• Vishnupriya murugesan

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Overview:

The aim of the book is to showcase how optimization techniques, computational intelligence, other mathematical techniques and models have been successfully applied to solve real-world industrial optimization problems. These techniques are helpful for designing the mathematical models and finding the input parameters to a function which results in the maximum or minimum output of the function for various society problems that occur in medical sciences, various industries, engineering and many more. For example, the most popular type of problems in machine learning are continuous functions, where the input perimeter are numerical values. To study the performance of a program in machine learning, these techniques play a ground breaking role. The most commonly used mathematical technique is the gradient descent method which guides the model to find the target and convergence to the optimal value of the objective function. Similarly stockiest gradient descent is math odd with a large number of samples and removes a certain amount of computational redundancy. Conjugate gradient method is also used for solving large scale linear systems of equations and non-linear optimization problems. As the initial solution plays an important role in finding the solution of these problems, here we are giving some techniques to achieve the best initial solution and hence get the better final solutions. Presently, we are struggling with the Covid-19 pandemic. The book also incorporates a model to distinguish Covid-19 patients with different symptoms. Similarly, many other mathematical models and techniques are being presented in this book. The main objective of the book is to equip the knowledge to beginners as well as for advanced readers about various mathematical models and techniques.


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