Advanced Analytics and Deep Learning Models (Next Generation Computing and Communication Engineering) - Tapa dura

Acerca del autor

Archana Mire, PhD, is an assistant professor in the Computer Engineering Department, Terna Engineering College, Navi Mumbai, India. She has published many research articles in peer-reviewed journals.

Shaveta Malik, PhD, is an associate professor in the Computer Engineering Department (NBA accredited), Terna Engineering College, Nerul, India. She has published many research articles in peer-reviewed journals.

Amit Kumar Tyagi, PhD, is an assistant professor and senior researcher at Vellore Institute of Technology (VIT), Chennai Campus, India. He received his PhD in 2018 from Pondicherry Central University, India. He has published more than 8 patents in the area of deep learning, Internet of Things, cyber-physical systems, and computer vision.

De la contraportada

The book provides readers with an in-depth understanding of concepts and technologies related to the importance of analytics and deep learning in many useful real-world applications such as e-healthcare, transportation, agriculture, stock market, etc.

Advanced analytics is a mixture of machine learning, artificial intelligence, graphs, text mining, data mining, semantic analysis. It is an approach to data analysis. Beyond the traditional business intelligence, it is a semi and autonomous analysis of data by using different techniques and tools.

However, deep learning and data analysis both are high centers of data science. Almost all the private and public organizations collect heavy amounts of data, i.e., domain-specific data. Many small/large companies are exploring large amounts of data for existing and future technology. Deep learning is also exploring large amounts of unsupervised data making it beneficial and effective for big data. Deep learning can be used to deal with all kinds of problems and challenges that include collecting unlabeled and uncategorized raw data, extracting complex patterns from a large amount of data, retrieving fast information, tagging data, etc.

This book contains 16 chapters on artificial intelligence, machine learning, deep learning, and their uses in many useful sectors like stock market prediction, a recommendation system for better service selection, e-healthcare, telemedicine, transportation. There are also chapters on innovations and future opportunities with fog computing/cloud computing and artificial intelligence.

Audience

Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in healthcare, telemedicine, transportation, and the financial sector. The book will also be a great source for software engineers and advanced students who are beginners in the field of advanced analytics in deep learning.