World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
41
Citations
8798
World Ranking
8713
National Ranking
144

Overview

Moshe Sipper is affiliated with Ben-Gurion University of the Negev in Israel and has contributed extensively to the field of computer science, with a particular focus on artificial intelligence and machine learning. Their body of research includes a significant number of publications centered on evolutionary algorithms, machine learning classification techniques, and adversarial robustness.

Their main fields of study include:

  • Computer Science

Within computer science, Moshe Sipper has a strong focus on subfields such as:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Computational Theory and Mathematics
  • Information Systems

The scientist's primary research topics reflect a specialization in:

  • Evolutionary Algorithms and Applications
  • Machine Learning and Data Classification
  • Adversarial Robustness in Machine Learning
  • Metaheuristic Optimization Algorithms Research
  • Anomaly Detection Techniques and Applications
  • Neural Networks and Applications
  • Advanced Malware Detection Techniques

Moshe Sipper has contributed papers to various publication venues, including:

  • arXiv (Cornell University)
  • Applied Sciences
  • Algorithms
  • Proceedings of the Genetic and Evolutionary Computation Conference Companion
  • SN Computer Science

The scientist has collaborated frequently with other researchers such as Raz Lapid, Achiya Elyasaf, Jason H. Moore, Tomer Halperin, and Itai Tzruia, indicating active engagement in collaborative projects within their research community.

Selected recent publications by Moshe Sipper include:

  • Conservation machine learning: a case study of random forests, 2021, Scientific Reports
  • AddGBoost: A gradient boosting-style algorithm based on strong learners, 2021, Machine Learning with Applications
  • High Per Parameter: A Large-Scale Study of Hyperparameter Tuning for Machine Learning Algorithms, 2022, Algorithms

Best Publications

  • Encyclopedia of Machine Learning and Data Mining

    Unknown

  • Evolution of Parallel Cellular Machines

    Moshe Sipper

  • A fuzzy-genetic approach to breast cancer diagnosis.

    Carlos Andrés Peña-Reyes;Moshe Sipper

  • Evolution of Parallel Cellular Machines: The Cellular Programming Approach

    Moshe Sipper

  • A phylogenetic, ontogenetic, and epigenetic view of bio-inspired hardware systems

    M. Sipper;E. Sanchez;D. Mange;M. Tomassini

  • Fifty years of research on self-replication: an overview

    Moshe Sipper

  • Toward robust integrated circuits: The embryonics approach

    D. Mange;M. Sipper;A. Stauffer;G. Tempesti

  • On the generation of high-quality random numbers by two-dimensional cellular automata

    M. Tomassini;M. Sipper;M. Perrenoud

  • Design, observation, surprise! A test of emergence

    Edmund M.A. Ronald;Moshe Sipper;Mathieu S. Capcarrère

  • Evolutionary computation in medicine: an overview.

    Carlos Andrés Peña-Reyes;Moshe Sipper

  • The emergence of cellular computing

    M. Sipper

  • Phylogeny, Ontogeny, and Epigenesis: Three Sources of Biological Inspiration for Softening Hardware

    Eduardo Sanchez;Daniel Mange;Moshe Sipper;Marco Tomassini

  • Fuzzy CoCo: a cooperative-coevolutionary approach to fuzzy modeling

    C.A. Pena-Reyes;M. Sipper

  • Co-evolving non-uniform cellular automata to perform computations

    Moshe Sipper

  • Toward a viable, self-reproducing universal computer

    Jean-Yves Perrier;Moshe Sipper;Jacques Zahnd

  • Generating Parallel Random Number Generators By Cellular Programming

    Moshe Sipper;Marco Tomassini

  • Static and dynamic configurable systems

    E. Sanchez;M. Sipper;J.-O. Haenni;J.-L. Beuchat

  • Machine Nature: The Coming Age of Bio-Inspired Computing

    Moshe Sipper

  • Towards Robust Integrated Circuits: The Embryonics Approach

    Daniel Mange;Moshe Sipper;André Stauffer;Gianluca Tempesti

  • Flight of the FINCH Through the Java Wilderness

    M Orlov;M Sipper

  • Necessary conditions for density classification by cellular automata

    Mathieu S. Capcarrere;Moshe Sipper

Frequent Co-Authors

Jason H. Moore
Jason H. Moore University of Pennsylvania
Marco Tomassini
Marco Tomassini University of Lausanne
Eytan Ruppin
Eytan Ruppin National Institutes of Health
Avishai Henik
Avishai Henik Ben-Gurion University of the Negev
Peter I. Cowling
Peter I. Cowling Queen Mary University of London
Martin V. Butz
Martin V. Butz University of Tübingen
Dana S. Nau
Dana S. Nau University of Maryland, College Park
Héctor Muñoz-Avila
Héctor Muñoz-Avila Lehigh University
Lee Spector
Lee Spector Hampshire College

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