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Books on the topic 'FOV PREDICTION'

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Published: 11 September 2023

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1

Houston, Walter. Central prediction systems for predicting specific course grades. Iowa City: American College Testing Program, 1988.

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2

Predicting Prehistory: Predictive models and field research methods for detecting prehistoric contexts. Firenze: Museo e istituto fiorentino di preistoria "Paolo Graziosi,", 2015.

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3

Cherdanceva, Tat'yana, Vladimir Klimechev, and Igor' Bobrov. Pathological and molecular biological analysis of renal cell carcinoma. Diagnosis and prognosis. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1020785.

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The monograph is devoted to the study of pathomorphological and molecular-biological characteristics of renal cell carcinoma and peritumoral zone depending on the degree of malignancy, and determine prognostic significance of criteria for predicting the postoperative survival of patients. Of interest to urologists, oncologists, pathologists, researchers, graduate students, dealing with the diagnosis of renal cell carcinoma and subsequent prediction of postoperative survival of patients.
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4

Altmisdort, F. Nadir. Development of a new prediction algorithm and a simulator for the Predictive Read Cache (PRC). Monterey, Calif: Naval Postgraduate School, 1996.

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5

Casey, Douglas R. Predictions for 1988. 2nd ed. Alexandria, VA: KCI Communications, 1988.

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6

United States. National Weather Service, ed. National Centers for Environmental Prediction. [Silver Spring, Md.?]: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Weather Service, 1996.

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7

Vasil'eva, Natal'ya. Mathematical models in the management of copper production: ideas, methods, examples. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1014071.

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Presents the current status in modelling of metallurgical processes considered by the model the mathematical model used in the description of the processes of copper production and their classification. Set out a system of methods and models in the field of mathematical modeling of technological processes, including balance sheet, statistics, optimization models, forecasting models and predictive models. For specific technological processes are developed: the model of the balance of the cycle of pyrometallurgical production of copper, polynomial model for prediction of matte composition on the basis of the passive experiment, predictive model of quantitative estimation of the copper content in the matte based on fuzzy logic. Of interest to students, postgraduates, teachers of technical universities, engineers and research workers who use mathematical methods for processing of data of laboratory and industrial experiments.
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8

Rathore, Santosh Singh, and Sandeep Kumar. Fault Prediction Modeling for the Prediction of Number of Software Faults. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7131-8.

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9

Hanson, R. Karl. Prediction statistics for psychological assessment. Washington: American Psychological Association, 2022. http://dx.doi.org/10.1037/0000275-000.

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10

Bolfarine, Heleno, and Shelemyahu Zacks. Prediction Theory for Finite Populations. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2904-9.

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11

Majoros, William H. Methods for computational gene prediction. Cambridge: Cambridge University Press, 2007.

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12

Manski, Charles F. Identification for prediction and decision. United States: Harvard U Pr, 2007.

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13

Dailey, Daniel J. Automata model for congestion prediction. Olympia, Wash: Washington Dept. of Transportation, 2003.

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14

United States. National Weather Service., ed. National Centers for Environmental Prediction. [Silver Spring, Md.?]: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Weather Service, 1996.

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15

Auerbach, Jonathan Lyle. Some Statistical Models for Prediction. [New York, N.Y.?]: [publisher not identified], 2020.

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16

Bolfarine, Heleno. Prediction theory for finite populations. New York: Springer-Verlag, 1992.

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17

Methods for computational gene prediction. Cambridge: Cambridge University Press, 2007.

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18

Olson, David. Data Mining for Bankruptcy Prediction. 1 Oliver’s Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications Ltd, 2023. http://dx.doi.org/10.4135/9781529668322.

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19

Meyers, Kenneth H. Documentation of the basis for selection of prediction methods for organometallic compounds in Manual for predicting chemical process design data. New York: American Institute of Chemical Engineers, 1991.

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20

Håkanson, Lars. Predictive limnology: Methods for predictive modelling. Amsterdam: SPB Academic, 1995.

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21

Kevin, Browne, Davies Cliff, Stratton Peter, and Society for Reproductive and Infant Psychology (Great Britain), eds. Early prediction and prevention of child abuse. Chichester: Wiley, 1988.

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22

Five scenarios for the year 2000. New York: Greenwood Press, 1986.

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23

service), SpringerLink (Online, ed. Designing Quantitative Experiments: Prediction Analysis. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2010.

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24

Kirchsteiger, Harald, John Bagterp Jørgensen, Eric Renard, and Luigi del Re, eds. Prediction Methods for Blood Glucose Concentration. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-25913-0.

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25

Bhattacharjee, Shrutilipi, Soumya Kanti Ghosh, and Jia Chen. Semantic Kriging for Spatio-temporal Prediction. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8664-0.

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26

Feldbauer, Roman. Machine Learning for Microbial Phenotype Prediction. Wiesbaden: Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-14319-0.

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27

Kihara, Daisuke, ed. Protein Function Prediction for Omics Era. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-0881-5.

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28

K. Halgamuge, Saman, and Lipo Wang, eds. Computational Intelligence for Modelling and Prediction. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/b93960.

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29

Smith, Richard E. Genetic algorithms for share price prediction. Manchester: University of Manchester, Department of Computer Science, 1997.

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30

McGrath, M. Neural networks for financial performance prediction. Dublin: University CollegeDublin, 1995.

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31

Krishnamoorthy, Srikumar. Novel features for review helpfulness prediction. Ahmedabad: Indian Institute of Management, 2014.

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32

Laboratory, Los Alamos National, ed. Science-based prediction for complex systems. Los Alamos, NM: Los Alamos National Laboratory, 2005.

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33

Walree, F. van. Resistance prediction method for hydrofoil craft. Delft: Delft University of Technology, Ship Hydromechanics Laboratory, 1985.

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34

Tyl, Noel. Predictions for a new millennium. St. Paul, Minn: Llewellyn Publications, 1996.

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35

Birch, Jonathan. The Rule under Attack. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198733058.003.0003.

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HRG has been criticized for being an ‘empty statement’ or tautology, for failing to yield predictions, and for failing to yield causal explanations of change. There is some justification for these charges, yet they do not undermine the value of HRG as an organizing framework. In response to the ‘tautology’ complaint, we should admit that HRG is tautology-like, in that it avoids detailed dynamical assumptions. But this is an advantage in an organizing framework, because it ensures its compatibility with a wide range of more detailed models. In response to the ‘prediction’ complaint, we should concede that HRG is not very useful for prediction, but the role of an organizing framework is not predictive. In response to the ‘causal explanation’ complaint, this chapter argues that HRG, by organizing our thinking about ultimate causes, generates understanding of those causes. It also compares favourably to other possible organizing frameworks.
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36

Anjum, Rani Lill, and Stephen Mumford. Risky Predictions. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198733669.003.0022.

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Prediction is often seen as explanation’s poor relation. Yet prediction can be more useful than explanation because it is often future-directed and a reason for action. Predictions can still be useful even with a degree of fallibility, which is important since there is no necessity in nature upon which infallible prediction could rest. Indeed, it is an evident weakness of any theory of prediction if it cannot account for the possibility that the very best theory, given the evidence, could still be false.
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37

Lockwood, Jonathan S. Lockwood Analytical Method for Prediction: A Method for Predictive Intelligence Analysis. Bloomsbury Academic & Professional, 2013.

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38

Gould, JS, WL McCaw, NP Cheney, PF Ellis, and S. Matthews. Field Guide: Fire in Dry Eucalypt Forest. CSIRO Publishing, 2008. http://dx.doi.org/10.1071/9780643101289.

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An effective response to bushfires relies on accurate predictions of fire behaviour, particularly the rate of spread, intensity and ‘spotting’. This field guide has been developed to provide a systematic method for assessing fuel hazard and predicting potential fire behaviour in dry eucalypt forest. It will assist in making vital decisions that ensure the protection of fire crews and the community. This guide integrates Project Vesta research findings with the Victorian Overall Fuel Hazard Guide and is applicable to dry eucalypt forests throughout southern Australia. Fuel assessment is based on the hazard scoring system employed during Project Vesta which investigated the effects of fuel age and understorey vegetation structure on fire behaviour in these forests. Information provided in this guide can be used to: Define and identify different fuel layers and components of fuel structure and hazard; Determine the hazard score of surface and near-surface fuel layers and the height of the near-surface fuel for fire spread prediction; Determine elevated fuel height for flame height prediction; and determine surface fuel hazard score and bark hazard score for spotting distance prediction. The Field Guide provides tables to predict the potential rate of spread of a bushfire burning in dry eucalypt forest under summer conditions, and can also be used to predict flame height and maximum spotting distance. The guide also allows users to determine the moisture content of fine dead fuels throughout the day, and to account for the effect of slope on the rate of spread of a fire.
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39

Trainor, Laurel J., and Robert J. Zatorre. The neurobiological basis of musical expectations. Edited by Susan Hallam, Ian Cross, and Michael Thaut. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199298457.013.0016.

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This article explores how the auditory system processes incoming information and generates perceptual representations that allow it to make predictions about future sound events from past context, and how music appears to make use of this general processing mechanism. It focuses on expectation formation in auditory cortex because this is where the most research has been done, but there is also evidence for prediction mechanisms at subcortical levels and at levels beyond sensory areas. The article presents a framework for thinking about the neurological basis of expectation and prediction in musical processing using selected examples.
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40

The Lockwood Analytical Method For Prediction Lamp A Method For Predictive Intelligence Analysis. Continuum Publishing Corporation, 2013.

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41

Blanc, Marc, Talib Esmail, Caroline Mascarell, and Rukshan Rodriguez. Predicting Project Outcomes: A Simple Methodology for Predictions Based on Project Ratings. World Bank, Washington, DC, 2016. http://dx.doi.org/10.1596/1813-9450-7800.

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42

Development of a New Prediction Algorithm and a Simulator for the Predictive Read Cache (PRC). Storming Media, 1996.

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43

Horn, Christian, Marcel Bogers, and Alexander Brem*. Prediction Markets for Crowdsourcing. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198816225.003.0012.

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Crowdsourcing is an increasingly important phenomenon that is fundamentally changing how companies create and capture value. There are still important questions with respect to how crowdsourcing works and can be applied in practice, especially in business practice. In this chapter, we focus on prediction markets as a mechanism and tool to tap into a crowd in the early stages of an innovation process. The act of opening up to external knowledge sources is also in line with the growing interest in open innovation. One example of a prediction market, a virtual stock market, is applied to open innovation through an online platform. We show that use of mechanisms of internal crowdsourcing with prediction markets can outperform use of external crowds.
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44

Rajchel, Elizabeth. Conceptual Design Reliability Prediction Method: Establishing Functional-Physical Reliability Relationships for System Reliability Predictions During Conceptual Design. Independently Published, 2021.

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45

Aircraft noise prediction program (ANOPP) fan noise prediction for small engines. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1996.

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46

Kumar, Sandeep, and Santosh Singh Rathore. Fault Prediction Modeling for the Prediction of Number of Software Faults. Springer, 2019.

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47

Ghosh, Soumya K., and Monidipa Das. Enhanced Bayesian Network Models for Spatial Time Series Prediction: Recent Research Trend in Data-Driven Predictive Analytics. Springer, 2020.

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48

Ghosh, Soumya K., and Monidipa Das. Enhanced Bayesian Network Models for Spatial Time Series Prediction: Recent Research Trend in Data-Driven Predictive Analytics. Springer, 2019.

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49

Majoros, William H. Methods for Computational Gene Prediction. Cambridge University Press, 2012.

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50

Manski, Charles F. Identification for Prediction and Decision. Harvard University Press, 2008. http://dx.doi.org/10.4159/9780674033665.

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