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Books on the topic 'Data-driven techniques'

Author: Grafiati

Published: 30 November 2024

Last updated: 19 July 2025

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1

Damper, Robert I., ed. Data-Driven Techniques in Speech Synthesis. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-3413-3.

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2

Damper, R. I. Data-Driven Techniques in Speech Synthesis. Springer US, 2001.

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3

I, Damper R., ed. Data-driven techniques in speech synthesis. Kluwer Academic Publishers, 2001.

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4

Si, Xiao-Sheng, Zheng-Xin Zhang, and Chang-Hua Hu. Data-Driven Remaining Useful Life Prognosis Techniques. Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-54030-5.

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5

Kumar Rana, Arun, Sumit Kumar Rana, Vishnu Sharma, and Ritu Dewan. Intelligent Data-Driven Techniques for Security of Digital Assets. CRC Press, 2025. https://doi.org/10.1201/9781003590439.

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6

P, Ford Michael, and Erekson James A, eds. Accessible assessment: How 9 sensible techniques can power data-driven reading instruction. Heinemann, 2011.

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7

W, Stoughton John, Mielke Roland R, and Langley Research Center, eds. Strategies for concurrent processing of complex algorithms in data driven architectures. National Aeronautics and Space Administration, Langley Research Center, 1990.

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8

Data-Driven Techniques in Speech Synthesis. Island Press, 2001.

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9

Winston, Wayne L. Marketing Analytics: Data-Driven Techniques with Microsoft Excel. Wiley & Sons, Incorporated, John, 2014.

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10

Winston, Wayne L. Marketing Analytics: Data-Driven Techniques with Microsoft Excel. Wiley & Sons, Incorporated, John, 2014.

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11

Winston, Wayne L. Marketing Analytics: Data-Driven Techniques with Microsoft Excel. Wiley & Sons, Incorporated, John, 2014.

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12

Marketing Analytics: Data-Driven Techniques with Microsoft Excel. Wiley, 2014.

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13

Tools and Techniques for Effective Data-Driven Decision Making. ScarecrowEducation, 2004.

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14

Tripathy, Rajesh Kumar. Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing. Elsevier Science & Technology Books, 2024.

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15

Tripathy, Rajesh Kumar. Signal Processing Driven Machine Learning Techniques for Cardiovascular Data Processing. Elsevier Science & Technology, 2024.

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16

Miller, Keith Lawrence. Career Code: Data-Driven Strategies to Achieve Job Search Success. Ivy League Resumes, LLC, 2023.

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17

Si, Xiao-Sheng, Zheng-Xin Zhang, and Chang-Hua Hu. Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications. Springer, 2017.

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18

Si, Xiao-Sheng. Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications. Springer, 2018.

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19

Hachmann, Andreas. Machine Learning and Data-Driven Research in Chemistry: Concepts, Techniques, and Applications. Wiley & Sons, Incorporated, John, 2024.

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20

Hachmann, Andreas. Machine Learning and Data-Driven Research in Chemistry: Concepts, Techniques, and Applications. Wiley & Sons, Incorporated, John, 2024.

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21

Hachmann, Andreas. Machine Learning and Data-Driven Research in Chemistry: Concepts, Techniques, and Applications. Wiley & Sons, Incorporated, John, 2024.

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22

Safdar, Mutahar, Guy Lamouche, and Gentry Wood. Engineering of Additive Manufacturing Features for Data-Driven Solutions: Sources, Techniques, Pipelines, and Applications. Springer, 2023.

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23

Mirza-Babaei, Pejman, and Thomas Galati. Affordable and data-driven user research for indie studios. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198794844.003.0021.

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User testing games does not have to be expensive and time-consuming. In this chapter we focus on the specific situation where the resources, expertise, and time available for user testing is limited, as is usually the case for indie studios. This means adapting affordable and accessible user testing processes for indie studios. We emphasize the contribution of analytics techniques in this adaptation process, and describe different methods of how to incorporate analytics into other Games User Research methods.

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24

Ghonge, Mangesh M. Predictive Analytics for Data Driven Decision Making: Tools and Techniques For Solving Real World Problems. Nova Science Publishers, Incorporated, 2022.

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25

Predictive Analytics for Data Driven Decision Making - Tools and Techniques for Solving Real World Problems. Nova Science Publishers, Incorporated, 2022.

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26

Rivera, Keanna. Your Life in Numbers: How to Make Data-Driven Decisions That Will Lead You to Your Best and Happiest Self. Publishing, Self, 2023.

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27

Rivera, Keanna. Your Life in Numbers: How to Make Data-Driven Decisions That Will Lead You to Your Best and Happiest Self. Publishing, Self, 2023.

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28

Perez, Gerald Augusto Corzo. Hybrid Models for Hydrological Forecasting : Integration of Data-Driven and Conceptual Modelling Techniques: UNESCO-IHE PhD Thesis. Taylor & Francis Group, 2017.

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29

Hybrid Models for Hydrological Forecasting : Integration of Data-Driven and Conceptual Modelling Techniques: UNESCO-IHE PhD Thesis. CRC Press LLC, 2012.

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30

Kumar, L. Ashok. Predictive Analytics for Data Driven Decision Making - Tools and Techniques for Solving Real World Problems: Tools and Techniques for Solving Real World Problems. Nova Science Publishers, Incorporated, 2022.

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31

Decker, Marita. Outsmart Doubt: The Data-Driven Analysis of Confidence Erosion for Women at Work, and Breakthrough Techniques for Sustainable Career Confidence. Palmetto Publishing, 2023.

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32

Decker, Marita. Outsmart Doubt: The Data-Driven Analysis of Confidence Erosion for Women at Work, and Breakthrough Techniques for Sustainable Career Confidence. Palmetto Publishing, 2023.

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33

Decker, Marita. Outsmart Doubt: The Data-Driven Analysis of Confidence Erosion for Women at Work, and Breakthrough Techniques for Sustainable Career Confidence. Palmetto Publishing, 2023.

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34

Martens, David. Data Science Ethics. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192847263.001.0001.

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Data science ethics is all about what is right and wrong when conducting data science. Data science has so far mainly been used for positive outcomes for businesses and society. However, just as with any technology, data science has also come with some negative consequences: an increase of privacy invasion, data-driven discrimination against sensitive groups, and decision making by complex models without explanations. This book looks at the different concepts and techniques related to data science ethics. Data scientists and business managers are not inherently unethical, but at the same time

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35

Miksza, Peter, and Kenneth Elpus. Exploratory and Confirmatory Factor Analysis. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199391905.003.0013.

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This chapter consists of data-driven examples of how factor analysis as a statistical tool can be applied in music education research. The chapter presents examples of how factor analysis methods can be used to identify latent variables, which in turn can be used to represent a broad set of measured variables. Exploratory and confirmatory factor analysis techniques are compared and illustrated with data-driven examples. The examples highlight some of the major considerations and basic steps for performing factor analyses so that the reader can begin to imagine how to apply this technique to th

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36

Hilliges, Otmar. Input Recognition. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.003.0004.

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Sensing of user input lies at the core of HCI research. Deciding which input mechanisms to use and how to implement them such that they work in a way that is easy to use, robust to various environmental factors and accurate in reconstruction of the users intent is a tremendously challenging problem. The main difficulties stem from the complex nature of human behavior which is highly non-linear, dynamic and context dependent and can often only be observed partially. Due to these complexities, research has turned its attention to data-driven techniques in order to build sophisticated and robust

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37

Scaletti, Carla. Sonification ≠ Music. Edited by Roger T. Dean and Alex McLean. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190226992.013.9.

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Starting from the observation that symbolic language is not the only channel for human communication, this chapter examines ‘data sonification’, a means of understanding, reasoning about, and communicating meaning that extends beyond that which can be conveyed by symbolic language alone. Data sonification is a mapping from data generated by a model, captured in an experiment, or otherwise gathered through observation to one or more parameters of an audio signal or sound synthesis model for the purpose of better understanding, communicating, or reasoning about the original model, experiment, or

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38

Watson-Lakamp, Paula. Marketing Moxie for Librarians. ABC-CLIO, LLC, 2015. http://dx.doi.org/10.5040/9798400683039.

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Fun and easy to read, this marketing and promotion guide offers you the "big picture" of how best to spend your limited funds and energy to create a successful marketing strategy from traditional promotions such as advertising and posters to social media marketing. Robust, resilient, and flexible marketing is an absolute necessity for today's libraries. Fortunately, marketing can be fun. Through this savvy guide, you'll discover a wealth of fresh, actionable ideas and approaches that can be combined with tried-and-true marketing techniques to serve any library. Focusing on building platforms r

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39

Lee, Juneseok, and Jonathan Keck, eds. Embracing Analytics in the Drinking Water Industry. IWA Publishing, 2022. http://dx.doi.org/10.2166/9781789062380.

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Abstract Analytics can support numerous aspects of water industry planning, management, and operations. Given this wide range of touchpoints and applications, it is becoming increasingly imperative that the championship and capability of broad-based analytics needs to be developed and practically integrated to address the current and transitional challenges facing the drinking water industry. Analytics will contribute substantially to future efforts to provide innovative solutions that make the water industry more sustainable and resilient. The purpose of this book is to introduce analytics to

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40

Zabel, Diane, and Lauren Reiter, eds. Envisioning the Future of Reference. Libraries Unlimited, 2020. http://dx.doi.org/10.5040/9798400647024.

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Offering a broad overview of consequential changes in the landscape of reference services, this guide also provides practical guidance on how to meet the new challenges they present. For the past decade, librarians have been lamenting the demise of reference services. Encouraging recent research shows that reference librarians are actually in more demand than ever; however, nearly everything about reference has changed—from technologies, tools, and techniques to models of service. What are these changes, and how can the profession respond to and prepare for shifting priorities and user needs?

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41

Mueller-Hanson, Rose, Evan F. Sinar, and Elaine D. Pulakos, eds. Evolving the Employee Experience. Oxford University PressNew York, NY, 2025. https://doi.org/10.1093/9780197780251.001.0001.

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Abstract This book explores the evolving landscape of employee experience and leadership in the face of unprecedented global disruption. Drawing on firsthand research and case studies, it examines how organizations are adapting to meet changing employee needs and expectations in the new world of work. The volume is structured around three interconnected themes: Expanding the Employee Experience, Inclusive Talent Practices, and Developing Leaders for the Future. It offers insights into critical areas such as human connection, remote work, organizational agility, diversity and inclusion, data-dr

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42

Bucy, Erik P., and Patrick Stewart. The Personalization of Campaigns: Nonverbal Cues in Presidential Debates. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228637.013.52.

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Nonverbal cues are important elements of persuasive communication whose influence in political debates are receiving renewed attention. Recent advances in political debate research have been driven by biologically grounded explanations of behavior that draw on evolutionary theory and view televised debates as contests for social dominance. The application of biobehavioral coding to televised presidential debates opens new vistas for investigating this time-honored campaign tradition by introducing a systematic and readily replicated analytical framework for documenting the unspoken signals tha

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