The paper presents a neutrosophic decomposition of a Random Forest + XGBoost + Logistic Regression ensemble for bearing fault detection, yielding four uncertainty indicators: top‑class evidence (T‑hat), best‑competitor evidence (F‑hat), predictive entropy (I1‑hat), and decision disagreement (I2‑hat). Evaluated on CWRU and JNU benchmarks under a leave‑one‑condition‑out protocol, the ensemble achieves perfect accuracy on most CWRU loads but performs poorly on JNU, where Logistic Regression outperforms the tree ensembles. Additional experiments show that fusing time‑ and frequency‑domain models and using Jensen‑Shannon divergence improves uncertainty estimation, and that envelope‑spectrum features can separate fault classes with 99.57 % accuracy on CWRU. "whyItMatters":"The study demonstrates that refined neutrosophic uncertainty indicators can reveal limitations of conventional confidence scores and guide more reliable fault detection in variable‑speed industrial settings."
arXiv:2607. 22797v1 Announce Type: cross Abstract: Trustworthy deployment of AI-based diagnosis in safety-critical mechanical systems hinges on validation: whether a prediction can be checked against physical reality before it is acted upon.
The paper proposes a Diagnostic Evidence Network (DENet) that augments traditional fault‑diagnosis outputs with structured evidence, including classification, predicted characteristic frequency, and temporal localization of impulses. This evidence aligns with theoretical bearing physics and can be validated at inference time, achieving high AUROC for misclassification detection without sacrificing accuracy. A QLoRA‑adapted language model then translates DENet’s evidence into maintenance reports, markedly reducing unsupported claims.
arXiv:2605. 20716v5 Announce Type: replace Abstract: Random forests construct each tree with a different, randomised representation of the feature space.
arXiv:2609.35929v1 Announce Type: new Abstract: Time series classification (TSC) exhibits a sharp trade-off between accuracy and computational scalability. Meta-ensembles like HIVE-COTE 2.0 reach sta...
The paper introduces a hybrid two‑stage machine learning pipeline for fault detection and classification in high‑voltage transmission networks. Stage 1 uses an Isolation Forest anomaly detector combined with an optional supervised binary detector, while Stage 2 applies a Random Forest multiclass classifier only to samples flagged by Stage 1. Feature engineering maps six raw channels to eighteen features, including zero‑sequence symmetrical components, achieving end‑to‑end accuracies of 95.8 % on the TLFaultDataset and 97.25 % on an independent single‑point dataset, surpassing federated benchmarks without GPU or federated infrastructure.
arXiv:2509. 22267v5 Announce Type: replace Abstract: Reliable detection of bearing faults is essential for maintaining the safety and operational efficiency of rotating machinery.
arXiv:2608. 08126v1 Announce Type: new Abstract: Credit scoring increasingly relies on models whose decision logic cannot be read off their parameters, in tension with supervisory expectations that adverse decisions be explainable.
arXiv:2607. 16493v1 Announce Type: new Abstract: Multi-task deep learning models that jointly perform fault classification and remaining useful life (RUL) regression are increasingly used in predictive maintenance, yet reported performance can be strongly affected by how sliding-window sequences are split into training and test sets.
arXiv:2608. 05859v1 Announce Type: cross Abstract: Interpretable classification often requires more than accurate predictions for real-life deployment: models should be transparent about the evidence behind their decisions and abstain when they cannot decide reliably.
arXiv:2609.17545v1 Announce Type: new Abstract: Deep learning models for cervical cytology are almost always evaluated as if every prediction must be acted upon, yet a screening system deployed along...
The paper introduces Counterfactual Fragility Certificates (CFC), a model‑agnostic audit protocol that maps each prediction to an evidence‑failure trajectory, summarizing it with metrics such as greedy flip budget, margin‑collapse area, degradation thresholds, and fragility dominance score. CFC is shown to identify brittle high‑confidence predictions on seven tabular benchmarks with an AUROC of 0.915, outperforming existing scalar scores by up to +0.405. The method remains effective across various perturbation and review‑budget scenarios, and can also inform fragility‑aware regularization and temperature correction.
arXiv:2606. 06871v1 Announce Type: new Abstract: Diagnosing 802.