Rule Based Systems For Big Data

The ideas introduced in this book explore the relationships among rule based systems, machine learning and big data. Rule based systems are seen as a special type of expert systems, which can be built by using expert knowledge or learning from real data. The book focuses on the development and evaluation of rule based systems in terms of accuracy, efficiency and interpretability. In particular, a unified framework for building rule based systems, which consists of the operations of rule generation, rule simplification and rule representation, is presented. Each of these operations is detailed using specific methods or techniques. In addition, this book also presents some ensemble learning frameworks for building ensemble rule based systems.

Big Data in Psychiatry and Neurology provides an up-to-date overview of achievements in the field of big data in Psychiatry and Medicine, including applications of big data methods to aging disorders (e.g., Alzheimer’s disease and Parkinson’s disease), mood disorders (e.g., major depressive disorder), and drug addiction. This book will help researchers, students and clinicians implement new methods for collecting big datasets from various patient populations. Further, it will demonstrate how to use several algorithms and machine learning methods to analyze big datasets, thus providing individualized treatment for psychiatric and neurological patients. As big data analytics is gaining traction in psychiatric research, it is an essential component in providing predictive models for both clinical practice and public health systems. As compared with traditional statistical methods that provide primarily average group-level results, big data analytics allows predictions and stratification of clinical outcomes at an individual subject level. Discusses longitudinal big data and risk factors surrounding the development of psychiatric disorders Analyzes methods in using big data to treat psychiatric and neurological disorders Describes the role machine learning can play in the analysis of big data Demonstrates the various methods of gathering big data in medicine Reviews how to apply big data to genetics

Anomaly Detection and Complex Event Processing over IoT Data Streams: With Application to eHealth and Patient Data Monitoring presents advanced processing techniques for IoT data streams and the anomaly detection algorithms over them. The book brings new advances and generalized techniques for processing IoT data streams, semantic data enrichment with contextual information at Edge, Fog and Cloud as well as complex event processing in IoT applications. The book comprises fundamental models, concepts and algorithms, architectures and technological solutions as well as their application to eHealth. Case studies, such as the bio-metric signals stream processing are presented –the massive amount of raw ECG signals from the sensors are processed dynamically across the data pipeline and classified with modern machine learning approaches including the Hierarchical Temporal Memory and Deep Learning algorithms. The book discusses adaptive solutions to IoT stream processing that can be extended to different use cases from different fields of eHealth, to enable a complex analysis of patient data in a historical, predictive and even prescriptive application scenarios. The book ends with a discussion on ethics, emerging research trends, issues and challenges of IoT data stream processing. Provides the state-of-the-art in IoT Data Stream Processing, Semantic Data Enrichment, Reasoning and Knowledge Covers extraction (Anomaly Detection) Illustrates new, scalable and reliable processing techniques based on IoT stream technologies Offers applications to new, real-time anomaly detection scenarios in the health domain

The recent pursuits emerging in the realm of big data processing, interpretation, collection and organization have emerged in numerous sectors including business, industry and government organizations. Data sets such as customer transactions for a mega-retailer, weather monitoring, intelligence gathering, quickly outpace the capacities of traditional techniques and tools of data analysis. The 3V (volume, variability and velocity) challenges led to the emergence of new techniques and tools in data visualization, acquisition, and serialization. Soft Computing being regarded as a plethora of technologies of fuzzy sets (or Granular Computing), neurocomputing and evolutionary optimization brings forward a number of unique features that might be instrumental to the development of concepts and algorithms to deal with big data. This carefully edited volume provides the reader with an updated, in-depth material on the emerging principles, conceptual underpinnings, algorithms and practice of Computational Intelligence in the realization of concepts and implementation of big data architectures, analysis, and interpretation as well as data analytics. The book is aimed at a broad audience of researchers and practitioners including those active in various disciplines in which big data, their analysis and optimization are of genuine relevance. One focal point is the systematic exposure of the concepts, design methodology, and detailed algorithms. In general, the volume adheres to the top-down strategy starting with the concepts and motivation and then proceeding with the detailed design that materializes in specific algorithms and representative applications. The material is self-contained and provides the reader with all necessary prerequisites and augments some parts with a step-by-step explanation of more advanced concepts supported by a significant amount of illustrative numeric material and some application scenarios to motivate the reader and make some abstract concepts more tangible.

This book unravels the mystery of Big Data computing and its power to transform business operations. The approach it uses will be helpful to any professional who must present a case for realizing Big Data computing solutions or to those who could be involved in a Big Data computing project. It provides a framework that enables business and technical managers to make optimal decisions necessary for the successful migration to Big Data computing environments and applications within their organizations.

This book is a delight for academics, researchers and professionals working in evolutionary and swarm computing, computational intelligence, machine learning and engineering design, as well as search and optimization in general. It provides an introduction to the design and development of a number of popular and recent swarm and evolutionary algorithms with a focus on their applications in engineering problems in diverse domains. The topics discussed include particle swarm optimization, the artificial bee colony algorithm, Spider Monkey optimization algorithm, genetic algorithms, constrained multi-objective evolutionary algorithms, genetic programming, and evolutionary fuzzy systems. A friendly and informative treatment of the topics makes this book an ideal reference for beginners and those with experience alike.

This book constitutes the post-conference proceedings of the 12th International Workshop on Fuzzy Logic and Applications, WILF 2018, held in Genoa, Italy, in September 2018. The 17 revised full papers and 9 short papers were carefully reviewed and selected from 26 submissions. The papers are organized in topical sections on fuzzy logic theory, recent applications of fuzzy logic, and fuzzy decision making. Also included are papers from the round table "Zadeh and the future of logic" and a tutorial.

This timely text/reference reviews the state of the art of big data analytics, with a particular focus on practical applications. An authoritative selection of leading international researchers present detailed analyses of existing trends for storing and analyzing big data, together with valuable insights into the challenges inherent in current approaches and systems. This is further supported by real-world examples drawn from a broad range of application areas, including healthcare, education, and disaster management. The text also covers, typically from an application-oriented perspective, advances in data science in such areas as big data collection, searching, analysis, and knowledge discovery. Topics and features: Discusses a model for data traffic aggregation in 5G cellular networks, and a novel scheme for resource allocation in 5G networks with network slicing Explores methods that use big data in the assessment of flood risks, and apply neural networks techniques to monitor the safety of nuclear power plants Describes a system which leverages big data analytics and the Internet of Things in the application of drones to aid victims in disaster scenarios Proposes a novel deep learning-based health data analytics application for sleep apnea detection, and a novel pathway for diagnostic models of headache disorders Reviews techniques for educational data mining and learning analytics, and introduces a scalable MapReduce graph partitioning approach for high degree vertices Presents a multivariate and dynamic data representation model for the visualization of healthcare data, and big data analytics methods for software reliability assessment This practically-focused volume is an invaluable resource for all researchers, academics, data scientists and business professionals involved in the planning, designing, and implementation of big data analytics projects. Dr. Mohammed M. Alani is an Associate Professor in Computer Engineering and currently is the Provost at Al Khawarizmi International College, Abu Dhabi, UAE. Dr. Hissam Tawfik is a Professor of Computer Science in the School of Computing, Creative Technologies & Engineering at Leeds Beckett University, UK. Dr. Mohammed Saeed is a Professor in Computing and currently is the Vice President for Academic Affairs and Research at the University of Modern Sciences, Dubai, UAE. Dr. Obinna Anya is a Research Staff Member at IBM Research – Almaden, San Jose, CA, USA.