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Автор: Grafiati
Опубліковано: 11 вересня 2023

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Статті в журналах з теми "BASED DATA"

1

Gültepe, Yasemin. "Querying Bibliography Data Based on Linked Data." Journal of Software 10, no. 8 (August 2015): 1014–20. http://dx.doi.org/10.17706//jsw.10.8.1014-1020.

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2

N.J., Anjala. "Algorithmic Assessment of Text based Data Classification in Big Data Sets." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 1231–34. http://dx.doi.org/10.5373/jardcs/v12sp4/20201598.

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3

C, Ajitha, DivyaLakshmi K, and Jothi Jayashree M. "MicroRNA-Disease Predictions Based On Genomic Data." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1646–51. http://dx.doi.org/10.31142/ijtsrd11386.

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4

Mohite, Ms Nikita S., and Mr H. P. Khandagale. "Social Group Recommendation based on Big Data." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1118–21. http://dx.doi.org/10.31142/ijtsrd7097.

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5

Baidari, Dr Ishwar, and S. P. Sajjan. "Location Based Crime Detection Using Data Mining." Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (October 31, 2016): 208–12. http://dx.doi.org/10.9756/bijsesc.8279.

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6

K., Ashfaq Ahmed, and Dr Shaheda Akthar. "Ridge Regression based Missing Data Estimation with Dimensionality Reduction: Microarray Gene Expression Data." Webology 19, no. 1 (January 20, 2022): 4113–28. http://dx.doi.org/10.14704/web/v19i1/web19271.

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Анотація:
Data is considered to be the important element in the field of Data Science and Machine Learning. Performance of Machine Learning and Data Mining algorithms greatly influenced by the characteristics of data and data with missing values. Performance of all these Machine Learning algorithms greatly improved and they can give accurate results when the data is in full without missing values. So before applying these algorithms; dataset and its missing values are completely filled. To impute these missing values in the dataset there are numerous methods were proposed. In this paper we used micro array gene expression dataset; by introducing various percentages of missing values a new methodology is proposed to impute these missing values in the data set. The nature of micro array gene expression dataset is huge in dimensionality, so at first, we used recursive feature elimination method to select the best features which contributes much for model was selected then we apply the Ridge Regression for imputation. Imputations with other methods are compared. We evaluate the performance of all models by using the metrics like MSE, MAE, R-square. To select the best model in the set of models we used Normalized Criteria Distance (NCD) to rank the models under proposed metrics. The model with least NCD rank selected as the best model among other models, in our paper proposed model has got the lowest value among other models and considered to be the best model among other models.
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7

Pantos, C., M, Schroeder, and P. Ivanova. "Open Access to Quality Biomedical Experimental and Clinical Data and Data-based Models." Biomedical Data Journal 01, no. 1 (January 2015): 1–2. http://dx.doi.org/10.11610/bmdj.01100.

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8

Yong-Qiong Zhu, Yong-Qiong Zhu, Ye-Ming Cai Yong-Qiong Zhu, and Fan Zhang Ye-Ming Cai. "Motion Capture Data Denoising Based on LSTNet Autoencoder." 網際網路技術學刊 23, no. 1 (January 2022): 011–20. http://dx.doi.org/10.53106/160792642022012301002.

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<p>This paper proposes a novel deep learning-based optical motion capture denoising model encoder-LSTNet- decoder (ELD). ELD uses an autoencoder for manifold learning and decoder to remove jitter noise and missing noise effectively. It uses recurrent units in LSTNet to effectively obtain the spatial-temporal information of motion sequences, especially the periodic long-term and short-term features. In the denoising procedure, the kinetical characteristics of the motion are also considered so that the reconstructed deviation is smaller and can more accurately reflect the real action. We simulated ELD with the CMU database and compared it with the art-of-state methods. The experiment shows that ELD is a very effective denoising technique with lower reconstruction error, stronger robustness, and shorter running time.</p> <p>&nbsp;</p>
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9

S, Revathy, Sangavi A, Surekha U, Sushmitha R, and Nandhagopal N. "Li-Fi Based Data Transfer in Underwater System." SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 05, no. 04 (August 4, 2017): 08–10. http://dx.doi.org/10.9756/sijcsea/v5i4/05010190101.

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N, Rajkumar, Janhavi V, and Rajendra M.A.B. "Cryptography Policy-Based Data Communication in Trusted Environment." Indian Journal of Science and Technology 12, no. 48 (December 20, 2019): 11–4. http://dx.doi.org/10.17485/ijst/2019/v12i48/149385.

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Дисертації з теми "BASED DATA"

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Niggemann, Oliver. "Visual data mining of graph based data." [S.l. : s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=962400505.

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Li, Liangchun. "Web-based data visualization for data mining." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/MQ35845.pdf.

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Albarakati, Rayan. "Density Based Data Clustering." CSUSB ScholarWorks, 2015. https://scholarworks.lib.csusb.edu/etd/134.

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Анотація:
Data clustering is a data analysis technique that groups data based on a measure of similarity. When data is well clustered the similarities between the objects in the same group are high, while the similarities between objects in different groups are low. The data clustering technique is widely applied in a variety of areas such as bioinformatics, image segmentation and market research. This project conducted an in-depth study on data clustering with focus on density-based clustering methods. The latest density-based (CFSFDP) algorithm is based on the idea that cluster centers are characterized by a higher density than their neighbors and by a relatively larger distance from points with higher densities. This method has been examined, experimented, and improved. These methods (KNN-based, Gaussian Kernel-based and Iterative Gaussian Kernel-based) are applied in this project to improve (CFSFDP) density-based clustering. The methods are applied to four milestone datasets and the results are analyzed and compared.
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4

Garingo, Gary D. "JAVA based data connectivity." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1997. http://handle.dtic.mil/100.2/ADA342181.

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Анотація:
Thesis (M.S. in Software Engineering) Naval Postgraduate School, September 1997.
"September 1997." Thesis advisor(s): LuQi, V. Berzins. Includes bibliographical references (p. 63). Also available online.
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5

Young, G. A. "Data-based statistical methods." Thesis, University of Cambridge, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.383307.

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Jäkel, Tobias. "Role-based Data Management." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-224416.

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Анотація:
Database systems build an integral component of today’s software systems and as such they are the central point for storing and sharing a software system’s data while ensuring global data consistency at the same time. Introducing the primitives of roles and their accompanied metatype distinction in modeling and programming languages, results in a novel paradigm of designing, extending, and programming modern software systems. In detail, roles as modeling concept enable a separation of concerns within an entity. Along with its rigid core, an entity may acquire various roles in different contexts during its lifetime and thus, adapts its behavior and structure dynamically during runtime. Unfortunately, database systems, as important component and global consistency provider of such systems, do not keep pace with this trend. The absence of a metatype distinction, in terms of an entity’s separation of concerns, in the database system results in various problems for the software system in general, for the application developers, and finally for the database system itself. In case of relational database systems, these problems are concentrated under the term role-relational impedance mismatch. In particular, the whole software system is designed by using different semantics on various layers. In case of role-based software systems in combination with relational database systems this gap in semantics between applications and the database system increases dramatically. Consequently, the database system cannot directly represent the richer semantics of roles as well as the accompanied consistency constraints. These constraints have to be ensured by the applications and the database system loses its single point of truth characteristic in the software system. As the applications are in charge of guaranteeing global consistency, their development requires more effort in data management. Moreover, the software system’s data management is distributed over several layers, which results in an unstructured software system architecture. To overcome the role-relational impedance mismatch and bring the database system back in its rightful position as single point of truth in a software system, this thesis introduces the novel and tripartite RSQL approach. It combines a novel database model that represents the metatype distinction as first class citizen in a database system, an adapted query language on the database model’s basis, and finally a proper result representation. Precisely, RSQL’s logical database model introduces Dynamic Data Types, to directly represent the separation of concerns within an entity type on the schema level. On the instance level, the database model defines the notion of a Dynamic Tuple that combines an entity with the notion of roles and thus, allows for dynamic structure adaptations during runtime without changing an entity’s overall type. These definitions build the main data structures on which the database system operates. Moreover, formal operators connecting the query language statements with the database model data structures, complete the database model. The query language, as external database system interface, features an individual data definition, data manipulation, and data query language. Their statements directly represent the metatype distinction to address Dynamic Data Types and Dynamic Tuples, respectively. As a consequence of the novel data structures, the query processing of Dynamic Tuples is completely redesigned. As last piece for a complete database integration of a role-based notion and its accompanied metatype distinction, we specify the RSQL Result Net as result representation. It provides a novel result structure and features functionalities to navigate through query results. Finally, we evaluate all three RSQL components in comparison to a relational database system. This assessment clearly demonstrates the benefits of the roles concept’s full database integration.
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Ji, Yongnan. "Data-driven fMRI data analysis based on parcellation." Thesis, University of Nottingham, 2001. http://eprints.nottingham.ac.uk/12645/.

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Анотація:
Functional Magnetic Resonance Imaging (fMRI) is one of the most popular neuroimaging methods for investigating the activity of the human brain during cognitive tasks. As with many other neuroiroaging tools, the group analysis of fMRI data often requires a transformation of the individual datasets to a common stereotaxic space, where the different brains have a similar global shape and size. However, the local inaccuracy of this procedure gives rise to a series of issues including a lack of true anatomical correspondence and a loss of subject specific activations. Inter-subject parcellation of fMRI data has been proposed as a means to alleviate these problems. Within this frame, the inter-subject correspondence is achieved by isolating homologous functional parcels across individuals, rather than by matching voxels coordinates within a stereotaxic space. However, the large majority of parcellation methods still suffer from a number of shortcomings owing to their dependence on a general linear model. Indeed, for all its appeal, a GLM-based parcellation approach introduces its own biases in the form of a priori knowledge about such matters as the shape of the Hemodynamic Response Function (HRF) and taskrelated signal changes. In this thesis, we propose a model-free data-driven parcellation approach to singleand multi-subject parcellation. By modelling brain activation without an relying on an a priori model, parcellation is optimized for each individual subject. In order to establish correspondences of parcels across different subjects, we cast this problem as a multipartite graph partitioning task. Parcels are considered as the vertices of a weighted complete multipartite graph. Cross subject parcel matching becomes equivalent to partitioning this graph into disjoint cliques with one and only one parcel from each subject in each clique. In order to solve this NP-hard problem, we present three methods: the OBSA algorithm, a method with quadratic programming and an intuitive approach. We also introduce two quantitative measures of the quality of parcellation results. We apply our framework to two fMRI data sets and show that both our single- and multi-subject parcellation techniques rival or outperform model-based methods in terms of parcellation accuracy.
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8

Wang, Yi. "Data Management and Data Processing Support on Array-Based Scientific Data." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1436157356.

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Hong, Yili. "Reliability prediction based on complicated data and dynamic data." [Ames, Iowa : Iowa State University], 2009.

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10

Chepetan, Adrian. "Microcontroller based Data Acquisition System." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ62200.pdf.

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Книги з теми "BASED DATA"

1

Holcomb, Edie L. Data-based decision making. 3rd ed. Bloomington, IN: Solution Tree PRess, 2012.

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2

O'Hare, William P. Data-Based Child Advocacy. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07830-4.

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Holcomb, Edie L. Data-based decision making. 3rd ed. Bloomington, IN: Solution Tree PRess, 2012.

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4

Jandris, Thomas P. Data-based decision-making. Arlington, Va: National Association of Elementary School Principals, 2001.

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5

P, Jandris Thomas, ed. Data-based decision making. 2nd ed. Alexandria, VA: Educational Research Service, 2008.

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6

Garingo, Gary D. JAVA based data connectivity. Monterey, Calif: Naval Postgraduate School, 1997.

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7

Kaoudi, Zoi, Ioana Manolescu, and Stamatis Zampetakis. Cloud-Based RDF Data Management. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-031-01875-6.

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8

Calo, Seraphin, Elisa Bertino, and Dinesh Verma, eds. Policy-Based Autonomic Data Governance. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17277-0.

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9

Cirrincione, Giansalvo, and Maurizio Cirrincione. Neural-Based Orthogonal Data Fitting. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470638286.

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10

Quintarelli, Elisa. Model-Checking Based Data Retrieval. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/b94902.

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Частини книг з теми "BASED DATA"

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Grefenstette, Gregory, and Laura Wilber. "Data Processing." In Search-Based Applications, 45–56. Cham: Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-02274-6_6.

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2

Grefenstette, Gregory, and Laura Wilber. "Data Retrieval." In Search-Based Applications, 57–67. Cham: Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-02274-6_7.

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3

Grefenstette, Gregory, and Laura Wilber. "Data Collection/Population." In Search-Based Applications, 38–44. Cham: Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-02274-6_5.

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4

Hurter, Christophe. "Data Density Maps." In Image-Based Visualization, 23–38. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-031-02601-0_3.

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Stonelake, Nancy. "Component-Based Development." In Data Management, 491–99. 3rd ed. Boca Raton: Auerbach Publications, 2021. http://dx.doi.org/10.1201/9780429114878-45.

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Raheem, Nasir. "Data Virtualization." In Big DataA Tutorial-Based Approach, 115–36. First edition. | Boca Raton, FL : Taylor & Francis Group, [2019]: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429060939-9.

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Cheung, Bosco. "Server-Based Computing Architecture." In Data Management, 561–69. 3rd ed. Boca Raton: Auerbach Publications, 2021. http://dx.doi.org/10.1201/9780429114878-52.

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Hagen, H., H. Barthel, A. Ebert, and M. Bender. "Component-based Intelligent Visualization." In Data Visualization, 439–51. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-1177-9_30.

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Richter, Michael M., and Rosina O. Weber. "Sensor Data and Speech." In Case-Based Reasoning, 443–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40167-1_19.

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Grefenstette, Gregory, and Laura Wilber. "Data Models & Storage." In Search-Based Applications, 23–37. Cham: Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-02274-6_4.

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Тези доповідей конференцій з теми "BASED DATA"

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Vehkalahti, Kimmo. "Engaging everyone with open data science." In Decision Making Based on Data. International Association for Statistical Education, 2019. http://dx.doi.org/10.52041/srap.19419.

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Анотація:
Teaching of statistics should focus more on practical data science, with a special emphasis on data wrangling: Preparing the data for the analyses, looking at the data via clever visualizations, and learning the principles and practices of open science and reproducible research. The statistics curriculum should be updated and the term “data science” used as a synonym to statistics. In all possible fields, there is a huge need to have more data scientists. To engage everyone with “open data science” (open data, open science, and data science), we have created a new course, where students from all levels and fields work together and share their ideas with openly available data sets and freely available state-of-the-art software tools, such as RStudio, R Markdown, and GitHub. The new course has been quite successful in engaging extremely heterogeneous groups of students to challenge themselves to a “next level” by learning new skills of open data science.
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2

Frischemeier, Daniel. "Developing data competence in primary school." In Decision Making Based on Data. International Association for Statistical Education, 2019. http://dx.doi.org/10.52041/srap.19502.

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Анотація:
In the present Data Science era, competent data handling is inevitably and important to become a responsible citizen. Therefore the development of data competence should be realized as early as possible in the curriculum. Our main idea is to introduce primary school students into real statistical projects and to experience the phases, problem, plan, data, analysis and conclusions of the PPDAC cycle on their own. Especially comparing groups, an important activity in statistics, takes into account the application of many fundamental statistical ideas like distribution, representation or variability and even at primary school level students can be engaged in such activities by offering pre- stages for formal comparison concepts like center and spread.
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3

Ocampo, Shirlee, and Bladimir Ocampo. "Capacity building through project based learning in Bayesian statistics." In Decision Making Based on Data. International Association for Statistical Education, 2019. http://dx.doi.org/10.52041/srap.19411.

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Анотація:
Outcome-based education requires statistics education in the 21st century to be structured holistically by allowing the students to work with real life data along with visualization, computation, and learning outputs. The new K-12 curriculum resulted to a chain reaction in the Statistics undergraduate program by including alternative statistical frameworks such as Bayesian statistics. This paper focuses on project-based learning approach on designing learning outputs for undergraduate and graduate students in Bayesian statistics. The stages of project-based learning in completing these Bayesian learning outputs have helped in building the capacities of the students to understand the essential concepts in Bayesian inference and do computations using software. Some of the learning outputs are cited. Insights are helpful in making the syllabus of Bayesian Statistics and Inference of undergraduate Statistics program. Project based learning incapacitates the students to do Statistics research in an organized manner and make decisions based on data.
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4

Engel, Joachim, Tim Erickson, and Laura Martignon. "Teaching about decision trees for classification problems." In Decision Making Based on Data. International Association for Statistical Education, 2019. http://dx.doi.org/10.52041/srap.19303.

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Анотація:
In times of big data tree-based algorithms are an important method of machine learning which supports decision making, e.g., in medicine, finance, public policy and many more. Trees are a versatile method to represent decision processes that mirror human decision-making more closely than sophisticated traditional statistical methods like multivariate regression or neural networks. We introduce and illustrate the tool ARBOR, a digital learning tool which is a plug-in to the freely available data science education software CODAP. It is designed to critically appreciate and explore the steps of automatically generated decision trees.
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5

Ekol, George. "Perspectives on foundation statistics: some examples from prospective secondary mathematics teachers." In Decision Making Based on Data. International Association for Statistical Education, 2019. http://dx.doi.org/10.52041/srap.19409.

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Анотація:
The qualitative case-study reported in this paper is framed by the ‘big’ ideas in introductory statistics, which emphasise statistical thinking, reasoning and literacy. We analysed secondary qualitative data from prospective secondary mathematics teachers’ statements, based on six themes in statistics: the influence of secondary-school experience and the teacher; difficult topics; avoidance of statistics; lack of coverage; teacher knowledge; reasons associated with the difficulty of statistics; and the contributions associated with school practice. We used five elements of the foundations of statistical thinking to enable us to analyse the prospective teachers’ statements from each of the six themes. Data analysis suggests that representing data to enable a clear understanding of statistical concepts emerged more frequently in the pre-service teachers’ statements than the other five foundation elements. The consideration of variability emerged the least number of times. Implications for statistics education are discussed.
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6

Berens, Florian. "Statistics beliefs of advanced social science students – a qualitative evaluation of focus groups." In Decision Making Based on Data. International Association for Statistical Education, 2019. http://dx.doi.org/10.52041/srap.19402.

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Анотація:
Unlike mathematics education, statistics education has given little attention to students’ beliefs. In comparison it is possible that statistics may open up another domain-specific horizon of possible beliefs. However, there is no explicit theory about beliefs on statistics. In order to gain insight into students' beliefs about statistics, focus groups of advanced social science students were conducted. The focus groups were analyzed by content analysis and then partly by hermeneutics in order to identify types of beliefs. As a result well-known belief systems from mathematics can also be found in statistics. There are students who view statistics as a system of terms and rules, and there are also students who understand statistics dynamically. The last group can be subdivided into those that extract information out of data and those that want to check theory using data. A fourth group sees statistics as a form of systematic description of reality.
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7

Budgett, Stephanie, and Malia Puloka. "Making sense of categorical data – question confusion." In Decision Making Based on Data. International Association for Statistical Education, 2019. http://dx.doi.org/10.52041/srap.19405.

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When students encounter categorical data, lessons often focus on computing probabilities from two- way tables. These computations may involve simple, joint, and conditional probabilities, and the calculation of relative risk. However, little attention has been given to the questions posed. The purpose of this paper is to explore the questions that undergraduate students pose of categorical data, and their reasoning with a variety of representations of categorical data. Results from a small pilot study suggest that when the questions posed involved making comparisons, students were often confused as to whether they should compare proportions between conditions, or compare proportions within a condition.
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8

Frischemeier, Daniel, and Rolf Biehler. "Design of a teaching unit to develop primary school students ́ reasoning about uncertainty in multi-step chance experiments." In Decision Making Based on Data. International Association for Statistical Education, 2019. http://dx.doi.org/10.52041/srap.19304.

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Statistical reasoning and the confrontation with first ideas of uncertainty can already be enhanced in primary school. A challenge is how to relate theoretical-combinatorial aspects to empirical frequency aspects, given that fraction concepts are usually not available at primary school. In the frame of a Design Based Research approach we have designed and realized a teaching sequence consisting of seven lessons to develop statistical reasoning about uncertainty of grade 4 students (age 10-11). To supervise their learning processes we collected data on different levels: (a) written pre/post-tests, (b) working notes after each lesson and (c) interviews after the teaching unit. In this paper we will mainly present the design of teaching unit and first results from the analysis of pre- and posttests.
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9

Apata, T., G. N’Guessan, K. Ayantoye, and O. Idowu. "Agricultural land-use systems and climate change among small Farmers in nigeria." In Decision Making Based on Data. International Association for Statistical Education, 2019. http://dx.doi.org/10.52041/srap.19301.

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In sub-Saharan-Africa (SSA), agriculture land-use supports the livelihoods of the majority of people. Land- use for agricultural-activity is an economic-activity that is highly dependent upon weather and climate that produce food and fibre necessary to sustain human life. Hence, land-use for agriculture is expected to be vulnerable to climate variability. This paper examines this relationship. The paper presents data and generated evidence-based decision making under risk and uncertainty as influenced by climate change and its effects on agricultural land-use/outputs. Farm-level cost-route survey of cross-sectional national-data of 800 respondents was used for analysis. Data were analyzed and presented using the tools of descriptive statistics, trans-logarithms model and multivariate probit model (MVP). The study indicated a strong relationship between efficient use of agricultural-land and adaptive-processes to climate-change. Thus, providing data and analysis that strengthen policy decisions on land-use and climate change. Hence, policies of promoting and motivating sustainable land-use management need to be entrenched.
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10

Martignon, Laura, Ulrich Hoffrage, Jan Woike, Tim Erickson, and Joachim Engel. "Fast-and-frugal trees for decision-making." In Decision Making Based on Data. International Association for Statistical Education, 2019. http://dx.doi.org/10.52041/srap.19307.

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Fast-and-frugal trees for classification/decision are at the intersection of three families of models: lexicographic, linear and tree-based. We briefly examine the classification performance of simple models when making inferences out of sample, in 11 medical data sets in terms of Receiver Operating Characteristics diagrams and predictive accuracy. The heuristic approaches, Naïve Bayes and fast- and-frugal trees, outperform models that are normatively optimal when fitting data. The success of fast-and-frugal trees lies in their ecological rationality: their construction exploits the structure of information in the data sets. The tool ARBOR, a digital learning tool, which is a plug-in to the freely available data-science education software CODAP can be used for constructing and interpreting fast- and-frugal classification and decision trees. This paper is an abridged version of work by Woike, Hoffrage & Martignon on the integration of classification and decision models into a common framework (Woike, Hoffrage & Martignon, 2017).
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Звіти організацій з теми "BASED DATA"

1

Koeppel, W., and P. Jessl. Generically Based Mobility-Terrain Data Bases. Fort Belvoir, VA: Defense Technical Information Center, April 1985. http://dx.doi.org/10.21236/ada157738.

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2

Dilks, Kelly, Jeffery Miller, Amit Patel, and Jim Cookas. Geospatial Data Computer-Based Training. Fort Belvoir, VA: Defense Technical Information Center, February 2000. http://dx.doi.org/10.21236/ada375198.

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3

Hartwell, Peter, Uija Yoon, Norman C. Tien, and Charles E. Hunt. Microelectromechanical (MEMS)-Based Data Storage. Fort Belvoir, VA: Defense Technical Information Center, February 2003. http://dx.doi.org/10.21236/ada411620.

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4

Heffetz, Ori, and Katrina Ligett. Privacy and Data-Based Research. Cambridge, MA: National Bureau of Economic Research, September 2013. http://dx.doi.org/10.3386/w19433.

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5

Li, Jianchao, and K. Larner. Differential equation-based seismic data filtering. Office of Scientific and Technical Information (OSTI), May 1992. http://dx.doi.org/10.2172/10159092.

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Li, Jianchao, and K. Larner. Differential equation-based seismic data filtering. Office of Scientific and Technical Information (OSTI), May 1992. http://dx.doi.org/10.2172/7235596.

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7

Shen, Han-Wei. Extreme-Scale Distribution-Based Data Analysis. Office of Scientific and Technical Information (OSTI), November 2019. http://dx.doi.org/10.2172/1576750.

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8

Cogan, James L., and Patrick A. Haines. METCM-Based Messages from Climatological Data. Fort Belvoir, VA: Defense Technical Information Center, March 2013. http://dx.doi.org/10.21236/ada585005.

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9

Gotthold, David W., Nigel Browning, Eric Jensen, Alan Joly, and Nathan Canfield. Optically Stimulated Luminescence Based Optical Data Storage. Office of Scientific and Technical Information (OSTI), May 2016. http://dx.doi.org/10.2172/1474181.

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10

Tang, Q., and S. Xie. Station-based Surface Data Value-Added Product. Office of Scientific and Technical Information (OSTI), July 2015. http://dx.doi.org/10.2172/1238067.

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