Advances in Computational Methods and Modelling in Science and Engineering explores the application of computational techniques and modeling approaches in science and engineering, providing practical knowledge and skills for tackling complex problems using numerical simulations and data analysis. This book addresses the need for a cohesive and up-to-date resource in the rapidly evolving field of computational methods. It consolidates diverse topics, serving as a one-stop guide for individuals seeking a comprehensive understanding of the subject matter. Sections focus on mathematical techniques that provide global solutions for models arising in engineering and scientific research applications by considering their long-term benefits.
The mathematical treatment of these models is very helpful in understanding these models and their real-world applications. The methods and modeling techniques presented are useful for mathematicians, engineers, scientists, and researchers working on the mathematical treatment of models in a wide range of applications, including disciplines such as engineering, physics, chemistry, computer science, and applied mathematics.
1. 15.1 Introduction - p. 193 2. 15.2 Mathematical model - p. 195 3. 15.3 Mathematical preliminaries - p. 196 4. 15.4 Mathematical analysis - p. 196 5. 15.5 Existence and stability conditions of equilibrium points - p. 201 6. 15.6 Numerical simulations - p. 213 7. 15.7 Conclusion - p. 212 8. References - p. 216 9. Chapter 16. Modeling and analyzing delay in plant responses under toxicity - p. 219 10. Chapter 17. Mathematical modeling of coral–algal competition under environmental pressures in aquatic ecosystems: a review - p. 231 11. Chapter 18. Effect of time delay on directional and stability analysis of plant competition for allelochemicals study - p. 241 12. Chapter 19. Harnessing the potentials of machine learning models in Alzheimer's disease prediction and detection - p. 259 13. Chapter 20. Impact of deforestation and environmental degradation on Nipah virus outbreaks: a critical review - p. 269 14. Chapter 21. Current advances in data-driven modeling and computational tools for novel materials - p. 277 15. Chapter 22. Onset of bioconvection in the presence of magnetic nanoparticles and gyrotactic microorganisms in porous media - p. 287 16. Chapter 23. Predicting diabetes using artificial neural networks and K-nearest neighbors - p. 297 17. Chapter 24. Real coded GA for finding stable structures of two small molecules - p. 305 18. Chapter 25. Cost optimization of a batch retrial queue with a working vacation - p. 315 19. Chapter 26. Future now: unleashing the potentials of machine learning algorithms in 6G - p. 331 20. Index - p. 341
Advances in Computational Methods and Modelling in Science and Engineering explores the application of computational techniques and modeling approaches in science and engineering, providing practical knowledge and skills for tackling complex problems using numerical simulations and data analysis. This book addresses the need for a cohesive and up-to-date resource in the rapidly evolving field of computational methods. It consolidates diverse topics, serving as a one-stop guide for individuals seeking a comprehensive understanding of the subject matter. Sections focus on mathematical techniques that provide global solutions for models arising in engineering and scientific research applications by considering their long-term benefits.
The mathematical treatment of these models is very helpful in understanding these models and their real-world applications. The methods and modeling techniques presented are useful for mathematicians, engineers, scientists, and researchers working on the mathematical treatment of models in a wide range of applications, including disciplines such as engineering, physics, chemistry, computer science, and applied mathematics.
1. 15.1 Introduction - p. 193 2. 15.2 Mathematical model - p. 195 3. 15.3 Mathematical preliminaries - p. 196 4. 15.4 Mathematical analysis - p. 196 5. 15.5 Existence and stability conditions of equilibrium points - p. 201 6. 15.6 Numerical simulations - p. 213 7. 15.7 Conclusion - p. 212 8. References - p. 216 9. Chapter 16. Modeling and analyzing delay in plant responses under toxicity - p. 219 10. Chapter 17. Mathematical modeling of coral–algal competition under environmental pressures in aquatic ecosystems: a review - p. 231 11. Chapter 18. Effect of time delay on directional and stability analysis of plant competition for allelochemicals study - p. 241 12. Chapter 19. Harnessing the potentials of machine learning models in Alzheimer's disease prediction and detection - p. 259 13. Chapter 20. Impact of deforestation and environmental degradation on Nipah virus outbreaks: a critical review - p. 269 14. Chapter 21. Current advances in data-driven modeling and computational tools for novel materials - p. 277 15. Chapter 22. Onset of bioconvection in the presence of magnetic nanoparticles and gyrotactic microorganisms in porous media - p. 287 16. Chapter 23. Predicting diabetes using artificial neural networks and K-nearest neighbors - p. 297 17. Chapter 24. Real coded GA for finding stable structures of two small molecules - p. 305 18. Chapter 25. Cost optimization of a batch retrial queue with a working vacation - p. 315 19. Chapter 26. Future now: unleashing the potentials of machine learning algorithms in 6G - p. 331 20. Index - p. 341
