explainable ai

Advancing Brain Tumor Management Using Generative AI: A Comprehensive Review of MRI-Based Applications

Purpose: To systematically review the integration of generative artificial intelligence (GenAI) in MRI-based brain tumor management and evaluate its impact on detection, segmentation, diagnosis, treatment planning, and outcome prediction. The review addresses the research question like How effectively can GenAI enhance...

Ethical and Safety Considerations

This chapter addresses essential questions of how to govern and deploy generative artificial intelligence (GenAI) responsibly in connected and autonomous vehicles (CAV) by examining principles guiding responsible system design, regulatory frameworks shaping technology deployment, and methodological practices supporting...

When AI Becomes the Travel Agent: How Tourists Negotiate Trust, Autonomy, and Authenticity in AI-Generated Travel Decisions

Generative artificial intelligence (GenAI) is increasingly transforming how tourists search for information, plan itineraries, and make travel decisions. However, limited qualitative research explains how tourists negotiate their agency when AI becomes actively involved in travel decision-making. This study explores ho...

Tecnología basada en IA para el diagnóstico de la madurez de la comunicación digital de las PYMES en Cuba

The digital transformation of small and medium-sized enterprises (SMEs) requires tools that can diagnose, explain, and improve communication management without relying on massive datasets or expensive infrastructures. This paper develops an artificial-intelligence (AI)-based technology for diagnosing digital communicat...

Neuro-Symbolic Artificial Intelligence: Integrating Large Language Models with SMT Solvers, Knowledge Graphs, and Formal Automated Theorem Provers

Abstract—Modern deep neural networks, particularly autoregressive Large Language Models (LLMs), have achieved state-of-the-art empirical performance across natural language generation, semantic translation, and broad pattern recognition. However, pure statistical connectionist architectures suffer from fundamental epis...

Analysis of the Influence of the Use of Artificial Intelligence (AI) on Students' Critical Thinking Abilities in Learning

This study examines how Artificial Intelligence (AI) use affects students' critical thinking skills in learning. The study uses a quantitative, explanatory research design. The study population consisted of students in the Economics Education Study Program who used AI-based applications for learning. The study collecte...

Innovación en Inteligencia Artificial y Nuevas Tecnologías en la Medicina del Deporte y el Fútbol

Background: Innovation in artificial intelligence (AI) in sports medicine integrates algorithms, sensors, computer vision, predictive analytics and decision-support systems to improve athlete health, performance, safety and competitive fairness. Objective: To reconstruct the historical development of AI and emerging te...

Explainable AI identifies recombination and chromatin environment as key predictors of subgenome evolution in maize and Brassica

Polyploidy, or whole genome duplication, reshapes genomes through biased gene loss and regulatory rewiring, yet the drivers of biased fractionation among subgenomes remain unclear. Using maize and Brassica rapa as model allopolyploids, we compiled 60 genomic and epigenomic features in maize and 45 in B. rapa and constr...

The rise of artificial intelligence in algorithmic trading: a bibliometric study

This study provides the first large-scale, longitudinal bibliometric mapping of artificial intelligence research in algorithmic trading, covering more than three decades of scientific production, and uniquely integrates publication trends, citation impact, collaboration networks, and thematic clustering within a single...

AI-Powered Adaptive Learning and Smart Education Platform

An AI-powered adaptive learning platform designed to provide personalized and interactive learning support that uses Artificial Intelligence to explain questions in detail, generate short exam-oriented answers and recall keywords, analyze students' performance, identify knowledge gaps, and recommend suitable learning t...

EBAROF: An explainable AI framework for robust multi-objective construction project optimization under simulated uncertainty

Predicting health insurance coverage among women in Sierra Leone using machine learning and explainable artificial intelligence applied to demographic and health survey data

Ensemble machine learning models, particularly Gradient Boosting and XGBoost, demonstrate strong predictive utility for identifying uninsured women in low-coverage, resource-limited settings, and bridges the gap between algorithmic performance and actionable policy insight.