[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 …
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 …
Semantics of the black-box: Can knowledge graphs help make deep learning systems more interpretable and explainable?
The recent series of innovations in deep learning (DL) have shown enormous potential to
impact individuals and society, both positively and negatively. DL models utilizing massive …
impact individuals and society, both positively and negatively. DL models utilizing massive …
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 …
Big Data and precision agriculture: a novel spatio-temporal semantic IoT data management framework for improved interoperability
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 …
A survey of knowledge graph approaches and applications in education
This paper presents a comprehensive survey of knowledge graphs in education. It covers
the patterns and prospects of research in this area. A total of 48 relevant publications …
the patterns and prospects of research in this area. A total of 48 relevant publications …
Explainable AI in 6G O-RAN: A Tutorial and Survey on Architecture, Use Cases, Challenges, and Future Research
The recent o-ran specifications promote the evolution of ranran 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 …
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 …
Image translation as diffusion visual programmers
We introduce the novel Diffusion Visual Programmer (DVP), a neuro-symbolic image
translation framework. Our proposed DVP seamlessly embeds a condition-flexible diffusion …
translation framework. Our proposed DVP seamlessly embeds a condition-flexible diffusion …
Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach
Social media is used to categorise products or services, but analysing vast comments is time-
consuming. Researchers use sentiment analysis via natural language processing …
consuming. Researchers use sentiment analysis via natural language processing …