Relevant bibliographies by topics / Objective data

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Objective data'

Author: Grafiati
Published: 29 June 2021
Last updated: 29 July 2025

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Journal articles on the topic "Objective data"

1

Stix, Gary. "Objective Data." Scientific American 266, no. 3 (1992): 108. http://dx.doi.org/10.1038/scientificamerican0392-108.

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Trapp, R. Jeffrey, and Charles A. Doswell. "Radar Data Objective Analysis." Journal of Atmospheric and Oceanic Technology 17, no. 2 (2000): 105–20. http://dx.doi.org/10.1175/1520-0426(2000)017<0105:rdoa>2.0.co;2.

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Gray, P. W., T. D. Mac Mahon, and M. U. Rajput. "Objective data evaluation procedures." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 286, no. 3 (1990): 569–75. http://dx.doi.org/10.1016/0168-9002(90)90918-v.

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Carr, Edward L. "Objective Data Analysis Conference." Bulletin of the American Meteorological Society 68, no. 5 (1987): 481–85. http://dx.doi.org/10.1175/1520-0477-68.5.481.

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The Air Force Global Weather Central (AFGWC) hosted a conference on objective data analysis 2–4 October 1985. This was a continuation in a series of conferences on data analysis. Participants included both operational meteorologists and research meteorologists from various government's numerical weather prediction (NWP) facilities. The conference consisted of each participating facility reviewing their current status and future plans. Individual topics were then presented concerning data analysis, followed by small group discussions on meteorological and computational aspects of objective data
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A, Bharathi, Siva C, Suguna Angamuthu, and Thangamani M. "Multi Objective Concerned Network Coding Technique for Optimal Data Transmission." Webology 18, no. 05 (2021): 1191–203. http://dx.doi.org/10.14704/web/v18si05/web18300.

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The Internet of Things (IoT) seems to developed in the real-world scenario due to increased utilization of sensor driven technologies. There are various research works has been proposed earlier for the network coding to ensure the reliable data transmission. However, existing research techniques doesn’t focus on the reliable route path selection which might affect the rate of data transmission. These issues are focused in the proposed research method by introducing the method namely Multi-Objective concerned Network Coding Technique (MO-NCT). In this work initially multicast tree construction
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K. Holland, Erin, and Major Major L. King. "Sleep Studies Need Objective Data." Journal of Psychosocial Nursing and Mental Health Services 46, no. 2 (2008): 13–14. http://dx.doi.org/10.3928/02793695-20080201-07.

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Smith, G. D., and Y. Ben-Shlomo. "Objective data trials are needed." BMJ 312, no. 7044 (1996): 1479–80. http://dx.doi.org/10.1136/bmj.312.7044.1479c.

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OBAYASHI, Shigeru. "Multi-Objective Optimization and Data Mining." Journal of the Society of Mechanical Engineers 109, no. 1050 (2006): 383–85. http://dx.doi.org/10.1299/jsmemag.109.1050_383.

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Huber, Jessica E., Elaine Stathopoulos, Joan Sussman, and Kris Tjaden. "Obtaining Objective Data in Clinical Settings." ASHA Leader 15, no. 12 (2010): 12–15. http://dx.doi.org/10.1044/leader.ftr2.15122010.12.

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M’lan, Cyr Emile, and Ming-Hui Chen. "Objective Bayesian Inference for Bilateral Data." Bayesian Analysis 10, no. 1 (2015): 139–70. http://dx.doi.org/10.1214/14-ba890.

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Dissertations / Theses on the topic "Objective data"

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Kwoh, Chee Keong. "Probabilistic reasoning from correlated objective data." Thesis, Imperial College London, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307686.

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Kirkland, Oliver. "Multi-objective evolutionary algorithms for data clustering." Thesis, University of East Anglia, 2014. https://ueaeprints.uea.ac.uk/51331/.

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In this work we investigate the use of Multi-Objective metaheuristics for the data-mining task of clustering. We �first investigate methods of evaluating the quality of clustering solutions, we then propose a new Multi-Objective clustering algorithm driven by multiple measures of cluster quality and then perform investigations into the performance of different Multi-Objective clustering algorithms. In the context of clustering, a robust measure for evaluating clustering solutions is an important component of an algorithm. These Cluster Quality Measures (CQMs) should rely solely on the structur
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PERRA, SILVIA. "Objective bayesian variable selection for censored data." Doctoral thesis, Università degli Studi di Cagliari, 2013. http://hdl.handle.net/11584/266108.

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In this thesis we study the problem of selecting a set of regressors when the response variable follows a parametric model (such as Weibull or lognormal) and observations are right censored. Under a Bayesian approach, the most widely used tools are the Bayes Factors (BFs) which are, however, undefined when using improper priors. Some commonly used tools in literature, which solve the problem of indeterminacy in model selection, are the Intrinsic Bayes factor (IBF) and the Fractional Bayes factor (FBF). The two proposals are not actual Bayes factors but it can be shown that they asymptoticall
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Fieldsend, Jonathan E. "Novel algorithms for multi-objective search and their application in multi-objective evolutionary neural network training." Thesis, University of Exeter, 2003. http://hdl.handle.net/10871/11706.

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Brown, Nathan C. (Nathan Collin). "Early building design using multi-objective data approaches." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123573.

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Thesis: Ph. D. in Architecture: Building Technology, Massachusetts Institute of Technology, Department of Architecture, 2019<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 201-219).<br>During the design process in architecture, building performance and human experience are increasingly understood through computation. Within this context, this dissertation considers how data science and interactive optimization techniques can be combined to make simulation a more effective component of a natural early design process. It focuses on conceptual design, since
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Mostaghim, Sanaz. "Multi-objective evolutionary algorithms data structures, convergence, and diversity /." [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=974405604.

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Furst, Séverine. "Multi-objective optimization for joint inversion of geodetic data." Thesis, Montpellier, 2018. http://www.theses.fr/2018MONTS017/document.

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La surface terrestre est affectée par de nombreux processus locaux tels que des événements volcaniques, des glissements de terrain ou des tremblements de terre. Parallèlement à ces processus naturels, les activités anthropiques, y compris l’extraction et le stockage des ressources profondes (par exemple, les minéraux ou les hydrocarbures) façonnent la Terre à différentes échelles spatiales et temporelles. Ces mécanismes produisent une déformation du sol qui peut être détectée par divers instruments et techniques géodésiques tel que le GNSS, l’InSAR, les inclinomètres. Le but de cette thèse est
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Fan, Qi. "Multi-Objective Optimization for Data Analytics in the Cloud." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAX069.

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Le traitement des requêtes Big Data est devenu de plus en plus important, ce qui a conduit au développement et au déploiement dans le cloud de nombreux systèmes. Cependant, le réglage automatique des nombreux paramètres de ces systèmes Big Data introduit une complexité croissante pour répondre aux objectifs de performance et aux contraintes budgétaires des utilisateurs. La détermination des configurations optimales est un défi en raison de la nécessité de prendre en compte : 1) plusieurs objectifs de performances et contraintes budgétaires concurrents, tels qu'une faible latence et un faible c
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Ray, Subhasis. "Multi-objective optimization of an interior permanent magnet motor." Thesis, McGill University, 2008. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=116021.

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In recent years, due to growing environmental awareness regarding global warming, green cars, such as hybrid electric vehicles, have gained a lot of importance. With the decreasing cost of rare earth magnets, brushless permanent magnet motors, such as the Interior Permanent Magnet Motor, have found usage as part of the traction drive system in these types of vehicles. As a design issue, building a motor with a performance curve that suits both city and highway driving has been treated in this thesis as a multi-objective problem; matching specific points of the torque-speed curve to the desired
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Golchin, Maryam. "Bicluster Analysis of Biomedical Data based on Multi-objective Evolutionary Optimization." Thesis, Griffith University, 2018. http://hdl.handle.net/10072/376812.

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Knowledge discovery is the process of finding hidden knowledge from a large volume of data that involves data mining. Data mining unveils interesting relationships among data and the results can help to make valuable predictions or recommendation in various applications. Recently, biclustering has become a common method in data mining and pattern recognition. Biclustering is an unsupervised machine learning method that can uncover and extract accurate and useful information from high-dimensional sparse data. Biclustering has found many useful applications for visualization and exploratory anal
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Books on the topic "Objective data"

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Gleim, Irvin N. Auditing & EDP: Objective questions and explanations. 2nd ed. Accounting Publications, 1985.

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Lieberman, Elliot R. Multi-objective programming in the USSR. Academic, 1991.

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Gleim, Irvin N. Auditing & EDP: Objective questions and explanations. 4th ed. Gleim Publications, 1991.

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Gleim, Irvin N. Auditing & systems: Objective questions and explanations. 7th ed. Gleim Publications, 1997.

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Lieberman, Elliot R. Multi-objective programming in the USSR. Academic Press, 1991.

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Coello, Carlos Artemio Coello, Satchidananda Dehuri, and Susmita Ghosh, eds. Swarm Intelligence for Multi-objective Problems in Data Mining. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03625-5.

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Coello, Carlos A. Coello. Swarm intelligence for multi-objective problems in data mining. Springer Verlag, 2009.

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Coello, Carlos A. Coello. Advances in multi-objective nature inspired computing. Springer, 2010.

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Chakraborty, Sanjay, and Lopamudra Dey. Multi-objective, Multi-class and Multi-label Data Classification with Class Imbalance. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-9622-9.

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Franke, Richard H. Laplacian smoothing splines with generalized cross validation for objective analysis of meteorological data. Naval Postgraduate School, 1985.

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Book chapters on the topic "Objective data"

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Dovey, James, and Ash Furrow. "Data Management with Core Data." In Beginning Objective-C. Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-4369-4_8.

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Campbell, Matthew. "Core Data." In Objective-C Recipes. Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-4372-4_10.

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bin Uzayr, Sufyan. "Data Types." In Mastering Objective-C. CRC Press, 2023. http://dx.doi.org/10.1201/9781003358534-4.

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Lee, Keith. "Foundation Functions and Data Types." In Pro Objective-C. Apress, 2013. http://dx.doi.org/10.1007/978-1-4302-5051-7_13.

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Bennett, Gary, Brad Lees, and Mitchell Fisher. "Comparing Data." In Objective-C for Absolute Beginners. Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-1904-1_9.

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Kaczmarek, Stefan, Brad Lees, Gary Bennett, and Mitch Fisher. "Comparing Data." In Objective-C for Absolute Beginners. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3429-7_9.

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Bennett, Gary, Mitch Fisher, and Brad Lees. "Comparing Data." In Objective-C for Absolute Beginners. Apress, 2011. http://dx.doi.org/10.1007/978-1-4302-3654-2_9.

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Bennett, Gary, Mitch Fisher, and Brad Lees. "Comparing Data." In Objective-C for Absolute Beginners. Apress, 2010. http://dx.doi.org/10.1007/978-1-4302-2833-2_9.

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Dovey, James, and Ash Furrow. "Networking: Connections, Data, and the Cloud." In Beginning Objective-C. Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-4369-4_6.

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Mukhopadhyay, Anirban. "Incorporating Gene Ontology Information in Gene Expression Data Clustering Using Multiobjective Evolutionary Optimization: Application in Yeast Cell Cycle Data." In Multi-Objective Optimization. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1471-1_3.

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Conference papers on the topic "Objective data"

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Hua, Yukai, Binrong Liao, Hao Zhang, Liming Liu, Yuxuan Li, and Yongkuan Yang. "Optimal Scheduling of Microgrid Using Constrained Multi-Objective Optimization with Additional Objectives." In 2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS). IEEE, 2024. http://dx.doi.org/10.1109/docs63458.2024.10704436.

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Garofalo, Marco, Alessio Catalfamo, Mario Colosi, and Massimo Villari. "Federated Objective: Assessing Client Truthfulness in Federated Learning." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825652.

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Shih, Mulaine, Ray Secondo, Maggie Lankford, Peter Stevenson, Jonathan Slagle, and Eric S. Harper. "Physics-driven multi-objective optimization of multi-angle resonant metasurface beamsplitter." In AI and Optical Data Sciences VI, edited by Masaya Notomi and Tingyi Zhou. SPIE, 2025. https://doi.org/10.1117/12.3040708.

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Makropoulos, Athanasios, Dimitrios Gunopulos, Vana Kalogeraki, and Nikolaos Zygouras. "CONCERTO: Constrained Linear Multi-Objective Routing Path Optimization." In 2025 26th IEEE International Conference on Mobile Data Management (MDM). IEEE, 2025. https://doi.org/10.1109/mdm65600.2025.00051.

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Rossi, Alvise Dei, Davide Marzorati, Radoslava Švihrová, Michal Bechny, Silvia Santini, and Francesca Faraci. "Modeling Perceived Sleep Quality Through Objective and Subjective Data." In 2024 12th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW). IEEE, 2024. https://doi.org/10.1109/aciiw63320.2024.00046.

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Benouaret, Idir, Sihem Amer-Yahia, Christiane Kamdem-Kengne, and Jalil Chagraoui. "A Bi-Objective Approach for Product Recommendations." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006503.

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Itonaga, Makoto, Fumihiko Ito, and Toshiya Saito. "Analysis of chromatic aberration of single objective lens and correction of that of a NA=0.85 objective lens." In Optical Data Storage. OSA, 2003. http://dx.doi.org/10.1364/ods.2003.wa7.

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Burmester, G., and H. Rohler. "Objective-based Image Data Management." In Second EAGE Borehole Geology Workshop. EAGE Publications BV, 2017. http://dx.doi.org/10.3997/2214-4609.201702393.

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Attaoui, Mohammed Oualid, Hanene Azzag, Mustapha Lebbah, and Nabil Keskes. "Multi-objective data stream clustering." In GECCO '20: Genetic and Evolutionary Computation Conference. ACM, 2020. http://dx.doi.org/10.1145/3377929.3389930.

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Enouen, Eric, Katja Mathesius, Sean Wang, Arielle Carr, and Sihong Xie. "Efficient Multiple Objective Optimization for Fair Misinformation Detection." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020909.

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Reports on the topic "Objective data"

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Hunt, J. W. Tank safety screening data quality objective. Revision 1. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/61107.

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Gydesen, S. Status of document search and data quality objective efforts. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/10107852.

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Gydesen, S. Status of document search and data quality objective efforts. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/6992873.

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Irvine, Nelson. Analysis of the Objective Data From Fleet Battle Experiment Hotel. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada389211.

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Irvine, Nelson. Objective Data from Fleet Battle Experiment Foxtrot, Golf, and Hotel. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada389356.

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Gates, C. M., and M. R. Beckette. Identification of physical properties for the retrieval data quality objective process. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/86993.

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Tung, S. L. Users Guide for Normal Mode Objective Analysis of Global Data Assimilation,. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada160373.

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Meacham, J. E. Data quality objective to support resolution of the organic solvent safety issue. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/341237.

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Kirkbride, R. A. Technical work plan for the privatization waste characterization data quality objective process. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/341255.

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Mulkey, C. H. ,. Westinghouse Hanford. Data quality objective for regulatory requirements for dangerous waste sampling and analysis. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/659219.

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