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Optimization of Sustainable Enzymes Production
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Optimization of Sustainable Enzymes Production : Artificial Intelligence and Machine Learning Techniques

Book Details

Format Hardback or Cased Book
ISBN-10 1032273372
ISBN-13 9781032273372
Publisher Taylor & Francis Ltd
Imprint Chapman & Hall/CRC
Country of Manufacture GB
Country of Publication GB
Publication Date Nov 29th, 2022
Print length 220 Pages
Weight 472 grams
Dimensions 13.70 x 24.10 x 2.20 cms
Ksh 18,000.00 Werezi Extended Catalogue

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This book presents a systematic approach to analyze evolutionary and nature-inspired population-based search algorithms. The book presents current research on various applications of machine learning and discusses optimization techniques to solve real-life problems.

This book is designed as a reference book and presents a systematic approach to analyze evolutionary and nature-inspired population-based search algorithms. Beginning with an introduction to optimization methods and algorithms and various enzymes, the book then moves on to provide a unified framework of process optimization for enzymes with various algorithms. The book presents current research on various applications of machine learning and discusses optimization techniques to solve real-life problems.

  • The book compiles the different machine learning models for optimization of process parameters for production of industrially important enzymes. The production and optimization of various enzymes produced by different microorganisms are elaborated in the book
  • It discusses the optimization methods that help minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making
  • Covers the best-performing methods and approaches for optimization sustainable enzymes production with AI integration in a real-time environment
  • Featuring valuable insights, the book helps readers explore new avenues leading towards multidisciplinary research discussions

The book is aimed primarily at advanced undergraduates and graduates studying machine learning, data science and industrial biotechnology. Researchers and professionals will also find this book useful.


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