"Machine Learning: Advanced Techniques and Emerging Applications" ed. by Hamed Farhadi

This book covers recent advances of machine learning techniques in a broad range of applications in smart cities, automated industry, and emerging businesses.

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.


Contents
1.Hardware Accelerator Design for Machine Learning
2.Regression Models to Predict Air Pollution from Affordable Data Collections
3.Multiple Kernel-Based Multimedia Fusion for Automated Event Detection from Tweets
4.Using Sentiment Analysis and Machine Learning Algorithms to Determine Citizens’ Perceptions
5.Overcoming Challenges in Predictive Modeling of Laser-Plasma Interaction Scenarios. The Sinuous Route from Advanced Machine Learning to Deep Learning
6.Machine Learning Approaches for Spectrum Management in Cognitive Radio Networks
7.Machine Learning Algorithm for Wireless Indoor Localization
8.Classification of Malaria-Infected Cells Using Deep Convolutional Neural Networks
9.Machine Learning in Educational Technology
10.Sentiment-Based Semantic Rule Learning for Improved Product Recommendations
11.A Multilevel Evolutionary Algorithm Applied to the Maximum Satisfiability Problems



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