctuation Analysis) were extracted. Principal Component Analysis was applied for dimensionality reduction. K-Means, hierarchical clustering, and Density-based spatial clustering of applications with noise (DBSCAN) were used to uncover natural groupings within the data. DBSCAN identified outliers associated with atypical responses, suggesting potential for early anomaly detection. The combination of HRV descriptors enabled unsupervised classification with over 90% consistency between clusters and physiological conditions. The proposed approach successfully differentiated the three physiological conditions based on HRV and fractal features, with a clear separation between clusters in terms of DFA α1, α2, LF/HF, and RMSSD (with high agreement to physiological labels (Purity ≈ 0.93; ARI = 0.89; NMI = 0.92)). Furthermore, DBSCAN identified three outliers with atypical autonomic profiles, highlighting the potential of the method for early warning detection in real-time monitoring systems. Full article

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38 pages, 4467 KB

Open AccessArticle

Causal Decoupling for Temporal Knowledge Graph Reasoning via Contrastive Learning and Adaptive Fusion

by Siling Feng, Housheng Lu, Qian Liu, Peng Xu, Yujie Zheng, Bolin Chen and Mengxing Huang

Information 2025, 16(9), 717; https://doi.org/10.3390/info16090717 - 22 Aug 2025

Abstract

Temporal knowledge graphs (TKGs) are crucial for modeling evolving real-world facts and are widely applied in event forecasting and risk analysis. However, current TKG reasoning models struggle to separate causal signals from noisy observations, align temporal dynamics with semantic structures, and integrate long-term [...] Read more.

Temporal knowledge graphs (TKGs) are crucial for modeling evolving real-world facts and are widely applied in event forecasting and risk analysis. However, current TKG reasoning models struggle to separate causal signals from noisy observations, align temporal dynamics with semantic structures, and integrate long-term and short-term knowledge effectively. To address these challenges, we propose the Temporal Causal Contrast Graph Network (TCCGN), a unified framework that disentangles causal features from noise via orthogonal decomposition and adversarial learning; applies dual-domain contrastive learning to enhance both temporal and semantic consistency; and introduces a gated fusion module for adaptive integration of static and dynamic features across time scales. Extensive experiments on five benchmarks (ICEWS14/05-15/18, YAGO, GDELT) show that TCCGN consistently outperforms prior models. On ICEWS14, it achieves 42.46% MRR and 31.63% Hits@1, surpassing RE-GCN by 1.21 points. On the high-noise GDELT dataset, it improves MRR by 1.0%. These results highlight TCCGN’s robustness and its promise for real-world temporal reasoning tasks involving fine-grained causal inference under noisy conditions. Full article

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33 pages, 2223 KB

Open AccessArticle

Modelling the Behavioural Side of Textile Waste Collection: From Individual Habits to Systemic Design

by Francesco Zammori, Francesco Moroni and Giovanni Romagnoli

Information 2025, 16(9), 716; https://doi.org/10.3390/info16090716 - 22 Aug 2025

Abstract

This paper contributes to the field of urban waste collection systems, which are crucial for advancing sustainability, urban cleanliness, and the aesthetic quality of cities. Specifically, it introduces a novel framework designed to support planners and decision makers in the design of efficient [...] Read more.

This paper contributes to the field of urban waste collection systems, which are crucial for advancing sustainability, urban cleanliness, and the aesthetic quality of cities. Specifically, it introduces a novel framework designed to support planners and decision makers in the design of efficient and responsive textile waste collection systems, aligned with both environmental objectives and citizen engagement. To this end, the framework exploits a hybrid simulation platform that realistically models the logistics infrastructure in a spatially explicit environment. Also, within the framework, citizens are represented as adaptive agents whose environmental attitudes evolve through personal experience, social influence, and perceived service quality. The behavioural layer is the core element of the framework. It enables dynamic analysis of the two-way feedback between citizen participation and service effectiveness to underscore the often-overlooked role of citizen behaviour in shaping overall system performance. The model was tested in a representative urban scenario under varying operational conditions. The results highlight how policy incentives and smart collection infrastructure can significantly boost participation, while social segregation may hinder the adoption of sustainable practices. The framework ultimately offers a generalisable decision-support tool to explore the behavioural dimension of circular economy initiatives and develop robust, scenario-based strategies. Full article

(This article belongs to the Special Issue Intelligent Agent and Multi-Agent System)

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16 pages, 2093 KB

Open AccessArticle

Neuromarketing and Health Marketing Synergies: A Protection Motivation Theory Approach to Breast Cancer Screening Advertising

by Dimitra Skandali, Ioanna Yfantidou and Georgios Tsourvakas

Information 2025, 16(9), 715; https://doi.org/10.3390/info16090715 - 22 Aug 2025

Abstract

This study investigates the psychological and emotional mechanisms underlying women’s reactions to breast cancer awareness advertisements through the dual lens of Protection Motivation Theory (PMT) and neuromarketing methods, addressing a gap in empirical research on the integration of biometric and cognitive approaches in [...] Read more.

This study investigates the psychological and emotional mechanisms underlying women’s reactions to breast cancer awareness advertisements through the dual lens of Protection Motivation Theory (PMT) and neuromarketing methods, addressing a gap in empirical research on the integration of biometric and cognitive approaches in health marketing. Utilizing a lab-based experiment with 78 women aged 40 and older, we integrated Facial Expression Analysis using Noldus FaceReader 9.0 with semi-structured post-exposure interviews. Six manipulated health messages were embedded within a 15 min audiovisual sequence, with each message displayed for 5 s. Quantitative analysis revealed that Ads 2 and 5 elicited the highest mean fear scores (0.45 and 0.42) and surprise scores (0.35 and 0.33), while Ad 4 generated the highest happiness score (0.31) linked to coping appraisal. Emotional expressions—including fear, sadness, surprise, and neutrality—were recorded in real time and analyzed quantitatively. The facial analysis data were triangulated with thematic insights from interviews, targeting perceptions of threat severity, vulnerability, response efficacy, and self-efficacy. The findings confirm that fear-based appeals are only effective when paired with actionable coping strategies, providing empirical support for PMT’s dual-process model. By applying mixed-methods analysis to the evaluation of health messages, this study makes three contributions: (1) it extends PMT by validating the emotional–cognitive integration framework through biometric–qualitative convergence; (2) it offers practical sequencing principles for combining threat and coping cues; and (3) it proposes cross-modal methodology guidelines for future health campaigns. Full article

(This article belongs to the Special Issue Human–Computer Interaction in Marketing: Emerging Interfaces, Cognitive Engagement, and Child–Computer Interaction)

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15 pages, 373 KB

Open AccessArticle

Diagnosing Structural Change in Digital Interventions: A Configurational Evaluation Framework

by Nachiket Mor, Ritika Ramasuri and Divya Saraf

Information 2025, 16(9), 714; https://doi.org/10.3390/info16090714 - 22 Aug 2025

Abstract

Digital interventions are widely promoted as levers of institutional change, yet their effects often prove fragile. We examine why some interventions persist while others fade. Using crisp-set Qualitative Comparative Analysis (csQCA) on 13 large-scale cases from India and abroad, we identify the configurations [...] Read more.

Digital interventions are widely promoted as levers of institutional change, yet their effects often prove fragile. We examine why some interventions persist while others fade. Using crisp-set Qualitative Comparative Analysis (csQCA) on 13 large-scale cases from India and abroad, we identify the configurations of conditions under which digital systems become self-sustaining. We conceptualise persistence as a shift in the Nash equilibrium: when incentives realign, the new behaviour maintains itself without continuing external push. The analysis shows that software openness is neither necessary nor sufficient for durable change. Instead, six non-technological conditions—regulatory enablement, a credible revenue model, substantial scale, a clearly targeted systemic barrier, presence of enabling prerequisites, and sufficient time—are each necessary and, in combination, sufficient for an equilibrium shift; no single condition is enough on its own. Successful cases (e.g., Aadhaar, UPI, Chalo, Swiggy) meet these conditions in combination, whereas others (e.g., ONDC, DIKSHA, ICDS-CAS) illustrate how missing elements limit institutional embedding. The paper contributes a theory-informed diagnostic that links game-theoretic stability to configurational evaluation and provides practical “if–then” decision rules for appraisal. We argue that policy and investment decisions should prioritise incentive-compatible ecosystems over software attributes, and judge success by whether interventions reconfigure the rules of the game rather than by short-term uptake. This perspective clarifies when digital systems can contribute to sustainable, inclusive institutional transformation. Full article

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Open AccessArticle

A Bibliometric Analysis of AI-Driven Performance Prediction in Higher Education

by Berat Ujkani, Sead Ujkani, Daniela Minkovska and Nikolay Hinov

Information 2025, 16(9), 713; https://doi.org/10.3390/info16090713 - 22 Aug 2025

Abstract

This study presents a comprehensive bibliometric analysis of research publications on artificial intelligence (AI) applications in higher education, with a particular focus on student performance-related studies. Drawing on 1431 documents retrieved from the Web of Science (WoS) Core Collections, advanced bibliometric tools, such [...] Read more.

This study presents a comprehensive bibliometric analysis of research publications on artificial intelligence (AI) applications in higher education, with a particular focus on student performance-related studies. Drawing on 1431 documents retrieved from the Web of Science (WoS) Core Collections, advanced bibliometric tools, such as VOSViewer (1.6.19) and Biblioshiny, were used to explore research trends, citation networks, co-authorship patterns, and keyword co-occurrences. The results revealed an increase in AI-related educational research during the COVID-19 pandemic, reflecting the rise in the reliance on AI for enhancing learning outcomes in times of educational disruption. The findings also indicated that the research focus is gradually moving towards using AI for educational assessment, emphasizing the importance of accurate and data-driven evaluation of student performance. The co-occurrence of keywords and citation analyses confirmed that machine learning, deep learning, and predictive modeling are among the dominant AI techniques applied to assess and predict student outcomes. Furthermore, the study highlights AI’s potential in identifying learning gaps and enabling personalized interventions, allowing educators to address students’ specific needs more effectively. This new trend suggests a growing recognition of AI’s role in refining educational methodologies and improving performance evaluations at the tertiary level. Full article

(This article belongs to the Special Issue Artificial Intelligence and Games Science in Education)

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