Researchers developed a reduced NEXI protocol for quantifying human gray matter microstructure on the Connectome 2.0 scanner, using an Explainable AI framework to cut scan time from 27 to 14 minutes. …
Researchers used interpretable machine learning to predict Parkinson's disease motor severity from QSM and fMRI features, achieving 45.4% variance explained and 75% of predictions within 5 points of c…
Researchers proposed an uncertainty-aware reinforcement learning framework for algorithmic trading that integrates distributional, epistemic, and aleatoric uncertainty estimations using SHAP-weighted …
A tutorial demonstrates building an autonomous AI co-scientist for EGFR C797S inhibitor discovery using ChEMBL, RDKit, SHAP, and BRICS. The system mines IC50 data, trains a scaffold-split Random Fores…
Researchers at the University of Benghazi are conducting a systematic review comparing interpretable statistical models with explainable AI approaches for predicting breast cancer recurrence. The revi…
Two independent studies published June 27, 2026, developed machine learning classifiers to distinguish life-threatening EBV-associated hemophagocytic lymphohistiocytosis (EBV-HLH) from self-limited Ep…
Researchers Antoine Pesenti and Aidan O'Sullivan combined deep neural networks with explainable AI techniques to analyze electricity price formation across 39 European bidding zones, identifying renew…
A study from arXiv finds that linear regression outperforms the naive random walk in forecasting the monthly USD/CAD exchange rate, while machine learning ensemble models show only marginal difference…
Watch Cortex, a threat-classification AI, runs on-agent in under 8ms without cloud calls or GPUs. The system uses a gradient-boosted decision tree ensemble for primary classification and a lightweight…
A new PDF analysis tool detects parser and representation differences in documents, using 47 forensic passes with MITRE ATT&CK mapping and LightGBM machine learning to identify exploits, tampering, an…
A developer built TrustGuard AI, a fraud detection system that catches 99.76% of fraudulent transactions on the PaySim dataset, achieving an AUC-ROC of 0.9995 and Recall of 0.9976 on a 6.3 million row…
Researchers have developed a framework combining Shapley-value attributions with large language model rationales to explain automated rubric-based scoring of teaching quality. Testing on 6,000 classro…
An arXiv preprint (arXiv:2606.03995) reports an explainable machine learning model that achieved near-perfect three-class classification of Alzheimer's disease, mild cognitive impairment, and normal c…
Researchers developed an XGBoost machine learning model using eight routine clinical features from the Alzheimer's Disease Neuroimaging Initiative dataset that achieved 94.3% accuracy and a 0.982 macr…
Researchers have introduced BOHM, a zero-cost attribution method for compound AI systems that extracts hierarchical attribution trees directly from existing routing weights. The method provides multi-…