explainable ai

Emotion detection using AI with explainable AI based perception: A comparative analysis between machine learning and deep learning

Engineering an integrated biosensing interface combining DNA-assisted clustering and explainable AI for biomarker detection.

An integrated biosensing framework that treats readout reliability as an explicit engineering objective rather than a post hoc correction problem, and establishes a generalizable strategy for constructing trustworthy POCT systems in which chemical signal generation and digital interpretation are co-designed.

Transparency and Explainability in Human Factors — A Systematic Review of Usability Assessment Practices for AI Medical Devices

Results identify a "symmetry of modality": qualitative interviews correlate with written text explanations, while Think-Aloud protocols better assess cognitively demanding tools like SHAP values.

Towards Driver-Centered Human–AI interaction: Examining the impact of transparency and explainability on driver experience in level 3 automated vehicles

Deep-synthesized image detection via multi-domain feature fusion and explainable AI guided color model selection

Dynamic risk assessment of riverbank collapse: A cascading framework integrating explainable AI and InSAR

EXPLAINING TOURISM AND HOSPITALITY STUDENTS' ADOPTION OF LARGE LANGUAGE MODELS IN HIGHER EDUCATION: AN INTEGRATED TAM–UTAUT FRAMEWORK USING PLS-SEM AND NECESSARY CONDITION ANALYSIS

The rapid integration of large language models (LLMs) in higher education has transformed students' learning practices, particularly in applied disciplines such as tourism and hospitality education. Yet, limited empirical research explains the factors driving their adoption. Drawing on the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), this study examines tourism and hospitality students' behavioural intention to use LLM-based learning tools by incorporating content reliability, learner motivation, and social influence as extended antecedents. Data were collected from 365 university students enrolled in tourism, hospitality and management courses in addition to the students enrolled in other allied programs having tourism as an elective course in India and analysed using partial least squares structural equation modelling (PLS-SEM) and Necessary Condition Analysis (NCA). The findings indicate that perceived usefulness remains central to adoption, while learner motivation and social influence play critical enabling roles. NCA further reveals that perceived usefulness, learner motivation, and social influence constitute necessary conditions for achieving high adoption intention. By integrating net-effect and necessity-based approaches, the study advances technology acceptance theory in AI-enabled education in tourism and hospitality. It offers practical insights for the responsible integration of LLMs in professional learning contexts.

Exchange rate expectations and explainable AI

Multivariable temporal-frequency reconstruction model with two-stage decomposition and explainable AI-driven feature selection for highly volatile time-series forecasting

PsoriaNET: An attention-based deep convolutional neural network for automated psoriasis detection with explainable AI

An explainable AI framework for EEG-based schizophrenia detection using MVMD and phase space dynamics

Heart disease diagnosis via a feature-separated dual-channel integrated model of machine learning, deep Learning, and explainable AI