2020 Taylor & Francis Award Winner for Outstanding New Textbook! Featuring recent advances in the field, this new textbook presents probability and statistics, and their applications in stochastic processes. This book presents key information for understanding the essential aspects of basic probability theory and concepts of reliability as an application. The purpose of this book is to provide an option in this field that combines these areas in one book, balances both theory and practical applications, and also keeps the practitioners in mind. Features Includes numerous examples using current technologies with applications in various fields of study Offers many practical applications of probability in queueing models, all of which are related to the appropriate stochastic processes (continuous time such as waiting time, and fuzzy and discrete time like the classic Gambler's Ruin Problem) Presents different current topics like probability distributions used in real-world applications of statistics such as climate control and pollution Different types of computer software such as MATLAB (R), Minitab, MS Excel, and R as options for illustration, programing and calculation purposes and data analysis Covers reliability and its application in network queues

This textbook provides a comprehensive, thorough and up-to-date treatment of topics of mathematics that an engineer and scientist would need, at the basic levels that contents of engineering and sciences are built by. For this purpose, natural readers would be junior and senior undergraduate students, who normally have the content of this book under different names on their degree plans. Also, engineers and scientists will benefit from this book since the book is a comprehensive volume for such audiences. This book is written in a way that it balances both theory and practical applications of topics from linear algebra, matrix theory, calculus of multivariable, theory of complex variables, several transforms, ordinary and partial differential equations, difference equations, optimization, probability, statistics, theory of reliability and finally, applications from variety of areas of sciences and engineering.

This book comprehensively discusses the modeling of real-world industrial problems and innovative optimization techniques such as heuristics, finite methods, operation research techniques, intelligent algorithms, and agent-based methods. Discusses advanced techniques like key cell, Mobius inversion, and zero suffix techniques to find initial feasible solutions to optimization problems. Provides a useful guide towards the development of a sustainable model for disaster management. Presents optimized hybrid block method techniques to solve mathematical problems existing in the industries. Covers mathematical techniques like Laplace transformation, stochastic process, and differential techniques related to reliability theory. Highlights application on smart agriculture, smart healthcare, techniques for disaster management, and smart manufacturing. Advanced Mathematical Techniques in Computational and Intelligent Systems is primarily written for graduate and senior undergraduate students, as well as academic researchers in electrical engineering, electronics and communications engineering, computer engineering, and mathematics.

2020 Taylor & Francis Award Winner for Outstanding New Textbook! Featuring recent advances in the field, this new textbook presents probability and statistics, and their applications in stochastic processes. This book presents key information for understanding the essential aspects of basic probability theory and concepts of reliability as an application. The purpose of this book is to provide an option in this field that combines these areas in one book, balances both theory and practical applications, and also keeps the practitioners in mind. Features Includes numerous examples using current technologies with applications in various fields of study Offers many practical applications of probability in queueing models, all of which are related to the appropriate stochastic processes (continuous time such as waiting time, and fuzzy and discrete time like the classic Gambler's Ruin Problem) Presents different current topics like probability distributions used in real-world applications of statistics such as climate control and pollution Different types of computer software such as MATLAB (R), Minitab, MS Excel, and R as options for illustration, programing and calculation purposes and data analysis Covers reliability and its application in network queues

Studies on queuing models and their publication in professional journals and textbooks have been sparse over the past eleven decades. Collections of some of these studies have appeared either as single volumes or just chapters of single volumes and/or monographs. This book is an attempt to present some queuing models, especially those applicable in business and industry, in a style between a monograph and a textbook. Also the need of researchers and practitioners for a handbook-type text and the current lack of it explain the need for a book of this kind. Most of the basic terminologies and concepts that appear throughout the text are introduced in a systematic way in the first two chapters; nevertheless, previous exposition to a first course in probability and statistics is advised for later chapters. The principal audiences for the book are senior undergraduates (first three chapters) and graduate students (all six chapters). It is also a hope that the volume will appeal to a wider audience of specialists and researchers in mathematical science, engineering, physics, business and economics.

Studies on queueing models and their publication in professional journals and textbooks have been sparse over the past eleven decades. Collections of some of these studies have appeared either as single volumes or just chapters of single volumes and/or monographs. This book is an attempt to present some queuing models, especially those applicable in business and industry, in a style between a monograph and a textbook. Also the need of researchers and practitioners for a handbook-type text and the current lack of it explain the need for a book of this kind. Most of the basic terminologies and concepts that appear throughout the text are introduced in a systematic way in the first two chapters; nevertheless, previous exposition to a first course in probability and statistics is advised for later chapters.

Topics in advanced mathematics for engineers, probability and statistics typically span three subject areas, are addressed in three separate textbooks and taught in three different courses in as many as three semesters. Due to this arrangement, students taking these courses have had to shelf some important and fundamental engineering courses until much later than is necessary. This practice has generally ignored some striking relations that exist between the seemingly separate areas of statistical concepts, such as moments and estimation of Poisson distribution parameters. On one hand, these concepts commonly appear in stochastic processes - for instance, in measures on effectiveness in queuing models. On the other hand, they can also be viewed as applied probability in engineering disciplines - mechanical, chemical, and electrical, as well as in engineering technology. There is obviously, an urgent need for a textbook that recognizes the corresponding relationships between the various areas and a matching cohesive course that will see through to their fundamental engineering courses as early as possible. This book is designed to achieve just that. Its seven chapters, while retaining their individual integrity, flow from selected topics in advanced mathematics such as complex analysis and wavelets to probability, statistics and stochastic processes.