NCU

International Conference on
Computational Intelligence and Data Science (ICCIDS 2018)

7-8th April, 2018

The NorthCap University, Gurugram

"The conference proceedings will be published in Procedia Computer Science Journal, Elsevier."

Workshop on “Big Data and Deep Learning” by IBM Resource Persons, IBM Research Lab, India

TRACKS:

ICCIDS2018 provides a platform for researchers, scientists and academia from Computational Intelligence and Data Science to meet and exchange ideas, collaborate and work together. It also proposes to focus on all aspects of computation, Intelligence and data sciences with modern and emerging computational techniques especially areas which require very high precision.
Topics of interest include but are not limited to:

THEORETICAL FOUNDATIONS:
  • Probabilistic and statistical models and theories
  • Learning theory
  • Optimization Methods
  • Data Compression and Sampling
  • Statistical learning
  • Evolutionary Computation
  • Deep Learning
  • Learning Classifiers
  • Parallel and distributed learning
  • Scientific data and Big Data analytics
  • Artificial Intelligence
  • Scalable analysis and learning
  • Data pre-processing, sampling and reduction
  • High dimensional data, feature selection and feature transformation
  • High performance computing for data analytics
  • Architecture, management and process for data science
MACHINE LEARNING AND KNOWLEDGE DISCOVERY:
  • Knowledge discovery theories, models and systems
  • Human-machine interaction for knowledge discovery and management
  • Biomedical knowledge discovery, analysis of micro-array and gene deletion data
  • Machine Learning for High-Performance Computing
  • Learning for streaming data
  • Machine learning over the Cloud
  • Knowledge based neural networks
  • Spatial/Temporal Data
  • Knowledge discovery from heterogeneous, unstructured and multimedia data
  • Knowledge discovery in network and link data
  • Knowledge discovery in social networks
  • Data and knowledge visualization
  • Cross media data analytics
  • Big data visualization, modeling and analytics
  • Multimedia/stream/text/visual analytics
COMPUTATIONAL INTELLIGENCE AND DATA SCIENCE:
  • Computational theories for big data analysis
  • Computational Intelligence for Pattern Recognition and Medical Imaging
  • Incremental learning – theory, algorithms and applications in Big data
  • Sparse data, feature selection and feature transformation
  • Intelligent Information Retrieval
  • Probabilistic and information-theoretic methods
  • Support vector machines and kernel methods
  • Time series analysis
  • High Performance/Parallel Computing
  • Search and Mining
  • Data Acquisition, Integration, Cleaning
  • Data Visualizations
  • Semantic based Data Mining
  • Data Wrangling
  • Decision making from insights, Hidden patterns
  • Optimization for Data Analytics
APPLICATIONS:
  • Bioinformatics
  • Biomedical informatics
  • Computational neuroscience
  • Information retrieval
  • Healthcare
  • Collaborative filtering
  • Computer vision
  • Human activity recognition
  • Natural language processing
  • Web search