Book Image

Artificial Intelligence for Big Data

By : Anand Deshpande, Manish Kumar
Book Image

Artificial Intelligence for Big Data

By: Anand Deshpande, Manish Kumar

Overview of this book

In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems. By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems.
Table of Contents (19 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Big Data for critical infrastructure protection


Critical infrastructure (CI) is a term used by enterprises and government agencies to define the assets and working models that need to function at their optimal level in order for a seamless and harmonious experience for the stakeholders who directly or indirectly benefit from or are impacted by these systems. Examples include the power grid, water supply, transportation, law enforcement, and many such systems that need to work seamlessly around the clock. Over the last few decades, most of the CI has become digitized and is generating more and more data from heterogeneous sources. These additional data assets result in continuous improvement and elimination of the need for human intervention and thereby reduce error.

The data generated by these systems is used as an asset for descriptive and predictive analytics in order to schedule preventive maintenance and prevent failures. With a data- driven approach for core functioning of the CI, we...