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The organelle-specific signaling sequences direct proteins synthesized in the cytosol to their final destination like ER, mitochondria, peroxisomes, etc. Some of the proteins directed to ER are then trafficked via vesicles to other organelles within the cell or the extracellular environment through the Golgi complex. For example, the rough ER synthesizes soluble proteins for transportation to the lysosomes or secretion out of the cell. It can also synthesize transmembrane proteins that can...
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Rough sets: past, present, and future.

Andrzej Skowron1,2, Soma Dutta3,4

  • 11Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland.

Natural Computing
|December 8, 2018
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Summary
This summary is machine-generated.

Rough set theory, celebrating 35 years, offers methods for complex concept approximation. Future research focuses on interactive granular computing for intelligent and complex adaptive systems.

Keywords:
(Approximate) Boolean reasoningAdaptive judgmentComplex adaptive systemGranular computingInteractionNatural computingRough set

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Area of Science:

  • * Computational intelligence and data analysis.
  • * Artificial intelligence and machine learning.
  • * Mathematical foundations of computing.

Background:

  • * Rough set theory, introduced by Professor Zdzisław Pawlak, marks 35 years of development.
  • * The theory has garnered global attention from researchers and practitioners.
  • * Rough set methods, individually or combined with other approaches, have found diverse applications.

Purpose of the Study:

  • * To review selected past and present research directions in rough set theory.
  • * To highlight the significance of searching strategies for approximation spaces.
  • * To explore new challenges in problem-solving for intelligent systems (IS) and complex adaptive systems (CAS).

Main Methods:

  • * Emphasizing the role of approximation spaces in discovering computational building blocks (granules/patterns).
  • * Discussing interactive granular computing as an extension of rough set theory.
  • * Focusing on strategies for effective computation realization in IS and CAS.

Main Results:

  • * Identification of key research trends in rough set theory.
  • * Underscoring the importance of approximation spaces for concept approximation.
  • * Highlighting the potential of granular computing for advanced intelligent systems.

Conclusions:

  • * Rough set theory continues to evolve, offering powerful tools for data analysis.
  • * Interactive granular computing presents a promising avenue for tackling complex problems in IS and CAS.
  • * These advancements are crucial for the progress of natural computing.