Impact of word embedding models on text analytics in deep learning environment: a review
The selection of word embedding and deep learning models for better outcomes is vital.
Word embeddings are an n-dimensional distributed representation of a text that attempts to …
Word embeddings are an n-dimensional distributed representation of a text that attempts to …
HURON: a quantitative framework for assessing human readability in ontologies
F Abad-Navarro, C Martínez-Costa… - IEEE …, 2023 - ieeexplore.ieee.org
The increasing use of ontologies requires their quality assurance. Ontology quality
assurance consists of a set of activities that allow analyzing the ontology, identifying …
assurance consists of a set of activities that allow analyzing the ontology, identifying …
[HTML][HTML] Extending the description logic EL with threshold concepts induced by concept measures
In applications of AI systems where exact definitions of the important notions of the
application domain are hard to come by, the use of traditional logic-based knowledge …
application domain are hard to come by, the use of traditional logic-based knowledge …
Do Large GPT Models Discover Moral Dimensions in Language Representations? A Topological Study Of Sentence Embeddings
S Fitz - ar** a sentence level fairness metric using word embeddings
Fairness is a principal social value that is observable in civilisations around the world. Yet, a
fairness metric for digital texts that describe even a simple social interaction, eg,'The boy hurt …
fairness metric for digital texts that describe even a simple social interaction, eg,'The boy hurt …
Anomaly Detection Using Machine Learning Forintrusion Detection
V Rudraraju - 2024 - search.proquest.com
This thesis examines machine learning approaches for anomaly detection in network
security, particularly focusing on intrusion detection using TCP and UDP protocols. It uses …
security, particularly focusing on intrusion detection using TCP and UDP protocols. It uses …