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Applied Big Data Analytics(Revised): Business Intelligence, Health Informatics, Capital Market, Analytics for Life Sciences

Applied Big Data Analytics(Revised): Business Intelligence, Health Informatics, Capital Market, Analytics for Life Sciences Kindle Edition by ajit roy (Author)

https://www.amazon.com/dp/B010SSKSP0


Preface
Data pours into millions of computers every moment of every day. It is estimated that the total accumulated data stored on computers worldwide is about 300 exabytes. The annual transmission of data is estimated at about 1.9 zettabytes i.e.1900 billion gigabytes. Due to the tremendous amount of data generated daily from fields such as business, research, and sciences, big data is everywhere and represents huge opportunities to those who can use it effectively. In the past, this information was simply ignored and opportunities were missed. Realizing the great importance of big data, organizations scramble to find hidden information buried in big data and try to make the best use of it. Presently “big data” is a hot topic and getting a lot of media and business attention. Therefore, alternative management and processing methods have to be created to handle this complex and unstructured data size. Over recent times, the concepts of “big data” and “big data analytics” have become ubiquitous. It is hard to visit a web site, open a newspaper, or read a magazine that does not refer to one or both of those phrases. Yet the technologies that are incorporated into big data are massive parallelism, huge data volumes, data distribution, high-speed networks, high-performance computing, task and thread management, and data mining and analytics. Big Data is the result of practically everything in the world being monitored and measured; creating data faster than the available technologies can store, process or manage it. Presently there has been a surge of unstructured data, making up as much as 80% of new data that requires attention for management. Big Data results in three basic challenges: storing, processing and managing it efficiently. Scale-out architectures have been developed to store large amount of data and purpose-built appliances have improved the processing capability. The next frontier is learning how to manage Big Data throughout its entire lifecycle. To tackle the challenges of Big Data, novel approaches and tools have emerged. The technology required for big-data computing is developing at a satisfactory rate due to market forces and technological evolution. This book presents a mix of theory and real world cases that discuss the technical and practical issues related to Big Data in intelligent information management. This book also provides rich topics of big data management, technologies, and applications.
The edited book on 'Applied Big data Analytics' reveals how to fully apply the tools and techniques in various fields. The book will certainly be invaluable to wide audiences of professionals, decision-makers, and consultants involved in analytics particularly the new comers in the areas. It will also be exceptionally useful to students of analytics in any graduate, undergraduate, or certificate program. The project could not have been successful without the whole hearted support of the valued contributors of the book. Simply, I am grateful to all of them.
AJIT KUMAR ROY    

Table of Contents
SECTION I:      BASICS OF BIG DATA ANALYTICS                     1
Chapter – 1:     AN OVERVIEW OF BIG DATA ANALYTICS       3
Chapter – 2:     CLOUD COMPUTING, INFRA-STRUCTURE AND APPLICATION PLATFORMS        47
Chapter – 3:     CLOUD COMPUTING FOR BIOINFORMATICS: MEETING THE CHALLENGES OF BIG DATA ANALYSIS, STORAGE AND INTEGRATION          79
Chapter – 4:     BUSINESS INTELLIGENCE, BUSINESS ANALYTICS AND PREDICTIVE ANALYTICS IN THE ERA OF BIG DATA ANALYTICS           99
SECTION II:     TOOLS, TECHNIQUES and SOFTWARE for ANALYTICS          149
Chapter – 5:     AN INTRODUCTION TOR logo SOFTWARE WITH APPLICATIONS    151
Chapter – 6:     USE OF ARTIFICIAL INTELLIGENCE IN TIME-SERIES ANALYSIS THROUGH GENETIC ALGORITHM                                     191
Chapter – 7:     A STATISTICAL APPROACH FOR NEURAL-NETWORK-SECURITY (NNS) ALGORITHM TO HANDLE DATA DISTURBANCE AND VIRUS DETECTIONS OF CLOUD COMPUTING                                        201
 Chapter – 8:    REGRESSION ANALYSIS INCLUDING OLS, ROBUST AND FUZZY METHODS                                                                             223
Chapter – 9:     TIME-SERIES ANALYSIS USING WAVELETS 267
Chapter – 10:   A STUDY ON INDEPENDENT COMPONENT ANALYSIS (ICA) AND ITS APPLICATION IN DIVERSE FIELD OF SCIENCE AND TECHNOLOGY          289
Chapter – 11:   CORRESPONDENCE ANALYSIS: AN EMERGING TOOL IN APPLIED RESEARCH                                                                             321
Chapter – 12:   CROP PRODUCTION FORECASTING USING TIME SERIES MODELS            345
SECTION III:    BIG DATA ANALYTICS IN HEALTH CARE SECTOR      361
Chapter – 13:   TRENDS IN BIG DATA ANALYTICS IN HEALTH CARE         363
Chapter – 14:   HEALTHCARE THROUGH GENOMIC ANALYSIS        419
Chapter – 15:   A STUDY AND ANALYSIS OF MAMOGRAMS BASED BREAST CANCER DETECTION TECHNIQUES                               453
Chapter – 16:   HUMAN BEHAVIOR ANALYSIS THROUGH FACIAL EPRESSION FOR PAIN ESTIMATION                                                      479
Chapter – 17:   ANALYTICAL APPROACH FOR FUSION BASED BRAIN TUMOR DETECTION                                                                             505
Chapter – 18:   AN ANALYTICAL STUDY ON RESEARCH TRENDS ON BREAST CANCER IN NORTH-EAST INDIA                                         525
SECTION IV:    APPLICATION OF ANALYTICS IN INDUSTRIES, BIOINFORMATICS AND LIFE SCIENCES                                                      559
Chapter – 19:   APPLICATIONS OF BIG DATA TECHNOLOGY IN CAPITAL MARKET MOVEMENTS IN INDIA                                                             561
Chapter – 20:   BIG DATA ANALYTICS FOR SMART METERS 611
Chapter – 21:   SCOPE OF BIG DATA ANALYTICS IN BIOINFORMATICS      629
Chapter – 22:   KNOWLEDGEBASE MANAGEMENT IN BIODEGRADATION – A COMPARATIVE STUDY AND ANALYSIS OF INFORMATION  649
Chapter – 23:   MULTIVARIATE APPROACHES TO THE ANALYSIS OF GENETIC DIVERSITY IN BANANA PLANTS                                             681
Chapter – 24:   ANALYSIS ASSISTED DECISION SUPPORT SYSTEMS IN MICROBIOLOGY 711
Chapter – 25:   APPLICATION OF ANALYTICAL TOOLS IN DISASTER MANAGEMENT      729
Chapter – 26:   USE OF BIG DATA IN SOCIAL SCIENCES RESEARSH – CHALLENGES AND OPPORTUNITIES                                                745

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