IEEE Computational Intelligence Magazine

Papers
(The H4-Index of IEEE Computational Intelligence Magazine is 19. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-03-01 to 2024-03-01.)
ArticleCitations
Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users183
Evolutionary Transfer Optimization - A New Frontier in Evolutionary Computation Research114
Graph Neural Networks in TensorFlow and Keras with Spektral [Application Notes]98
A Self-Adaptive Mutation Neural Architecture Search Algorithm Based on Blocks92
A Survey on Differentially Private Machine Learning [Review Article]66
Multi-Scale Neural Network for EEG Representation Learning in BCI47
When Federated Learning Meets Blockchain: A New Distributed Learning Paradigm46
Surrogate-Assisted Evolutionary Multitasking for Expensive Minimax Optimization in Multiple Scenarios40
Half a Dozen Real-World Applications of Evolutionary Multitasking, and More38
Evolutionary Computation for Intelligent Transportation in Smart Cities: A Survey [Review Article]34
An Effective Feature Learning Approach Using Genetic Programming With Image Descriptors for Image Classification [Research Frontier]32
Difficulties in Fair Performance Comparison of Multi-Objective Evolutionary Algorithms [Research Frontier]28
Improving Depression Level Estimation by Concurrently Learning Emotion Intensity27
The General Combinatorial Optimization Problem: Towards Automated Algorithm Design27
Bridging the Gap Between AI and Explainability in the GDPR: Towards Trustworthiness-by-Design in Automated Decision-Making26
PaletteViz: A Visualization Method for Functional Understanding of High-Dimensional Pareto-Optimal Data-Sets to Aid Multi-Criteria Decision Making25
Computational Intelligence Techniques for Combating COVID-19: A Survey24
COVID-19 Time Series Forecast Using Transmission Rate and Meteorological Parameters as Features19
Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning With Shapley Values19
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