International Conference on Data Mining, Neural Networks and Deep Learning (DMNNDL 2023)

The International Conference on Data Mining, Neural Networks and Deep Learning (DMNNDL 2023) covers topics such as:

• Neural Networks
  • Hebbian theory
  • Convolutional neural networks
  • Residual neural networks
  • Long short term memory
  • Biological neural networks
  • Self-organizing feature maps
  • Feedforward neural networks
  • Cellular neural networks
  • Multilayer perceptrons
  • Extreme learning machines
  • Multi-layer neural network
  • Graph neural networks
  • Radial basis function networks
  • Neural network hardware
  • Hopfield neural networks
  • Recurrent neural networks
• Data Mining
  • Image data mining
  • Data processing
  • Human, domain, organizational
  • Agent-based data mining
  • Scalable data preprocessing
  • And social factors in data mining
  • Methodologies on large-scale data mining
  • Parallel and distributed data mining algorithms
  • Novel models and algorithms
  • High performance data mining algorithms
  • Opinion mining and sentiment analysis
  • OLAP and data mining
  • And cloud-based high performance data mining
  • Parallel, distributed
  • Statistical methods for data mining
  • Privacy preserving data mining
  • Feature extraction and selection
  • Visual data mining
  • Data mining systems in finance and e-commerce
  • Competitive analysis of mining algorithms
  • Web mining
  • Text data mining
  • Multimedia data mining
  • Video data mining
• Deep Learning
  • Evolutionary Methods
  • Neuro-Fuzzy Algorithms
  • Deep Hierarchical Networks (DHN)
  • Convolutional Neural Networks (CNN)
  • Deep Boltzmann Machines
  • Unsupervised Feature Learning
  • Deep Metric Learning Methods
  • Generative Adversarial Networks (GAN)
  • Learning Deep Generative Models
  • Deep Reinforcement Learning
  • Active learning
  • Graph Representation Learning
  • Manifold learning
  • Agent-based learning
  • Statistical models and learning
  • Multi-task learning
  • Evolutionary algorithms and learning
  • Computational learning
  • Parallel and distributed learning
  • Fuzzy logic-based learning
  • Deep Learning for Computing and Network Platforms