Название: Machine Learning and Deep Learning in Efficacy Improvement of Healthcare Systems
Автор: Om Prakash Jena, Bharat Bhushan, Nitin Rakesh
Издательство: CRC Press
Год: 2022
Страниц: 397
Язык: английский
Формат: pdf (true)
Размер: 27.4 MB

Rapid population growth coupled with the evolution of numerous diseases is a matter of concern worldwide. Due to this, the healthcare industry has emerged as an essential service sector. The generation of a large amount of healthcare data and the lack of insight from that data are significant problems in the healthcare sector. Therefore, there is a need for a fully effective and automated system that can help medical stakeholders to take prompt action at the right time. Artificial intelligence (AI) and machine learning (ML) have a very long association with the healthcare sector dating back to 1980s. It gained momentum with the emergence of rule-based systems, hierarchical clustering, and various regression models. ML is an important utility of AI that provides systems with the capacity to automatically examine and enhance action without being specially programmed. However, neither the computers nor the algorithms were efficient enough to enable effective ML based systems. The last five years had seen tremendous rise in the adoption of ML techniques mainly due to emergence of neural network that enhanced the overall computational power. Deep Learning (DL) is a subset of ML where innovations have led to the construction of several novel deep neural network architectures that can be used for the classification of large data sets. AI, ML, and DL techniques can be employed for efficient knowledge discovery from healthcare data.

Название: Hybrid Quantum Metaheuristics: Theory and Applications
Автор: Siddhartha Bhattacharyya, Mario Koppen, Elizabeth Behrman, Ivan Cruz-Aceves
Издательство: CRC Press
Серия: Quantum Machine Intelligence
Год: 2022
Страниц: 276
Язык: английский
Формат: pdf (true)
Размер: 15.3 MB

A metaheuristic is a heuristic (partial search) algorithm that is more or less an efficient optimization algorithm to real-world problems. Hybrid metaheuristics refer to a proper and judicious combination of several other metaheuristics and Machine Learning algorithms. The hybrid metaheuristics have been found to be more robust and failsafe owing to the complementary character of the individual metaheuristics in the resultant combination. This is primarily due to the fact that the vision of hybridization is to combine different metaheuristics such that each of the combination supplements the other in order to achieve the desired performance. Typical examples use fuzzy-evolutionary, neuro-evolutionary, neuro-fuzzy evolutionary, rough-evolutionary approaches to name a few. Recently, chaos theory has also found wide applications in evolving efficient hybrid metaheuristics. Quantum computer, as the name suggests, principally works on several quantum physical features. These could be used as an immense alternative to today’s apposite computers since they possess faster processing capability (even exponentially) than classical computers. The term quantum computing stems from the synergistic combination of quantum mechanical principles and classical information theory conjoined with principles of Computer Science.

Название: Modern Applied Regressions: Bayesian and Frequentist Analysis of Categorical and Limited Response Variables with R and Stan
Автор: Jun Xu
Издательство: CRC Press
Год: 2023
Страниц: 298
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB

Modern Applied Regressions creates an intricate and colorful mural with mosaics of categorical and limited response variable (CLRV) models using both Bayesian and Frequentist approaches. Written for graduate students, junior researchers, and quantitative analysts in behavioral, health, and social sciences, this text provides details for doing Bayesian and frequentist data analysis of CLRV models. Each chapter can be read and studied separately with R coding snippets and template interpretation for easy replication. Along with the doing part, the text provides basic and accessible statistical theories behind these models and uses a narrative style to recount their origins and evolution. We use R as the statistical analysis environment exclusively for all the models discussed in this text, accompanied with discussions about other software applications for Bayesian analysis. R is an open-source free software for statistical analysis, and it is a member of the GNU Project that advocates users’ freedom to create, extend, and use the software.

Название: Modern Adaptive Fuzzy Control Systems
Автор: Ardashir Mohammadzadeh, Mohammad Hosein Sabzalian, Chunwei Zhang
Издательство: Springer
Год: 2023
Страниц: 161
Язык: английский
Формат: pdf (true)
Размер: 10.3 MB

This book explains the basic concepts, theory and applications of fuzzy systems in control in a simple unified approach with clear ex-amples and simulations in the MATLAB programming language. Fuzzy systems, especially, type-2 neuro-fuzzy systems, are now used extensively in various engineering fields for different purposes. In plain language, this book aims to practically explain fuzzy sys-tems and different methods of training and optimizing these systems. For this purpose, type-2 neuro-fuzzy systems are first analyzed along with various methods of training and optimizing these systems through implementation in MATLAB. These systems are then em-ployed to design adaptive fuzzy controllers. The authors aim at pre-senting all the well-known optimization methods clearly and code them in the MATLAB language.

Название: Bayesian Reasoning and Gaussian Processes for Machine Learning Applications
Автор: Hemachandran K., Shubham Tayal
Издательство: CRC Press
Год: 2022
Страниц: 149
Язык: английский
Формат: pdf (true)
Размер: 10.9 MB

This book introduces Bayesian reasoning and Gaussian processes into Machine Learning applications. Bayesian methods are applied in many areas, such as game development, decision making, and drug discovery. It is very effective for Machine Learning algorithms in handling missing data and extracting information from small datasets.Bayesian Reasoning and Gaussian Processes for Machine Learning Applications uses a statistical background to understand continuous distributions and how learning can be viewed from a probabilistic framework. The chapters progress into such machine learning topics as belief network and Bayesian reinforcement learning, which is followed by Gaussian process introduction, classification, regression, covariance, and performance analysis of Gaussian processes with other models. This book is aimed at graduates, researchers, and professionals in the field of Data Science and Machine Learning.

Название: Beginning MATLAB and Simulink: From Beginner to Pro, 2nd Edition
Автор: Sulaymon Eshkabilov
Издательство: Apress
Год: 2022
Страниц: 627
Язык: английский
Формат: pdf (true), epub (true)
Размер: 41.8 MB, 46.1 MB

Employ essential tools and functions of the MATLAB and Simulink packages, which are explained and demonstrated via interactive examples and case studies. This revised edition covers features from the latest MATLAB 2022b release, as well as other features that have been released since the first edition published. This book contains dozens of simulation models and solved problems via m-files/scripts and Simulink models which will help you to learn programming and modelling essentials. You’ll become efficient with many of the built-in tools and functions of MATLAB/Simulink while solving engineering and scientific computing problems.

Название: Problems on Algorithms: A Comprehensive Exercise Book for Students in Software Engineering
Автор: Habib Izadkhah
Издательство: Springer
Год: 2022
Страниц: 519
Язык: английский
Формат: pdf (true), epub
Размер: 21.2 MB

With approximately 2500 problems, this book provides a collection of practical problems on the basic and advanced data structures, design, and analysis of algorithms. To make this book suitable for self-instruction, about one-third of the algorithms are supported by solutions, and some others are supported by hints and comments. This book is intended for students wishing to deepen their knowledge of algorithm design in an undergraduate or beginning graduate class on algorithms, for those teaching courses in this area, for use by practicing programmers who wish to hone and expand their skills, and as a self-study text for graduate students who are preparing for the qualifying examination on algorithms for a Ph.D. program in Computer Science or Computer Engineering. About all, it is a good source for exam problems for those who teach algorithms and data structure.

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. 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.

Название: Random Process Analysis with R
Автор: Marco Bittelli, Roberto Olmi
Издательство: Oxford University Press
Год: 2022
Страниц: 513
Язык: английский
Формат: pdf (true)
Размер: 10.5 MB

Random process analysis (RPA) is used as a mathematical model in physics, chemistry, biology, Computer Science, information theory, economics, environmental science, and many other disciplines. Over time, it has become more and more important for the provision of computer code and data sets. This book presents the key concepts, theory, and computer code written in R, helping readers with limited initial knowledge of random processes to become confident in their understanding and application of these principles in their own research. Consistent with modern trends in university education, the authors make readers active learners with hands-on computer experiments in R code directing them through RPA methods and helping them understand the underlying logic.

Название: Statistical Modeling With R: a dual frequentist and Bayesian approach for life scientists
Автор: Pablo Inchausti
Издательство: Oxford University Press
Год: 2023
Страниц: 519
Язык: английский
Формат: pdf (true)
Размер: 36.0 MB

To date, statistics has tended to be neatly divided into two theoretical approaches or frameworks: frequentist (or classical) and Bayesian. Scientists typically choose the statistical framework to analyse their data depending on the nature and complexity of the problem, and based on their personal views and prior training on probability and uncertainty. Although textbooks and courses should reflect and anticipate this dual reality, they rarely do so. This accessible textbook explains, discusses, and applies both the frequentist and Bayesian theoretical frameworks to fit the different types of statistical models that allow an analysis of the types of data most commonly gathered by life scientists. It presents the material in an informal, approachable, and progressive manner suitable for readers with only a basic knowledge of calculus and statistics. This book will make extensive use of the R programming environment. This is an open-source (one can access and edit the code of all the R functions and save a revised version in one’s computer), interpreted (it does not require compilation to be executed) programming language environment for statistical computing and graphics.