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        <title>Frontiers in Big Data | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/big-data</link>
        <description>RSS Feed for Frontiers in Big Data | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-08-04T14:31:05.80+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1899143</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1899143</link>
        <title><![CDATA[A computational analysis of human rights discourse in news media using institutional theory]]></title>
        <pubdate>2026-07-31T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Joel Andrew B. Cruz Jr</author><author>Ma. Rowena R. Caguiat</author>
        <description><![CDATA[IntroductionNews coverage of politically sensitive events involves framing choices that accumulate across thousands of headlines, years, and media institutions. Likewise, no single reader or journalist can track these patterns as they happen. This study examines how English-language news media framed Rodrigo Duterte's anti-drug campaign in the Philippines from June 2016 to June 2025.MethodsThe study applies BERTopic topic modeling and VADER sentiment analysis to 1,133 headlines collected through Google News's RSS endpoint. Institutional Theory organizes the results around three pressures: normative, coercive, and mimetic.ResultsTen topic clusters emerged from the corpus, centered on human rights violations, International Criminal Court (ICC) legal proceedings, and post-presidency accountability narratives. Domestic Philippine outlets covered human rights themes in a larger share of headlines than international outlets during the campaign's early years, but that pattern reversed after Duterte left office in 2022. By 2024 and 2025, international outlets covered human rights themes in roughly 64 percent of headlines, compared with about half for domestic outlets. Sentiment analysis classified 807 headlines as negative, 203 as neutral, and 123 as positive. Negative coverage rose sharply in early 2025, around Duterte's arrest on an ICC warrant. Headline language also clustered around three institutional events: the 2018 ICC filing, the 2020 United Nations (UN) Human Rights Report, and the 2021 ICC investigation authorization, each marked by distinct word patterns across positive, negative, and neutral coverage.DiscussionNormative pressure explains why domestic outlets led on human rights framing before any international body had acted. Coercive pressure from the ICC and UN corresponds to the sentiment spikes around each event. Mimetic pressure explains how framing conventions moved between domestic and international outlets over time, first from domestic to international sources and later in the other direction. The study also considers how Google News's own curation choices shape which outlets and frames appear in the corpus. These findings suggest computational analysis is both a research method and a way to hold a media record open to scrutiny after the news cycle moves on.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1886902</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1886902</link>
        <title><![CDATA[Shifting supply chain interdependencies among global automakers]]></title>
        <pubdate>2026-07-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hiromitsu Goto</author><author>Wataru Souma</author>
        <description><![CDATA[Against the backdrop of electrification and supply chain resilience, the global automotive industry is in an era of great transformation. Using component supply information provided by MarkLines, this study investigated the impact on inter-firm dependencies among major global automakers from 2018 to 2024. Specifically, it analyzed changes in the community structure of the interdependency network between manufacturers, based on common suppliers for each model year and component category. The results revealed the impact of electrification: a reduction of roughly two-thirds (about 64%) in transactions for internal combustion engine (ICE) powertrains and an approximately eight-fold increase in e-powertrain transactions. Furthermore, the results of community detection also revealed a structural reorganization: while geographical clustering among manufacturers intensified for ICE components, new cross-border interdependencies formed for e-powertrain components, leading to the fragmentation of the traditionally integrated Japan-US-Europe bloc and the emergence of a China-centric ecosystem. This study provides new empirical evidence on the structural realignment of the global automotive value chain, offering important implications for management strategy and industrial policy in an era of great technological and geopolitical change.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1878242</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1878242</link>
        <title><![CDATA[A cost-sensitive random forest framework for ARP spoofing detection in Internet of Medical Things networks]]></title>
        <pubdate>2026-07-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Siddhartha Singhal</author><author>Kakelli Anil Kumar</author>
        <description><![CDATA[IntroductionARP spoofing poses a major security threat to Internet of Medical Things (IoMT) networks by enabling man-in-the-middle attacks that compromise the integrity of life-critical communications. Existing intrusion detection methods fail to simultaneously address temporal attack dynamics, unequal medical safety requirements, and explicit control of false negative rates.MethodsThis study proposes the Self-Healing IoT-Optimized Random Forest (SH-IORF) framework, which integrates temporal behavioral feature engineering, validation-guided cost-sensitive learning, and medical safety-constrained threshold optimization. To ensure methodological rigor and prevent information leakage, a stratified three-way partitioning strategy consisting of training, validation, and completely held-out testing datasets was employed. Class penalty weights and operating thresholds were determined exclusively from the validation dataset.ResultsExperimental evaluation on the CICIoMT2024 benchmark demonstrated that the proposed SH-IORF framework achieved 99.90% accuracy, 99.83% recall, 99.95% precision, a 0.9989 F1-score, and an AUC-ROC of 0.9996. The framework limited the false negative rate to 0.17%, satisfying the predefined medical safety constraint (FNR ≤ 0.5%), corresponding to 40 missed detections among 23,390 attack samples and 12 false alarms across 28,768 benign traffic instances.DiscussionThe results demonstrate that the proposed framework provides stable and safety-oriented intrusion detection capability under heterogeneous IoMT deployment conditions while maintaining strict testing independence and robust performance under rigorous evaluation settings.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1846964</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1846964</link>
        <title><![CDATA[AI applied to Saudi Arabia higher education: systematic literature review—SLR with PRISMA and VOSviewer]]></title>
        <pubdate>2026-07-22T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Jehad Alqurni</author>
        <description><![CDATA[This study explored the role of artificial intelligence in higher education in Saudi Arabia. It aims to identify trends, key contributors, influential papers, collaborations, and important research areas from January 2022 to February 2026 to guide future research. The researchers used bibliometric and content analyses. This combined quantitative descriptive methods and network analysis with qualitative content analysis of the most-cited articles. They extracted data from Scopus and the Web of Science, resulting in 66 documents after removing duplicates, editorials, and notes. The analytical techniques included the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), co-word analysis, citation analysis, co-authorship analysis, and bibliographic coupling. VOSviewer supported the visualization. The key findings show that King Abdulaziz University, Qassim University, and King Saud University are the top contributors. A total of 52 papers have been published in journals indexed by WOS and Scopus, while 14 papers are indexed only by Scopus. Additionally, five major groups revealed significant correlations among various word pairs: High-education, Artificial intelligence, AI Chatbot, learning systems, and ChatGPT. The leading journals include Sustainability, Acta Psychological, and Systems, with notable authors such as Al-Harbi. Content analysis highlights the potential of AI to improve learning, boost administrative efficiency, and drive innovation in Saudi Arabia. This study offers practical recommendations for students, universities, and policy makers. This study has several limitations that should be addressed in future studies.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1832790</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1832790</link>
        <title><![CDATA[Machine learning approach for predicting the severity risk of obstructive sleep apnea syndrome]]></title>
        <pubdate>2026-07-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Qi Wang</author><author>Xiaoyu Yang</author><author>Shuran Xu</author><author>Haohao Wu</author><author>Guixuan Wang</author><author>Huixian Liu</author><author>Ronghua Chen</author><author>Fengming Xu</author><author>Cheng Wang</author><author>Kang Du</author>
        <description><![CDATA[BackgroundObstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) has a high global prevalence and is prone to causing various serious complications. Our objective is to develop severity stratification of OSAHS by integrating multiple commonly available clinical features based on machine learning (ML).Materials and methodsThis study collected data from 432 cases at Qujing Central Hospital in Yunnan Province, integrating 25 clinical feature variables. The cases were randomly split into training (70%) and validation (30%) sets. The importance of the 25 features was analyzed.ResultsIt showed that the HCY, TBIL, BMI, GGT, and Age made significant contributions to OSAHS severity. We established five machine learning models-Multilayer Perceptron (MLP), Random Forest, XGBoost, LightGBM, and Support Vector Machine (SVM)-by integrating 25 clinical features. Through cross-validation and continuous adjustment of model parameters, the optimal predictive model was determined. By calculating model accuracy and F1-score, XGBoost was identified as the best-performing model, achieving an area under the curve (AUC) of 0.63, an accuracy of 75% and an F1-score of 65.60.ConclusionIn this study, we established a predictive model for the severity stratification of OSAHS based on machine learning algorithms. The XGBoost model demonstrated superior predictive performance.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1878260</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1878260</link>
        <title><![CDATA[A dataset-centric review of IoT and IIoT intrusion detection: realism, evaluation biases, and future research directions]]></title>
        <pubdate>2026-07-10T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Dwarsala Sreedhar Reddy</author><author>Kakelli Anil Kumar</author>
        <description><![CDATA[The rapid growth of IoT and IIoT expands the cyber-attack surface of interconnected and safety-critical systems, and, as such, IDSs have become a fundamental security mechanism. Although very impressive results have been reported for machine learning and deep learning-based IDS in benchmark datasets, these gains often do not generalize to real-world deployments owing to dataset design limitations, realism deficits, and evaluation biases, rather than inherent flaws in detection algorithms, which can lead to significant vulnerabilities in actual operational environments. This study presents a dataset-centric review of widely used intrusion detection datasets from the IIoT, IoT, and traditional network domains. A unified taxonomy differentiates datasets based on the domain context, traffic representation, protocol semantics, and attack modeling assumptions. Based on a common analytical framework, each dataset was reviewed regarding its realism, coverage of the threats, class imbalance, temporal continuity, and modern ML/DL-based evaluation of the IDS. The cross-dataset analysis conducted in this study shows that, in addition to the fact that model architecture and feature engineering play a major role, several studies indicate that the simplicity of the datasets, the class imbalance, and the repetitive attack patterns as well as the evaluation methods can affect accuracy of the IDS. This work further underlines the remaining gaps, such as zero-day and adaptive attacks, limited encrypted traffic, weak temporal evolution, poor support for federated learning, and sparse annotations for explainable IDSs. Finally, this study presents future directions for dataset design aligned with the requirements of next-generation IDSs by highlighting digital twin-based IIoT environments, edge-cloud collaborative data generation, sequential traffic modeling, and explainability-oriented annotations that can ensure robust, trustworthy, and deployment-ready IDS solutions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1728498</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1728498</link>
        <title><![CDATA[A prognostic tool for pulmonary collapse: nomogram-based prediction of 28-day mortality]]></title>
        <pubdate>2026-07-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xinming He</author><author>Wenchong Yu</author><author>Yuling Li</author><author>Ao Ma</author><author>Zhichao Meng</author><author>Jiehao Zhu</author><author>Minghui Tan</author><author>Xiaodong Zhao</author><author>Mu Chen</author>
        <description><![CDATA[BackgroundPulmonary collapse is a common and serious respiratory condition, but there is no dedicated bedside tool to estimate prognosis. This study aimed to develop a nomogram to predict 28-day mortality in patients with pulmonary collapse.MethodsWe extracted data for patients with pulmonary collapse from MIMIC-III, identified predictors using regression analyses, and used MIMIC-IV for temporal validation. We then built a nomogram based on the selected predictors. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), AUC comparisons using the DeLong test, reclassification (NRI and IDI), calibration (calibration curves, calibration slope, and Brier score), and decision curve analysis (DCA).ResultsA total of 4,088 patients with pulmonary collapse were included in the study. Logistic regression analysis identified twelve independent predictive factors associated with 28-day mortality: age (OR = 1.01, P =0.040), married status (OR =0 .63, P =0 .040), Glasgow Coma Scale score (OR = 0.94, P = 0.02), creatinine (OR = 0.82, P = 0.03), chloride ions (OR = 0.85, P = 0.03), sodium ions (OR = 1.19, P = 0.02), blood urea nitrogen (OR = 1.02, P < 0.001), white blood cell count (OR = 1.04, P < 0.001), heart rate (OR = 1.02, P = 0.02), respiratory rate (OR = 1.05, P = 0.03), temperature (OR = 0.54, P < 0.001), and metastatic cancer (OR = 6.66, P < 0.001). The nomogram showed moderate discrimination and consistently higher AUC than Age+Gender, SOFA, and SAPSII across cohorts.ConclusionThis study identified factors associated with 28-day mortality in patients with pulmonary collapse and developed a nomogram for early risk stratification.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1837706</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1837706</link>
        <title><![CDATA[Noise-robust temporal–spectral fusion transformers for EEG-based cognitive state classification in aviation environments]]></title>
        <pubdate>2026-07-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Quynh Anh Nguyen</author><author>Nam Anh Dao</author><author>Long Nguyen</author>
        <description><![CDATA[Attention-related Pilot Performance Decrements (APPD) contribute substantially to aviation incidents, yet existing electroencephalography (EEG)-based monitoring methods often lack generalization, robustness to noise, and effective temporal–spectral integration. We propose a temporal–spectral fusion transformer (TF-T) combining multi-scale preprocessing, dual-stream temporal and spectral feature extraction, and transformer-based fusion with enhanced temporal–spectral integration and multi-resolution feature processing for multiclass cognitive state recognition. Three variants (TF-T1–TF-T3) are evaluated on controlled and ecologically realistic EEG datasets under clean and noise-augmented (Gaussian, Uniform, COMBO) conditions, using chronological partitioning to avoid temporal leakage. TF-T2 achieves the highest clean-data accuracy (99.2%), while TF-T3 offers superior robustness, improving Macro-F1 by ~4.5–4.7 points across all noise types and outperforming state-of-the-art baselines by up to +8 Macro-F1 under COMBO noise, supporting its deployment in perturbation-prone aviation environments.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1842233</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1842233</link>
        <title><![CDATA[Evolutionary multi-agent reinforcement learning for crisis-aware demographic policy optimization]]></title>
        <pubdate>2026-07-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Anton V. Dozhdikov</author><author>Arseniy M. Sitkovskiy</author>
        <description><![CDATA[Demographic systems face unprecedented challenges from simultaneous crises. Conventional statistical demography techniques and agent–based models often struggle to capture nonlinear inter–regional interactions during periods of severe socio–economic disruption. To address this, we propose MADDPG–EVO–DGM, a hybrid algorithm that integrates multi–agent deep reinforcement learning with evolutionary optimisation and meta–learning principles to model regional demographic processes under multiple crisis scenarios. Each region is treated as an autonomous agent learning to steer demographic policy levers, while periodic evolutionary “boosters” overcome local optima via population–based perturbations of actor network parameters. Additionally, a Darwin–Gödel Machine–inspired meta–learning mechanism adapts the booster triggers, enabling self–improvement in the learning process. We evaluate MADDPG–EVO–DGM on a simulation environment calibrated with real demographic data for eight federal regions of the Russian Federation over the period 2000–2024 and subject to ten concurrent crisis scenarios (e.g., pandemic, geopolitical conflict, economic collapse). Experiments demonstrate significantly faster convergence and improved performance over a baseline MADDPG: the hybrid approach achieves a higher final average reward (252.57 vs. 243.07) and 3.4 × lower convergence variance (σ = 0.24 vs. 0.80), indicating more reliable training. It also exhibits qualitative performance jumps of +68% during evolutionary phases and maintains 35%–45% greater resilience under crisis shocks compared to the baseline. To our knowledge, this is the first application of multi–agent reinforcement learning to large–scale demographic modeling under crises, opening new possibilities for evidence–based, crisis–resilient population policy design. Code, data, and logs are provided to ensure reproducibility.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1883452</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1883452</link>
        <title><![CDATA[Cross-model evaluation of phishing detectors against LLM-generated emails]]></title>
        <pubdate>2026-07-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Rommel Gutierrez</author><author>William Villegas-Ch</author><author>Jaime Govea</author>
        <description><![CDATA[Phishing remains a prevalent cyberattack vector, and the widespread adoption of large language models (LLMs) has enabled adversaries to generate grammatically correct and contextually coherent phishing emails at scale, against which conventional detection systems are less effective. Although stylometric methods achieve over 95% accuracy within a single generator, their performance has not been systematically evaluated when the source model changes between training and deployment. This represents a significant gap, as adversaries can switch generators rapidly. A balanced corpus of 9,986 phishing emails was assembled, comprising 4,986 emails generated by three modern LLMs (GPT-4.1, DeepSeek 3.2, and Llama 3.3 70B) across five thematic categories, and 5,000 human phishing emails sampled in a stratified manner from five public sources. Seventeen stylometric features were extracted, and Logistic Regression and XGBoost classifiers were evaluated under intra-model, cross-model, threshold-recalibrated, cross-dataset, and aggregated-pool settings. Intra-model F1 scores reached 0.96 under stratified cross-validation and 0.999 on held-out splits used for the cross-model matrix. However, cross-model F1 dropped by 28.0 percentage points under the default decision threshold of 0.5. Notably, the area under the receiver operating characteristic curve remained above 0.96 in every off-diagonal cell, indicating that discriminative information is preserved even though the decision threshold is generator-specific. Recalibrating the threshold on a small target subset reduced the gap to 4.0 percentage points (an 86% reduction), and an aggregated-pool detector achieved F1 = 0.997 on each generator. This work reframes cross-model phishing detection from a problem of model incompatibility to one of practical calibration, and provides two deployable solutions, threshold recalibration on a small target slice and aggregated-pool training, along with a publicly released multi-LLM corpus.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1871346</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1871346</link>
        <title><![CDATA[Adaptive class-aware feature selection for high-dimensional and imbalanced multi-class network intrusion detection]]></title>
        <pubdate>2026-06-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Joseph P. Mchina</author><author>Neema Mduma</author><author>Ramadhani S. Sinde</author>
        <description><![CDATA[High-dimensional feature spaces and severe class imbalance remain fundamental challenges for Machine Learning-based Network Intrusion Detection Systems (ML-NIDS), where minority attack categories are frequently overlooked during feature selection. Existing feature selection approaches commonly rely on global feature relevance measures and manually specified feature counts, which favor majority traffic classes and reduce sensitivity toward rare but critical attack categories. To address these limitations, this study proposes the Adaptive Class-Aware Feature Selection (ACAFS) framework for multi-class intrusion detection. Unlike conventional approaches, ACAFS introduces a data-driven adaptive feature count mechanism based on permutation null hypothesis testing, a Class-Aware Composite Mutual Information scoring strategy that explicitly preserves minority-class discriminative information, and a coordinated two-stage feature selection framework that combines statistical filtering with XGBoost-based model refinement. The framework was evaluated independently on the CSE-CIC-IDS2018 benchmark dataset and a Simulated University Network Environment (SUNE) dataset representing Tanzanian higher learning institution networks. Experimental results demonstrate that ACAFS substantially reduces feature dimensionality while improving balanced intrusion detection performance. On CSE-CIC-IDS2018, ACAFS reduced the feature space from 74 to 22 features, representing a 70.3% dimensionality reduction, while the Two-Stage CNN achieved 99.39% accuracy, 99.40% F1-score, and a false positive rate of 0.09%. The framework further achieved 98.59% recall for Web_Attacks despite severe class imbalance, demonstrating effective preservation of minority-class discriminative features. On the SUNE dataset, ACAFS independently selected 18 features and maintained stable detection performance without dataset-specific manual tuning, confirming its adaptability across heterogeneous network environments. These results confirm that adaptive and class-aware feature selection can simultaneously reduce feature redundancy, improve minority attack detection, and maintain robust intrusion detection performance across diverse network traffic environments.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1807559</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1807559</link>
        <title><![CDATA[Influence of localized roadway surface obstacles on vehicular emissions under real-world urban driving conditions]]></title>
        <pubdate>2026-06-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Victor Cardoso Oliveira</author><author>Thiago Iachiley Araújo de Souza</author><author>Nicole Souza Batista</author><author>Bruno Vieira Bertoncini</author><author>Verônica Teixeira Franco Castelo Branco</author>
        <description><![CDATA[IntroductionVehicular emissions are a major source of air pollution in tropical urban environments. While the impacts of technology, traffic flow, and driving behavior on pollutant formation are well established, the influence of pavement surface remains insufficiently understood. Pavement defects such as potholes, cracks, and depressions disturb vehicle operation and may increase real-world emissions. This study evaluates the influence of pavement obstacles on emissions of CO2, CO, and NOx across five urban road segments in Fortaleza, Brazil.MethodsA portable emissions measurement system (PEMS) collected second-by-second exhaust data during real driving. Roadway surface obstacles were captured through windshield-mounted images acquired at 1 Hz. A broader image pool comprising 44,175 roadway images was assembled from multiple urban roads for model development. From this pool, a final annotated dataset comprising 1,812 images with 3,158 labeled obstacles was used for training, validation, and testing the YOLOv8n detector, which was then applied to the five monitored road sections used in the synchronized emission analysis. Emission data and obstacle locations were synchronized, enabling comparison of pollutant rates along obstacle-present and obstacle-free segments, with emphasis on features likely to influence short-term driving behavior. Detection performance was evaluated using precision, recall, and mAP metrics.ResultsRoad segments with higher obstacle occurrence presented elevated emission rates. In the full dataset, maximum values reached 478.2 g/km for CO2, 491.96 mg/km for CO, and 100.266 mg/km for NOx. A filtered analysis excluding curves, intersection buffers, and visible traffic or pedestrian interference showed that obstacle-present observations still exhibited higher emissions than obstacle-free observations, with average increases of 26% for CO2, 31% for NOx, and 42% for CO. Spatial mapping showed that emission hotspots tended to occur in areas with frequent roadway surface obstacles and operational disturbances.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1687969</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1687969</link>
        <title><![CDATA[Deep learning model to predict COPD hospital admissions based on meteorological data: a medical meteorological forecast]]></title>
        <pubdate>2026-06-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Lei Zhang</author><author>Mingjie Zhang</author><author>Jinghong Zhang</author><author>Yajie Zhang</author><author>Tian Xie</author><author>Yipeng Ding</author><author>Shuyuan Chu</author><author>Haihong Wu</author>
        <description><![CDATA[BackgroundChronic obstructive pulmonary disease (COPD) has placed a substantial health burden on the world. Meteorological conditions are associated with hospital admissions for COPD. In this study, we aim to develop a model of medical meteorological forecasting for COPD hospital admissions.MethodsA predictive model was developed using a Long Short-Term Memory (LSTM) algorithm applied to time series data on COPD hospital admissions and meteorological conditions. Data were collected daily from 25 September 2016 to 26 December 2020. Performance of the model was assessed using the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and R2. The association between the risk of COPD hospital admissions and meteorological conditions was assessed using a conditional logistic regression analysis and a conditional Poisson regression analysis in a time-stratified case-crossover design.ResultsA total of 17,555 hospital admissions for COPD from 1 January 2017 to 31 December 2019 were included in the final LSTM model. Regarding the performance of the LSTM model, the MSE was 0.028, the RMSE was 0.167, the MAE was 0.134, and R2 was 0.416. Regression analysis revealed that the maximum temperature was positively associated with COPD hospital admissions.ConclusionThe LSTM model offers potential for medical meteorological forecasting to predict COPD hospital admissions among the general population according to the local climate. Higher maximum temperature may be a risk factor for COPD hospital admissions.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1857064</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1857064</link>
        <title><![CDATA[Where diverse populations gather: transit accessibility and the spatial structure of social mixing]]></title>
        <pubdate>2026-06-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yuan Liao</author>
        <description><![CDATA[Urban venues serve as arenas for social mixing, yet less is known about how public transit infrastructure shapes the geography of mixing at specific locations. This study examines how transit catchment diversity—the socioeconomic heterogeneity of populations reachable by public transit—associates with visitor diversity at points of interest (POIs) in nine Swedish and three US cities. Using mobile phone GPS traces and aggregated foot traffic data from 2024, we compute visitor diversity indices based on visitors' home-neighborhood birth-background composition and employ spatial regression models and geographically weighted regression (GWR). Transit catchment diversity positively predicts visitor diversity across nearly all cities, but this association is robust only in the largest metropolitan areas; in smaller cities, the coefficient attenuates to insignificance once geographic catchment composition, centrality, and venue density are controlled. Spatial spillovers in visitor diversity follow general geographic proximity rather than shared transit-stop connectivity, suggesting that the association operates through catchment population composition rather than station-level linkages. Transit–diversity hotspots occur not in already-diverse venues, but in lower-diversity POIs with lower commercial density, greater distance from transit in US cities, and greater centrality in Sweden. These patterns are consistent with transit-accessible population composition being associated with visitor diversity, particularly where alternative pathways to diverse co-presence are limited.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1826953</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1826953</link>
        <title><![CDATA[Inner layer security reinforcement for instant payment systems: a dual layer encryption-steganography evaluation in Brunei's digital payment context]]></title>
        <pubdate>2026-06-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ampuan Shazani bin Ampuan Haji Sadikin</author><author>Heru Susanto</author>
        <description><![CDATA[IntroductionThe rapid adoption of real-time digital payment systems introduces cybersecurity risks that extend beyond technical vulnerabilities to include significant human and organizational factors.MethodsThis study evaluates a dual-layer data protection mechanism combining AES-128 encryption with spread spectrum audio steganography within Brunei's digital payment context. Stakeholder interviews with Cyber Security Brunei (CSB), National Digital Payments Network (NDPx), and a local bank were conducted alongside experimental testing using Stripe Sandbox.ResultsHuman factor issues accounted for 47% of identified cybersecurity concerns. Experimental results demonstrated that spread spectrum steganography achieved an 87.5% robustness rate across eight attack scenarios while maintaining near real-time performance with an average processing time of 568.82 ms and acceptable audio quality (PSNR 26.30 dB). Sandbox validation confirmed feasibility within realistic payment workflows.DiscussionThe findings support data-centric security as a compensating control in human-dominated threat environments and demonstrate the viability of combining encryption and steganography to reinforce instant payment security.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1838191</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1838191</link>
        <title><![CDATA[Measuring the impact of virtualization and containerization on the environment when using GPUs for processing the AI models]]></title>
        <pubdate>2026-06-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Safaa Hriez</author><author>Mohammad Haikal</author>
        <description><![CDATA[IntroductionThe rapid growth of artificial intelligence (AI) has significantly increased computing demand, intensifying the operational strain on the computing environment. While virtualization and containerization are established technologies for resource optimization, their comparative energy efficiency and environmental impact, particularly under GPU-accelerated AI workloads, are not well-quantified.MethodsThis study evaluates the energy consumption and environmental impact of virtualization and containerization technologies when using Graphics Processing Units (GPUs) for AI model execution. Employing a computer vision benchmark, the performance, GPU resource utilization, and power consumption were measured. The experiment involved training a DenseNet-121 model on the MNIST dataset within a VirtualBox virtual machine and a Docker container environment.ResultsThe analysis indicates that containerization consistently surpasses virtualization in energy efficiency. Specifically, Docker container configuration demonstrated an approximately 21.6% reduction in total energy consumption, and a corresponding reduction in carbon dioxide (CO2) emissions compared to a VirtualBox virtual machine. Furthermore, containerization exhibited lower average and peak GPU utilization and power consumption.DiscussionThese findings demonstrate that containerization offers a more energy-efficient and environmentally sustainable approach than VirtualBox virtualization for the specific GPU-enabled AI workload evaluated in this study. Statistical significance testing indicates that the observed performance differentials are significant, supporting the validity of the results within the experimental scope of this work.ConclusionImplementing containerization in this experimental setup may reduce energy consumption and environmental impact without compromising computational performance. Future studies should extend these analyses to larger neural network models, diverse AI workloads, and heterogeneous GPU platforms to enhance the generalizability of these findings beyond the current single-system experimental configuration.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1835663</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1835663</link>
        <title><![CDATA[Using artificial intelligence to improve governance and public services in Africa]]></title>
        <pubdate>2026-06-15T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>David Mhlanga</author>
        <description><![CDATA[African governments continue to face persistent challenges in delivering efficient, transparent, and inclusive public services due to institutional constraints, rapid population growth, and fragmented administrative systems. At the same time, accelerating digital transformation and expanding data ecosystems create new opportunities for governance reform. This study examines the role of Artificial Intelligence (AI) in enhancing public sector performance across welfare targeting, healthcare delivery, tax administration, and urban governance. Drawing on a structured narrative literature review, the paper develops a conceptual framework that conceptualizes governance outcomes as a function of data availability, AI capability, institutional capacity, and human oversight. The findings suggest that AI can improve service delivery by enabling predictive decision-making, reducing administrative inefficiencies, and enhancing targeting accuracy. However, these benefits depend on the alignment between technological adoption and institutional readiness, as weak governance systems may amplify risks such as bias, exclusion, and accountability gaps. The study concludes that AI must be embedded in inclusive, context-sensitive governance strategies to support sustainable development outcomes in Africa.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1752468</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1752468</link>
        <title><![CDATA[Data field theory: a geometric framework for learning on Riemannian manifolds with synthetic validation and limitation analysis]]></title>
        <pubdate>2026-06-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mohammadreza Nehzati</author>
        <description><![CDATA[IntroductionConventional machine learning treats learning as parameter optimization, lacking a first-principles framework for phenomena like criticality, generalization, and causal structure. We introduce Data Field Theory (DFT), a mathematical framework modelling learning as the evolution of a data field governed by stochastic partial differential equations on Riemannian manifolds. This work aims to validate DFT's core predictions in settings where its geometric assumptions hold, while honestly assessing its empirical limitations.MethodsWe formulate learning as a field φ:M×ℝ≥0→ℝk evolving on a spherical manifold. To test DFT, we implement a hierarchical classification task using synthetic data drawn from von Mises-Fisher distributions, ensuring match with the manifold geometry. We derive four key predictions: (1) critical exponents near concept formation, (2) a spectral robustness law linking Eigen gaps to out-of-distribution (OOD) error, (3) finite-speed causal propagation from hyperbolic regularization, and (4) approximate rotational equivariance via a Ward identity. We also conduct a preliminary real-data experiment projecting MNIST digits onto the sphere.ResultsSynthetic experiments validate all four predictions: (1) Correlation length diverges as ξ(t)~|t-tc|-ν with ν = 0.63 ± 0.04, accompanied by 1/f fluctuations; (2) OOD generalization error scales as ϵOOD∝mgap-2 (ρ = −0.78, p < 10−6); (3) Causal propagation speed ceff = 0.98 ± 0.03 (theory maximum cmax = 1.0) under hyperbolic regularization; and (4) Ward identity residual R = 0.0032 ± 0.0008 converging as R∝h1.02. However, on real-world MNIST-sphere data, DFT achieves only 15.7% accuracy versus 51.7% for k-NN, revealing critical limitations.DiscussionDFT successfully predicts emergent phenomena criticality, spectral robustness, bounded causality, and approximate equivariance under ideal geometric conditions, supporting its theoretical validity. The poor real-data performance highlights key gaps: the current framework lacks adaptive metric learning, noise robustness, and hierarchical feature extraction present in real images. These results establish DFT as a principled mathematical foundation for learning as field dynamics while clearly delineating necessary extensions for practical applicability.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1736939</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1736939</link>
        <title><![CDATA[Case count metric for comparative analysis of entity resolution results]]></title>
        <pubdate>2026-06-11T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>John R. Talburt</author><author>Muzakkiruddin Ahmed Mohammed</author><author>Mert Can Cakmak</author><author>Onais Khan Mohammed</author><author>Mahboob Khan Mohammed</author><author>Khizer Syed</author><author>Leon Claassens</author>
        <description><![CDATA[IntroductionEntity resolution (ER) systems often produce different clustering outcomes when applied to the same dataset, especially when parameters, algorithms, or system configurations change. However, in many real-world settings, the true linking structure is unknown, making traditional accuracy-based evaluation difficult.MethodsThis paper presents the Case Count Metric System (CCMS), a process and software system for comparing two cluster ER outcomes without requiring a truth set. CCMS classifies how each cluster from the first ER process is transformed by the second process into four mutually exclusive cases: unchanged, merged, partitioned, or overlapping.ResultsCCMS produces aggregate case counts, singleton summaries, and per-cluster transformation details to support diagnostic analysis. Example applications using synthetic demographic data and an industrial materials dataset show that CCMS can identify how clustering outcomes change under parameter adjustments and alternative ER systems.DiscussionCCMS provides a practical and interpretable method for comparing ER clustering results when labeled ground truth is unavailable. By distinguishing between over-linking, under-linking, and more complex cluster reorganizations, CCMS offers more actionable insight than single-value similarity measures and supports both research analysis and operational ER evaluation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fdata.2026.1883246</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fdata.2026.1883246</link>
        <title><![CDATA[Correction: Explainable gradient convolutional vector fuzzy pattern analysis based on ensemble model for facial expression recognition]]></title>
        <pubdate>2026-06-10T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Lakshmi Sarvani Videla</author><author>Babu Reddy Mukamalla</author>
        <description></description>
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