explainable ai

Internal Audit of Pension Funds: The Case of Georgias Funded Pension Scheme

DOI kodi: 10.52340/ekonomisti.2026.03.03 Annotation. Funded pension schemes in emerging economies are growing rapidly and accumulating substantial assets on behalf of millions of participants, placing exceptional demands on governance, risk management, and the internal audit function. This paper examines the extent to...

Eco-Sustainable Water Practice through Confidence-Bounded, Sensitive-Aware, and Explainable Machine Learning for Weir Oxygen Transfer

Autonomous Multi-agent Governance: AI Sentinel Framework for Mitigating Psychological Restraints in AI-Driven Fall Management

A Statistical-Signature Framework for Detecting Irreducible Stochasticity in CFD Simulations

Summary This preprint presents a 2D proof of concept for deciding, before an expensive simulation campaign, whether a pointwise (trajectory-level) grid-convergence study is an appropriate verification target. In chaotic flows, small differences, including those introduced by discretization, grow until pointwise agreeme...

Artificial Intelligence for Lacrimal Drainage Disorders: Advances in Diagnosis, Management, and Clinical Translation.

PURPOSE Lacrimal drainage disorders (LDDs) are common in oculoplastic practice, but their diagnostic evaluation continues to depend on tests that are variably subjective, operator-dependent, and at times invasive. In recent years, artificial intelligence (AI), spanning conventional machine learning (ML), deep learning...

A Clinically Grounded and Uncertainty-Aware Explainable Artificial Intelligence Detailed Analysis for Skin Lesion Diagnosis

A conceptual framework is proposed that integrates dermoscopic image analysis, lesion segmentation, deep learning-based prediction, clinical concept recognition, uncertainty estimation, clinical reasoning, and structured explanation and distinguishes the reliability of the prediction from the reliability of the explana...

Clinically aligned explainable AI for medical imaging using multi-scale attention and prototype reasoning

Artificial Intelligence vs. Traditional Models in Computational Thinking Gap Analytics: A BERT-Driven Approach with XAI Diagnostics

This study investigates computational thinking (CT) proficiency in programming-based learning environments using a unified analytical framework that combines statistical analysis and predictive modeling, and positions deep learning as a complementary tool to statistical analysis for understanding and predicting CT prof...

A Review of Explainable AI Techniques in Machine Learning

Explainable AI for identifying global solar-weather regimes using UMAP-HDBSCAN and SHAP across climate zones

A systematic survey of artificial intelligence methods for ECG-based cardiovascular disease prediction

This systematic review examines how artificial intelligence methods are being applied to ECG-based cardiovascular disease prediction, with particular attention to data handling practices, modeling approaches, interpretability techniques, and evaluation strategies, to accelerate the development of reliable, fair, and cl...

The renaissance of information retrieval and the limitations of artificial intelligence

This paper critically examines the limitations of generative AI in medicine and challenges the popular narrative that multimodality alone can rescue its current shortcomings. It argues for a shift toward evidence-based AI grounded in structured, high-quality clinical data rather than unsupported generation. Retrieval-A...