Aims and scope

Our primary aims and scope encompass, but are not limited to, the following key areas:

Foundational Theories in Information Science:

  • Information Theory: Exploration of the quantification, storage, and communication of information.
  • Mathematical Linguistics: Study of mathematical structures and techniques within the linguistic context.
  • Automata Theory: Investigation of abstract machines and the computational problems they can solve.
  • Cognitive Science: An interdisciplinary examination of the mind and its processes.
  • Theories of Qualitative Behaviour: Analysis of systems that do not rely on numerical measures.
  • Artificial Intelligence and Computational Intelligence: Focus on creating systems capable of intelligent behavior.
  • Soft Computing: Techniques encompassing approximate reasoning and computation, such as fuzzy logic.
  • Semiotics: Study of signs and symbols and their use and interpretation.
  • Computational Biology and Bioinformatics: Application of computational techniques to solve biological problems.

 

Technological Implementations and Innovations:

  • Intelligent Systems: Development and application of systems capable of autonomous decision-making.
  • Genetic Algorithms and Modelling: Use evolutionary algorithms to solve optimization and search problems.
  • Fuzzy Logic and Approximate Reasoning: Systems that use fuzzy set theory to reason about data.
  • Artificial Neural Networks: Systems inspired by the biological neural networks that constitute animal brains.
  • Expert and Decision Support Systems: Computer-based systems that support decision-making activities.
  • Learning and Evolutionary Computing: Emphasis on algorithms that learn from and adapt to new data.
  • Biometrics: Techniques for uniquely recognizing humans based on intrinsic physical or behavioral traits.
  • Moleculoid Nanocomputing: Exploration of computing with molecular, atomic, and supramolecular scales.
  • Self-adaptation and Self-organizational Systems: Study systems that adapt and reorganize without external intervention.
  • Data Engineering and Data Fusion: Techniques for managing, merging, and making sense of large data sets.
  • Adaptive and Supervisory Control: Systems that adjust their operation in response to changing conditions.
  • Discrete Event Systems: Modeling and analyzing systems where changes occur at discrete points.
  • Symbolic/Numeric and Statistical Techniques: Methods for processing and analyzing numerical data.
  • Perceptions and Pattern Recognition: Techniques for identifying patterns in data.
  • Design of Algorithms: Creation of efficient algorithms for problem-solving.
  • Software Design and Computer Systems Architecture: Development of software and the architectural design of computer systems.
  • Human-Computer Interface: Design of user interfaces for maximizing usability and user experience.
  • Computer Communication Networks: Study digital networks, including their architecture and protocols.