explainable ai

Modeling tourism-driven coastal land-use change and habitat quality under alternative development pathways: Implications for environmental management from explainable AI

An efficient glaucoma diagnosis with explainable AI using ResNet152 and Swin Transformer attention-based fusion

Artificial intelligence in breast cancer imaging: a systematic review of detection, segmentation, explainability, and clinical translation

Artificial intelligence shows substantial potential to enhance breast cancer imaging, but broader clinical translation requires robust external and prospective validation, improved calibration, assessment of generalizability and bias, and integration into clinical workflows.

Evaluating Explainable Hybrid Intrusion Detection Models Under Zero-Day Conditions

The study demonstrates that integrating deep learning with stable explainable AI offers a practical and trustworthy solution for zero-day intrusion detection, contributing validated evidence to an area where explanation reliability is rarely examined.

A technology acceptance model extension of ERP use in Sri Lanka’s pharmaceutical industry

It is highlighted that in regulated contexts, ERP value depends more on real-time data, compliance and system usefulness than on customization, positioning ERP as a strategic decision-support system.

The Oligarch Barely Steers Model Collapse in Multi-Model Ecosystems

AI-generated text is flowing back into the training corpora of the next generation of models. Recursive training on it drives model collapse, and recent work extends the setting to many models feeding one another -- but almost always with the market split evenly, while real generative AI is an oligopoly. Concentration...

Engineered Persuasion: Evaluating Personalized Pretexts in LLM-Generated Spear Phishing

The results highlight that personalization is not simply a matter of adding more details: it depends on whether the pretext fits the recipient's work context, and how this distinction can inform workplace cybersecurity training.

Exercise adaptation and proactive health: an information-theoretic framework based on load entropy

Proactive Health is developed as a scientific framework in which exercise is the central means of cultivating adaptive capacity and an information-theoretic model – load entropy – is introduced to explain why individuals respond differently to the same exercise challenge.

Artificial Intelligence for Cerebral Aneurysm Management: Integrating Imaging, Hemodynamics, and Clinical Decision Support.

Overall, the reviewed evidence indicates that AI has considerable potential to support the detection, risk assessment, and management of cerebral aneurysms, while emphasizing the need for standardized datasets, prospective multicenter validation, and interpretable models to enable reliable clinical adoption.

AI Cautionary Guide: Pitfalls and Strategies for the Use of Machine Learning in Medical Research.

By outlining many of the common potential issues, this compilation of common pitfalls and practical safeguard considerations for ML-enabled medical research aims to provide the medical and research community with practical insights to help foster trustworthy ML-enabled medical research.

Explainability in Forecasting Hospital Readmissions with Machine Learning.

This framework illustrates how EHR-based classifiers enhanced with transparent XAI outputs can facilitate actionable, auditable, and clinically meaningful forecasting in the hospital environment.

Integrating SAOS, LAOS, and food tribology: Emerging frameworks for structure-function relationships and sensory prediction in food systems.

Food structure and its effect on deformation behaviour and sensory perception is still a challenge in food science. Small-Amplitude Oscillatory Shear (SAOS) is a common method for the determination of the linear viscoelastic properties and stability of food materials, but it is not applicable to large deformations, suc...