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

J Satya Eswari (Curatore)

Nisha Suryawanshi (Curatore)

Taylor & Francis Ltd, 2024

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.

• Curatore: J Satya Eswari, Nisha Suryawanshi
• Editore: Taylor & Francis Ltd
• Anno: 2024
• Rilegatura: Paperback / softback
• Pagine: 220 p.
• Dimensioni: 234 x 156 mm
• Peso: 430 gr.
• EAN: 9781032273433

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