Call For Paper

Track 1: Fundamentals and Theories

• Theoretical Models
• Spatial and temporal multi-models
• Multi-dimensional data
• Data acquisition and preprocessing
• Data inference
• Data Classification and Taxonomy
• Data Metrics
• Approaches for collaboration and competition
• Spatial reasoning
• Context awareness
• Approaches to supervised and unsupervised learning
• Multi-Agent Systems for data science
• Theories and approaches for Deep learning
• Approaches for Business Intelligence
• Fuzzy logic
• Decision trees
• Support vector machines
• Evolutionary computation
• Statistical methods
• Collaborative filtering
• Data engineering
• Content mining
• Indexing schemes
• Information retrieval

Track 2: Intelligent Systems and Interactions

• Smart grid computing
• Intelligent data management
• Internet of Things
• Cyber Physical Systems
• Blockchain
• Fog and Edge intelligence
• Parallel Computing systems  
• Open Source systems for data science
• Embedded intelligence
• Embedded data science
• In-Memory computing for structured and unstructured data
• Intelligent Drones
• Smart cities
• Data science for smart environments
• Robotics and autonomous systems
• Intelligent telecommunications
• Networks for high-performance computing platforms
• Intelligent and integrated design of complex systems
• Condition monitoring and fault diagnosis
• Network control and Autonomous Vehicles
• Computer vision
• Signal processing
• Simulation and Modeling

Track 3: Big Data Analytics

• Multi-level data processing
• Data analytics optimization
• Smart data mining
• Machine Learning
• Deep Learning
• Bio-Inspired Computing
• Business intelligence
• Visualization Analytics
• Intelligence as a Service (IaaS)
• Data Science as a Service (DSaaS)
• Natural Language Processing
• Data-Intensive Computing
• Distributed data systems
• Big Data computing
• Cloud computing
• Real-time data analytics
• High-performance data access toolkits
• Image Processing, Multimedia and Stream data computing
• Data intensive systems

Track 4: Security and privacy issues

• Secure data analytics
• Privacy in data analytics
• Trust and data science
• Authentication and data access authorization
• Data authenticity
• Security and Cyber-infrastructure 
• Forensic analysis and resolution of security incidents
• Proactive and reactive network traffic analyses
• Self-organization, self-healing, fault-tolerance approaches  
• Secure cloud services
• Intrusion and attack detection
• Cryptographic protocols
• Rights managements for data science
• Intelligent secure architectures