Renata De Paris

Orcid: 0000-0002-6662-2182

According to our database1, Renata De Paris authored at least 14 papers between 2011 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of five.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2026
HR-Agents: Using Multiple LLM-based Agents to Improve Q&A about Brazilian Labor Legislation.
CoRR, April, 2026

Comparing RAG, DPO and Agentic Approaches in Systems Performance on Q&A about Brazilian Labor Legislation.
Proceedings of the 18th International Conference on Agents and Artificial Intelligence, 2026

Comparing Automatic Speech Recognition Quality for Brazilian Portuguese in Multimodal Large Models.
Proceedings of the 18th International Conference on Agents and Artificial Intelligence, 2026

2023
eXplainable Artificial Intelligence on Medical Images: A Survey.
CoRR, 2023

Efficient Brazilian Sign Language Recognition: A Study on Mobile Devices.
Proceedings of the Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 2023

2020
SmartIX: A database indexing agent based on reinforcement learning.
Appl. Intell., 2020

2019

2018
A selective method for optimizing ensemble docking-based experiments on an InhA Fully-Flexible receptor model.
BMC Bioinform., 2018

2016
An effective method to optimize docking-based virtual screening in a clustered fully-flexible receptor model deployed on cloud platforms.
PhD thesis, 2016

2015
Clustering Molecular Dynamics Trajectories for Optimizing Docking Experiments.
Comput. Intell. Neurosci., 2015

A Cloud-Based Workflow Approach for Optimizing Molecular Docking Simulations of Fully-Flexible Receptor Models and Multiple Ligands.
Proceedings of the 7th IEEE International Conference on Cloud Computing Technology and Science, 2015

Clustering Molecular Dynamics Trajectories with a univariate estimation of distribution algorithm.
Proceedings of the IEEE Congress on Evolutionary Computation, 2015

2014
A strategic solution to optimize molecular docking simulations using Fully-Flexible Receptor models.
Expert Syst. Appl., 2014

2011
A Conceptual Many Tasks Computing Architecture to Execute Molecular Docking Simulations of a Fully-Flexible Receptor Model.
Proceedings of the Advances in Bioinformatics and Computational Biology, 2011


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