Advanced Mathematical Techniques in Computational and Intelligent Systems

Advanced Mathematical Techniques in Computational and Intelligent Systems

This book comprehensively discusses the modeling of real-world industrial problems and innovative optimization techniques such as heuristics, finite methods, operation research techniques, intelligent algorithms, and agent-based methods.

Detalles Bibliográficos
Autor principal: Singh, Sandeep (-)
Otros Autores: Haghighi, Aliakbar Montazer, Dalal, Sandeep
Formato: Libro electrónico
Idioma:Inglés
Publicado: Milton : Taylor & Francis Group 2023.
Edición:1st ed
Colección:Computational and Intelligent Systems Series
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009869134406719
Tabla de Contenidos:
  • Cover
  • Half Title
  • Series Page
  • Title Page
  • Copyright Page
  • Table of Contents
  • About the Editors
  • Contributors
  • Preface
  • 1 Increasing the Order of Convergence of Three-step-Modified Potra-Pták-Chebyshev Methods for Systems and Equations
  • 1.1 Introduction
  • 1.2 Development of the Method
  • 1.3 Multi-step Version Method
  • 1.4 Local Convergence Analysis
  • 1.5 Numerical Examples
  • References
  • 2 Mathematical Model to Distinguish the Symptomatic Patient of COVID-19
  • 2.1 Introduction
  • 2.2 Preliminaries
  • 2.2.1 Basic Definition
  • 2.2.2 Arithmetic Operations
  • 2.2.3 Max-min Principle
  • 2.3 Approach
  • 2.4 Model Representation
  • 2.5 Case Study
  • 2.6 Results and Discussion
  • 2.7 Conclusion and Future Scope
  • References
  • 3 Maximum Cost cell Method for IBFS of Transportation Problems
  • 3.1 Introduction
  • 3.2 Mathematical Representation
  • 3.3 Proposed Algorithm (PA)
  • 3.4 Illustrative Example
  • 3.5 Comparative Study
  • 3.6 Conclusion
  • References
  • 4 Optimization Techniques and Applications to Solve Real-world Industrial Optimization Problems
  • 4.1 Introduction
  • 4.1.1 Background
  • 4.2 Recent Works
  • 4.2.1 Optimizing Maintenance Schedule
  • 4.2.2 Search-based Software Testing (SBST)
  • 4.2.3 Cost Optimization of Turning Process
  • 4.3 Problem Statement
  • 4.3.1 Research Questions
  • 4.3.2 Research Objectives
  • 4.4 Using Industrial data for Optimization Practices
  • 4.4.1 Centralization and Collection of Data
  • 4.4.2 Automation of Monitoring
  • 4.4.3 Real-time Data Visualization
  • 4.4.4 Predictive Analysis
  • 4.5 Optimization Techniques and Applications to Solve Real-world Problems
  • 4.5.1 Optimization Models
  • 4.5.2 Optimization Methods
  • 4.5.3 The Complex Nature of Production Optimization
  • 4.5.4 Solving Optimization Problem with Machine Learning
  • 4.5.5 Components of ML-based Production Optimization.
  • 4.6 Discussion
  • 4.7 Conclusion
  • References
  • 5 A Method to Solve Trapezoidal Transshipment Problem under Uncertainty
  • 5.1 Introduction
  • 5.2 Preliminaries
  • 5.3 Arithmetic Operations
  • 5.4 Ranking Function
  • 5.5 Mathematical Formulation of the Transshipment Problem under Uncertainty
  • 5.6 Proposed Method
  • 5.7 Numerical Example
  • 5.8 Results and Discussion
  • 5.9 Comparative Studies
  • 5.10 Conclusion
  • References
  • 6 Enhancing the Security of Public key Cryptographic Model based on Integrated ElGamal-Elliptic Curve Diffe Hellman (EG-ECDH) Key Exchange Technique
  • 6.1 Introduction
  • 6.2 Generalized Fibonacci Matrix and its Inverse
  • 6.3 Technique for key Generation, Encryption and Decryption
  • 6.3.1 ElGamal Technique
  • 6.3.2 ECDH Technique
  • 6.3.3 Proposed Integrated ElGamal-ECDH Technique for key Generation
  • 6.4 Technique for Encryption and Decryption
  • 6.5 Algorithm for Proposed Model
  • 6.6 Numerical Example
  • 6.7 Complexity of this Model
  • 6.8 Conclusion
  • References
  • 7 An Investigation of Fractional Ordered Biological Systems using a Robust Semi-Analytical Technique
  • 7.1 Introduction
  • 7.2 Generalized time Fractional order Biological Population Model
  • 7.3 Preliminaries
  • 7.3.1 Basic Concept of Fractional Calculus
  • 7.3.2 Basic Definitions
  • 7.4 Implementation of Fractional Modified Differential Transform Method
  • 7.5 Conclusion
  • References
  • 8 Variable Selection in Multiple Nonparametric Regression Modelling
  • 8.1 Introduction
  • 8.2 Proposed Methodology and Statistical Properties
  • 8.2.1 Description of the Method
  • 8.2.2 Large Sample Statistical Property
  • 8.3 Simulation Study
  • 8.4 Real data Analysis
  • 8.4.1 QSAR Fish Toxicity Data Set
  • 8.4.2 Istanbul Stock Exchange Data Set
  • 8.4.3 SGEMM GPU Kernel Performance Data Set
  • 8.4.4 CSM (Conventional and Social Media Movies) Data Set.
  • 8.5 Concluding Remarks
  • Appendix A (Proofs)
  • Appendix B (Tables)
  • Acknowledgement
  • References
  • 9 Mathematical Modeling of Regulation of Sulfur Metabolic Pathways
  • 9.1 Introduction
  • 9.2 Review of Literature
  • 9.3 Materials and Methods
  • 9.3.1 Materials
  • 9.3.2 Methods
  • 9.4 Results
  • 9.4.1 KEGG Reported S-biochemical Pathways
  • 9.4.2 Construction of S-biochemical Network
  • 9.4.3 Model Formulation
  • 9.4.4 Steady State Behavior of S-biochemical System
  • 9.4.5 Parameter Dependency of the S-biochemical System
  • 9.4.6 Initial Condition Dependency of the S-biochemical System
  • 9.4.7 Flipping Dynamics of Sulfate and Sulfide
  • 9.4.8 Elementary Flux Modes
  • 9.5 Conclusion
  • References
  • 10 Some Results on Quasi-convergence in Gradual Normed linear Spaces
  • 10.1 Introduction: Background and Driving Forces
  • 10.2 Preliminaries
  • 10.3 Main Results
  • 10.4 Conclusion
  • References
  • 11 On Einstein Gyrogroup
  • 11.1 Introduction
  • 11.2 Preliminaries
  • 11.2.1 Gyrogroup and its Main Properties
  • 11.2.2 Einstein's Addition of Relativistically Admissible Velocities
  • 11.3 Gyrations: Repairing the Breakdown of Classical Laws
  • References
  • 12 On the Norms of Toeplitz and Hankel Matrices with Balancing and Lucas-balancing Numbers
  • 12.1 Introduction
  • 12.1.1 Norms and Bounds for Spectral Norm
  • 12.2 Main Results
  • 12.2.1 Bounds on Norms for Toeplitz Matrices
  • 12.2.2 Norms on Hankel Matrices
  • 12.3 Numerical Experiments
  • 12.4 Concluding Remarks
  • Acknowledgment
  • References
  • 13 Grey Wolf Optimizer for the Design Optimization of a DC-DC Buck Converter
  • 13.1 Introduction
  • 13.2 Algorithms for Optimization
  • 13.2.1 Grey Wolf Optimizer
  • 13.2.2 Moth Flame Optimization
  • 13.2.3 Particle Swarm Optimization
  • 13.2.4 Simulated Annealing
  • 13.2.5 Firefly Algorithm.
  • 13.3 Steady State Space Model of the DC-DC buck Converter
  • 13.4 Design Constraints of the DC-DC buck Converter
  • 13.5 Results and Discussion
  • 13.5.1 Solution Quality
  • 13.5.2 Robustness
  • 13.6 Conclusions
  • References
  • 14 A New Modified Iterative Transform Method for Solving Time-fractional Nonlinear Dispersive Korteweg-de Vries Equation
  • 14.1 Introduction
  • 14.2 Numerical Experiment
  • 14.3 Some Preliminaries
  • 14.3.1 Basics on Fractional Calculus
  • 14.3.2 Conformable Sumudu Transform
  • 14.4 Conformable New Iterative Transform Method
  • 14.5 Illustrative Example
  • 14.6 Conclusion
  • Acknowledgments
  • References
  • 15 Application of Graph Theory in Search of Websites and Web Pages
  • 15.1 Introduction
  • 15.2 Materials and Methods
  • 15.3 Solution Procedure
  • 15.4 Results and Discussion
  • 15.5 Conclusion
  • References
  • Index.

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