专题:Statistical and Computational Modeling

This cluster of papers focuses on the application of inductive modeling techniques, particularly GMDH-type neural networks and interval models, in various scientific research domains. The papers cover topics such as environmental monitoring, modeling and prediction of complex processes, application of machine learning algorithms, and the use of self-organization techniques. The overarching theme revolves around the utilization of advanced computational methods for sustainable development and scientific analysis.
最新文献
近5年高被引文献
Can Open Large Language Models Catch Vulnerabilities?

preprint Full Text OpenAlex 489 FWCI1094.0594

A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts

article Full Text OpenAlex 285 FWCI46.04

Towards Human-Centered Explainable AI: A Survey of User Studies for Model Explanations

review Full Text OpenAlex 209 FWCI33.7627

Wav-KAN: Wavelet Kolmogorov-Arnold Networks

article Full Text OpenAlex 163 FWCI0

Advancing Computational Toxicology by Interpretable Machine Learning

review Full Text OpenAlex 157 FWCI28.9044

Artificial Intelligence-Based Toxicity Prediction of Environmental Chemicals: Future Directions for Chemical Management Applications

review Full Text OpenAlex 132 FWCI17.1894

A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences

article Full Text OpenAlex 115 FWCI13.8771

A brief introduction to supervised, unsupervised, and reinforcement learning

book-chapter Full Text OpenAlex 110 FWCI31.7215

A Two-Step Data Normalization Approach for Improving Classification Accuracy in the Medical Diagnosis Domain

article Full Text OpenAlex 95 FWCI12.3492

The “Problem” of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation

article Full Text OpenAlex 88 FWCI11.1014