Tag: Explainable AI

Researchers built a dual-stream deep learning framework that localizes and classifies facial emotions and binary gender labels together, achieving 94.56% ...

Random Forest and XGBoost models predicted loss to follow-up among ART patients in Zimbabwe with high internal accuracy, while LIME ...

A new explainable AI framework for network intrusion detection audited its semantic rule layer on held-out benchmark data, reporting high ...

A structured narrative review finds that explainable AI techniques may improve transparency in diagnosing skin-manifesting neglected tropical diseases, while deep ...

An open-access study combined label-free brightfield microscopy, machine learning, and SHAP-based explainability to classify 17,781 red blood cells across ten ...

A new hybrid explainable machine learning framework combines satellite environmental data with health records to predict cardiovascular disease risk at ...

A systematic review of 64 studies finds that only about one in five AI prediction models built on electronic health ...

A scoping review of 50 studies finds that Egypt's cybercrime law recognizes digital evidence but lacks mandatory tool validation, laboratory ...

Researchers have built an explainable stacking machine learning framework that jointly forecasts electrical load and predicts transformer remaining lifespan in ...

A new review argues that trustworthy agricultural autonomy depends on integrating robot learning, safe control and human-robot interaction rather than ...

A new open access survey reviews AI methods for operating power grids with high penetration of distributed energy resources and ...

Researchers propose a conceptual framework called CyTS that organizes human–AI collaboration into four layers while explicitly withholding claims of validated ...

Researchers report a Mamba-BiLSTM framework with Granger-inspired directional causal fusion that reached 81.86 percent accuracy for multimodal depression detection on ...

A new survey in Artificial Intelligence Review proposes a unified framework linking hybrid forecasting, explainable AI and multi-agent optimization for ...

A federated learning framework combining MRI and clinical data classifies Alzheimer's disease with reported accuracy up to 94.1 percent while ...

Researchers propose a modular multi-agent framework, SoS-ML, that generates evidence-based explanations from internal model representations rather than post-hoc justifications.

A systematic review of 78 papers examines how machine learning methods support demand forecasting and inventory decision-making.

A new review maps how artificial intelligence is transforming mosquito identification, disease forecasting, and vector control worldwide.

A new promptable diffusion-enhanced framework achieved 98.1% classification accuracy and a 93.2% Dice score for explainable brain tumor segmentation and ...

A new editorial from the inaugural Lushan Summit argues that AI should personalize K-12 learning while human teachers remain irreplaceable ...