Relevant bibliographies by topics / Mathematics – Data Processing

Contents

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

Academic literature on the topic 'Mathematics – Data Processing'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 July 2024

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Journal articles on the topic "Mathematics – Data Processing"

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Jaworski, John, and Elizabeth Bliss. "Data Processing Mathematics." Mathematical Gazette 71, no. 458 (December 1987): 334. http://dx.doi.org/10.2307/3617092.

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Richards, B. "Data processing mathematics." Data Processing 28, no. 3 (April 1986): 162. http://dx.doi.org/10.1016/0011-684x(86)90015-8.

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Ivanek, J. "Mathematics for data processing and computing." European Journal of Operational Research 23, no. 3 (March 1986): 414–15. http://dx.doi.org/10.1016/0377-2217(86)90314-0.

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Ashcroft, W. A. "Mathematics for Seismic Data Processing and Interpretation." Physics of the Earth and Planetary Interiors 58, no. 2-3 (December 1989): 269–70. http://dx.doi.org/10.1016/0031-9201(89)90063-0.

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Kumkov, Sergey I., Vyacheslav S. Nikitin, Tatyana N. Ostanina, and Valentin M. Rudoy. "Interval processing of electrochemical data." Journal of Computational and Applied Mathematics 380 (December 2020): 112961. http://dx.doi.org/10.1016/j.cam.2020.112961.

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Hartati, Tuti, Nida Fitria, Maulana Al Aziz Harahap, and Dadan Dasari. "Data-Driven Education: Data Processing as a Key to Improving the Quality of Mathematics Education." ALSYSTECH Journal of Education Technology 2, no. 1 (December 20, 2023): 45–57. http://dx.doi.org/10.58578/alsystech.v2i1.2361.

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Education is a major pillar in the development of a nation, and the quality of education becomes a key factor in determining the progress of a society. In this digital era, there is a rapid development in the use of information technology, and this has a significant impact on the education sector. The purpose of this study is for data-driven education: data processing as the key to improving the quality of mathematics education. This research uses a mixed method that combines qualitative and quantitative methods. This approach was chosen to address the complexity of data management and the comprehensiveness of this topic. The research begins with an in-depth literature analysis to understand the potential of Data-Driven Education. By using data as a guide, educators can make smarter decisions, detail instructional strategies, and create an educational environment that allows each student to reach his or her full potential. This is done by combining the results of various data of educator learners who can make informational decisions to improve the quality of mathematics learning. The application of data management in the context of mathematics education can provide concrete solutions to overcome challenges and improve the quality of learning. The right use of data provides the basis for smarter decisions and more adaptive learning, creating a learning environment that fits the needs of each student. By integrating Data-Driven Education effectively, we can move towards a more inclusive, personalized and successful mathematics education. The use of data contributes significantly in improving the quality and inclusivity of mathematics education. The integration of data management in mathematics education is a critical step towards a more adaptive, inclusive, and effective education
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Oursatyev, Oleksii A. "Data Research in Industrial Data Mining Projects in the Big Data Generation Era." Control Systems and Computers, no. 3 (303) (2023): 33–53. http://dx.doi.org/10.15407/csc.2023.03.033.

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Introduction. The review material is based mainly on business intelligence (BI) solutions designed for tasks with corporate data. But all the main aspects of working with data discussed in the work are also used on data processing platforms (Data Science Platform). Many BI vendors have expanded the capabilities of their systems to perform more advanced analytics, including Data Science. They added the phrase “Data Science” to their marketing research, and the term “advanced analytics” lost some popularity in relation to corporate data. The Data Science Platform provides a comprehensive set of tools for use by advanced users who traditionally work with data. Capabilities that allow you to connect to multi-structured data across different types of storage platforms, both on-premises and in the cloud, and the infrastructure architecture of a modern BI analytics platform enable high-performance workloads, including business intelligence. It uses distributed architecture, massively parallel processing, data virtualization, in-memory computing, etc. The combination of traditional relational data processing with calculations on the well-known Apache Hadoop software infrastructure, which integrates a number of components of the Hadoop ecosystem (Apache Hive, HBase, Spark, Solr, etc.) with the necessary target functions, allows you to create a fully functional platform for storing and processing structured and non-structures data. Purpose. A review of data processing problems and an analysis of the use of world-class mathematical apparatus and tools for obtaining knowledge from information were carried out. Methods. The paper describes the use of Data Mining methods in big data processing tasks, as well as methods of business, recommendation and predictive analytics. Result. The study suggests that machine learning-enhanced master data management (MDM), data quality, data preparation, and data catalogs will converge into a single, modern Enterprise Information Management (EIM) platform applicable to most new analytics projects. The results of the analysis of the process of identifying useful data can be useful to researchers and developers of modern platforms for processing and researching data in various spheres of society. Conclusion. A review of data processing problems and an analysis of the use of world-class mathematical apparatus and tools for obtaining knowledge from information were carried out. It is shown that a high-quality solution to the problems of working with first-level data indicated in this review will be provided by data research in modern analytical platforms. Successful penetration into their essence at the level of obtaining knowledge using machine learning and artificial intelligence algorithms will make it possible to predict future results in managed objects (processes) and make informed decisions.
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Moshchenko, I., O. Nikitenko, Yu Kozlov, and Yu Zharko. "Feature of statistical data processing by computer mathematics systems tools." Radiotekhnika, no. 206 (September 24, 2021): 131–36. http://dx.doi.org/10.30837/rt.2021.3.206.12.

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Processes of error accumulation were analysed during arithmetic operations on statistical data obtained in the course of research on oscillations in cross-field electron vacuum devices. The features of statistical data processing obtained as a result of experimental research were investigated using the most widespread computer mathematical packages. The features of statistical data processing were investigated by processing a sample of 80 values of the magnetron generation frequency using popular mathematical packages Excel, Maple, Matlab and MathCad and comparing the results obtained with calculations using theoretical formulas. Calculation results for all packages give the same results for mean, variance and standard deviation. As for the coefficients of skewness and kurtosis, most of the results do not coincide. Analysis of the calculation results showed that the difference in the obtained values ​​of the skewness and kurtosis is due to different definitions of these indicators in mathematical packages Excel, Maple, Matlab and MathCad. It is proved that in Microsoft Excel we cannot correctly construct a histogram without using additional operations, because the interval limits are calculated with errors. It leads to an incorrect determination of the number of elements into these intervals. To build correctly a histogram using the Excel package, it is necessary to calculate the interval limits in advance. It is concluded that before using computer mathematical packages for processing statistical data, it is necessary to analyze first by what formulas the required parameters are calculated and take appropriate measures to eliminate possible discrepancies with the parameters calculated using theoretical formulas.
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Unaenah, Een, Gabriella Sinthia Marctines, and Nanda Nahzifa. "Pembelajaran Pengolahan Data dengan Metode Permainan Pos Berantai pada Siswa Kelas 4 Sekolah Dasar." MASALIQ 3, no. 6 (August 1, 2023): 1031–39. http://dx.doi.org/10.58578/masaliq.v3i6.1576.

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Data processing is an important aspect of learning mathematics at the elementary school level. Next will understand mathematics in data processing. Data processing is the process of collecting, compiling, analyzing, and presenting data in graphical or tabular form so that it is easy to understand. There are two types of data that are often used in data processing, namely qualitative data and quantitative data. Qualitative data are those that are measured based on research as they are and cannot be measured by numbers, such as color, smell, and taste. Meanwhile, quantitative data is data that can be measured based on numbers, such as numbers or sizes. There is a method that can be used to better understand interactive data processing, namely the serial post game method. With this method in order to be able to explore the use of the postal game method in teaching data processing to grade 4 students in elementary schools so that it is more fun and does not make students easily bored. There is also the purpose of this study is to know how to improve learning outcomes in mathematics lessons regarding data processing using the serial post method for class IV Elementary School and to find out the responses to mathematics lessons on data processing materials that link students' activeness.
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Gavrilović, Branko. "Innovative approach to teaching mathematics using data analytics." Zbornik radova Pedagoskog fakulteta Uzice, no. 25 (2023): 217–36. http://dx.doi.org/10.5937/zrpfu2325191g.

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With the development of information technology, the Internet, and social networks, the amount of collected data is growing year by year at a high rate. Data processing and analysis becomes a necessity without which quality decisions cannot be made. Education, as an important social segment, must follow global changes and provide an adequate response to new social needs. International tests have shown that students from Serbia have problems with processing and analyzing data as well as applying mathematical knowledge in modeling real-life situations. In this paper, an innovative approach to teaching mathematics based on data analytics will be presented. The goal of the paper is to show the possibility of using data analytics techniques in the teaching process. A teaching model using data analytics will be presented, as well as a complete preparation for the realization of a mathematics class. By building a mathematical model using data analytics, students will gain new knowledge in the field of financial mathematics. The possibility of using data analytics in the implementation of other teaching units in mathematics, as well as in the teaching of other subjects, will be presented.
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Dissertations / Theses on the topic "Mathematics – Data Processing"

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Turkmen, Muserref. "Digital Image Processing Of Remotely Sensed Oceanographic Data." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609948/index.pdf.

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Developing remote sensing instrumentation allows obtaining information about an area rapidly and with low costs. This fact offers a challenge to remote sensing algorithms aimed at extracting information about an area from the available re¬
mote sensing data. A very typical and important problem being interpretation of satellite images. A very efficient approach to remote sensing is employing discrim¬
inant functions to distinguish different landscape classes from satellite images. Various methods on this direction are already studied. However, the efficiency of the studied methods are still not very high. In this thesis, we will improve efficiency of remote sensing algorithms. Besides we will investigate improving boundary detection methods on satellite images.
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Farnham, Rodrigo Bouchardet. "Processing and inpainting of sparse data as applied to atomic force microscopy imaging." California State University, Long Beach, 2013.

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Cena, Bernard Maria. "Reconstruction for visualisation of discrete data fields using wavelet signal processing." University of Western Australia. Dept. of Computer Science, 2000. http://theses.library.uwa.edu.au/adt-WU2003.0014.

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The reconstruction of a function and its derivative from a set of measured samples is a fundamental operation in visualisation. Multiresolution techniques, such as wavelet signal processing, are instrumental in improving the performance and algorithm design for data analysis, filtering and processing. This dissertation explores the possibilities of combining traditional multiresolution analysis and processing features of wavelets with the design of appropriate filters for reconstruction of sampled data. On the one hand, a multiresolution system allows data feature detection, analysis and filtering. Wavelets have already been proven successful in these tasks. On the other hand, a choice of discrete filter which converges to a continuous basis function under iteration permits efficient and accurate function representation by providing a “bridge” from the discrete to the continuous. A function representation method capable of both multiresolution analysis and accurate reconstruction of the underlying measured function would make a valuable tool for scientific visualisation. The aim of this dissertation is not to try to outperform existing filters designed specifically for reconstruction of sampled functions. The goal is to design a wavelet filter family which, while retaining properties necessary to preform multiresolution analysis, possesses features to enable the wavelets to be used as efficient and accurate “building blocks” for function representation. The application to visualisation is used as a means of practical demonstration of the results. Wavelet and visualisation filter design is analysed in the first part of this dissertation and a list of wavelet filter design criteria for visualisation is collated. Candidate wavelet filters are constructed based on a parameter space search of the BC-spline family and direct solution of equations describing filter properties. Further, a biorthogonal wavelet filter family is constructed based on point and average interpolating subdivision and using the lifting scheme. The main feature of these filters is their ability to reconstruct arbitrary degree piecewise polynomial functions and their derivatives using measured samples as direct input into a wavelet transform. The lifting scheme provides an intuitive, interval-adapted, time-domain filter and transform construction method. A generalised factorisation for arbitrary primal and dual order point and average interpolating filters is a result of the lifting construction. The proposed visualisation filter family is analysed quantitatively and qualitatively in the final part of the dissertation. Results from wavelet theory are used in the analysis which allow comparisons among wavelet filter families and between wavelets and filters designed specifically for reconstruction for visualisation. Lastly, the performance of the constructed wavelet filters is demonstrated in the visualisation context. One-dimensional signals are used to illustrate reconstruction performance of the wavelet filter family from noiseless and noisy samples in comparison to other wavelet filters and dedicated visualisation filters. The proposed wavelet filters converge to basis functions capable of reproducing functions that can be represented locally by arbitrary order piecewise polynomials. They are interpolating, smooth and provide asymptotically optimal reconstruction in the case when samples are used directly as wavelet coefficients. The reconstruction performance of the proposed wavelet filter family approaches that of continuous spatial domain filters designed specifically for reconstruction for visualisation. This is achieved in addition to retaining multiresolution analysis and processing properties of wavelets.
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Tang, Cham-wing, and 鄧湛榮. "The attitudes of secondary school mathematics teachers towards the teaching of mathematics by using computers." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1996. http://hub.hku.hk/bib/B31958886.

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van, Schaik Sebastiaan Johannes. "A framework for processing correlated probabilistic data." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:91aa418d-536e-472d-9089-39bef5f62e62.

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The amount of digitally-born data has surged in recent years. In many scenarios, this data is inherently uncertain (or: probabilistic), such as data originating from sensor networks, image and voice recognition, location detection, and automated web data extraction. Probabilistic data requires novel and different approaches to data mining and analysis, which explicitly account for the uncertainty and the correlations therein. This thesis introduces ENFrame: a framework for processing and mining correlated probabilistic data. Using this framework, it is possible to express both traditional and novel algorithms for data analysis in a special user language, without having to explicitly address the uncertainty of the data on which the algorithms operate. The framework will subsequently execute the algorithm on the probabilistic input, and perform exact or approximate parallel probability computation. During the probability computation, correlations and provenance are succinctly encoded using probabilistic events. This thesis contains novel contributions in several directions. An expressive user language – a subset of Python – is introduced, which allows a programmer to implement algorithms for probabilistic data without requiring knowledge of the underlying probabilistic model. Furthermore, an event language is presented, which is used for the probabilistic interpretation of the user program. The event language can succinctly encode arbitrary correlations using events, which are the probabilistic counterparts of deterministic user program variables. These highly interconnected events are stored in an event network, a probabilistic interpretation of the original user program. Multiple techniques for exact and approximate probability computation (with error guarantees) of such event networks are presented, as well as techniques for parallel computation. Adaptations of multiple existing data mining algorithms are shown to work in the framework, and are subsequently subjected to an extensive experimental evaluation. Additionally, a use-case is presented in which a probabilistic adaptation of a clustering algorithm is used to predict faults in energy distribution networks. Lastly, this thesis presents techniques for integrating a number of different probabilistic data formalisms for use in this framework and in other applications.
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Smith, Sydney. "Approaches to Natural Language Processing." Scholarship @ Claremont, 2018. http://scholarship.claremont.edu/cmc_theses/1817.

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This paper explores topic modeling through the example text of Alice in Wonderland. It explores both singular value decomposition as well as non-­‐‑negative matrix factorization as methods for feature extraction. The paper goes on to explore methods for partially supervised implementation of topic modeling through introducing themes. A large portion of the paper also focuses on implementation of these techniques in python as well as visualizations of the results which use a combination of python, html and java script along with the d3 framework. The paper concludes by presenting a mixture of SVD, NMF and partially-­‐‑supervised NMF as a possible way to improve topic modeling.
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Ng, Yui-kin, and 吳銳堅. "Computers, Gödel's incompleteness theorems and mathematics education: a study of the implications of artificialintelligence for secondary school mathematics." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1994. http://hub.hku.hk/bib/B31957419.

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Akleman, Ergun. "Pseudo-affine functions : a non-polynomial implicit function family to describe curves and sufaces." Diss., Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/15409.

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Paterson, Judith Evelyn. "A study of nine girl's learning before, during and after their introduction to some of the basics of LOGO." Thesis, University of Cape Town, 1986. http://hdl.handle.net/11427/23348.

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Ngaye, Zonke. "User experience metrics for Dr Math." Thesis, Nelson Mandela Metropolitan University, 2012. http://hdl.handle.net/10948/d1012036.

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The purpose of this research study is to propose guidelines for providing a positive user experience for pupils using Dr Math®. User experience was found to have a positive impact on the acceptance and adoption of a product. Thus the proposed guidelines contribute in maximizing the adoption and acceptance of Dr Math® among pupils. This study begins with an introductory chapter that describes the problem that forms the basis for this research. The chapter defines the objectives that this study is intended to achieve in order to accomplish its ultimate goal. The methodology followed to conduct this research study as well as its scope are also defined here. The results from a preliminary survey revealed that despite its potential accessibility, Dr Math® has a low adoption rate. However, when compared to other mobile learning (m-learning) applications for mathematics learning, Dr Math® is more popular. Thus Dr Math® was selected as a case for study. Chapter 2 of this study provides a detailed description of Dr Math® as a local mobile application for mathematics learning. It was found that the affordability and accessibility of Dr Math® did not necessarily imply a high adoption rate. There are various possible barriers to its low adoption. User experience (UX), which is the focus of this study, is one of them. Thus, a subsequent chapter deals with UX. Chapter 3 discusses UX, its scope, components and definition and places particular emphasis on its significance in the success of any product. The chapter also highlights the characteristics of a positive UX and the importance of designing for this outcome. In Chapter 4, a discussion and justification of the methodology used to conduct this research is discussed. This study primarily employs a qualitative inductive approach within an interpretivism paradigm. An exploratory single case study was used to obtain an in-depth analysis of the case. Data was collected using Dr Math® log files as a documentary source. Gathered data was then analysed and organized into themes and categories using qualitative content analysis as outlined in Chapter 5. Also the findings obtained from the results, which are mainly the factors that were found to have an impact on the user interaction with Dr Math®, are presented here. The identified factors served as a basis from which the guidelines presented in Chapter 6 were developed. Chapter 7 presents the conclusions and recommendations of the research. From both theoretical and empirical work, it was concluded that Dr Math® has the potential to improve mathematics learning in South Africa. Its adoption rate, however, is not satisfying: hence, the investigation of the factors impacting on the user interaction with Dr Math®, from which the proposed guidelines are based.
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Books on the topic "Mathematics – Data Processing"

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Bliss, Elizabeth. Data processing mathematics. Englewood Cliffs, N.J: Prentice-Hall, 1985.

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McCullough, Robert N. Mathematics for data processing. Dubuque, Iowa: W.C. Brown, 1988.

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Price, Wilson T. Elements of data processing mathematics. 3rd ed. New York: Holt, Rinehart, and Winston, 1987.

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Kolatis, Maria Shopay. Mathematics for data processing and computing. Reading, Mass: Addison-Wesley Pub., 1985.

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Gray, Theodore W. Exploring mathematics with Mathematica: Dialogs concerning computers and mathematics. Redwood City, Calif: Addison-Wesley, 1991.

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Mignotte, Maurice. Mathematics for computer algebra. New York: Springer-Verlag, 1992.

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Goodman, I. R. Mathematics of Data Fusion. Dordrecht: Springer Netherlands, 1997.

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ʹois, Djindjian Franc, Ducasse Henri, and European Study Group on Physical, Chemical, Biological and Mathematical Techniques Applied to Archaeology., eds. Data processing and mathematics applied to archaeology. Ravello, Italia: Council of Europe, European University Center for the Cultural Heritage = Conseil de l'Europe, Centre universitaire Europe en pour les biens culturels, 1987.

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Ronald), Kincaid David (David, ed. Numerical mathematics and computing. 7th ed. Boston, MA: Brooks/Cole, Cengage Learning, 2013.

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Kaltofen, Erich. Computers and Mathematics. New York, NY: Springer US, 1989.

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Book chapters on the topic "Mathematics – Data Processing"

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Gorawski, Marcin, and Pawel Marks. "Resumption of Data Extraction Process in Parallel Data Warehouses." In Parallel Processing and Applied Mathematics, 478–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11752578_58.

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Gorawski, Marcin, and Rafal Malczok. "Distributed Spatial Data Warehouse." In Parallel Processing and Applied Mathematics, 676–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24669-5_88.

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Jackson, Colin. "Data Collection, Processing and Analysis." In All-Attainment Teaching in Secondary Mathematics, 75–84. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92361-7_6.

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Fox, Geoffrey C., Mehmet S. Aktas, Galip Aydin, Hasan Bulut, Harshawardhan Gadgil, Sangyoon Oh, Shrideep Pallickara, Marlon E. Pierce, Ahmet Sayar, and Gang Zhai. "Grids for Real Time Data Applications." In Parallel Processing and Applied Mathematics, 320–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11752578_39.

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Kwedlo, Wojciech. "Parallelizing Evolutionary Algorithms for Clustering Data." In Parallel Processing and Applied Mathematics, 430–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11752578_52.

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Cai, Min, Istvan Jonyer, and Marcin Paprzycki. "Improving Parallelism in Structural Data Mining." In Parallel Processing and Applied Mathematics, 455–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11752578_55.

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Głut, Barbara, and Tomasz Jurczyk. "Mesh Adaptation Based on Discrete Data." In Parallel Processing and Applied Mathematics, 559–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11752578_67.

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Słota, Renata, Darin Nikolow, Marcin Kuta, Mariusz Kapanowski, Kornel Skałkowski, Marek Pogoda, and Jacek Kitowski. "Replica Management for National Data Storage." In Parallel Processing and Applied Mathematics, 184–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14403-5_20.

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Puszyński, Krzysztof. "Parallel Implementation of Logical Analysis of Data (LAD) for Discriminatory Analysis of Protein Mass Spectrometry Data." In Parallel Processing and Applied Mathematics, 1114–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11752578_135.

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Angeletti, Mélodie, Jean-Marie Bonny, Franck Durif, and Jonas Koko. "Parallel Hierarchical Agglomerative Clustering for fMRI Data." In Parallel Processing and Applied Mathematics, 265–75. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78024-5_24.

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Conference papers on the topic "Mathematics – Data Processing"

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Nusantari, Diah Oga, Deni Nasir Ahmad, and Ihwan Zulkarnain. "Community Service: Processing Data Statistically." In SEMANTIK Conference of Mathematics Education (SEMANTIK 2019). Paris, France: Atlantis Press, 2020. http://dx.doi.org/10.2991/assehr.k.200827.108.

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Vashisht, Manisha, and Madhulika Bhatia. "Role of Mathematics in Image Processing." In 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon). IEEE, 2019. http://dx.doi.org/10.1109/comitcon.2019.8862438.

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Hussien, Nur Syahela, Sarina Sulaiman, and Siti Mariyam Shamsuddin. "Tools in data science for better processing." In ADVANCES IN INDUSTRIAL AND APPLIED MATHEMATICS: Proceedings of 23rd Malaysian National Symposium of Mathematical Sciences (SKSM23). Author(s), 2016. http://dx.doi.org/10.1063/1.4954530.

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Kumkov, Sergey I., Alexander A. Redkin, Svetlana V. Pershina, Evgeniya A. Il’ina, Alexander A. Kataev, and Yury P. Zaikov. "Interval approach to processing the noised thermophysical data." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2019. AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0028149.

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Bistrian, D. A., G. Dimitriu, and I. M. Navon. "Processing epidemiological data using dynamic mode decomposition method." In APPLICATION OF MATHEMATICS IN TECHNICAL AND NATURAL SCIENCES: 11th International Conference for Promoting the Application of Mathematics in Technical and Natural Sciences - AMiTaNS’19. AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5130825.

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Kaliyagina, L., J. Mopoz, Theodore E. Simos, George Psihoyios, and Ch Tsitouras. "Methods of Empirical Data Processing in Developing Processes." In ICNAAM 2010: International Conference of Numerical Analysis and Applied Mathematics 2010. AIP, 2010. http://dx.doi.org/10.1063/1.3497818.

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Chen, Keting. "Research on material big data processing based on optimization algorithm." In International Conference on Mathematics, Modeling and Computer Science (MMCS2022), edited by Kun Zhang. SPIE, 2023. http://dx.doi.org/10.1117/12.2670412.

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Wu, Ronghua. "Mathematics Education and Teaching Based on Big Data Analysis." In 2021 International Conference on Computers, Information Processing and Advanced Education (CIPAE). IEEE, 2021. http://dx.doi.org/10.1109/cipae53742.2021.00077.

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Zhelev, Svetoslav, and Anna Rozeva. "Big data processing in the cloud - Challenges and platforms." In PROCEEDINGS OF THE 43RD INTERNATIONAL CONFERENCE APPLICATIONS OF MATHEMATICS IN ENGINEERING AND ECONOMICS: (AMEE’17). Author(s), 2017. http://dx.doi.org/10.1063/1.5014007.

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Khokhar, Muhammad Saddam, Keyang Cheng, Misbah Ayoub, Zakria, and Nida E. Rub. "Data Driven Processing Via Two-Dimensional Spearman Correlation Analysis (2D-SCA)." In 2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS). IEEE, 2019. http://dx.doi.org/10.1109/macs48846.2019.9024798.

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Reports on the topic "Mathematics – Data Processing"

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DeVore, Ronald A., Peter G. Binev, and Robert C. Sharpley. Advanced Mathematical Methods for Processing Large Data Sets. Fort Belvoir, VA: Defense Technical Information Center, October 2008. http://dx.doi.org/10.21236/ada499985.

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Modlo, Yevhenii O., Serhiy O. Semerikov, Stanislav L. Bondarevskyi, Stanislav T. Tolmachev, Oksana M. Markova, and Pavlo P. Nechypurenko. Methods of using mobile Internet devices in the formation of the general scientific component of bachelor in electromechanics competency in modeling of technical objects. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3677.

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An analysis of the experience of professional training bachelors of electromechanics in Ukraine and abroad made it possible to determine that one of the leading trends in its modernization is the synergistic integration of various engineering branches (mechanical, electrical, electronic engineering and automation) in mechatronics for the purpose of design, manufacture, operation and maintenance electromechanical equipment. Teaching mechatronics provides for the meaningful integration of various disciplines of professional and practical training bachelors of electromechanics based on the concept of modeling and technological integration of various organizational forms and teaching methods based on the concept of mobility. Within this approach, the leading learning tools of bachelors of electromechanics are mobile Internet devices (MID) – a multimedia mobile devices that provide wireless access to information and communication Internet services for collecting, organizing, storing, processing, transmitting, presenting all kinds of messages and data. The authors reveals the main possibilities of using MID in learning to ensure equal access to education, personalized learning, instant feedback and evaluating learning outcomes, mobile learning, productive use of time spent in classrooms, creating mobile learning communities, support situated learning, development of continuous seamless learning, ensuring the gap between formal and informal learning, minimize educational disruption in conflict and disaster areas, assist learners with disabilities, improve the quality of the communication and the management of institution, and maximize the cost-efficiency. Bachelor of electromechanics competency in modeling of technical objects is a personal and vocational ability, which includes a system of knowledge, skills, experience in learning and research activities on modeling mechatronic systems and a positive value attitude towards it; bachelor of electromechanics should be ready and able to use methods and software/hardware modeling tools for processes analyzes, systems synthesis, evaluating their reliability and effectiveness for solving practical problems in professional field. The competency structure of the bachelor of electromechanics in the modeling of technical objects is reflected in three groups of competencies: general scientific, general professional and specialized professional. The implementation of the technique of using MID in learning bachelors of electromechanics in modeling of technical objects is the appropriate methodic of using, the component of which is partial methods for using MID in the formation of the general scientific component of the bachelor of electromechanics competency in modeling of technical objects, are disclosed by example academic disciplines “Higher mathematics”, “Computers and programming”, “Engineering mechanics”, “Electrical machines”. The leading tools of formation of the general scientific component of bachelor in electromechanics competency in modeling of technical objects are augmented reality mobile tools (to visualize the objects’ structure and modeling results), mobile computer mathematical systems (universal tools used at all stages of modeling learning), cloud based spreadsheets (as modeling tools) and text editors (to make the program description of model), mobile computer-aided design systems (to create and view the physical properties of models of technical objects) and mobile communication tools (to organize a joint activity in modeling).
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Shabelnyk, Tetiana V., Serhii V. Krivenko, Nataliia Yu Rotanova, Oksana F. Diachenko, Iryna B. Tymofieieva, and Arnold E. Kiv. Integration of chatbots into the system of professional training of Masters. [б. в.], June 2021. http://dx.doi.org/10.31812/123456789/4439.

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The article presents and describes innovative technologies of training in the professional training of Masters. For high-quality training of students of technical specialties, it becomes necessary to rethink the purpose, results of studying and means of teaching professional disciplines in modern educational conditions. The experience of implementing the chatbot tool in teaching the discipline “Mathematical modeling of socio-economic systems” in the educational and professional program 124 System Analysis is described. The characteristics of the generalized structure of the chatbot information system for investment analysis are presented and given: input information, information processing system, output information, which creates a closed cycle (system) of direct and feedback interaction. The information processing system is represented by accounting and analytical data management blocks. The investment analysis chatbot will help masters of the specialty system analysis to manage the investment process efficiently based on making the right decisions, understanding investment analysis in the extensive structure of financial management and optimizing risks in these systems using a working mobile application. Also, the chatbot will allow you to systematically assess the disadvantages and advantages of investment projects or the direction of activity of a system analyst, while increasing interest in performing practical tasks. A set of software for developing a chatbot integrated into training is installed: Kotlin programming, a library for network interaction Retrofit, receiving and transmitting data, linking processes using the HTTP API. Based on the results of the study, it is noted that the impact of integrating a chatbot into the training of Masters ensures the development of their professional activities, which gives them the opportunity to be competent specialists and contributes to the organization of high-quality training.
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Tanny, Josef, Gabriel Katul, Shabtai Cohen, and Meir Teitel. Micrometeorological methods for inferring whole canopy evapotranspiration in large agricultural structures: measurements and modeling. United States Department of Agriculture, October 2015. http://dx.doi.org/10.32747/2015.7594402.bard.

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Original objectives and revisions The original objectives as stated in the approved proposal were: (1) To establish guidelines for the use of micrometeorological techniques as accurate, reliable and low-cost tools for continuous monitoring of whole canopy ET of common crops grown in large agricultural structures. (2) To adapt existing methods for protected cultivation environments. (3) To combine previously derived theoretical models of air flow and scalar fluxes in large agricultural structures (an outcome of our previous BARD project) with ET data derived from application of turbulent transport techniques for different crops and structure types. All the objectives have been successfully addressed. The study was focused on both screenhouses and naturally ventilated greenhouses, and all proposed methods were examined. Background to the topic Our previous BARD project established that the eddy covariance (EC) technique is suitable for whole canopy evapotranspiration measurements in large agricultural screenhouses. Nevertheless, the eddy covariance technique remains difficult to apply in the farm due to costs, operational complexity, and post-processing of data – thereby inviting alternative techniques to be developed. The subject of this project was: 1) the evaluation of four turbulent transport (TT) techniques, namely, Surface Renewal (SR), Flux-Variance (FV), Half-order Time Derivative (HTD) and Bowen Ratio (BR), whose instrumentation needs and operational demands are not as elaborate as the EC, to estimate evapotranspiration within large agricultural structures; and 2) the development of mathematical models able to predict water savings and account for the external environmental conditions, physiological properties of the plant, and structure properties as well as to evaluate the necessary micrometeorological conditions for utilizing the above turbulent transfer methods in such protected environments. Major conclusions and achievements The major conclusions are: (i) the SR and FV techniques were suitable for reliable estimates of ET in shading and insect-proof screenhouses; (ii) The BR technique was reliable in shading screenhouses; (iii) HTD provided reasonable results in the shading and insect proof screenhouses; (iv) Quality control analysis of the EC method showed that conditions in the shading and insect proof screenhouses were reasonable for flux measurements. However, in the plastic covered greenhouse energy balance closure was poor. Therefore, the alternative methods could not be analyzed in the greenhouse; (v) A multi-layered flux footprint model was developed for a ‘generic’ crop canopy situated within a protected environment such as a large screenhouse. The new model accounts for the vertically distributed sources and sinks within the canopy volume as well as for modifications introduced by the screen on the flow field and microenvironment. The effect of the screen on fetch as a function of its relative height above the canopy is then studied for the first time and compared to the case where the screen is absent. The model calculations agreed with field experiments based on EC measurements from two screenhouse experiments. Implications, both scientific and agricultural The study established for the first time, both experimentally and theoretically, the use of four simple TT techniques for ET estimates within large agricultural screenhouses. Such measurements, along with reliable theoretical models, will enable the future development of lowcost ET monitoring system which will be attainable for day-to-day use by growers in improving irrigation management.
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