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Numerical Machine Learning
Numerical Machine Learning is a simple textbook on machine learning that bridges the gap between mathematics theory and practice. The book uses numerical examples with small datasets and simple Python codes to provide a complete walkthrough of the underlying mathematical steps of seven commonly used machine learning algorithms and techniques, including linear regression, regularization, logistic regression, decision trees, gradient boosting, Support Vector Machine, and K-means Clustering. Through a step-by-step exploration of concrete numerical examples, the students (primarily undergraduate and graduate students studying machine learning) can develop a well-rounded understanding of these al...
Advanced Methods for Processing and Visualizing the Renewable Energy
This book is a collection of research work conducted by researchers at Centre for Smart Grid Energy Research (CSMER), Institute of Autonomous System, Universiti Teknologi PETRONAS (UTP), and Seismic Modelling and Inversion Group, King Abdullah University of Science and Technology (KAUST), Saudi Arabia. The book covers topics in the field of renewable energy where visualization, artificial neural network and deep learning techniques have been applied to optimize the performance of various applications in energy-related industries. These examples include a natural gas vehicle (NGV), a single axis and a fixed axis solar tracker, seismic inversion enhanced oil recovery, viability of a PV system and construction of a septic B-spline tensor product scheme. Readers will benefit from these examples, which describe the current trend of energy optimization techniques in renewable energy applications making it a good reference for the researchers and industrial practitioners working in the field of renewable energy and optimization techniques.
Agricultural Sustainability through Nanotechnology
Agricultural Sustainability through Nanotechnology focuses on the innovative intersection of agriculture and nanotechnology, offering a comprehensive exploration of how nanotechnological applications are revolutionizing sustainable farming practices. This book is a pioneering work that not only elucidates the immense potential of nanotechnology in agriculture but also provides practical insights into its implementation for enhanced sustainability. With a focus on addressing pressing agricultural challenges, this book sets itself apart by bridging the gap between cutting-edge nanotechnology research and its real-world applications in sustainable agriculture for better productivity. Readers will discover a wealth of knowledge on how nanotechnology can optimize crop production, mitigate environmental impacts, and improve resource efficiency in farming practices. This book is essential reading for researchers, academics, and professionals in the fields of agriculture, nanotechnology, and environmental science. It serves as a valuable resource for readers seeking to understand and harness the momentum of nanotechnology for sustainable agricultural practices.
Towards Intelligent Systems Modeling and Simulation
This book creates the emergence of disruptive technologies that have led to a significant change in the role of mathematics and statistics for problem solving, with the use of sophisticated software and hardware in solving complex systems and process. In the era of digital technology, mathematics and statistics need to be highly relevant to be able to cater for the needs of IR4.0 such as big data analytics, simulation, autonomous system, and cloud computing. Motivated by this development, a total of 26 chapters are contributed by respectable experts for this book. The main scope of the book is to conduct a new system of modeling and simulations on solving differential equations, nonlinear equations, energy, epidemiology, and risk assessment. This book is of interest for postgraduate students, researchers as well as other scientists who are working in numerical modeling and simulations based on efficient mathematical and statistical techniques.
A Concise Guide to Geopressure
A concise guide to the origins and prediction of subsurface fluid pressures, emphasizing the interactions with geological processes.
Finite Difference Methods in Heat Transfer
Finite Difference Methods in Heat Transfer, Second Edition focuses on finite difference methods and their application to the solution of heat transfer problems. Such methods are based on the discretization of governing equations, initial and boundary conditions, which then replace a continuous partial differential problem by a system of algebraic equations. Finite difference methods are a versatile tool for scientists and for engineers. This updated book serves university students taking graduate-level coursework in heat transfer, as well as being an important reference for researchers and engineering. Features Provides a self-contained approach in finite difference methods for students and professionals Covers the use of finite difference methods in convective, conductive, and radiative heat transfer Presents numerical solution techniques to elliptic, parabolic, and hyperbolic problems Includes hybrid analytical–numerical approaches
Applied Machine Learning and Multi-Criteria Decision-Making in Healthcare
This book provides an ideal foundation for readers to understand the application of artificial intelligence (AI) and machine learning (ML) techniques to expert systems in the healthcare sector. It starts with an introduction to the topic and presents chapters which progressively explain decision-making theory that helps solve problems which have multiple criteria that can affect the outcome of a decision. Key aspects of the subject such as machine learning in healthcare, prediction techniques, mathematical models and classification of healthcare problems are included along with chapters which delve in to advanced topics on data science (deep-learning, artificial neural networks, etc.) and pr...
Degradation of Implant Materials
This book reviews the current understanding of the mechanical, chemical and biological processes that are responsible for the degradation of a variety of implant materials. All 18 chapters will be written by internationally renowned experts to address both fundamental and practical aspects of research into the field. Different failure mechanisms such as corrosion, fatigue, and wear will be reviewed, together with experimental techniques for monitoring them, either in vitro or in vivo. Procedures for implant retrieval and analysis will be presented. A variety of biomaterials (stainless steels, titanium and its alloys, nitinol, magnesium alloys, polyethylene, biodegradable polymers, silicone gel, hydrogels, calcium phosphates) and medical devices (orthopedic and dental implants, stents, heart valves, breast implants) will be analyzed in detail. The book will serve as a broad reference source for graduate students and researchers studying biomedicine, corrosion, surface science, and electrochemistry.
Numerical Machine Learning
Numerical Machine Learning is a simple textbook on machine learning that bridges the gap between mathematics theory and practice. The book uses numerical examples with small datasets and simple Python codes to provide a complete walkthrough of the underlying mathematical steps of seven commonly used machine learning algorithms and techniques, including linear regression, regularization, logistic regression, decision trees, gradient boosting, Support Vector Machine, and K-means Clustering. Through a step-by-step exploration of concrete numerical examples, the students (primarily undergraduate and graduate students studying machine learning) can develop a well-rounded understanding of these al...
Numerical Computation of Compressible and Viscous Flow
Written for those who want to calculate compressible and viscous flow past aerodynamic bodies, this book allows you to get started in programming for solving initial value problems and to understand numerical accuracy and stability, matrix algebra, finite volume formulations, and the use of flux split algorithms for solving the Euler equations.
