Machine Learning and Mathematical Models in Evolutionary Biology : Insights, Innovations, and Applications (Computational Biology)

Singh, Satyvir (EDT)/ Awasthi, Mukesh Kumar (EDT)

Springer, Berlin; Springer（2026/08発売）

• 製本 Hardcover:ハードカバー版
• 商品コード 9783032258502

Full Description

The book discusses the advantages of using mathematical modeling and machine learning in the context of the evolutionary biology domain to gain knowledge and develop further. It discusses the background ideas regarding evolutionary theory, population behavior and computation, and advances to the current topics of evolutionary algorithms, nonlinear modeling, and data-driven analysis.

The volume proposes the application of theoretical models and clever algorithms to the analysis of complex biological systems, ecological interactions, and real-world problems in health, genomics, and engineering. Combining classical theories with some new computational tools, the book proves that machine learning is able to make predictions more accurate and reduce some parameters and process large amounts of data more efficiently in biological studies. It also covers disease modelling, genomic prediction, tumor growth and socio-environmental dynamics and is also interdisciplinary in exploring network systems and biomedical engineering. On the whole, the book offers an integrative and prospective view to scientists and professionals regarding the innovative aspects at the interplay of biology, mathematics, and artificial intelligence and highlights the future of evolutionary science and intelligent models.

Contents

.- Foundations of Evolutionary Biology and Computational Methods.
.- Fundamentals of Evolutionary Biology and Algorithms.
.- Machine Learning Basics for Biological Applications.
.- Mathematical Models in Evolutionary Dynamics.
.- Computational and Mathematical Approaches in Modern Evolutionary Biology.
.- Evolutionary Algorithms and Computational Intelligence.
.- Evolutionary Algorithms Inspired by Nature.
.- Adaptive Evolution and Environmental Interactions.
.- Mathematical Modeling of Population and Ecological Dynamics.
.- Nonlinear Attrition Dynamics with Stability Analysis for Military and Civilian Populations.
.- Two dimensional predator-prey system with smartest predators: The killer whales.
.- Effect of Population Growth Driven by Female Education: A Mathematical Study With Machine Learning.
.- The Code of Biodiversity: Computational Modeling of Speciation and Extinction Dynamics.
.- Mathematical Modeling in Biological and Evolutionary Systems.
.- Fixed Point Non-linear Differential Solution for a 3-Manifold Mathematical Diffusion Gene Network Analyzing Stability and Oscillatory Dynamics Using Phase Portraits.
.- High-Order Computational Modeling of Tumor Growth and Evolutionary Dynamics.
.- Machine Learning Applications in Health.
.- Evolutionary Pathways in Infectious Diseases.
.- Disease-Related Genomic Prediction via Machine Learning: New Data Augmentation with Hybrid GAN Models.
.- Machine Learning Assisted Mathematical Analysis of Smoking Dynamics based on WHO Dataset.
.- An Epidemiological Analysis of Traffic Rule Violations and Their Impact on Public Health.
.- Cross-Disciplinary Computational Applications and Future Perspectives.
.- Estimating Femoral Artery Hemodynamics Using a Hybrid CFD-Machine Learning Approach.
.- A Modeling Study on Virus Dynamics in a Computer Network.
.- Future Directions in Evolutionary Biology and Artificial Intelligence.