Handbook of Research on Emerging Trends and Applications of Machine Learning

Handbook of Research on Emerging Trends and Applications of Machine Learning
  • eBook:
    Handbook of Research on Emerging Trends and Applications of Machine Learning
  • Author:
    Arun Solanki, Sandeep Kumar, Anand Nayyar
  • Edition:
    1 edition
  • Categories:
  • Data:
    December 13, 2019
  • ISBN:
  • ISBN-13:
  • Language:
  • Pages:
    674 pages
  • Format:
    PDF, ePUB

Book Description
As todays world continues to advance, Artificial Intelligence (AI) is a field that has become a staple of technological development and led to the advancement of numerous professional industries. An application within AI that has gained attention is machine learning. Machine learning uses statistical techniques and algorithms to give computer systems the ability to understand and its popularity has circulated through many trades. Understanding this technology and its countless implementations is pivotal for scientists and researchers across the world.
The Handbook of Research on Emerging Trends and Applications of Machine Learning provides a high-level understanding of various machine learning algorithms along with modern tools and techniques using Artificial Intelligence. In addition, this book explores the critical role that machine learning plays in a variety of professional fields including healthcare, business, and computer science. While highlighting topics including image processing, predictive analytics, and smart grid management, this book is ideally designed for developers, data scientists, business analysts, information architects, finance agents, healthcare professionals, researchers, retail traders, professors, and graduate students seeking current research on the benefits, implementations, and trends of machine learning.


Chapter 1. Method to Rank Academic Institutes by the Sentiment Analysis of Their Online Reviews
Chapter 2. Machine Learning in Higher Education: Predicting Student Attrition Status Using Educational Data Mining
Chapter 3. Machine Learning Optimization Techniques for 3D IC Physical Design
Chapter 4. A Novel Approach to Kinect-Based Gesture Recognition for HCI Applications
Chapter 5. A Survey on Precision Treatment for Humans Using Cognitive Machine Learning Techniques
Chapter 6. Text Classification Using Self-Structure Extended Multinomial Naive Bayes
Chapter 7. Applications of Machine Learning for Software Management
Chapter 8. Creating Paraphrase Identification Corpus for Indian Languages: Opensource Data Set for Paraphrase Creation
Chapter 9. Deep Learning Approaches for Textual Sentiment Analysis
Chapter 10. Deep Learning for Next-Generation Inventive Wireless Networks: Issues, Challenges, and Future Directions
Chapter 11. Demystifying Disease Identification and Diagnosis Using Machine Learning Classification Algorithms: Demystify Medical Data With Machine Learning Algorithms
Chapter 12. Evolution of Data Analytics in Healthcare
Chapter 13. Implementation of Machine Learning in Network Security
Chapter 14. Information Retrieval in Conjunction With Deep Learning
Chapter 15. Interactive Game-Based Motor Rehabilitation Using Hybrid Sensor Architecture
Chapter 16. Leveraging Natural Language Processing Applications Using Machine Learning: Text Summarization Employing Deep Learning
Chapter 17. Machine Learning in 5G Multimedia Communications: Open Research Challenges and Applications
Chapter 18. Machine Learning in Text Analysis
Chapter 19. Machine Learning in the Medical Industry
Chapter 20. Machine Learning in Video Games
Chapter 21. Multi-Layer Hybrid Credit Scoring Model Based on Feature Selection, Ensemble Learning, and Ensemble Classifier
Chapter 22. Overview of Machine Learners in Classifying of Speech Signals
Chapter 23. Self-Driving Cars: Role of Machine Learning
Chapter 24. Sentiment Analysis on Social Media: Recent Trends in Machine Learning
Chapter 25. Social Big Data Mining: A Survey Focused on Sentiment Analysis
Chapter 26. Tool Condition Monitoring Using Artificial Neural Network Models
Chapter 27. Computer Vision-Based Assistive Technology for Helping Visually Impaired and Blind People Using Deep Learning Framework

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