Neuro-Symbolic AI Makes Traffic Forecasts Explainable with Concept Lattices
Malaysian researchers have built a hybrid CNN-LSTM traffic forecasting system that pairs deep learning accuracy with Formal Concept Analysis to ...
Malaysian researchers have built a hybrid CNN-LSTM traffic forecasting system that pairs deep learning accuracy with Formal Concept Analysis to ...
A sweeping systematic review maps how explainable artificial intelligence is being applied to schizophrenia diagnosis, prognosis, and monitoring, and outlines ...
Researchers have built a framework that turns AI explanation maps into a single score capable of predicting when a breast ...
A spatiotemporal attention network that reads fNIRS blood-oxygen signals while patients listen to music classifies levels of consciousness with 88.72 ...
Researchers trained an explainable machine learning pipeline that predicts smartphone addiction from behavioral usage data with 97.8 percent accuracy, identifying ...
Researchers at Banasthali Vidyapith developed an explainable machine learning system that predicts university student academic performance with an R-squared of ...
Researchers have built a reuse-aware, package-level fault prediction model for Java applications that pairs architectural metrics with SHAP and LIME ...
A two-year field experiment in Ethiopia's Upper Blue Nile Basin shows that lime and balanced nitrogen and phosphorus fertilization can ...
A hybrid feature selection framework combining correlation analysis, PCA, neural sensitivity testing, and SHAP/LIME explainability reveals that average battery voltage ...
Researchers have built an explainable stacked ensemble of boosting algorithms that predicts Bitcoin's short-term closing prices with 97.5 percent explained ...
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© 2026 Scienmag - Science Magazine