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Computational Intelligence and Mathematics for Tackling Complex Problems 6 [Hardback]

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  • Formāts: Hardback, 194 pages, height x width: 235x155 mm, 20 Illustrations, color; 13 Illustrations, black and white; XII, 194 p. 33 illus., 20 illus. in color., 1 Hardback
  • Sērija : Studies in Computational Intelligence 1222
  • Izdošanas datums: 21-Oct-2025
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031978781
  • ISBN-13: 9783031978784
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Computational Intelligence and Mathematics for Tackling Complex Problems 6
  • Formāts: Hardback, 194 pages, height x width: 235x155 mm, 20 Illustrations, color; 13 Illustrations, black and white; XII, 194 p. 33 illus., 20 illus. in color., 1 Hardback
  • Sērija : Studies in Computational Intelligence 1222
  • Izdošanas datums: 21-Oct-2025
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031978781
  • ISBN-13: 9783031978784
Citas grāmatas par šo tēmu:

This book focuses on collecting some of the best results with the goal, dealing with the important and efficient combination of mathematics and computational intelligence, with a stress of fuzzy systems, and fuzzy logic.

Artificial intelligence appears nowadays very much in the media, and also in the practice. In addition, every day, thousands of automatic intelligent systems for decision making are designed and applied around the world. In their design, many advanced techniques based on mathematics and computational intelligence are considered and used. Therefore, it is fundamental to continuously work on novel approaches in these areas and research models and algorithms with foundation in the mutual relationship of these two fields. 

Naturally, an important part of this issue focuses on the applications of these both areas, taking a special interest in the development and application of mathematics, artificial intelligence, and automatic reasoning tools applicable to Digital Forensics, also following the main objective of the COST Action DigForASP-CA17124.

A note on aggregation of T-fuzzy subgroups.- Independent subcontexts in
fuzzy formal concept analysis .- Two sufficient conditions for having a
maximal solution to fuzzy relational equations.- Towards compatible
subcontext by means of classical attribute reduction.