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        <title>Frontiers in Artificial Intelligence | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/artificial-intelligence</link>
        <description>RSS Feed for Frontiers in Artificial Intelligence | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-11-06T09:00:19.987+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1919009</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1919009</link>
        <title><![CDATA[Developing and evaluating an AI-assisted chatbot for promotion and tenure guidance in an academic medical center]]></title>
        <pubdate>2026-11-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>May May Yeo</author><author>Deborah D. Navedo</author><author>Patrick J. Casey</author><author>Bernard C. Y. Tan</author>
        <description><![CDATA[IntroductionThe SingHealth Duke–NUS Academic Medical Center (AMC) oversees 3,000 clinical faculty members and processes 400 appointments and promotions annually; yet, because its promotion and tenure (P&T) policies are dispersed across 30 documents, the relevant information is time-consuming to locate.MethodsIn response, we designed and evaluated “AskADD,” an AI-assisted chatbot for P&T guidance using a two-phase (alpha-beta) approach. Our aim was to iteratively design and refine such a chatbot to meet faculty-identified requirements; capture faculty’s perceptions of its usability, responsiveness, empathy, and accessibility; assess the changes in these self-perceived evaluations between iterations; and determine faculty’s perceptions of its effectiveness relative to traditional methods of locating P&T information. Thirty-five faculty and staff, recruited by email, tested an initial prototype built on Microsoft low/no-code platforms, and following refinements informed by their feedback, they evaluated a subsequent custom generative pre-trained transformer built on the institutional platform. Feedback gathered via a structured survey was analyzed descriptively.ResultsParticipants reported improvements in the perceived usability, empathy, and query resolution, and the proportion of promoters rose from 23% to 51%. Faculty valued accurate, contextually relevant responses requiring minimal rephrasing, direct links to source documents, and integration of P&T and human-resource information; approximately half nonetheless remained passively satisfied, indicating a preference for personalization such as CV-based gap analysis.DiscussionThese preliminary findings serve as proof of concept and offer a transferable, platform-agnostic framework for developing and evaluating AI policy-guidance tools in academic medical centers, which merits confirmation with validated instruments and objective performance measures.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1918322</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1918322</link>
        <title><![CDATA[LARU-net: lesion-aware residual attention U-net for binary cucumber leaf lesion segmentation and continuous severity estimation]]></title>
        <pubdate>2026-11-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Batyrkhan Omarov</author><author>Nurbibi Imanbayeva</author><author>Arman Ibrayeva</author>
        <description><![CDATA[Plant leaf diseases threaten agricultural productivity and motivate accurate automated monitoring. This study presents LARU-Net, a unified architecture for binary cucumber lesion segmentation (lesion versus background) with an auxiliary continuous severity-regression output on a bounded 0–4 scale. The network integrates a Disease Feature Enhancement Autoencoder, Residual Multi-Scale Encoder, transformer bottleneck, Attention-Guided Decoder, and Boundary Refinement Module. On the declared held-out test partition (1,080 images), the archived full-model results were Dice 0.9230, IoU 0.8660, Boundary F1 0.9350, and precision 0.9460; the previously emphasized Dice value of 0.9456 is a validation value and is now identified as such. Severity estimates yielded an overall MAE of 0.29, RMSE of 0.37, and R2 of 0.90. These values are single-run point estimates from one greenhouse image collection. Plant/session identifiers, complete acquisition and annotation provenance, repeated-run logs, and an external dataset were not preserved in the supplied study archive; therefore, the results support in-domain evaluation but not broad cross-greenhouse or cross-season generalization. The split was stratified at image level; independence at plant, leaf, or acquisition-session level could not be verified. Confidence intervals and significance tests were not reconstructed because per-image predictions and repeated-run logs were unavailable.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1967344</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1967344</link>
        <title><![CDATA[A hybrid XGBoost-RF residual learning model for energy consumption forecasting]]></title>
        <pubdate>2026-11-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Monire Norouzi</author><author>Can Eyupoglu</author><author>Oktay Karakuş</author>
        <description><![CDATA[Recently, energy consumption forecasting plays an important role in power systems, smart grids, and power generation networks. Given the complex nature and time dependence of electricity consumption data, applying machine learning techniques improves energy forecasting accuracy and performance. In this research, a hybrid XGBoost–Random Forest (XGBoost–RF) model is proposed to make better the forecasting accuracy. To improve the performance of the proposed model, a residual learning-based forecasting framework is presented to learn the remaining prediction errors. In the forecasting procedure, important prediction metrics such as Mean Absolute Error (MAE), and coefficient of determination R2 are evaluated. The experimental results show that the proposed hybrid model achieves an MAE of 15.8683, Root Mean Square Error (RMSE) of 22.1588, R2 of 0.985807, and Mean Absolute Percentage Error (MAPE) of 1.3118%. Compared with the base XGBoost model, the proposed model is able to decrease MAE by 11.19% and RMSE by 11.30%. In addition, the SHapley Additive Explanations (SHAP) method is used to examine the effect of the input features on the model predictions and improve model interpretability. The SHAP results show that temporal features and lag-based variables have an important role in energy consumption forecasting. In particular, lag_1 and lag_168 are identified as the most important features. The experimental results confirmed that the proposed hybrid model achieved lower prediction errors and a higher R2 than the other evaluated models. These findings show that the proposed XGBoost-RF residual learning approach can improve energy consumption forecasting accuracy while providing useful information about the important forecasting features.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.2006672</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.2006672</link>
        <title><![CDATA[Correction: The rise of AI in the workplace: why perceived organizational support fails to protect long-term career satisfaction]]></title>
        <pubdate>2026-11-05T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>João Matias</author><author>Joana Carneiro Pinto</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1769631</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1769631</link>
        <title><![CDATA[Research on segmentation method for Lycium barbarum garden images during non-fruiting period]]></title>
        <pubdate>2026-11-05T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Maoqiang Li</author><author>Zhifeng Liu</author><author>Shuming Yang</author><author>Jiateng Qi</author><author>Yun Chen</author><author>Yonglong Ma</author>
        <description><![CDATA[IntroductionAccurate segmentation of plant-column and path regions is essential for visual navigation and baseline extraction of autonomous agricultural platforms in Lycium barbarum gardens. During the non-fruiting period, sparse foliage, exposed branches, fallen leaves, weeds, similar soil backgrounds, and variable illumination weaken the visual contrast between plant-column and path regions, rendering conventional segmentation methods based on color or texture unreliable.MethodsWe propose a deep learning-based semantic segmentation method tailored to non-fruiting L. barbarum garden images. A region-based annotation strategy was used to label continuous plant-column and path regions following the requirements for navigation baseline extraction. The encoder–decoder model incorporates depthwise separable convolutions, skip connections, and multi-scale contextual feature extraction to enhance feature representation of complex field scenes. Image data were collected from multiple gardens across different years, growth stages, lighting conditions, irrigation regimes, and soil types; data augmentation was implemented during model training. Model performance was validated using an independent internal test set containing 230 images, and cross-garden generalization was assessed on two unseen field sites with limited fine-tuning.ResultsOn the internal test set, the model achieved precision of 95.24 ± 2.66%, recall of 94.68 ± 3.15%, mean intersection over union (mIoU) of 90.10 ± 5.43%, Dice coefficient of 94.68 ± 3.15%, and mean pixel accuracy (mPA) of 94.64 ± 3.14%. For the two unseen gardens, the raw mIoU reached 86.84% and 84.60%, and increased to 92.43% and 90.23% after fine-tuning with a small set of newly annotated images.DiscussionThe proposed method yields reliable region-level segmentation for non-fruiting *L. barbarum* garden scenes, alleviating domain shifts while preserving strong pixel-level and region-overlap performance. Minor boundary discrepancies may arise from annotation ambiguity. This approach offers solid technical support for navigation baseline extraction in agricultural robotic systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1894755</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1894755</link>
        <title><![CDATA[Recognition is not taste: interpretive space as the missing object in AI for science]]></title>
        <pubdate>2026-11-04T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Liu Ben</author>
        <description><![CDATA[Artificial intelligence (AI) for Science is shifting from systems that execute scientific tasks to systems that judge and generate research questions. Recognition-trained systems, including Reinforcement Learning from Community Feedback (RLCF), convert citations, review scores, or preference signals into supervisory signals for future research. This Perspective argues that such systems risk optimizing the wrong evaluative object: recognition is an institutional compression of scientific value, recording what a community has already rewarded, whereas scientific judgment must also preserve interpretive space, the socially available future of a claim before institutional recognition stabilizes a dominant appraisal of its value. Taste is therefore recast as one mode of appraising interpretive space, and answerability as the governance principle that keeps it open. The article proposes an interpretive layer above reward models as a research agenda for preserving interpretive space rather than merely predicting recognized success.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1975513</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1975513</link>
        <title><![CDATA[Physics-guided multi-view hybrid depth-wise separable convolution and temporal LSTM network for fault diagnosis of rotor-bearing systems]]></title>
        <pubdate>2026-11-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sandhya Mitta</author><author>Poornima Nedunchezhian</author>
        <description><![CDATA[IntroductionRolling-element bearings are critical components of rotating machinery, and their failures can affect equipment safety, availability, and service life. However, non-stationary vibration signals and background noise make reliable automatic fault diagnosis challenging. This study proposes a physics-guided Hybrid Depth-wise Separable Spatial Convolution and Temporal Long Short-Term Memory network (HDSSC-TLST) for rotor-bearing fault diagnosis.MethodsThe proposed approach derives the minimum sample length from the characteristic defect frequencies of the inner race, outer race, and rolling elements. A leakage-free chronological multi-view sliding-window scheme generates large windows to capture impact periodicity and centered small windows to resolve high-frequency transients. Both views are converted into continuous wavelet transform (CWT) scalograms using a complex Morlet wavelet. Depth-wise separable convolution blocks extract spatial time-frequency features, while a stacked LSTM models their temporal evolution to classify four conditions: normal, inner-race fault, outer-race fault, and ball fault.ResultsExperiments using the Case Western Reserve University (CWRU) 48 kHz drive-end dataset show that HDSSC-TLST achieves 94% accuracy at a signal-to-noise ratio (SNR) of 19 dB and outperforms the hybrid adaptive fusion network (HAFDL) and nonlinear auto-regressive model with exogenous inputs (NARX) at every tested SNR from 1 to 19 dB. Averaged over ten data sizes, the model achieves precision, recall, and F1-score values of 0.90, compared with 0.79, 0.78, and 0.79 for HAFDL and 0.75, 0.74, and 0.74 for NARX, respectively. It also reduces training time by 14.5% relative to HAFDL and 18.8% relative to NARX. On a leakage-free chronological split of noise-free 1 hp data, it achieves 96.0 ± 0.8% accuracy and a macro-F1 score of 0.960 ± 0.009. Its mean accuracy on unseen 0, 2, and 3 hp loads is 88.1% without retraining.DiscussionThe results indicate that combining physics-guided sample selection, multi-view time-frequency representation, depth-wise separable convolution, and temporal modeling can provide an effective and computationally efficient approach to bearing fault diagnosis under noisy conditions and changing operating loads.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1892198</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1892198</link>
        <title><![CDATA[Composite Schatten-p norm factorization for efficient matrix completion]]></title>
        <pubdate>2026-11-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jiaoyan Zhao</author><author>Shuangyan Yi</author>
        <description><![CDATA[Schatten-p norm optimization has emerged as an effective non-convex relaxation for low-rank matrix completion. However, the primary computational bottleneck arises from repeated singular value decomposition (SVD) operations required during optimization, which leads to high computational cost. In this work, we propose a novel factorized reformulation of the Schatten-p norm by decomposing it into combinations of nuclear and Frobenius norms. Specifically, we investigate three distinct decomposition schemes: the sum of two nuclear norms, the sum of two Frobenius norms, and the combination of a nuclear norm and a Frobenius norm. Based on these formulations, we develop an alternating gradient descent algorithm with rigorous convergence guarantees. By avoiding SVD, the proposed method significantly reduces computational complexity. Extensive experiments on synthetic datasets, image inpainting tasks, and recommender systems demonstrate that our approach consistently outperforms several is the state-of-the-art matrix completion methods in terms of both accuracy and computational efficiency.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1890105</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1890105</link>
        <title><![CDATA[Evidence-grounded decision support for cleantech innovation scouting: a system-oriented design-science study]]></title>
        <pubdate>2026-11-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Andri Oliver Gerber</author><author>Janna Lipenkova</author><author>Susie Xi Rao</author><author>Guang Lu</author>
        <description><![CDATA[IntroductionCleantech innovation scouting requires analysts to integrate heterogeneous evidence while preserving traceability. We present GRaph-Augmented INnovation Scouting (GRAINS), a system-oriented design-science artifact that connects governed evidence preparation, hybrid retrieval, optional graph expansion, and staged cite-or-abstain synthesis. Its contribution combines an inspectable evidence-to-answer architecture, an explicit rationale linking design choices to scouting requirements, and empirical observations of system behavior and evaluation sensitivity.MethodsWe evaluate GRAINS through an exploratory comparison on a source-derived, in-corpus benchmark of 50 broad-cleantech queries with relevance judgments from two large language model judge pipelines and a separate exploratory case-based audit of six battery-domain cases assessed by one researcher.ResultsHybrid achieved Recall@100 of 0.951 and Precision@10 of 0.278 using the combined relevance judgments of the two judge pipelines, while none of the tested retrieval contrasts reached statistical significance after global Holm correction. Comparisons of top-10 ranking quality (nDCG@10) depended on the judge pipeline and whether query-source documents were excluded. Limiting the number of retrieved chunks per document increased document diversity with a modest reduction in nDCG@10; the evaluated graph-expanded configuration had lower observed early-ranking scores than Hybrid. Structural checks confirmed citation-identifier compliance in evaluated follow-up answers. The case audit illustrated how the system organizes supported actors, applications, and uncertainties, while revealing gaps in task-specific evidence.DiscussionTogether, these findings provide bounded system-design observations on the behavior and evaluation of evidence-grounded scouting, while independent human relevance assessment and analyst-workflow validation remain necessary for broader validation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1969037</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1969037</link>
        <title><![CDATA[Geography, geospatial analysis and artificial intelligence: between traditional methods and new spatial paradigms]]></title>
        <pubdate>2026-11-03T00:00:00Z</pubdate>
        <category>Opinion</category>
        <author>Cláudia M. Viana</author><author>Jorge Rocha</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1933918</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1933918</link>
        <title><![CDATA[SACX: Suppression–Absence–Competition eXplanations for the why-not question in visual recognition]]></title>
        <pubdate>2026-11-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Adithya JK Annamalai</author><author>L. RamaParvathy</author>
        <description><![CDATA[IntroductionExisting attribution-based explainable artificial intelligence (XAI) methods for convolutional neural network (CNN) classifiers primarily address why a particular class was predicted, while providing limited insight into why a competing class was not selected. To address the question “Why class A and not class B?”, we introduce Suppression-Absence-Competition eXplanations (SACX), a framework for structured contrastive visual explanations.MethodsSACX characterizes the contrastive margin za−zb using three spatially aligned heatmaps: Hcomp, representing positive evidence for the predicted class; Hsup, representing negative attribution for the competing foil class; and Hmiss, identifying regions where expected foil-class features are absent through prototype-based similarity. We introduce three direction-aware faithfulness metrics: suppression-removal AUC (SR-AUC), feature-injection AUC (FI-AUC), and localization fidelity under injection (LFI@k). We additionally evaluate suppression using a fixed-budget placement test (SR@k). Experiments are conducted on CUB-200-2011 and Pattern Analysis, Statistical Modelling and Computational Learning (PASCAL) Visual Object Classes (VOC) 2007 using fine-tuned ResNet-50 models and a frozen DINOv2 ViT-B/14 backbone with a linear probe.ResultsOn fine-tuned ResNet-50, injecting features at top-ranked Hmiss locations outperforms random placement, improving the foil-class logit by Δzb = +0.41 (95% CI: 0.39–0.42) on CUB and +1.29 (95% CI: 1.23–1.36) on VOC. On the frozen DINOv2 backbone, all injection variants reduce the foil-class logit, but top-ranked Hmiss locations remain the least disruptive, achieving an LFI@20% top-minus-random gap of +0.68. The LFI gaps remain stable across three K-means and three probe seeds (standard deviation ≤ 0.04). The SR@k evaluation shows that Hsup outperforms random removal at tight budgets (+0.09 at k = 5% on CUB Top-2), although suppression effects are generally weak for false-negative cases.DiscussionSACX provides an interpretable structural decomposition of the existing contrastive attribution signal rather than introducing a new aggregate map, as Hcontra is mathematically equivalent to Contrastive Grad-CAM. Its prototype-based missing-evidence component preserves spatial localization rankings even under frozen self-supervised representations, although absolute injection gains require task-specific supervision. The suppression component is interpreted as negative attribution rather than a guarantee of causal suppression. These findings demonstrate the value of separating competition, suppression, and missing evidence when explaining why a competing class was not predicted.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1966982</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1966982</link>
        <title><![CDATA[Machine learning prediction model for respiratory failure in patients with asthma]]></title>
        <pubdate>2026-11-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xiaojun Hao</author><author>Xiaohui Du</author><author>Si Tang</author><author>Hongxia Jiang</author>
        <description><![CDATA[BackgroundAsthma is one of the most common chronic respiratory diseases worldwide, and its global burden continues to increase. Although most patients can achieve good disease control with standardized treatment, some still experience severe acute exacerbations triggered by infection, environmental exposure, poor treatment adherence, or comorbid COPD, which may further progress to respiratory failure. Respiratory failure is one of the most serious adverse outcomes during hospitalization for asthma and is often associated with a greater need for mechanical ventilation, longer intensive care stays, and poorer clinical prognosis. Therefore, early identification of high-risk patients and the development of reliable prediction tools are of substantial clinical importance.ObjectiveTo develop and validate a machine learning-based risk prediction model using routinely available clinical data from hospitalized patients with asthma to identify those at high risk of developing respiratory failure during hospitalization, screen key predictive factors, and evaluate the model’s discrimination, calibration, clinical net benefit, and interpretability.MethodsThis was a single-center retrospective cohort study. A total of 2,098 patients hospitalized for asthma at Zhongnan Hospital of Wuhan University between November 2016 and December 2024 were retrospectively screened. After applying predefined inclusion and exclusion criteria, 1,405 patients were included in the final analysis, among whom 233 developed respiratory failure during hospitalization (respiratory failure group) and 1,172 did not (control group). Patients were randomly assigned by stratified sampling in an 8:2 ratio to a training set (n = 1,124) and an internal validation set (n = 281). In addition, 403 patients hospitalized for asthma at the same institution between January 2025 and June 2026 were screened using the same criteria, and 371 patients were ultimately included as an temporal validation cohort from an independent time period (72 with respiratory failure and 299 controls). A total of 62 candidate variables were collected, including demographic characteristics, smoking history, COPD history, and laboratory indicators such as complete blood count, coagulation function, liver and renal function, electrolytes, and blood lipids. Feature selection was performed using least absolute shrinkage and selection operator (LASSO) regression combined with the Boruta algorithm. Eleven machine learning models were then developed based on the selected variables, including logistic regression, random forest, naive Bayes, multilayer perceptron, support vector machine, k-nearest neighbors, decision tree, gradient boosting decision tree (GBDT), LightGBM, CatBoost, and XGBoost. Model performance was comprehensively evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), precision-recall (PR) curves, and calibration curves. Shapley additive explanations (SHAP) and a nomogram were further applied to interpret and visualize the optimal model.ResultsLASSO regression combined with the Boruta algorithm ultimately identified five core predictive variables from the 62 candidate features: D-dimer, bicarbonate concentration (HCO3−), neutrophil percentage, direct bilirubin, and lymphocyte percentage. Among the 11 candidate models, CatBoost showed the best overall performance. In the training set, the model achieved an AUC of 0.945 (95% CI: 0.928–0.962), accuracy of 0.923 (95% CI: 0.889–0.962), precision of 0.943 (95% CI: 0.896–0.989), specificity of 0.994 (95% CI: 0.989–0.996), recall of 0.556 (95% CI: 0.403–0.712), F1 score of 0.699 (95% CI: 0.678–0.714), and Matthews correlation coefficient (MCC) of 0.689. In the internal validation set, the model achieved an AUC of 0.796 (95% CI: 0.723–0.868), accuracy of 0.858 (95% CI: 0.812–0.899), precision of 0.842 (95% CI: 0.758–0.911), specificity of 0.987 (95% CI: 0.973–0.994), recall of 0.302 (95% CI: 0.217–0.381), and F1 score of 0.444 (95% CI: 0.369–0.513). In the temporal validation set, the model achieved an AUC of 0.878 (95% CI: 0.829–0.926), accuracy of 0.879 (95% CI: 0.832–0.924), precision of 0.909 (95% CI: 0.857–0.962), specificity of 0.989 (95% CI: 0.982–0.995), recall of 0.417 (95% CI: 0.333–0.485), and F1 score of 0.571 (95% CI: 0.549–0.602).ConclusionThe CatBoost machine learning model developed using routinely available clinical data can accurately predict the risk of respiratory failure in hospitalized patients with asthma. The model demonstrated good discrimination, acceptable temporal generalizability, and strong interpretability, and may provide useful support for early identification of severe progression and individualized management in patients with asthma.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1879422</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1879422</link>
        <title><![CDATA[Partitioned feature selection: locally uniform feature selection with structured feature grouping for explainable predictor identification]]></title>
        <pubdate>2026-11-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tumpa Rani Shaha</author><author>Fahmid Al Farid</author><author>Momotaz Begum</author><author>Abdullah Al Mamun</author><author>Tahmina Nazneen</author><author>Jia Uddin</author><author>Hezerul Bin Abdul Karim</author>
        <description><![CDATA[IntroductionFeature selection helps identify important features in high-dimensional mental health data. However, conventional feature selection (CFS) methods commonly rank all features within a single feature space using an overall statistical or predictive criterion. This global evaluation method does not take into account the fact that symptoms of depression are interrelated and fall within various distinct clinical domains. To address this limitation, we propose partitioned feature selection (PFS), a framework that integrates domain-based feature grouping with group-wise feature selection.MethodsPFS organizes depression symptoms into clinically relevant domains and applies four complementary feature selection methods to estimate individual feature importance within each domain. The importance scores are normalized and combined, then weighted according to each domain's association with global severity to calculate integrated group feature importance (IGFI). We evaluated PFS on two independent multiclass depression datasets using 16 classifiers. Validation-guided feature retention was used to select the final feature subsets. We also conducted a ten-trial random regrouping analysis, an exploratory Wilcoxon signed-rank comparison with CFS-RFE, and a comparison of IGFI rankings with SHAP explanations.ResultsPFS selected 21 features for Dataset 1 and 23 features for Dataset 2. Using the selected, untuned support vector classifier (SVC) configuration without synthetic minority over-sampling technique (SMOTE), PFS achieved test accuracies of 95.65% and 89.60% and Macro-F1 scores of 95.43% and 92.37%, respectively. The random regrouping analysis showed that the predictive effect of clinical partitioning varied between datasets. The exploratory Wilcoxon analysis also showed dataset-dependent statistical differences between PFS and CFS-RFE. Furthermore, IGFI and SHAP identified similar important features across clinical domains.DiscussionPFS provides a structured approach to identifying important depression symptoms while preserving their clinical domain information. The findings indicate that domain-based grouping can support interpretability, although its predictive benefits vary across datasets. This study focuses on questionnaire-based depression severity estimation rather than the prediction of an independently established clinical diagnosis.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1919101</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1919101</link>
        <title><![CDATA[P2-VGAN: a lightweight velocity-weighted graph attention network for privacy-conscious edge anomaly detection]]></title>
        <pubdate>2026-11-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Meena P.</author><author>Vidhyapathi C. M.</author>
        <description><![CDATA[IntroductionTwo persistent challenges hinder intelligent video surveillance in public spaces: dense pixel-based models incur heavy computational costs on edge infrastructure, and continuous capture of identifiable imagery creates substantial visual privacy liabilities. Skeleton-based pipelines, which abstract each video frame into articulated landmark coordinates, support visual data minimization by discarding appearance information. However, conventional graph backbones weight joint trajectories uniformly across time, frequently confusing benign high-speed locomotion (such as sprinting for transit) with violent anomalies.MethodsThis paper presents the Privacy-Conscious Velocity-Weighted Graph Attention Network (P2-VGAN; the prefix “P2” is retained as a legacy identifier designating privacy-conscious representation rather than a cryptographic guarantee), an efficient framework engineered for desktop-class CPU edge gateways. The pipeline decouples motion analysis from visual appearance by extracting 33 planar body landmarks via a single-shot pose estimator; the application does not intentionally store or transmit decoded RGB frames after pose extraction, ensuring downstream processing operates strictly on appearance-reduced pose coordinates. The primary contribution is the Velocity-Weighted Attention Mechanism (VW-AM), an ultra-lightweight gating module requiring only two shared parameters that converts inter-frame coordinate displacements into a bounded squared-displacement motion proxy. This proxy modulates node features prior to multi-head graph attention, directing message passing toward rapidly moving limbs while damping near-stationary joints. Trained under a weakly supervised Multiple Instance Learning objective with temporal smoothness and sparsity regularizers, P2-VGAN is evaluated across five random seeds on the ShanghaiTech Campus and a human-centric subset of UCF-Crime.ResultsOn ShanghaiTech, the model achieves a frame-level AUC of 84.72 ± 0.28% with 1.81 M downstream trainable parameters (pose-extractor parameters excluded) and sustains 34.01 frames per second (FPS) on an Intel Core i7-10700 CPU without hardware acceleration. When compared directly with an identical Vanilla GAT backbone without gating (83.14 ± 0.39%), VW-AM provides an unconfounded +1.58% absolute AUC gain (p = 0.00096). On the human-centric UCF-Crime subset (215 test videos), it attains 77.88 ± 0.35% micro-average AUC, with high-motion categories (Fighting, Assault) exceeding 82% AUC. Stepwise ablations document an incremental +2.52% gain from VW-AM over an adaptive ST-GCN baseline.DiscussionWe position P2-VGAN as a Pareto-efficient framework for lightweight CPU surveillance, acknowledging that coordinate streams reduce visual surface exposure but do not eliminate biometric gait re-identification.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1947204</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1947204</link>
        <title><![CDATA[Coarse-to-fine-grained insulator defect detection using two-stage deep learning framework]]></title>
        <pubdate>2026-11-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sultan Daud Khan</author><author>Atif Naseer</author><author>Habib Ullah</author><author>Mohib Ullah</author>
        <description><![CDATA[IntroductionIn power transmission and power distribution systems, insulators are the fundamental components which provide mechanical support and electrical isolation between conductors and supporting towers. However, these insulators are continuously exposed to extreme weather conditions, mechanical stress, pollution and dust, which can lead to surface damage and abnormal operation.MethodConventionally, manual on-site inspection is performed to identify faulty insulators. However, this method is labor intensive, risky, and reactive. With the advancement in unmanned aerial vehicle (UAV) technology and deep learning method, automatic visual inspection has replaced the conventional methods. However, automatic visual inspection from UAV images is challenging task due to complex background and small size of the insulators. To address these challenges, we proposed a framework that consists of two stages. In the first stage, you only look once (YOLO)-based is used for extracting candidate region of insulator to get coarse class prediction. During the second stage, the candidate regions are then cropped from the image into image patches and fed to a lightweight patch-level classifier. In order to enhance inter-class separability, hard-pair contrastive classification loss is proposed by adding hard-pair contrastive term to the cross-entropy loss in the embedding space.DiscussionThe proposed technique is evaluated using an open-source insulator defect image dataset. The experiment results demonstrate the superior performance of the proposed framework compared with the YOLO variants and representative two-stage detectors.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1949474</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1949474</link>
        <title><![CDATA[MoFed-Guard: a mobility-aware federated and explainable lightweight Transformer framework for misbehavior detection in VANETs]]></title>
        <pubdate>2026-11-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Fida Muhammad Khan</author><author>Asim Zeb</author><author>Rex Bacarra</author><author>Jamil Abedalrahim Jamil Alsayaydeh</author><author>Rostam Affendi Bin Hamzah</author><author>Maqsood Mahmud</author>
        <description><![CDATA[Authenticated vehicles can disseminate false safety messages that pass cryptographic verification but remain behaviorally untruthful, making insider misbehavior a challenge in Vehicular Ad Hoc Networks (VANETs). This study proposes MoFed-Guard, a data-local federated framework combining OBU-TFT, a compact temporal Transformer, with reliability-aware aggregation and on-demand SHAP explanation. Fixed RSUs act as training clients and OBUs perform inference. MoFed-Guard weights client updates using vehicle-association stability, communication reliability, and leave-one-client-out update consistency; these indicators are evaluated empirically rather than treated as theoretically derived trust variables. On the trajectory-disjoint 20-class VeReMi Extension test partition, the framework achieved 95.20% accuracy, 94.75% macro-precision, 94.30% macro-recall, and 94.52% macro-F1. With architecture and training fixed, MoFed-Guard improved macro-F1 by 1.45 points over FedAvg and 0.61 points over FedAdam; the five-seed improvement was significant [t(4) = 6.30, p = 0.003, dz = 2.82]. Five-seed convergence reached 98.0 ± 0.2% validation macro-F1 at round 100, while held-out client-scale macro-F1 increased from 93.10% with three clients to 94.52% with ten. Explicit FL-layer delivery tests and a road-network-aware partition preserved the aggregation benefit, whereas coordinated-client corruption exposed the limitation of cosine-based consistency. The joint reliability score correlated positively with update utility (ρ = 0.71, p < 0.001). OBU-TFT contains 85,460 trainable parameters, occupies 0.34 MB, and achieved 5.10 ± 0.42 ms T4 inference latency. Parameter-matched comparison showed that OBU-TFT achieved higher macro-F1 than Linformer and Performer, although Linformer remained smaller and faster. These results support empirical reliability-conditioned federated weighting under simulation without implying universal convergence, Byzantine robustness, formal privacy, or field deployment readiness.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1860893</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1860893</link>
        <title><![CDATA[Revisiting dynamic batch scheduling for grokking: the role of batch size, sample budget, and regularization]]></title>
        <pubdate>2026-11-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Roland Fang</author>
        <description><![CDATA[Grokking—the sudden generalization of neural networks long after overfitting—has inspired training strategies aimed at accelerating its onset. This paper revisits dynamic batch size scheduling and asks whether larger batches provide intrinsically better optimization dynamics, or whether they appear better because they process more examples per update. We evaluate three dynamic schedules (linear, exponential, and step-wise increases from 32 to 512) against fixed batch sizes of 32 and 512 across four algorithmic tasks, each with 10 random seeds. When measured by update steps, the fixed-512 baseline is highly competitive: on modular division it reaches 99.6% final accuracy with 10/10 runs grokking at a mean of 3,245 steps. However, this advantage changes substantially after normalizing by total examples processed. Under a matched budget of 240,000 training examples (equivalently 960,000 input tokens), fixed-512 performs poorly and never groks on any task, while smaller-batch and dynamic strategies remain competitive on the modular tasks. A focused hyperparameter sensitivity study additionally shows that grokking depends strongly on weight decay, model size, and maximum batch size: without weight decay, none of the tested strategies grok, while intermediate or strong weight decay can make both fixed-large and dynamic strategies reliable. These results show that larger batches improve update-step efficiency but are not necessarily more sample-efficient, and that their effect is conditional on regularization and capacity. We therefore revise the original interpretation: batch size magnitude is a major confound in grokking experiments, and claims about acceleration should report both update-step and sample-normalized metrics.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1942761</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1942761</link>
        <title><![CDATA[Sustainable Federated Continual Learning for environmental monitoring: a systematic review]]></title>
        <pubdate>2026-11-02T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>G. Suruthi</author><author>Rabindra Kumar Singh</author>
        <description><![CDATA[IntroductionThe use of distributed sensing infrastructures is increasingly becoming a key approach to environmental monitoring that is required to be mobile, flexible and adaptable to non-stationary conditions, preserve data privacy and consume as few resources as possible. The decentralized training paradigm provided by Federated Learning together with the ability of adapting sequentially over time without forgetting from Continual Learning has resulted in a concept of Federated Continual Learning (FCL) which offers a promising approach for privacy preserving environmental sensing systems that are capable of learning over time without forgetting. This systematic review provides an in-depth synthesis of the latest research, progress, environmental applications, sustainability, trustworthiness and future directions of Federated Continual Learning (FCL) for environmental monitoring.MethodsA total of 90 peer-reviewed publications were identified by conducting a systematic literature search using pre-defined inclusion/exclusion criteria, quality assessment, bibliometric analysis and qualitative evidence synthesis with the framework of PRISMA 2020.ResultsThe synthesized evidence suggests that Federated Learning has come of age in terms of optimization, communication complexity, robustness, and personalization, but Federated Continual Learning is still in its infancy with few applications in the real world. Typically, sustainability, trustworthiness, privacy, fairness, personalization and explainability are explored separately and not in parallel. Although most applications have been single-shot Federated Learning systems, without genuine continual adaptation, isolated examples include some applications from air quality to water quality, agriculture to disaster monitoring and remote sensing. Seven cross-cutting research gaps are identified: disjointed integration of research directions, few deployments in the real world, absence of standardized evaluation, unsolved multi-objective optimization, poor scaling in the long term, weak sustainability-trustworthiness coupling, and poor cross-domain generalization.DiscussionIt is not just about the evolution of single algorithmic breakthroughs anymore, but actual realization of Federated Continual Learning systems that can effectively assist in continual adaptation while ensuring communication efficiency, sustainability, trustworthiness, scalability and long-term deployment under real environmental conditions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1967588</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1967588</link>
        <title><![CDATA[Hybrid one-shot and vertical federated learning framework for privacy-preserving brain tumor classification and segmentation using the BRISC2025 dataset]]></title>
        <pubdate>2026-11-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Adhi Siva M.</author><author>Chiranji Lal Chowdhary</author>
        <description><![CDATA[IntroductionFrom MRI images, diagnosis of brain tumors may have limitations due to restriction over patient privacy and lack of annotated datasets so that models cannot be trained across multiple institutions.MethodsHOS-VFL: A Hybrid One-Shot and Vertical Federated Learning framework with a two-round protocol that jointly enjoys the advantages of prototype-based one-shot aggregation and encrypted vertical feature space augmentation. Tested on BRISC2025 MRimages of glioma, meningioma, pituitary and non-tumor, with T1-weighted images, expert-annotated, at five simulated institutions, with simulated IID and Dirichlet setups.ResultsHOS-VFL obtained values of 98.73% on accuracy and 35–50 for conventional federated learning, with a 94.3% reduction compared to the best baseline valued. HOS-VFL performed 98.73% accuracy and 35–50 for the conventional federated learning with an improvement of 94.3% when compared with its best baseline accuracy. The mean Dice and IoU of Segmentation was 0.9147, 0.8423 respectively. Performance did not wane differently in terms of competitive levels and number of institutions, and was able to withstand gradient inference attacks.DiscussionHOS-VFL philosophy is centralised performance with minimum communication with retention of raw data local. The method needs to be validated on larger real multi-center datasets before their clinical use.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1966841</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1966841</link>
        <title><![CDATA[StoneXAI-Net: an explainable weakly supervised framework for kidney stone classification, localization, and segmentation from CT images]]></title>
        <pubdate>2026-11-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>P. Karthick</author><author>Chiranji Lal Chowdhary</author>
        <description><![CDATA[IntroductionKidney stone disease (urolithiasis) is one of the most frequent urological diseases worldwide; it is highly recurrent and carries a significant clinical burden and economic cost. Non-contrast computed tomography (CT) is the primary diagnostic modality and offers the highest sensitivity and specificity. However, automated kidney stone analysis remains challenging because expert pixel-level annotations are scarce, hyperdense non-stone anatomy causes mislocalization in weakly supervised localization, and explainable artificial intelligence (XAI) methods are often used circularly, both for weakly supervised localization and for its subsequent interpretation.MethodsWe present StoneXAI-Net, an explainable, weakly supervised framework to classify, localize, and segment kidney stones from CT images. The framework consists of three modules: (i) a CT-physics augmentation strategy that diversifies training and mitigates overfitting; (ii) consensus pseudo-labeling that combines complementary explanation maps with uncertainty maps and anatomical priors to suppress false localizations; and (iii) a faithfulness audit module with deletion, insertion, and pointing-game metrics that objectively evaluates explanation quality. The resulting consensus pseudo-labels were used to train an attention-prior-conditioned Attention U-Net for segmentation. The framework was evaluated on 3,364 axial abdominal CT images (1,577 stone and 1,787 normal) using five-fold cross-validation.ResultsStoneXAI-Net achieved 97.4% accuracy, 98.4% precision, 96.0% recall, a 97.1% F1-score, and a 99.5% AUC. Against consensus pseudo-masks, the full segmentation configuration achieved a proxy Dice coefficient of 0.834, an IoU of 0.777, and an HD95 of 2.94 px; anatomical post-processing reduced HD95 by 6.26 px.DiscussionStoneXAI-Net is a feasible and interpretable framework for the automated analysis of kidney stones that substantially reduces the time-consuming and costly manual annotation of kidney stones by experts. Because segmentation was evaluated against pseudo-masks at the slice level, these results are proxy estimates that require confirmation with expert annotations and patient-level validation.]]></description>
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