Machine Learning and Mathematical Models in Evolutionary Biology
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.
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Persons
Dr. Satyvir Singh is currently working as a Research Associate Fellow in the Institute of Applied and Computational Mathematics (ACoM) at RWTH Aachen University, Germany. He earned his Ph.D. in Computational Fluid mechanics in the School of Mechanical and Aerospace Engineering at Gyeongsang National University, South Korea. Subsequently, He worked as a Senior Research Fellow at Research Center for Aircraft Parts Technology, Gyeongsang National University, South Korea in 2018. After then, Dr. Singh worked as a Research Fellow in School of Physical and Mathematical Sciences at Nanyang Technological University Singapore during 2018-2022. He completed Master Degree M.Tech. in Industrial Mathematics & Scientific Computing at Indian Institute of Technology Madras, India (QS ranking # 250), as well as Master Degree M.Sc. in Mathematics at CCS University Meerut, India. He qualified two highly competitive Indian examinations -Junior Research Fellowship and National Eligibility Test in Mathematical Sciences (2011) with All Indian Rank # 38, and Graduate Aptitude Test for Engineering in Mathematics (2012) with All Indian Rank - 244. Dr. Singh has a vast research area, including computational fluid dynamics, high order numerical methods, hydrodynamic instability, gas kinetic theory, heat and mass transfer, and computational biology.
Dr. Mukesh Kumar Awasthi has done his Ph.D. on the topic "Viscous Correction for the Potential Flow Analysis of Capillary and Kelvin-Helmholtz instability". He is working as an Assistant Professor in the Department of Mathematics at Babasaheb Bhimrao Ambedkar University, Lucknow. Dr. Awasthi is specialized in the mathematical modeling of flow problems. He has taught courses of Fluid Mechanics, Discrete Mathematics, Partial differential equations, Abstract Algebra, Mathematical Methods, and Measure theory to postgraduate students. He has acquired excellent knowledge in the mathematical modeling of flow problems and he can solve these problems analytically as well as numerically. He has a good grasp of the subjects like viscous potential flow, electro-hydrodynamics, magneto-hydrodynamics, heat, and mass transfer. He has excellent communication skills and leadership qualities. He is self-motivated and responds to suggestions in a more convincing manner. Dr. Awasthi has qualified National Eligibility Test (NET) conducted on all India level in the year 2008 by the Council of Scientific and Industrial Research (CSIR) and got Junior Research Fellowship (JRF) and Senior Research Fellowship (SRF) for doing research.
Content
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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.
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Evolutionary Algorithms and Computational Intelligence.
.- Evolutionary Algorithms Inspired by Nature.
.- Adaptive Evolution and Environmental Interactions.
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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.
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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.
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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.
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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.