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

This book provides a comprehensive guide to analyzing evolutionary and nature-inspired population-based search algorithms, covering optimization techniques, machine learning models, and their applications in enzyme production optimization. It is aimed at advanced undergraduates, graduates, and researchers in machine learning, data science, and industrial biotechnology.

Format: Hardback
Length: 220 pages
Publication date: 29 November 2022
Publisher: Taylor & Francis Ltd

This comprehensive book serves as a valuable reference guide, offering a systematic approach to analyze evolutionary and nature-inspired population-based search algorithms. It begins with an introductory exploration of optimization methods and algorithms, along with a comprehensive introduction to various enzymes. Subsequently, the book presents a unified framework for process optimization, encompassing a diverse range of algorithms tailored for enzymes. The content delves into current research on diverse applications of machine learning, highlighting optimization techniques for solving real-life problems.

The book also compiles a comprehensive collection of machine learning models specifically designed for optimizing process parameters in the production of industrially significant enzymes. It provides detailed explanations of the production and optimization of various enzymes produced by different microorganisms, emphasizing the use of optimization methods to minimize errors and enhance pattern development and classification accuracy. Furthermore, the book discusses the integration of artificial intelligence (AI) into real-time environments for optimizing sustainable enzyme production. It offers valuable insights and highlights the best-performing methods and approaches for achieving efficient and sustainable enzyme production through AI integration.

This book is primarily aimed at advanced undergraduates and graduates studying machine learning, data science, and industrial biotechnology. It also serves as a valuable resource for researchers and professionals in these fields. By presenting a multidisciplinary perspective, the book fosters new avenues for research discussions and contributes to the advancement of knowledge in these rapidly evolving domains.

Weight: 466g
Dimension: 137 x 241 x 22 (mm)
ISBN-13: 9781032273372