Machine learning

Artificial Intelligence and Edge Computing for Sustainable Ocean Health explores the transformative role of AI and edge computing in preserving and enhancing ocean health.

This interdisciplinary book incorporates various aspects of environment, ecology, and natural disaster management including cognitive informatics and computing.

Membrane computing is a class of distributed and parallel computing models inspired by living cells.

Deep learning has significantly reshaped a variety of technologies, such as image processing, natural language processing, and audio processing.

This book systematically elaborates on the intelligent information processing technology for a bioinspired polarization compass and inertial integrated navigation system.

The PC era is giving way to a new form of popular computing in which smart, globally-connected objects and environments are the new computational ground.

This book explores how visualization provides an effective way of improving not only the interpretability but also the generalization capabilities of machine learning models.

Making obscure knowledge about matrix decompositions widely available, Understanding Complex Datasets: Data Mining with Matrix Decompositions discusses the most common matrix decompositions and shows how they can be used to analyze large datasets in a broad range of application areas.

Due to increasing demands for dimensionality reduction, research on feature selection has deeply and widely expanded into many fields, including computational statistics, pattern recognition, machine learning, data mining, and knowledge discovery.

Collecting the latest developments in the field, Multimedia Data Mining: A Systematic Introduction to Concepts and Theory defines multimedia data mining, its theory, and its applications.

This volume encompasses many new types of constraints and clustering methods as well as delivers thorough coverage of the capabilities and limitations of constrained clustering.

Our physical world is grounded in three dimensions.

Data Fusion Mathematics: Theory and Practice offers a comprehensive overview of data fusion (DF) and provides a proper and adequate understanding of the basic mathematics directly related to DF.

This book aims to fully demonstrate the burnout of learners in online learning processes.

Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning.

Anomaly detection in video surveillance stands at the core of numerous real-world applications that have broad impact and generate significant academic and industrial value.

The Fourth Industrial Revolution is being accelerated by the digital twin technological revolution, which converges intelligent technologies and defines the connectivity between physical and digital items.

Enhance your data science programming and analysis with the Wolfram programming language and Mathematica, an applied mathematical tools suite.

Take a journey toward discovering, learning, and using Apache Spark 3.

Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning.

Design, develop, and validate machine learning models with streaming data using the Scikit-Multiflow framework.

Learn to develop and deploy dashboards as web apps using the Python programming language, and how to integrate algorithms into web apps.

Use this book as an on-ramp to the development of enterprise-grade chatbots and digital assistants.

Get introduced to ML.

Use TensorFlow 2.

Get insight into data science techniques such as data engineering and visualization, statistical modeling, machine learning, and deep learning.

While Robotic Process Automation (RPA) has been around for about 20 years, it has hit an inflection point because of the convergence of cloud computing, big data and AI.

What is an artificial intelligence (AI)-enabled drone and what can it do?

Comprehensively covers protein subcellular localization from single-label prediction to multi-label prediction, and includes prediction strategies for virus, plant, and eukaryote species.

Comprehensively covers protein subcellular localization from single-label prediction to multi-label prediction, and includes prediction strategies for virus, plant, and eukaryote species.

Use TensorFlow 2.

Automatic biometrics recognition techniques are increasingly important in corporate and public security systems and have increased in methods due to rapid field development.

Real-world engineering problems often require concurrent optimization of several design objectives, which are conflicting in cases.

In the last few years the scientific community has realized that obtaining a better understanding of interactions between natural systems and the man-made environment across different scales demands more research efforts in remote sensing.

Big data is presenting challenges to cybersecurity.

This book covers the fundamentals in designing and deploying techniques using deep architectures.

Differential equations play a vital role in the fields of engineering and science.

Hybrid Intelligent Techniques for Pattern Analysis and Understanding outlines the latest research on the development and application of synergistic approaches to pattern analysis in real-world scenarios.

In recent years bio-inspired computational theories and tools have developed to assist people in extracting knowledge from high dimensional data.

Data Mining: A Tutorial-Based Primer, Second Edition provides a comprehensive introduction to data mining with a focus on model building and testing, as well as on interpreting and validating results.

"e;Data Science Foundations is most welcome and, indeed, a piece of literature that the field is very much in need of.

Comprehensive Coverage of the Entire Area of ClassificationResearch on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning.

The utility of artificial neural network models lies in the fact that they can be used to infer functions from observations making them especially useful in applications where the complexity of data or tasks makes the design of such functions by hand impractical.

Artificial Intelligence Tools: Decision Support Systems in Condition Monitoring and Diagnosis discusses various white- and black-box approaches to fault diagnosis in condition monitoring (CM).