A survey on explainable ai for 6g o-ran: Architecture, use cases, challenges and research directions
The recent O-RAN specifications promote the evolution of RAN architecture by function
disaggregation, adoption of open interfaces, and instantiation of a hierarchical closed-loop …
disaggregation, adoption of open interfaces, and instantiation of a hierarchical closed-loop …
[HTML][HTML] Spatio-temporal semantic data management systems for IoT in agriculture 5.0: Challenges and future directions
Abstract The Agri-Food sector is in a stressful situation due to the high demand for food from
the growing population around the world. The agricultural sector is facing a challenging …
the growing population around the world. The agricultural sector is facing a challenging …
EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage …
Abstract The latest Deep Learning (DL) models for detection and classification have
achieved an unprecedented performance over classical machine learning algorithms …
achieved an unprecedented performance over classical machine learning algorithms …
Do you follow? a fully automated system for adaptive robot presenters
A Axelsson, G Skantze - Proceedings of the 2023 acm/ieee international …, 2023 - dl.acm.org
An interesting application for social robots is to act as a presenter, for example as a museum
guide. In this paper, we present a fully automated system architecture for building adaptive …
guide. In this paper, we present a fully automated system architecture for building adaptive …
Big Data and precision agriculture: a novel spatio-temporal semantic IoT data management framework for improved interoperability
M San Emeterio de la Parte, JF Martínez-Ortega… - Journal of Big Data, 2023 - Springer
Precision agriculture in the realm of the Internet of Things is characterized by the collection
of data from multiple sensors deployed on the farm. These data present a spatial, temporal …
of data from multiple sensors deployed on the farm. These data present a spatial, temporal …
Evolearner: Learning description logics with evolutionary algorithms
Classifying nodes in knowledge graphs is an important task, eg, for predicting missing types
of entities, predicting which molecules cause cancer, or predicting which drugs are …
of entities, predicting which molecules cause cancer, or predicting which drugs are …
KGTORe: tailored recommendations through knowledge-aware GNN models
Knowledge graphs (KG) have been proven to be a powerful source of side information to
enhance the performance of recommendation algorithms. Their graph-based structure …
enhance the performance of recommendation algorithms. Their graph-based structure …
[HTML][HTML] Knowledge-infused learning for entity prediction in driving scenes
Scene understanding is a key technical challenge within the autonomous driving domain. It
requires a deep semantic understanding of the entities and relations found within complex …
requires a deep semantic understanding of the entities and relations found within complex …
Knowledge graphs in practice: characterizing their users, challenges, and visualization opportunities
This study presents insights from interviews with nineteen Knowledge Graph (KG)
practitioners who work in both enterprise and academic settings on a wide variety of use …
practitioners who work in both enterprise and academic settings on a wide variety of use …
Knowledge Graphs: Constructing, Completing, and Effectively Applying Knowledge Graphs in Tourism
M Kejriwal - Applied Data Science in Tourism: Interdisciplinary …, 2022 - Springer
Since the introduction of the Google Knowledge Graph in the early 2010s, web search and
advertising have both undergone profound shifts. A Knowledge Graph (KG) is a graph …
advertising have both undergone profound shifts. A Knowledge Graph (KG) is a graph …