Aims and Scope

Data science and analytics has emerged as an important research topic in recent decades, with applications in many fields such as market basket analysis, social networks, the Internet of Things and cloud computing. Pattern recognition is also a very active research area in computer science and information theory, which is increasingly popular in recent years. The primary objective of the Data Science and Pattern Recognition (DSPR) journal is to give the opportunity to researchers, scientists, industry practitioners, and professionals to publish outstanding work in these fields. The journal welcomes manuscripts describing experimental and theoretical findings both on data science and pattern recognition, and encourages the application of theoretical models in real-life applications. The journal also welcomes the submission of survey papers that provides a detailed overview of specific research areas.

Topics of relevance include all aspects of the scientific foundations, technologies, theories, and applications in the fields of data science and pattern recognition, including but not limited to:

  •  Data mining
  •  Knowledge modeling and visualization
  •  Machine learning and deep learning
  •  Database management and query processing
  •  Social network analytics, behavior analytics and text analytics
  •  Big data mining and cloud computing
  •  Security, trust and privacy issues
  •  Bioinformatics
  •  Recomender systems
  •  Pattern recognition
  •  Image or signal processing
  •  Artificial intelligence and optimization methods
  •  Innovative software for data science or pattern recognition