explainable ai

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...

Judgment-Centred Software Engineering Education: A Post-Hype Review and Framework for AI-Augmented Learning

The concept of comprehension debt is extended: the deferred learning and maintenance cost that arises when AI-assisted production outpaces a learner's or team's ability to explain, test, modify, and justify the resulting software.

Risk Scoring and Algorithmic Systems in Law Enforcement: Explainability, Human Oversight and the Right to Challenge

This report examines the use of algorithmic risk assessments in law enforcement and the safeguards required when a score influences an individual's treatment. It analyses data quality, the object of prediction, classification errors, discriminatory effects and the distinction between an intermediate assessment and a de...

Hypersonics Explained: High-Speed Aerodynamics, Flow Systems & Advanced Flight

This academic curriculum module delivers an analytical, physical, and computational exposition of hypersonics, high-speed aerodynamics, and aerothermodynamic transport phenomena across Mach 5+ atmospheric and reentry flight regimes. Key Technical Topics & Curricular Areas Covered:1. High-Enthalpy Compressible Aerodynam...

UNIFYING CONVENTIONAL AND LIGHTWEIGHT CONCRETE: AN EXPLAINABLE AI WEB APPLICATION FOR COMPRESSIVE STRENGTH PREDICTION

Explainable Anomaly Detection for Privacy-Preserving IoT Assistive Environments in Neurocognitive Healthcare

The use of artificial intelligence in education is currently discussed through the prism of personalization, conversational assistance, automated evaluation, and adoption on an institutional level, yet the entrepreneurial and cybersecurity sides of the progress are not included exhaustively. This chapter is based on th...