Описание: Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner(R), Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies.
Описание: This book brings together multi-disciplinary research and practical evidence about the role and exploitation of big data in driving and supporting innovation in tourism.
Автор: Abu-Salih Bilal, Wongthongtham Pornpit, Zhu Dengya Название: Social Big Data Analytics: Practices, Techniques, and Applications ISBN: 9813366516 ISBN-13(EAN): 9789813366510 Издательство: Springer Цена: 19564.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Chapter 1: Big data technologies
Big data is no more "all just hype" but widely applied in nearly all aspects of our business, governments, and organizations with the technology stack of AI. Its influences are far beyond a simple technique innovation but involves all rears in the world. This chapter will first have historical review of big data; followed by discussion of characteristics of big data, i.e. the 3V's to up 10V's of big data. The chapter then introduces technology stacks for an organization to build a big data application, from infrastructure/platform/ecosystem to constructional units/components; following by several successful examples. Finally, we provide some big data online resources for reference.
Chapter 2: Credibility and influence in social big data
Online Social Networks (OSNs) are a fertile medium through which users can express their sentiments and share their opinions, experiences and knowledge of several topics. There is a deficiency of assessment mechanisms that incorporate domain-based trustworthiness. In OSNs, determining users' influence in a particular domain has been driven by its significance in a broad range of applications such as personalized recommendation systems, opinion analysis, expertise retrieval, to name a few. This chapter presents a comprehensive framework that aims to infer value from BSD by measuring the domain-based trustworthiness of OSN users, addressing the main features of big data, and incorporating semantic analysis and the temporal factor.
Chapter 3: Semantic data discovery from social big data
The challenge of managing and extracting useful knowledge from social media data sources has attracted much attention from academia and industry. Social big data is an important big data island; thus, social data analytics are intended to make sense of data and to obtain value from data. Social big data provides a wealth of information that businesses, political governments, organisations, etc. can mine and analyse to exploit value in a variety of areas. This chapter discusses the development of an approach that aims to semantically analyse social content, thus enriching social data with semantic conceptual representation for domain-based discovery.
Chapter 4: Predictive analytics using social big data and machine learning
Previous works in the area of topic distillation and discovery lack an appropriate and applicable technical solution that can handle the complex task of obtaining an accurate interpretation of the contextual social content. This is evident through the inadequacy of these endeavours in addressing the topics of microblogging short messages like tweets, and their inability to classify and predict the messages' actual and precise domains of interest at the user level. Hence, this chapter intends to address this problem by presenting solutions to domain-based classification and prediction of social big data at the user and tweet levels incorporating comprehensive knowledge discovery tools and well-known machine learning algorithms.
Chapter 5: Affective design in the era of big social data
In today's competitive market, product designers not only need to optimize functional qualities when developing a new product, but also they need to optimize the affective qualities of the product. The reason is that products with high affective qualities is more likely to attract more potential consumers to buy. In the past, affective design is generally conducted based on the limited amount of customer survey data which is collected from marketing questionnaires and consumer interviews. Since the data amount is limited, the affective design cannot fully reflect the current or even the recent situation of the marketplaces. Thanks to the advanced computing and web technologies, big data from social media or product reviews in w
Описание: This book comprises theoretical foundations to deep learning, machine learning and computing system, deep learning algorithms, and various deep learning applications.
As data holdings get bigger and questions get harder, data scientists and analysts must focus on the systems, the tools and techniques, and the disciplined process to get the correct answer, quickly Whether you work within industry or government, this book will provide you with a foundation to successfully and confidently process large amounts of quantitative data.
Here are just a dozen of the many questions answered within these pages:
What does quantitative analysis of a system really mean?
What is a system?
What are big data and analystics?
How do you know your numbers are good?
What will the future data science environment look like?
How do you determine data provenance?
How do you gather and process information, and then organize, store, and synthesize it?
How does an organization implement data analytics?
Do you really need to think like a Chief Information Officer?
What is the best way to protect data?
What makes a good dashboard?
What is the relationship between eating ice cream and getting attacked by a shark?
The nine chapters in this book are arranged in three parts that address systems concepts in general, tools and techniques, and future trend topics. Systems concepts include contrasting open and closed systems, performing data mining and big data analysis, and gauging data quality. Tools and techniques include analyzing both continuous and discrete data, applying probability basics, and practicing quantitative analysis such as descriptive and inferential statistics. Future trends include leveraging the Internet of Everything, modeling Artificial Intelligence, and establishing a Data Analytics Support Office (DASO).
Many examples are included that were generated using common software, such as Excel, Minitab, Tableau, SAS, and Crystal Ball. While words are good, examples can sometimes be a better teaching tool. For each example included, data files can be found on the companion website. Many of the data sets are tied to the global economy because they use data from shipping ports, air freight hubs, largest cities, and soccer teams. The appendices contain more detailed analysis including the 10 T's for Data Mining, Million Row Data Audit (MRDA) Processes, Analysis of Rainfall, and Simulation Models for Evaluating Traffic Flow.
Описание: Features innovative research and implementation practices of analytics in marketing research. Highlighting various techniques in acquiring and deciphering marketing data, this publication is a pivotal reference for professionals, managers, market researchers, and practitioners interested in the observation and utilization of data on marketing trends to promote positive business practices.
Описание: Chapter 1. Secure Multimodal Access with 2D and 3D Ears.- Chapter 2. Efficient and Low Overhead Detection of Brain Diseases using Deep Learning based Sparse MRI Image Classification.- Chapter 3. Continual Deep Learning Framework for Medical Media Screening and Archival.- Chapter 4. KannadaRes-NeXt: a Deep Residual Network for Kannada Numeral Recognition.- Chapter 5. Secure Image Transmission in Wireless Network using Conventional Neural Network and DOST.- Chapter 6. Robust General Twin Support Vector Machine with Pinball Loss Function.- Chapter 7. Noise Resilient Thresholding based on Fuzzy Logic and Non-linear Filtering.- Chapter 8. Deep Learning Methods for Audio Events Detection.- Chapter 9. A Framework for Multi-lingual Scene Text Detection using K-means++ and Memetic Algorithms.- Chapter 10. Recent Advancements in Medical Imaging: A Machine Learning Approach.- Chapter 11. Solving Image Processing Critical Problems using Machine Learning.- Chapter 12. Spoken Language Identification of Indian Languages using MFCC Features.- Chapter 13. Performance Evaluation of One-Class Classifiers (OCC) for Damage Detection in Structural Health Monitoring.- Chapter 14. Brain Tumor Classification in MRI Images using Transfer Learning.- Chapter 15. Semantic based Vectorization Technique for Hindi Language.
Автор: Gunter Wallner Название: Data Analytics Applications in Gaming and Entertainment ISBN: 1138104434 ISBN-13(EAN): 9781138104433 Издательство: Taylor&Francis Рейтинг: Цена: 16078.00 р. Наличие на складе: Поставка под заказ.
Описание: Over the last decade big data and data mining has received growing interest and importance in game production to process and draw actionable insights from large volumes of player-related data in order to inform game design, to ensure customer satisfaction, to maximize revenues, and to drive technical innovation.
Автор: Mohammed M. Alani; Hissam Tawfik; Mohammed Saeed; Название: Applications of Big Data Analytics ISBN: 3030094979 ISBN-13(EAN): 9783030094973 Издательство: Springer Рейтинг: Цена: 13275.00 р. Наличие на складе: Поставка под заказ.
Описание: 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 slicingExplores methods that use big data in the assessment of flood risks, and apply neural networks techniques to monitor the safety of nuclear power plantsDescribes a system which leverages big data analytics and the Internet of Things in the application of drones to aid victims in disaster scenariosProposes a novel deep learning-based health data analytics application for sleep apnea detection, and a novel pathway for diagnostic models of headache disordersReviews techniques for educational data mining and learning analytics, and introduces a scalable MapReduce graph partitioning approach for high degree verticesPresents a multivariate and dynamic data representation model for the visualization of healthcare data, and big data analytics methods for software reliability assessmentThis 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.
Автор: C.S.R. Prabhu; Aneesh Sreevallabh Chivukula; Adity Название: Big Data Analytics: Systems, Algorithms, Applications ISBN: 9811500932 ISBN-13(EAN): 9789811500930 Издательство: Springer Рейтинг: Цена: 9083.00 р. Наличие на складе: Поставка под заказ.
Описание:
This book provides a comprehensive survey of techniques, technologies and applications of Big Data and its analysis. The Big Data phenomenon is increasingly impacting all sectors of business and industry, producing an emerging new information ecosystem. On the applications front, the book offers detailed descriptions of various application areas for Big Data Analytics in the important domains of Social Semantic Web Mining, Banking and Financial Services, Capital Markets, Insurance, Advertisement, Recommendation Systems, Bio-Informatics, the IoT and Fog Computing, before delving into issues of security and privacy.
With regard to machine learning techniques, the book presents all the standard algorithms for learning – including supervised, semi-supervised and unsupervised techniques such as clustering and reinforcement learning techniques to perform collective Deep Learning. Multi-layered and nonlinear learning for Big Data are also covered.
In turn, the book highlights real-life case studies on successful implementations of Big Data Analytics at large IT companies such as Google, Facebook, LinkedIn and Microsoft. Multi-sectorial case studies on domain-based companies such as Deutsche Bank, the power provider Opower, Delta Airlines and a Chinese City Transportation application represent a valuable addition.
Given its comprehensive coverage of Big Data Analytics, the book offers a unique resource for undergraduate and graduate students, researchers, educators and IT professionals alike.
Автор: Thuraisingham, Bhavani Parveen, Pallabi Masud, Mohammad Mehedy Khan, Latifur Название: Big data analytics with applications in insider threat detection ISBN: 0367657422 ISBN-13(EAN): 9780367657420 Издательство: Taylor&Francis Рейтинг: Цена: 6889.00 р. Наличие на складе: Поставка под заказ.
Описание: Antivirus software uses algorithms to detect viruses Reactively adaptive malware deploys those algorithms to outwit antivirus defenses and to go undetected. This book provides details of the tools, the types of malware the tools will detect, implementation of the tools in a cloud framework, and the applications for insider threat detection.
Описание: The accepted chapters covered many themes including artificial intelligence and data mining applications, machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and security with big data.
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