Название: Neural Networks, Machine Learning, and Image Processing: Mathematical Modeling and Applications
Автор: Manoj Sahni, Ritu Sahni
Издательство: CRC Press
Год: 2022
Страниц: 221
Язык: английский
Формат: pdf (true)
Размер: 16.5 MB

Mathematical modeling is a field that provides fresh insights into natural phenomena by approximating and formulating physical situations. Scientists gather real-world data relevant to a specific topic through observations or experiments and then develop mathematical models to explain and predict the behavior of the real-world object whose scientific model they created. These models are close representations of real objects, not exact replicas. Thus, it is essential to work on the development of more precise models by using various mathematical tools. Mathematical modeling becomes easier with the help of machine learning tools and neural network algorithms.

Neural network algorithms, in fact, work in the same way that our brains do. We begin by observing any real-life phenomenon with our eyes or collecting data with machines such as microscopes, telescopes, and cameras, and then we process that data by hypothesizing the underlying principles hidden in the phenomenon. We perform more and more experiments for further verification and then provide the results. The neural network also receives inputs in the form of numerical data, text, images, or any type of pattern, then processes the inputs by translating those data through various algorithms, and finally generates outputs. The output was evaluated by using Simulink/MATLAB software.

This book aims to provide the most recent research on the development of various mathematical techniques in the area of neural networks, as well as the use of various machine learning techniques for better natural science modeling. It contains predictive models related to day-to-day problems, biological problems, engineering problems, and many other advancements in mathematical techniques. The aim is that models may be able to provide a more precise view, or at the very least a better understanding, of a real object or system. This book is divided into two parts, viz. Mathematical Modeling and Neural Network’s Mathematical Essence and Simulations in Machine Learning and Image Processing.

In this way, this book is very important for students, researchers, engineers, and computer scientists as it contains various applications of mathematical modeling containing strategies for the solutions of the problems as well as a systematic understanding of the modeling of any real-life problems and also the model of the latest system and technologies. It also provides techniques for the efficient use of latest computerized mathematical techniques for the betterment of the world. This book not only contains real-life problems, but also provides precise theory related to the latest research on mathematical modeling, Machine Learning, and numerical techniques in an uncertain environment.