Conventional computational methods, and even the latest soft computing paradigms, often fall short in their ability to offer solutions to many real-world problems due to uncertainty, imprecision, and circumstantial data. Hybrid intelligent computing is a paradigm that addresses these issues to a considerable extent. The Handbook of Research on Advanced Research on Hybrid Intelligent Techniques and Applications highlights the latest research on various issues relating to the hybridization of artificial intelligence, practical applications, and best methods for implementation. Focusing on key interdisciplinary computational intelligence research dealing with soft computing techniques, pattern mining, data analysis, and computer vision, this book is relevant to the research needs of academics, IT specialists, and graduate-level students.

Recently, the event-based paradigm, a burgeoning technology receiving attention in research as well as industry, has gained momentum in the commercial world causing a need for further research in the field. Principles and Applications of Distributed Event-Based Systems showcases event-based systems in real-world applications, providing an overview of relevant terminology and content approachable for readers from a variety of backgrounds. Containing expert international contributions, this advanced publication provides professionals, researchers, and students in systems design with a rich compendium of latest applications in the field.

In the literature of information science, a number of studies have been carried out attempting to model cognitive, affective, behavioral, and contextual factors associated with human information seeking and retrieval. On the other hand, only a few studies have addressed the exploration of creative thinking in music, focusing on understanding and describing individuals' information seeking behavior during the creative process. Trends in Music Information Seeking, Behavior, and Retrieval for Creativity connects theoretical concepts in information seeking and behavior to the music creative process. This publication presents new research, case studies, surveys, and theories related to various aspects of information retrieval and the information seeking behavior of diverse scholarly and professional music communities. Music professionals, theorists, researchers, and students will find this publication an essential resource for their professional and research needs.

Privacy protection within large databases can be a challenge. By examining the current problems and challenges this domain is facing, more efficient strategies can be established to safeguard personal information against invasive pressures. HCI Challenges and Privacy Preservation in Big Data Security is an informative scholarly publication that discusses how human-computer interaction impacts privacy and security in almost all sectors of modern life. Featuring relevant topics such as large scale security data, threat detection, big data encryption, and identity management, this reference source is ideal for academicians, researchers, advanced-level students, and engineers that are interested in staying current on the advancements and drawbacks of human-computer interaction within the world of big data.

Stunning Digital Photography is much more than a book; it's a hands-on, self-paced photography class with over three hours of online training videos and free help from the author and other readers. That's why award-winning author and photographer Tony Northrup's book quickly became #1 photography e-book of 2012 with over 100,000 readers.

This book gives you four innovations no other book offers:

1) Free video training. Watch over three hours of fast-paced, hands-on video tutorials integrated into the book to support and reinforce the lessons. View the videos using any web browser or by scanning QR codes with your smartphone.

2) Hands-on practices. Complete the practices at the end of every chapter to get the real world experience you need.

3) Classroom support. Join an author led private community of supportive, helpful people who also want to improve their photography.

4) Free ebook with updates. When you buy the book and join the private Stunning Digital Photography readers group.

Big data consists of data sets that are too large and complex for traditional data processing and data management applications. Therefore, to obtain the valuable information within the data, one must use a variety of innovative analytical methods, such as web analytics, machine learning, and network analytics. As the study of big data becomes more popular, there is an urgent demand for studies on high-level computational intelligence and computing services for analyzing this significant area of information science. Big Data Analytics for Sustainable Computing is a collection of innovative research that focuses on new computing and system development issues in emerging sustainable applications. Featuring coverage on a wide range of topics such as data filtering, knowledge engineering, and cognitive analytics, this publication is ideally designed for data scientists, IT specialists, computer science practitioners, computer engineers, academicians, professionals, and students seeking current research on emerging analytical techniques and data processing software.

Build and deploy intelligent applications for natural language processing with Python by using industry standard tools and recently popular methods in deep learning Key Features A no-math, code-driven programmer's guide to text processing and NLP Get state of the art results with modern tooling across linguistics, text vectors and machine learning Fundamentals of NLP methods from spaCy, gensim, scikit-learn and PyTorch Book DescriptionNLP in Python is among the most sought after skills among data scientists. With code and relevant case studies, this book will show how you can use industry-grade tools to implement NLP programs capable of learning from relevant data. We will explore many modern methods ranging from spaCy to word vectors that have reinvented NLP. The book takes you from the basics of NLP to building text processing applications. We start with an introduction to the basic vocabulary along with a workflow for building NLP applications. We use industry-grade NLP tools for cleaning and pre-processing text, automatic question and answer generation using linguistics, text embedding, text classifier, and building a chatbot. With each project, you will learn a new concept of NLP. You will learn about entity recognition, part of speech tagging and dependency parsing for Q and A. We use text embedding for both clustering documents and making chatbots, and then build classifiers using scikit-learn. We conclude by deploying these models as REST APIs with Flask. By the end, you will be confident building NLP applications, and know exactly what to look for when approaching new challenges. What you will learn Understand classical linguistics in using English grammar for automatically generating questions and answers from a free text corpus Work with text embedding models for dense number representations of words, subwords and characters in the English language for exploring document clustering Deep Learning in NLP using PyTorch with a code-driven introduction to PyTorch Using an NLP project management Framework for estimating timelines and organizing your project into stages Hack and build a simple chatbot application in 30 minutes Deploy an NLP or machine learning application using Flask as RESTFUL APIs Who this book is forProgrammers who wish to build systems that can interpret language. Exposure to Python programming is required. Familiarity with NLP or machine learning vocabulary will be helpful, but not mandatory.

Optimization techniques have developed into a significant area concerning industrial, economics, business, and financial systems. With the development of engineering and financial systems, modern optimization has played an important role in service-centered operations and as such has attracted more attention to this field. Meta-heuristic hybrid optimization is a newly development mathematical framework based optimization technique. Designed by logicians, engineers, analysts, and many more, this technique aims to study the complexity of algorithms and problems. Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance explores the emerging study of meta-heuristics optimization algorithms and methods and their role in innovated real world practical applications. This book is a collection of research on the areas of meta-heuristics optimization algorithms in engineering, business, economics, and finance and aims to be a comprehensive reference for decision makers, managers, engineers, researchers, scientists, financiers, and economists as well as industrialists.

During the COVID-19 pandemic, computational intelligence and computer-aided diagnosis (CAD) systems have supported the effective treatment of the virus. Artificial intelligence (AI) has been playing a significant role in the rapidly emerging healthcare sector in terms of CAD, software algorithms, hardware implementation, and applications in the medical field. Through this, the constraints of the traditional system must be addressed to innovate and shed light on emerging healthcare technologies. Computational Intelligence and Applications for Pandemics and Healthcare explores the state-of-the-art computational intelligence approaches in medical data and classifies existing computational techniques used in medical areas. It discusses the tactics and methods as well as the limitations and performances of computational intelligence applications for healthcare. The constraints of traditional healthcare systems are addressed by using CAD and computationally-intelligent medical data. Covering topics such as cloud-based monitoring systems, detection and diagnosis, and intelligent medical systems, this book is an excellent resource for computer scientists, government officials, medical students, medical professionals, hospitals, researchers, and academicians.

The exploitation of theoretical results in knowledge representation, language standardization by W3C and data publication initiatives such as Linked Open Data have given a level of concreteness to the field of ontology research. In light of these recent outcomes, ontology development has also found its way to the forefront, benefiting from years of R&D on development tools. Semi-Automatic Ontology Development: Processes and Resources includes state-of-the-art research results aimed at the automation of ontology development processes and the reuse of external resources becoming a reality, thus being of interest for a wide and diversified community of users. This book provides a thorough overview on the current efforts on this subject and suggests common directions for interested researchers and practitioners.

Uncovering and analyzing data associated with the current business environment is essential in maintaining a competitive edge. As such, making informed decisions based on this data is crucial to managers across industries. Integration of Data Mining in Business Intelligence Systems investigates the incorporation of data mining into business technologies used in the decision making process. Emphasizing cutting-edge research and relevant concepts in data discovery and analysis, this book is a comprehensive reference source for policymakers, academicians, researchers, students, technology developers, and professionals interested in the application of data mining techniques and practices in business information systems.

The book will benefit a reader with a background in physical sciences and applied mathematics interested in the mathematical models of genetic evolution. In the first chapter, we analyze several thought experiments based on a basic model of stochastic evolution of a single genomic site in the presence of the factors of random mutation, directional natural selection, and random genetic drift. In the second chapter, we present a more advanced theory for a large number of linked loci. In the third chapter, we include the effect of genetic recombination into account and find out the advantage of sexual reproduction for adaptation. These models are useful for the evolution of a broad range of asexual and sexual populations, including virus evolution in a host and a host population.

Advances in machine learning techniques and ever-increasing computing power has helped create a new generation of hardware and software technologies with practical applications for nearly every industry. As the progress has, in turn, excited the interest of venture investors, technology firms, and a growing number of clients, implementing intelligent automation in both physical and information systems has become a must in business. Handbook of Research on Smart Technology Models for Business and Industry is an essential reference source that discusses relevant abstract frameworks and the latest experimental research findings in theory, mathematical models, software applications, and prototypes in the area of smart technologies. Featuring research on topics such as digital security, renewable energy, and intelligence management, this book is ideally designed for machine learning specialists, industrial experts, data scientists, researchers, academicians, students, and business professionals seeking coverage on current smart technology models.

This publication contributes to the serious games field by investigating original contributions and methods that use serious games in various domains. This comprehensive and timely publication works as an essential reference source, building on the available literature in the field of Serious Games for the economic and social development of countries while providing for further research opportunities in this dynamic and growing field. Thus, the book provides the opportunity for a reflection on this important issue, increasing the understanding of the importance of Serious Games in the context of organizations' improvements, providing relevant academic work, empirical research findings, and an overview of this relevant field of study. This text provides the resources necessary for policy makers, technology developers and managers to adopt and implement solutions for a more digital era.

This is an overview of the end-to-end data cleaning process. Data quality is one of the most important problems in data management, since dirty data often leads to inaccurate data analytics results and incorrect business decisions. Poor data across businesses and the U.S. government are reported to cost trillions of dollars a year. Multiple surveys show that dirty data is the most common barrier faced by data scientists. Not surprisingly, developing effective and efficient data cleaning solutions is challenging and is rife with deep theoretical and engineering problems. This book is about data cleaning, which is used to refer to all kinds of tasks and activities to detect and repair errors in the data. Rather than focus on a particular data cleaning task, this book describes various error detection and repair methods, and attempts to anchor these proposals with multiple taxonomies and views. Specifically, it covers four of the most common and important data cleaning tasks, namely, outlier detection, data transformation, error repair (including imputing missing values), and data deduplication. Furthermore, due to the increasing popularity and applicability of machine learning techniques, it includes a chapter that specifically explores how machine learning techniques are used for data cleaning, and how data cleaning is used to improve machine learning models. This book is intended to serve as a useful reference for researchers and practitioners who are interested in the area of data quality and data cleaning. It can also be used as a textbook for a graduate course. Although we aim at covering state-of-the-art algorithms and techniques, we recognize that data cleaning is still an active field of research and therefore provide future directions of research whenever appropriate.

The long-standing debate on public vs. private healthcare systems has forced an examination of these organisations, in particular whether these approaches play corresponding or conflicting roles in service to global citizens. Healthcare Management and Economics: Perspectives on Public and Private Administration discusses public and private healthcare organisations by gathering perspectives on the differences in service, management, delivery, and efficiency. Highlighting the impact of citizens and information technology in these healthcare processes, this book is a vital collection of research for practitioners, academics, and scholars in the healthcare management field.