Databases

A concise text that presents and analyzes the fundamental techniques and methods in optimization that are useful in data science.

Explains methods behind machine learning systems to personalize predictions to individual users, from recommendation to dating and fashion.

A diverse selection of data science topics explored through a mathematical lens.

This book highlights various evolutionary algorithm techniques for various medical conditions and introduces medical applications of evolutionary computation for real-time diagnosis.

Machine Learning: Theory and Practice provides an introduction to the most popular methods in machine learning.

The significant objective of this edited book is to bridge the gap between smart manufacturing and big data by exploring the challenges and limitations.

Cybersecurity needs a change in communication.

Interest in applying analytics, machine learning, and artificial intelligence to sales and marketing has grown dramatically, with no signs of slowing down.

In recent years, there has been steady increase in the interest shown in both big data analytics and the use of information technology (IT) solutions to improve healthcare services.

In this multidisciplinary book, experts from around the globe examine how data-driven political campaigning works, what challenges it poses for personal privacy and democracy, and how emerging practices should be regulated.

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today's most popular machine learning methods.

Interdisciplinary approaches using Machine Learning and Deep Learning techniques are smartly addressing real life challenges and have emerged as an inseparable element of disruption in current times.

Large-Scale 3D Data Integration: Challenges and Opportunities examines the fundamental aspects of 3D geo-information, focusing on the latest developments in 3D GIS (geographic information) and AEC (architecture, engineering, construction) systems.

This book presents soft computing techniques and applications used in healthcare systems, along with the latest advancements.

Essential to database design, entity-relationship (ER) diagrams are known for their usefulness in data modeling and mapping out clear database designs.

Nowadays, raw biological data can be easily stored as databases in computers but extracting the required information is the real challenge for researchers.

Cultivating Professional Identity in Design is a nuanced, comprehensive companion for designers across disciplines honing their identities, self-perception, personal strengths, and essential attributes.

This book focusses on the Internet of Things (IoT) and Data Mining for Modern Engineering and Healthcare Applications and the recent technological advancements in Microwave Engineering, Communication and applicability of newly developed Solid State Technologies in Bio-medical Engineering and Health-Care.

This textbook places cyber security management within an organizational and strategic framework, enabling students to develop their knowledge and skills for a future career.

Disruptive technologies are gaining importance in healthcare systems and health informatics.

Databases have become an integral part of modern life.

Approximately 15% of the global population is affected by some sort of disability, according to the World Report on Disability.

This book looks at industry change patterns and innovations (such as artificial intelligence, machine learning, big data analysis, and blockchain support and efficiency technology) that are speeding up industrial transformation, industrial infrastructure, biodiversity, and productivity.

This book examines the use of big data in regenerative urban environment and how data helps in functional planning and design solutions.

This book provides a detailed description of machine learning algorithms in data analytics, data science life cycle, Python for machine learning, linear regression, logistic regression, and so forth.

Data Science: A First Introduction focuses on using the R programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference.

This book introduces Bayesian reasoning and Gaussian processes into machine learning applications.

Python Packages introduces Python packaging at an introductory and practical level that's suitable for those with no previous packaging experience.

This handbook provides a comprehensive collection of knowledge for emerging multidisciplinary research areas such as cybersecurity, IoT, Blockchain, Machine Learning, Data Science, and AI.

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Industrial Internet of Things: Technologies, Design, and Applications addresses the complete functional framework workflow in IoT technology.

This book brings insight into data science and offers applications and implementation strategies.

A database management system (DBMS) is a collection of programs that enable users to create and maintain a database; it also consists of a collection of interrelated data and a set of programs to access that data.

Bioinformatics: A Practical Guide to NCBI Databases and Sequence Alignments provides the basics of bioinformatics and in-depth coverage of NCBI databases, sequence alignment, and NCBI Sequence Local Alignment Search Tool (BLAST).

This book discusses IoT in healthcare and how it enables interoperability, machine-to-machine communication, information exchange, and data movement.

Cancer Prediction for Industrial IoT 4.

There are many books written for the accounting and finance community.

A number of approaches are being defined for statistics and machine learning.

Deep Learning: A Comprehensive Guide provides comprehensive coverage of Deep Learning (DL) and Machine Learning (ML) concepts.

Focused on the mechanics of managing environmental data, this book provides guidelines on how to evaluate data requirements, assess tools and techniques, and implement an effective system.

This book addresses topics related to the Internet of Things (IoT), machine learning, cyber-physical systems, cloud computing, and autonomous vehicles in Industry 4.

This book is a refreshingly practical yet theoretically sound roadmap to leveraging data analytics and data science.

DataOps is a new way of delivering data and analytics that is proven to get results.

Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language.

Physics of Data Science and Machine Learning links fundamental concepts of physics to data science, machine learning, and artificial intelligence for physicists looking to integrate these techniques into their work.

It is becoming increasingly important to design and develop adaptive, robust, scalable, reliable, security and privacy mechanisms for IoT applications and for Industry 4.

Everyday technology is constantly changing, and it's hard to keep up with it at times.

Everyday technology is constantly changing, and it's hard to keep up with it at times.

The authors provide an understanding of big data and MapReduce by clearly presenting the basic terminologies and concepts.

Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization describes such algorithms as Locally Linear Embedding (LLE), Laplacian Eigenmaps, Isomap, Semidefinite Embedding, and t-SNE to resolve the problem of dimensionality reduction in the case of non-linear relationships within the data.