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Found 6 papers, 2 papers with code
Towards Enabling FAIR Dataspaces Using Large Language Models
Dataspaces have recently gained adoption across various sectors, including traditionally less digitized domains such as culture.
Data Augmentation Scheme for Raman Spectra with Highly Correlated Annotations
This is of interest in scenarios where large amounts of historical data are available but are currently not used for model training.
Enhancing Data Space Semantic Interoperability through Machine Learning: a Visionary Perspective
Our vision paper outlines a plan to improve the future of semantic interoperability in data spaces through the application of machine learning.
Explainable AI for Bioinformatics: Methods, Tools, and Applications
Explainable artificial intelligence (XAI) aims to overcome the opaqueness of black-box models and provide transparency in how AI systems make decisions.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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Template-Based Question Answering over Linked Geospatial Data
We give a detailed description of the system's architecture, its underlying algorithms, and its evaluation using a set of 201 natural language questions.
OncoNetExplainer: Explainable Predictions of Cancer Types Based on Gene Expression Data
In this paper, we propose a new approach called OncoNetExplainer to make explainable predictions of cancer types based on GE data.
