Open access peer-reviewed Edited Volume

Machine Learning

Advanced Techniques and Emerging Applications

Edited by Hamed Farhadi

Royal Institute of Technology, Sweden

The volume of data that is generated, stored, and communicated across different industrial sections, business units, and scientific research communities has been rapidly expanding. The recent developments in cellular telecommunications and distributed/parallel computation technology have enabled real-time collection and processing of the generated data across different sections. On the one hand, the internet of things (IoT) enabled by cellular telecommunication industry connects various types of sensors that can collect heterogeneous data. On the other hand, the recent advances in computational capabilities such as parallel processing in graphical processing units (GPUs) and distributed processing over cloud computing clusters enabled the processing of a vast amount of data. There has been a vital need to discover important patterns and infer trends from a large volume of data (so-called Big Data) to empower data-driven decision-making processes. Tools and techniques have been developed in machine learning to draw insightful conclusions from available data in a structured and automated fashion. Machine learning algorithms are based on concepts and tools developed in several fields including statistics, artificial intelligence, information theory, cognitive science, and control theory. The recent advances in machine learning have had a broad range of applications in different scientific disciplines. This book covers recent advances of machine learning techniques in a broad range of applications in smart cities, automated industry, and emerging businesses.

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Machine LearningAdvanced Techniques and Emerging ApplicationsEdited by Hamed Farhadi

Published: September 19th 2018

DOI: 10.5772/intechopen.69783

ISBN: 978-1-78923-753-5

Print ISBN: 978-1-78923-752-8

eBook (PDF) ISBN: 978-1-83881-418-2

Copyright year: 2018

Books open for chapter submissions

10827 Total Chapter Downloads

20 Crossref Citations

13 Web of Science Citations

32 Dimensions Citations


Open access peer-reviewed

1. Hardware Accelerator Design for Machine Learning

By Li Du and Yuan Du


Open access peer-reviewed

2. Regression Models to Predict Air Pollution from Affordable Data Collections

By Yves Rybarczyk and Rasa Zalakeviciute


Open access peer-reviewed

3. Multiple Kernel-Based Multimedia Fusion for Automated Event Detection from Tweets

By Suhuai Luo, Samar M. Alqhtani and Jiaming Li


Open access peer-reviewed

4. Using Sentiment Analysis and Machine Learning Algorithms to Determine Citizens’ Perceptions

By Sherrene Bogle


Open access peer-reviewed

5. Overcoming Challenges in Predictive Modeling of Laser-Plasma Interaction Scenarios. The Sinuous Route from Advanced Machine Learning to Deep Learning

By Andreea Mihailescu


Open access peer-reviewed

6. Machine Learning Approaches for Spectrum Management in Cognitive Radio Networks

By Ahmed Mohammed Mikaeil


Open access peer-reviewed

7. Machine Learning Algorithm for Wireless Indoor Localization

By Osamah Ali Abdullah and Ikhlas Abdel-Qader


Open access peer-reviewed

8. Classification of Malaria-Infected Cells Using Deep Convolutional Neural Networks

By W. David Pan, Yuhang Dong and Dongsheng Wu


Open access peer-reviewed

9. Machine Learning in Educational Technology

By Ibtehal Talal Nafea


Open access peer-reviewed

10. Sentiment-Based Semantic Rule Learning for Improved Product Recommendations

By Dandibhotla Teja Santosh and Bulusu Vishnu Vardhan


Open access peer-reviewed

11. A Multilevel Evolutionary Algorithm Applied to the Maximum Satisfiability Problems

By Noureddine Bouhmala, Kjell Ivar Øvergård and Karina Hjelmervik


Edited Volume and chapters are indexed in

  • Worldcat
  • OpenAIRE
  • Google Scholar
  • AZ ebsco
  • Base
  • CNKI

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