Advanced Deep Learning Applications In Big Data Analytics

Advanced Deep Learning Applications in Big Data Analytics
Publisher IGI Global
Release Date
Category Computers
Total Pages 351
ISBN 9781799827931
Rating 4/5 from 21 reviews

Interest in big data has swelled within the scholarly community as has increased attention to the internet of things (IoT). Algorithms are constructed in order to parse and analyze all this data to facilitate the exchange of information. However, big data has suffered from problems in connectivity, scalability, and privacy since its birth. The application of deep learning algorithms has helped process those challenges and remains a major issue in today’s digital world. Advanced Deep Learning Applications in Big Data Analytics is a pivotal reference source that aims to develop new architecture and applications of deep learning algorithms in big data and the IoT. Highlighting a wide range of topics such as artificial intelligence, cloud computing, and neural networks, this book is ideally designed for engineers, data analysts, data scientists, IT specialists, programmers, marketers, entrepreneurs, researchers, academicians, and students.

Advanced Deep Learning Applications in Big Data Analytics
  • Author : Bouarara, Hadj Ahmed
  • Publisher : IGI Global
  • Release Date : 2020-10-16

Interest in big data has swelled within the scholarly community as has increased attention to the internet of things (IoT). Algorithms are constructed in order to parse and analyze all this data to facilitate the exchange of information. However, big data has suffered from problems in connectivity, scalability, and privacy

Big Data Analysis and Deep Learning Applications
  • Author : Thi Thi Zin,Jerry Chun-Wei Lin
  • Publisher : Springer
  • Release Date : 2018-06-06

This book presents a compilation of selected papers from the first International Conference on Big Data Analysis and Deep Learning Applications (ICBDL 2018), and focuses on novel techniques in the fields of big data analysis, machine learning, system monitoring, image processing, conventional neural networks, communication, industrial information, and their applications. Readers

Deep Learning  Convergence to Big Data Analytics
  • Author : Murad Khan,Bilal Jan,Haleem Farman
  • Publisher : Springer
  • Release Date : 2018-12-30

This book presents deep learning techniques, concepts, and algorithms to classify and analyze big data. Further, it offers an introductory level understanding of the new programming languages and tools used to analyze big data in real-time, such as Hadoop, SPARK, and GRAPHX. Big data analytics using traditional techniques face various

Deep Learning for Data Analytics
  • Author : Himansu Das,Chittaranjan Pradhan,Nilanjan Dey
  • Publisher : Academic Press
  • Release Date : 2020-05-29

Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design

Deep Learning in Data Analytics
  • Author : Debi Prasanna Acharjya,Anirban Mitra,Noor Zaman
  • Publisher : Springer
  • Release Date : 2021-09-21

This book comprises theoretical foundations to deep learning, machine learning and computing system, deep learning algorithms, and various deep learning applications. The book discusses significant issues relating to deep learning in data analytics. Further in-depth reading can be done from the detailed bibliography presented at the end of each chapter.

Machine Learning and Big Data Analytics Paradigms  Analysis  Applications and Challenges
  • Author : Aboul Ella Hassanien,Ashraf Darwish
  • Publisher : Springer Nature
  • Release Date : 2020-12-14

This book is intended to present the state of the art in research on machine learning and big data analytics. The accepted chapters covered many themes including artificial intelligence and data mining applications, machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and security with

Applications of Machine Learning in Big Data Analytics and Cloud Computing
  • Author : Subhendu Kumar Pani,Somanath Tripathy,George Jandieri,Sumit Kundu,Talal Ashraf Butt
  • Publisher : River Publishers Information S
  • Release Date : 2020-07-30

Cloud Computing and Big Data technologies have become the new descriptors of the digital age. The global amount of digital data has increased more than nine times in volume in just five years and by 2030 its volume may reach a staggering 65 trillion gigabytes. This explosion of data has led to

Machine Learning and Big Data
  • Author : Uma N. Dulhare,Khaleel Ahmad,Khairol Amali Bin Ahmad
  • Publisher : John Wiley & Sons
  • Release Date : 2020-09-01

Currently many different application areas for Big Data (BD) and Machine Learning (ML) are being explored. These promising application areas for BD/ML are the social sites, search engines, multimedia sharing sites, various stock exchange sites, online gaming, online survey sites and various news sites, and so on. To date,

Nature Inspired Algorithms for Big Data Frameworks
  • Author : Banati, Hema,Mehta, Shikha,Kaur, Parmeet
  • Publisher : IGI Global
  • Release Date : 2018-09-28

As technology continues to become more sophisticated, mimicking natural processes and phenomena becomes more of a reality. Continued research in the field of natural computing enables an understanding of the world around us, in addition to opportunities for manmade computing to mirror the natural processes and systems that have existed

Deep Learning Techniques and Optimization Strategies in Big Data Analytics
  • Author : Thomas, J. Joshua,Karagoz, Pinar,Ahamed, B. Bazeer,Vasant, Pandian
  • Publisher : IGI Global
  • Release Date : 2019-11-29

Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there’s a need for research on

Machine Learning for Big Data Analysis
  • Author : Siddhartha Bhattacharyya,Hrishikesh Bhaumik,Anirban Mukherjee,Sourav De
  • Publisher : Walter de Gruyter GmbH & Co KG
  • Release Date : 2018-12-17

This volume comprises six well-versed contributed chapters devoted to report the latest fi ndings on the applications of machine learning for big data analytics. Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them.

Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches
  • Author : K. Gayathri Devi,Mamata Rath,Nguyen Thi Dieu Linh
  • Publisher : CRC Press
  • Release Date : 2020-10-08

Artificial Intelligence (AI), when incorporated with machine learning and deep learning algorithms, has a wide variety of applications today. This book focuses on the implementation of various elementary and advanced approaches in AI that can be used in various domains to solve real-time decision-making problems. The book focuses on concepts

Big Data  IoT  and Machine Learning
  • Author : Rashmi Agrawal,Marcin Paprzycki,Neha Gupta
  • Publisher : CRC Press
  • Release Date : 2020-09-01

The idea behind this book is to simplify the journey of aspiring readers and researchers to understand Big Data, IoT and Machine Learning. It also includes various real-time/offline applications and case studies in the fields of engineering, computer science, information security and cloud computing using modern tools. This book

Big Data Technologies and Applications
  • Author : Borko Furht,Flavio Villanustre
  • Publisher : Springer
  • Release Date : 2016-09-16

The objective of this book is to introduce the basic concepts of big data computing and then to describe the total solution of big data problems using HPCC, an open-source computing platform. The book comprises 15 chapters broken into three parts. The first part, Big Data Technologies, includes introductions to big

Fog Computing  Deep Learning and Big Data Analytics Research Directions
  • Author : C.S.R. Prabhu
  • Publisher : Springer
  • Release Date : 2019-02-04

This book provides a comprehensive picture of fog computing technology, including of fog architectures, latency aware application management issues with real time requirements, security and privacy issues and fog analytics, in wide ranging application scenarios such as M2M device communication, smart homes, smart vehicles, augmented reality and transportation management.