We invite submissions of papers addressing theoretical aspects of Sentimental Analysis, Deep Learning and related topics. We strongly support a broad definition of learning theory, including, but not limited to:

Sentiment Analysis
  • Cognitive Computing
  • Deep Learning
  • Semantics & Syntactic Analysis
  • Natural Language Processing
  • Emotion-Driven Systems
  • Data Mining for ontology matching, instance matching and search
  • Recommender systems Applications in Social Networks
  • AI Driven Sentiment Analysis
  • Information Retrieval and Document Analysis
  • Intelligent Decision Making
Deep Learning
  • Fog Computing
  • Data Analytics and Computing
  • Reinforcement learning
  • Image Processing
  • Cognitive Networks
  • Evolutionary Computing
  • Genetic Algorithms
  • Statistical learning methods
  • Fuzzy models
  • Security and Privacy Methods in Deep Learning
Big Data Analytics
  • Foundational theoretical or computational models for big data
  • Big data quality evaluation and assurance technologies
  • Artificial Intelligence for big data
  • Visualization analytics for big data
  • Real-time Big Data Services and Applications
  • Big data services and applications for healthcare
  • Web Intelligence
  • Cryptographic Algorithms and Protocols
  • Web mining and Graph Mining
  • Machine learning in cloud computing