Optimization and Computing using Intelligent Data-Driven Approaches for Decision-Making - Ali, Irfan; Modibbo, Umar Muhammad; Bolaji, Asaju La?aro;(ed.) - Prospero Internet Bookshop
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Optimization and Computing using Intelligent Data-Driven Approaches for Decision-Making

Artificial Intelligence Applications
 
Edition number: 1
Publisher: CRC Press
Date of Publication:
 
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Short description:

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.

Long description:

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. 

Table of Contents:

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. 13. The impact of blockchain technology and decentralized supply chain on production.