Tag: explainable AI

A new scoping review of 73 studies argues that explanations in AI-driven 5G and 6G networks must balance fidelity, latency, ...

FedSPA combines Shapley-value analysis and model parameter similarity to identify malicious clients in federated learning under data poisoning attacks.

A class-adaptive ensemble of three neural networks classifies brain tumors in MRI scans with 99.39 percent benchmark accuracy, validated by ...

A hybrid TransUNet–DenseNet121 deep learning framework classifies brain tumors from MRI scans with reported accuracies up to 99.56 percent while ...

An explainable TabNet framework achieved an R² of 0.9880 when predicting employee performance from 100,000 workforce records, while its authors ...

A proof-of-concept study shows explainable AI can reconstruct dairy cow welfare-risk scores from routine milk records while revealing how, and ...

An explainable transformer model fusing MRI with texture descriptors reached over 99 percent accuracy in Alzheimer's disease staging benchmarks, with ...

Researchers report an entropy-driven adaptive spatial modeling framework that classifies drought, salinity, heat and nutrient-deficiency stress in grapevine leaf images ...

A federated, differentially private multimodal framework detects depression and anxiety across three benchmarks with F1 up to 0.806 while cutting ...

A scoping review of 20 studies finds machine learning clinical decision support systems show strong diagnostic performance but face barriers ...