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Books on the topic 'Data-driven techniques'

Author: Grafiati
Published: 30 November 2024

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1

Damper, Robert I., ed. Data-Driven Techniques in Speech Synthesis. Boston, MA: 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. Boston, MA: Springer US, 2001.

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3

I, Damper R., ed. Data-driven techniques in speech synthesis. Boston: 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. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-54030-5.

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5

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

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6

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

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7

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

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8

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

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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

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

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12

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

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13

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

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14

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

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15

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

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16

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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17

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

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18

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

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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

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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22

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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23

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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24

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

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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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

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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28

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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29

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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30

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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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

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 not trained to think this through either. This book aims to address this important gap. The techniques discussed range from k-anonymity and differential privacy to homomorphic encryption and zero-knowledge proofs to address privacy concerns, measurements to assess, and techniques to remove discrimination against sensitive groups, and various explainable AI techniques. The real-life cautionary tales further illustrate the importance and potential impact of data science ethics, including tales of racist bots, search censoring, government backdoors, discrimination in recruitment, predicting pregnancy, redlining, re-identification of persons based on movie viewing and location data, cheating academics, and face recognition. Additionally, 10 discussions are provided with hypothetical scenarios, for example:‘you are the founder of a startup …’, which then present an ethical dilemma related to data science. These can serve as structured exercises to be completed with your fellow colleagues, and will teach you how to balance the ethical concerns and the utility of your data.
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34

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 their own research questions.
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35

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 input recognition mechanisms. In this chapter we discuss the most important aspects that constitute data-driven signal analysis approaches. The aim is to provide the reader with an overall understanding of the process irrespective of the exact choice of sensor or machine learning algorithm.
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36

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 system. Although data sonification shares techniques and materials with data-driven music, it is in the interests of the practitioners of both music composition and data sonification to maintain a distinction between the two fields.
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37

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 rather than chasing trends, the book offers low- and no-budget ideas for those in small libraries as well as information that can be used by libraries that have a staff of professionals. The guide opens with an overview of the basics of marketing and continues through the numerous channels that should be incorporated into a modern-day marketing strategy mix. Branding, merchandising, and media relations are covered, as are social media, new technologies, fundraising, and advocacy. You'll also learn how to use tools such as data-driven information gathering and email segmentation to help your library compete and stay relevant. Perfect for beginners, the book will be equally useful to seasoned communicators who are looking for creative ideas, new techniques, and innovative approaches to boost the effectiveness of their existing marketing efforts.
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38

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 practicing water engineers so they can deploy the covered subjects, approaches, and detailed techniques in their daily operations, management, and decision-making processes. Also, undergraduate students as well as early graduate students who are in the water concentrations will be exposed to established analytical techniques, along with many methods that are currently considered to be new or emerging/maturing. This book covers a broad spectrum of water industry analytics topics in an easy-to-follow manner. The overall background and contexts are motivated by (and directly drawn from) actual water utility projects that the authors have worked on numerous recent years. The authors strongly believe that the water industry should embrace and integrate data-driven fundamentals and methods into their daily operations and decision-making process(es) to replace established “rule-of-thumb” and weak heuristic approaches – and an analytics viewpoint, approach, and culture is key to this industry transformation. ISBN: 9781789062373 (paperback) ISBN: 9781789062380 (eBook) ISBN: 9781789062397 (ePub)
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39

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? In this volume, business librarians Diane Zabel and Lauren Reiter bring together a host of experts to answer these timely questions. Topics range from the education and training of professionals to meeting the needs and wants of employers. Covered are trends in chat reference, research consultations, do-it-yourself reference, tracking trends with user populations, assessment, and data-driven decisions about reference services. Grounded in the principle that, regardless of the evolutions in service, the user remains at the center of reference, this guide offers readers an exciting look at the future of this important public service.
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40

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 that are a continuous feature of competitive candidate encounters. As research utilizing biobehavioral measures of presidential debates and other political communication progresses, studies are becoming increasingly characterized by the use of multiple methodologies and merging of disparate data into combined systems of coding that support predictive modeling.Key elements of nonverbal persuasion include candidate appearance, communication style and behavior, as well as gender dynamics that regulate candidate interactions. Together, the use of facial expressions, voice tone, and bodily gestures form uniquely identifiable display repertoires that candidates perform within televised debate settings. Also at play are social and political norms that govern candidate encounters. From an evaluative standpoint, the visual equivalent of a verbal gaffe is the commission of a nonverbal expectancy violation, which draws viewer attention and interferes with information intake. Through second screens, viewers are able to register their reactions to candidate behavior in real time, and merging biobehavioral and social media approaches to debate effects is showing how such activity can be used as an outcome measure to assess the efficacy of candidate nonverbal communication during televised presidential debates.Methodological approaches employed to investigate nonverbal cues in presidential debates have expanded well beyond the time-honored technique of content analysis to include lab experiments, focus groups, continuous response measurement, eye tracking, vocalic analysis, biobehavioral coding, and use of the Facial Action Coding System to document the muscle movements that comprise leader expressions. Given the tradeoffs and myriad considerations involved in analyzing nonverbal cues, critical issues in measurement and methodology must be addressed when conducting research in this evolving area. With automated coding of nonverbal behavior just around the corner, future research should be designed to take advantage of the growing number of methodological advances in this rapidly evolving area of political communication research.
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