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Bio-inspired Neurocomputing

Über Bio-inspired Neurocomputing

This book covers the latest technological advances in neuro-computational intelligence in biological processes where the primary focus is on biologically inspired neuro-computational techniques. The theoretical and practical aspects of biomedical neural computing, brain-inspired computing, bio-computational models, artificial intelligence (AI) and machine learning (ML) approaches in biomedical data analytics are covered along with their qualitative and quantitative features. The contents cover numerous computational applications, methodologies and emerging challenges in the field of bio-soft computing and bio-signal processing. The authors have taken meticulous care in describing the fundamental concepts, identifying the research gap and highlighting the problems with the strategical computational approaches to address the ongoing challenges in bio-inspired models and algorithms. Given the range of topics covered, this book can be a valuable resource for students, researchersas well as practitioners interested in the rapidly evolving field of neurocomputing and biomedical data analytics.

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  • Sprache:
  • Englisch
  • ISBN:
  • 9789811554971
  • Einband:
  • Taschenbuch
  • Seitenzahl:
  • 427
  • Veröffentlicht:
  • 23. Juli 2021
  • Ausgabe:
  • 12021
  • Abmessungen:
  • 155x235x0 mm.
  • Gewicht:
  • 789 g.
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Beschreibung von Bio-inspired Neurocomputing

This book covers the latest technological advances in neuro-computational intelligence in biological processes where the primary focus is on biologically inspired neuro-computational techniques. The theoretical and practical aspects of biomedical neural computing, brain-inspired computing, bio-computational models, artificial intelligence (AI) and machine learning (ML) approaches in biomedical data analytics are covered along with their qualitative and quantitative features. The contents cover numerous computational applications, methodologies and emerging challenges in the field of bio-soft computing and bio-signal processing. The authors have taken meticulous care in describing the fundamental concepts, identifying the research gap and highlighting the problems with the strategical computational approaches to address the ongoing challenges in bio-inspired models and algorithms. Given the range of topics covered, this book can be a valuable resource for students, researchersas well as practitioners interested in the rapidly evolving field of neurocomputing and biomedical data analytics.

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