| ISBN13: | 9781032781112 |
| ISBN10: | 1032781114 |
| Binding: | Hardback |
| No. of pages: | 228 pages |
| Size: | 234x156 mm |
| Weight: | 580 g |
| Language: | English |
| Illustrations: | 37 Illustrations, black & white; 1 Halftones, black & white; 36 Line drawings, black & white; 39 Tables, black & white |
| 700 |
Applied mathematics
Electrical engineering and telecommunications, precision engineering
Energy industry
Theory of computing, computing in general
Computer architecture, logic design
Computer programming in general
Computer networks in general
Artificial Intelligence
Environmental sciences
Applied mathematics (charity campaign)
Electrical engineering and telecommunications, precision engineering (charity campaign)
Energy industry (charity campaign)
Theory of computing, computing in general (charity campaign)
Computer architecture, logic design (charity campaign)
Computer programming in general (charity campaign)
Computer networks in general (charity campaign)
Artificial Intelligence (charity campaign)
Environmental sciences (charity campaign)
Optimization and Computing using Intelligent Data-Driven Approaches for Decision-Making
GBP 84.99
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This book comprehensively discusses nature-inspired algorithms, deep learning methods, applications of mathematical programming and artificial intelligence techniques. It will further cover important topic such as linking green supply chain management practices with competitiveness, industry 4.0 and social responsibility.
This book comprehensively discusses nature-inspired algorithms, deep learning methods, applications of mathematical programming and artificial intelligence techniques. It will further cover important topic such as linking green supply chain management practices with competitiveness, industry 4.0, and social responsibility.
This book:
- Addresses solving practical problems such as supply chain management, take-off, and healthcare analytics using intelligent computing
- Presents a comparative analysis of machine learning algorithms for power consumption prediction
- Discusses a machine learning-based multi-objective optimization technique for load balancing in an integrated fog cloud environment
- Illustrates a data-driven optimization concept for modeling environmental and economic sustainability
- Explains the use of heuristics and metaheuristics in supply chain networks and the use of fuzzy optimization in sustainable development goals
The text is primarily written for graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, mathematics and statistics, computer science and engineering.
1. Linking green supply chain management practices with competitiveness, Industry 4.0, and social responsibility. 2. Comparative analysis of machine learning algorithms for power consumption prediction. 3. Prediction of cardiovascular disease using information gain, artificial neural network, and CART 5.0 algorithm. 4. Weighting of logistics management factors by SWARA and DELPHI methods. 5. Evaluation of daily management with standard deviation and MOOSRA tools in hospitals. 6. Machine learning-based multi-objective optimization technique for load balancing in integrated fog cloud environment. 7. Perishable inventory fuzzy optimization model with uncertain deterioration and preservation investment. 8. Employing fuzzy inference system in ant colony optimization for travelling salesman problems. 9. Type-2 Gaussian neurofuzzy VIKOR technique in multi-criteria decision making for medical diagnostics. 10. Overviewing AI and explainable AI decision-making (XAIDM). 11. Harnessing the power of Industry 4.0: synergizing smart manufacturing, supply chain, and reshoring strategies. 12. Digital revolution in cold supply chain management related to the food industry.
