World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
34
Citations
4893
World Ranking
12192
National Ranking
39

Overview

Sara Silva is affiliated with the University of Lisbon in Portugal and has contributed extensively to the field of Computer Science, with a particular focus on Artificial Intelligence. Their research spans several subfields including Molecular Biology, Plant Science, Pulmonary and Respiratory Medicine, and Global and Planetary Change.

Their most recent publications include:

  • A Machine Learning Approach to Predict Air Quality in California, 2020, Complexity
  • Evolving knowledge graph similarity for supervised learning in complex biomedical domains, 2020, BMC Bioinformatics
  • A Comparative Study of Automated Deep Learning Segmentation Models for Prostate MRI, 2023, Cancers
  • An Intelligent Intrusion Detection System for 5G-Enabled Internet of Vehicles, 2023, Electronics
  • Comparing Machine Learning Methods for Classifying Plant Drought Stress from Leaf Reflectance Spectra in Arabidopsis thaliana, 2021, Applied Sciences

Their collaborative network includes frequent co-authors such as:

  • Leonardo Vanneschi (38 publications)
  • Nuno M. Rodrigues (18 publications)
  • João E. Batista (17 publications)
  • Rita T. Sousa (12 publications)
  • Cátia Pesquita (12 publications)

Frequently chosen venues for publishing include:

  • Zenodo (CERN European Organization for Nuclear Research) with 7 publications
  • arXiv (Cornell University) with 7 publications
  • Climate Services with 3 publications
  • Computers in Biology and Medicine with 3 publications
  • Preprints.org with 3 publications

Sara Silva has also published a book titled Lectures on Intelligent Systems in 2023 under Springer Science+Business Media, which has accumulated 25 citations.

The main topics of their work cover:

  • Evolutionary Algorithms and Applications
  • Metaheuristic Optimization Algorithms Research
  • Bioinformatics and Genomic Networks
  • Biomedical Text Mining and Ontologies
  • Horticultural and Viticultural Research
  • Viral Infectious Diseases and Gene Expression in Insects
  • Machine Learning and Data Classification

Best Publications

  • GPLAB A Genetic Programming Toolbox for MATLAB

    Sara Silva

  • A Machine Learning Approach to Predict Air Quality in California

    Mauro Castelli;Fabiana Martins Clemente;Aleš Popovič;Aleš Popovič;Sara Silva

  • Comparative phylogenetic analyses uncover the ancient roots of Indo-European folktales

    Sara Graça da Silva;Jamshid J. Tehrani

  • A survey of semantic methods in genetic programming

    Leonardo Vanneschi;Mauro Castelli;Sara Silva

  • Combination of Cell-Penetrating Peptides with Nanoparticles for Therapeutic Application: A Review

    Sara Silva;António J Almeida;Nuno Vale

  • Dynamic limits for bloat control in genetic programming and a review of past and current bloat theories

    Sara Silva;Ernesto Costa

  • Prediction of high performance concrete strength using Genetic Programming with geometric semantic genetic operators

    Mauro Castelli;Mauro Castelli;Leonardo Vanneschi;Leonardo Vanneschi;Sara Silva

  • Measuring bloat, overfitting and functional complexity in genetic programming

    Leonardo Vanneschi;Mauro Castelli;Sara Silva

  • A C++ framework for geometric semantic genetic programming

    Mauro Castelli;Sara Silva;Leonardo Vanneschi

  • Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

    Sara Silva;Anna I. Esparcia-Alcázar

  • A new implementation of geometric semantic GP and its application to problems in pharmacokinetics

    Leonardo Vanneschi;Mauro Castelli;Luca Manzoni;Sara Silva

  • Dynamic Maximum Tree Depth

    Sara Silva;Jonas S. Almeida;Jonas S. Almeida

  • Geometric Semantic Genetic Programming for Real Life Applications

    Leonardo Vanneschi;Leonardo Vanneschi;Leonardo Vanneschi;Sara Silva;Sara Silva;Mauro Castelli;Mauro Castelli;Luca Manzoni

  • Dynamic maximum tree depth: a simple technique for avoiding bloat in tree-based GP

    Sara Silva;Jonas Almeida

  • Balancing learning and overfitting in genetic programming with interleaved sampling of training data

    Ivo Gonçalves;Sara Silva

  • Random sampling technique for overfitting control in genetic programming

    Ivo Gonçalves;Sara Silva;Joana B. Melo;João M. B. Carreiras

  • Operator equalisation for bloat free genetic programming and a survey of bloat control methods

    Sara Silva;Stephen Dignum;Leonardo Vanneschi

  • Dynamic Limits for Bloat Control

    Sara Silva;Ernesto Costa

  • Prediction of the Unified Parkinson’s Disease Rating Scale assessment using a genetic programming system with geometric semantic genetic operators

    Mauro Castelli;Leonardo Vanneschi;Sara Silva

  • Burned area estimations derived from Landsat ETM+ and OLI data: Comparing Genetic Programming with Maximum Likelihood and Classification and Regression Trees

    Ana I.R. Cabral;Sara Silva;Sara Silva;Pedro C. Silva;Leonardo Vanneschi

  • neat Genetic Programming

    Leonardo Trujillo;Luis Muñoz;Edgar Galván-López;Sara Silva

Frequent Co-Authors

Leonardo Vanneschi
Leonardo Vanneschi Universidade Nova de Lisboa
Carlos M. Fonseca
Carlos M. Fonseca University of Coimbra
Mait Metspalu
Mait Metspalu University of Tartu
Stefania Sarno
Stefania Sarno University of Bologna
Jason H. Moore
Jason H. Moore University of Pennsylvania
Lee Spector
Lee Spector Hampshire College
Hermínia de Lencastre
Hermínia de Lencastre Rockefeller University
Julian Togelius
Julian Togelius New York University
Christophe Rosenberger
Christophe Rosenberger Université de Caen Normandie
Dora Brites
Dora Brites University of Lisbon

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