Big Data, IoT, and Machine Learning: Tools and Applications

Big Data, IoT, and Machine Learning: Tools and Applications
PDF
  • eBook:
    Big Data, IoT, and Machine Learning: Tools and Applications
  • Author:
    Rashmi Agrawal, Marcin Paprzycki, Neha Gupta
  • Edition:
    1 edition
  • Categories:
  • Data:
    July 30, 2020
  • ISBN:
    036733674X
  • ISBN-13:
    9780367336745
  • Language:
    English
  • Pages:
    337 pages
  • Format:
    PDF

Book Description
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 consists of two sections: Section I contains the topics related to Applications of Machine Learning, and Section II addresses issues about Big Data, the Cloud and the Internet of Things. This brings all the related technologies into a single source so that undergraduate and postgraduate students, researchers, academicians and people in industry can easily understand them.
Features
  • Addresses the complete data science technologies workflow
  • Explores basic and high-level concepts and services as a manual for those in the industry and at the same time can help beginners to understand both basic and advanced aspects of machine learning
  • Covers data processing and security solutions in IoT and Big Data applications
  • Offers adaptive, robust, scalable and reliable applications to develop solutions for day-to-day problems
  • Presents security issues and data migration techniques of NoSQL databases

Content

Section I Applications of Machine Learning
1. Machine Learning Classifers
2. Dimension Reduction Techniques
3. Reviews Analysis of Apple Store Applications Using Supervised Machine Learning
4. Machine Learning for Biomedical and Health Informatics
5. Meta-Heuristic Algorithms: A Concentration on the Applications in Text Mining
6. Optimizing Text Data in Deep Learning: An Experimental Approach

Section II Big Data, Cloud and Internet of Things
7. Latest Data and Analytics Technology Trends That Will Change Business Perspectives
8. A Proposal Based on Discrete Events for Improvement of the Transmission Channels in Cloud Environments and Big Data
9. Heterogeneous Data Fusion for Healthcare Monitoring: A Survey
10. Discriminative and Generative Model Learning for Video Object Tracking
11. Feature, Technology, Application, and Challenges of Internet of Things
12. Analytical Approach to Sustainable Smart City Using IoT and Machine Learning
13. Traffc Flow Prediction with Convolutional Neural Network Accelerated by Spark Distributed Cluster

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