ISBN13: | 9781032273433 |
ISBN10: | 1032273437 |
Binding: | Paperback |
No. of pages: | 232 pages |
Size: | 234x156 mm |
Weight: | 426 g |
Language: | English |
Illustrations: | 34 Illustrations, black & white; 7 Halftones, black & white; 27 Line drawings, black & white; 32 Tables, black & white |
694 |
Electrical engineering and telecommunications, precision engineering
Theory of computing, computing in general
Data management in computer systems
Computer architecture, logic design
Supercomputers
Operating systems and graphical user interfaces
Computer programming in general
Artificial Intelligence
Computer modeling and simulation
Environmental sciences
Electrical engineering and telecommunications, precision engineering (charity campaign)
Theory of computing, computing in general (charity campaign)
Data management in computer systems (charity campaign)
Computer architecture, logic design (charity campaign)
Supercomputers (charity campaign)
Operating systems and graphical user interfaces (charity campaign)
Computer programming in general (charity campaign)
Artificial Intelligence (charity campaign)
Computer modeling and simulation (charity campaign)
Environmental sciences (charity campaign)
Optimization of Sustainable Enzymes Production
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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.
1. Industrially Important Enzymes. 2. Applications of Industrially important enzymes. 3. Optimization of Fermentation Process: Influence on Industrial Production of Enzymes. 4. Reforming process optimization of enzyme production using artificial intelligence and machine learning. 5. Scale-up models for chitinase production, enzyme kinetics, and optimization. 6. Genetic Algorithm for optimization of fermentation process of various enzyme production. 7. Optimization of process parameter of various classes of enzymes using artificial neural network. 8. Advanced Evolutionary Differential Evolution and Central Composite Design: Comparative Study for process optimization of chitinase production. 9. Artificial bee colony for optimization of process parameters for various enzyme productions.