Open Science Research Excellence

ICADMTEE 2021 : International Conference on Applications of Data Mining Techniques in Electrical Engineering

New York, USA
June 3 - 4, 2021

Call for Papers

ICADMTEE 2021 : International Conference on Applications of Data Mining Techniques in Electrical Engineering is the premier interdisciplinary platform for the presentation of new advances and research results in the fields of Applications of Data Mining Techniques in Electrical Engineering. The conference will bring together leading academic scientists, researchers and scholars in the domain of interest from around the world. Topics of interest for submission include, but are not limited to:
  • Data mining theory in electrical engineering
  • Fault analysis
  • Data mining theory
  • Electric circuit principle
  • Machine learning
  • Information theory for data mining
  • Decision trees and hierarchical partitioning
  • Variable reduction and data visualization
  • Regression in large noisy databases
  • Classification in the data mining
  • Fault analysis based on data mining
  • Fault diagnosis based on node phase voltage
  • Fault diagnosis based on node negative sequence voltage
  • Neural network applications in electrical engineering
  • Artificial neural network in electrical based power industry
  • Neural networks in control engineering
  • Fixed stabilizing controllers
  • Signal classification with perceptron
  • Limitations of neural networks
  • Neural network design
  • Neural network signal processing
  • Fuzzy logic in electrical engineering
  • Neuron model and network architectures
  • Linear transformations for neural networks
  • Optimal linear associative memories
  • Principal-components analysis
  • Backpropagation learning algorithms
  • Variations on backpropagation
  • Radial basis networks
  • Continuous hopfield networks
  • Competitive networks
  • Counterpropagation networks
  • Performance optimization
  • Performance surfaces and optimum points
  • Perceptron learning rule
  • Artificial neural networks