Mathematical theory of computation Books

The first book to focus on logo recognition, this text presents a new logo recognition system based on line pattern features. To achieve the desired accuracy and efficiency, the system employs a conceptually simple three-stage hierarchy that includes...

This book discusses the major approaches, algorithms, and technologies used in automated face detection and recognition. Explaining the theory and practice of systems currently in vogue, the text covers face detection with colour and infrared face...

<p>The ACM Symposium on Principles of Distributed Computing (PODC) is the premier interna-tional forum on the theory, design, analysis, implementation and application of distributed systemsand networks. PODC is sponsored by the ACM Special Interest...

This book develops a framework that shows how uncertainty in Artificial Intelligence (AI) expands and generalizes traditional AI. It explores the uncertainties of knowledge and intelligence. The authors focus on the importance of natural language –...

Automatic biometrics recognition techniques are increasingly important in corporate and public security systems and have increased in methods due to rapid field development. This book discusses classic behavioral biometrics as well as collects the latest...

Present anti-virus technologies do not have the symmetrical weaponry to defeat massive DDoS attacks on smart cities. Smart cities require a new set of holistic and AI-centric cognitive technology, such as autonomic components that replicate the human...

For optimal computer vision outcomes, attention to image pre-processing is required so that one can improve image features by eliminating unwanted falsification. This book emphasizes various image pre-processing methods which are necessary for early...

Computability theory is at the heart of theoretical computer science. Yet, ironically, many of its basic results were discovered by mathematical logicians prior to the development of the first stored-program computer. As a...

This reference text aims to systematically collect quality research spanning AI, ML and Deep Learning (DL) applications to diverse sub-topics of computer networks, communications, and security, under a single cover. It also aspires to provide more...

This book focuses on the application of soft computing in materials and manufacturing sectors with the objective to offer an intelligent approach to improve the manufacturing process, material selection and characterization techniques for developing...

This book divides edge intelligence into AI for edge (intelligence-enabled edge computing) and AI on edge (artificial intelligence on edge). It focuses on providing optimal solutions to the key concerns in edge computing through effective AI...

Praise for the first edition:"[This book] succeeds singularly at providing a structured introduction to this active field of research. ... it is arguably the most accessible overview yet published of the mathematical ideas and principles that one needs...

It is widely believed that Artificial Intelligence (AI) and its applications will revolutionize healthcare and medicine. This book provides a comprehensive overview on the recent developments on clinical decision support systems, precision health and...

Biomedical and Health Informatics is an important field that brings tremendous opportunities and helps address challenges due to an abundance of available biomedical data. This book examines and demonstrates state-of-the-art approaches for IOT and...

Machine learning approaches has the capability to learn and adapt to the constantly evolving demands of large Internet-of-energy (IoE) network. The focus of this book is on using the machine learning approaches to present various solutions for IoE...

Written by specialists in teaching computer animation, this text addresses key international topics of computer animation, such as: mathematics, modelling, rendering, and compositing. Each chapter discusses a particular topic...

Are you interested in Machine Learning? Are you fascinated by how robots work? Are you ready to open up to the dynamics of technological change?Machine Learning has been approached in a definitive manner as a subset falling under a larger set of...

This book presents a comprehensive treatment of visual analysis of behaviour from computational-modelling and algorithm-design perspectives. Topics: covers learning-group activity models, unsupervised behaviour profiling,...

This book provides a thorough introduction to both individual decision tree algorithms (Part I) and ensembles thereof (Part II). Part I of the book brings several different tree algorithms into focus, both conventional and contemporary. 

Master MATLAB tools for creating machine learning applications through effective code writing, guided by practical examples showcasing the versatility of machine learning in real-world applicationsKey FeaturesWork with the MATLAB Machine Learning Toolbox...

Take your machine learning models to the next level by learning how to leverage hyperparameter tuning, allowing you to control the model's finest detailsKey Features:Gain a deep understanding of how hyperparameter tuning worksExplore exhaustive search,...

Gain the key knowledge and skills required to manage data science projects using CometKey Features:Discover techniques to build, monitor, and optimize your data science projectsMove from prototyping to production using Comet and DevOps toolsGet to grips...

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has recently been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine.Deep RL...

Space and time are inextricably linked. Reasoning about space often involves reasoning about change in spatial configurations. Qualitative spatial information theory encompasses spatial as well as temporal representation and reasoning. Qualitative...

Random matrix theory plays a central role in statistical physics, computational mathematics and engineering sciences, including data assimilation, signal processing, combinatorial optimization, compressed sensing, econometrics and mathematical finance,...

Technological tools and computational techniques have enhanced the healthcare industry. These advancements have led to significant progress in the diagnosis of heart disorders. Electrocardiogram Signal Classification and Machine Learning: Emerging...

Analytical tools and algorithms are essential in business data and information systems. Efficient economic and financial forecasting in machine learning techniques increases gains while reducing risks. Providing research on predictive models with high...

In the fast pace of the modern world it is important, more than ever, for factories to know how and why their machines are failing and what can be done to prevent it. As such, it is imperative that new research is conducted to make sure that factories...

The expansion of digital data has transformed various sectors of business such as healthcare, industrial manufacturing, and transportation. A new way of solving business problems has emerged through the use of machine learning techniques in conjunction...

Constant improvements in technological applications have allowed for more opportunities to develop automated systems. This not only leads to higher success in smart data analysis, but also ensures that technological progression will continue. Ubiquitous...

A practical, step-by-step guide to using Microsoft's AutoML technology on the Azure Machine Learning service for developers and data scientists working with the Python programming languageKey Features:Create, deploy, productionalize, and scale automated...

The Book presents an overview of newly developed watermarking techniques in various independent and hybrid domains Covers the basics of digital watermarking, its types, domain in which it is implemented and the application of machine learning algorithms...

Discover how TPOT can be used to handle automation in machine learning and explore the different types of tasks that TPOT can automateKey Features:Understand parallelism and how to achieve it in Python.Learn how to use neurons, layers, and activation...

Comprehensive recipes to give you valuable insights on Transformers, Reinforcement Learning, and moreKey Features:Deep Learning solutions from Kaggle Masters and Google Developer ExpertsGet to grips with the fundamentals including variables, matrices,...

Thompson sampling is an algorithm for online decision problems where actions are taken sequentially in a manner that must balance between exploiting what is known to maximize immediate performance and investing to accumulate new information that may...

Kernel methods are among the most popular techniques in machine learning. From a regularization theory perspective, they provide a natural choice for the hypotheses space and the regularization functional through the notion of reproducing kernel Hilbert...

Evolutionary multi-objective optimization (EMO) has emerged as a sub-discipline of multi-objective optimization, combining the fields of evolutionary computation and classical multiple criteria decision making. This field has applications in artificial...

The beginning of the age of artificial intelligence and machine learning has created new challenges and opportunities for data analysts, statisticians, mathematicians, econometricians, computer scientists and many others. At the root of these techniques...

This book provides an introduction to the growing field of sparse modeling, including application examples, problem formulations that yield sparse solutions, algorithms for finding such solutions, and recent theoretical results on sparse recovery. It...

The Virtual Mind: Designing the Logic to Approximate Human Thinking, through an in-depth and multidisciplinary review, outlines and defines the underpinnings for modelling human thinking through approximating the mind. Whilst there are plenty of efforts...

In today's age of wireless and mobile computing, network and computer security is paramount. Case Studies in Secure Computing: Achievements and Trends gathers the latest research from researchers who share their insights and best practices through...

Get to grips with deep learning techniques for building image processing applications using PyTorch with the help of code notebooks and test questionsKey FeaturesImplement solutions to 50 real-world computer vision applications using PyTorchUnderstand...

Create scalable and reliable data pipelines easily with PachydermKey Features:Learn how to build an enterprise-level reproducible data science platform with PachydermDeploy Pachyderm on cloud platforms such as AWS EKS, Google Kubernetes Engine, and...