**Preface XI**


Preface

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 dif‐ ferent sections. On the one hand, the internet of things (IoT) enabled by cellular telecommu‐ nication industry connects various types of sensors that can collect heterogeneous data and communicate them to the distributed processing units. On the other hand, the recent advan‐ ces in computational capabilities such as parallel processing in graphical processing units (GPUs) and distributed processing over cloud computing clusters have enabled the process‐ ing 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 deci‐ sion-making processes. Tools and techniques have been developed in machine learning to draw insightful conclusions from available data in a structured and automated fashion. Ma‐ chine learning algorithms are based on concepts and tools developed in several fields includ‐ ing statistics, artificial intelligence, information theory, cognitive science, and control theory. The recent advances in the area of machine learning have had a broad range of applications in different scientific disciplines (e.g., bioinformatics, particle physics, and neuroscience), technology (e.g., autonomous cyber physical systems, and computer-assisted diagnosis sys‐

tems), and business (e.g., online financial trading tools and data-driven marketing).

This book is an attempt to bring together the recent developments in machine learning tools and techniques, and the emerging applications of machine learning across different research disciplines, industry sections, and business areas. The book starts with a chapter on hard‐ ware accelerator design for the implementation of machine learning algorithms. The chapter addresses hardware architectures to enable parallel processing and hence accelerating the execution of these algorithms. The book continues with subsequent chapters, each of which addresses a specific area where machine learning can achieve substantial improvements. Predictive models for air quality prediction are discussed in Chapter 2. The chapter presents several algorithms that are developed to predict the concentration of particles suspended in the air. The predictions are based on heterogeneous datasets collected with various sensors that measure traffic status, weather condition, and trace gases. The presented techniques would be able to advance public health by providing accurate air quality prediction in fu‐ ture smart cities. Automated event detection is another area where machine learning can improve spreading awareness and organizing responses in emerging smart cities. Automat‐ ed event detection using multimedia information in tweets is addressed in Chapter 3. A multiple kernel learning (MKL) algorithm is presented to automatically combine extracted features from texts and images in tweets and detect events. This would assist risk manage‐

Chapter 11 **A Multilevel Evolutionary Algorithm Applied to the Maximum Satisfiability Problems 203** Noureddine Bouhmala, Kjell Ivar Øvergård and Karina Hjelmervik
