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Готові списки джерел за темами / Metadata mining / Статті в журналах

Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: Metadata mining.

Статті в журналах з теми "Metadata mining"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 19 лютого 2022

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1

Sutton, Stuart A. "Mining the Metadata Quarries." Bulletin of the American Society for Information Science and Technology 29, no. 2 (January 31, 2005): 11. http://dx.doi.org/10.1002/bult.267.

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2

Illien, Gildas. "Metadata mining : fouiller les données des catalogues ?" Enrichir pour partager, no. 76 (October 1, 2014): 15–16. http://dx.doi.org/10.35562/arabesques.890.

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3

Şah, Melike, and Vincent Wade. "Automatic metadata mining from multilingual enterprise content." Journal of Web Semantics 11 (March 2012): 41–62. http://dx.doi.org/10.1016/j.websem.2011.11.001.

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4

LI, G., H. SHENG, and X. FAN. "Incorporating Metadata into Data Mining with Ontology." IEICE Transactions on Information and Systems E90-D, no. 6 (June 1, 2007): 983–85. http://dx.doi.org/10.1093/ietisy/e90-d.6.983.

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5

Murraças, Adriana, Paula Maria Vaz Martins, Carlos Daniel Cipriani Ferreira, Tiago Marques Godinho, and Augusto Marques Ferreira da Silva. "Data Mining of MR Technical Parameters." International Journal of E-Health and Medical Communications 12, no. 1 (January 2021): 16–33. http://dx.doi.org/10.4018/ijehmc.2021010102.

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Exposure to radiofrequency (RF) energy during a magnetic resonance imaging exam is a safety concern related to biological thermal effects. Estimation of the specific absorption rate (SAR) is done by manufacturer scanner integrated tools to monitor RF energy. This work presents an exploratory approach of DICOM metadata focused in whole-body SAR values, patient dependent parameters, and pulse sequences. Previously acquired abdominopelvic and head studies were retrieved from a 3 Tesla scanner. Dicoogle tool was used for metadata indexing, mining, and extraction. Specifically weighted pulse sequen

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6

Nurandini, Indri, and Arief Fatchul Huda. "Klastering Dokumen dengan Menambahkan Metadata Menggunakan Algoritma COATES." Kubik: Jurnal Publikasi Ilmiah Matematika 2, no. 2 (November 30, 2017): 39–44. http://dx.doi.org/10.15575/kubik.v2i2.1859.

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Text mining adalah proses ekstraksi pola berupa informasi dan pengetahuan yang berguna dari sejumlah besar sumber data tak terstruktur. Salah satu perkembangan text mining adalah ruang lingkup perbaikan dari pemanfaatan sebuah “side information” yang digunakan untuk membantu proses klastering yang lebih efisien. “side information” yang dimiliki data dapat membantu proses text mining jika “side information” tersebut bersifat informatif. Di dalam “side information” , metadata merupakan bagian dari “side information” yang dimiliki oleh data. Oleh karena itu, algoritma klastering partisi klasik da

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7

Wang, Fei Chao. "A Novel Approach to Mine Knowledge from Social Images." Advanced Materials Research 430-432 (January 2012): 1068–71. http://dx.doi.org/10.4028/www.scientific.net/amr.430-432.1068.

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With the popularity of various social media website, currently, lots of social images attached with different kinds of metadata have been uploaded to social media websites. Mining useful knowledge from social images has been an emerging important research topic in web search and data mining. In this paper, we propose a novel approach to find geographical difference of a given concept from social image community. We put a given concept to social image community, and then downloaded social images with metadata, particularly, the place where the photo was taken should be provided in advance. Firs

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8

Intagorn, Suradej, and Kristina Lerman. "Mining Geospatial Knowledge on the Social Web." International Journal of Information Systems for Crisis Response and Management 3, no. 2 (April 2011): 33–47. http://dx.doi.org/10.4018/jiscrm.2011040103.

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Up-to-date geospatial information can help crisis management community to coordinate its response. In addition to data that is created and curated by experts, there is an abundance of user-generated, user-curated data on Social Web sites such as Flickr, Twitter, and Google Earth. User-generated data and metadata can be used to harvest knowledge, including geospatial knowledge that will help solve real-world problems including information discovery, geospatial information integration and data management. This paper proposes a method for acquiring geospatial knowledge in the form of places and r

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9

Su, Shian, Vincent J. Carey, Lori Shepherd, Matthew Ritchie, Martin T. Morgan, and Sean Davis. "BiocPkgTools: Toolkit for mining the Bioconductor package ecosystem." F1000Research 8 (May 29, 2019): 752. http://dx.doi.org/10.12688/f1000research.19410.1.

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Motivation: The Bioconductor project, a large collection of open source software for the comprehension of large-scale biological data, continues to grow with new packages added each week, motivating the development of software tools focused on exposing package metadata to developers and users. The resulting BiocPkgTools package facilitates access to extensive metadata in computable form covering the Bioconductor package ecosystem, facilitating downstream applications such as custom reporting, data and text mining of Bioconductor package text descriptions, graph analytics over package dependenc

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10

Algur, Siddu P., and Prashant Bhat. "Web Video Mining: Metadata Predictive Analysis using Classification Techniques." International Journal of Information Technology and Computer Science 8, no. 2 (February 8, 2016): 69–77. http://dx.doi.org/10.5815/ijitcs.2016.02.09.

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11

Bhanuse, Shraddha S., Shailesh D. Kamble, and Sandeep M. Kakde. "Text Mining Using Metadata for Generation of Side Information." Procedia Computer Science 78 (2016): 807–14. http://dx.doi.org/10.1016/j.procs.2016.02.061.

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12

Davulcu, Hasan, Srinivas Vadrevu, and Saravanakumar Nagarajan. "OntoMiner: automated metadata and instance mining from news websites." International Journal of Web and Grid Services 1, no. 2 (2005): 196. http://dx.doi.org/10.1504/ijwgs.2005.008320.

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13

Mastroianni, Carlo, Domenico Talia, and Paolo Trunfio. "Metadata for Managing Grid Resources in Data Mining Applications." Journal of Grid Computing 2, no. 1 (March 2004): 85–102. http://dx.doi.org/10.1007/s10723-004-2809-x.

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14

Wang, Zichen, Alexander Lachmann, and Avi Ma’ayan. "Mining data and metadata from the gene expression omnibus." Biophysical Reviews 11, no. 1 (December 29, 2018): 103–10. http://dx.doi.org/10.1007/s12551-018-0490-8.

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15

Goudannavar, Basavaraj A., and Prashant Bhat. "Frequent Itemset Mining A Metadata Based Approach for Knowledge Discovery." International Journal of Computer Sciences and Engineering 6, no. 3 (March 30, 2018): 316–20. http://dx.doi.org/10.26438/ijcse/v6i3.316320.

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16

Gemmeren, P. van, and D. Malon. "Event metadata records as a testbed for scalable data mining." Journal of Physics: Conference Series 219, no. 4 (April 1, 2010): 042057. http://dx.doi.org/10.1088/1742-6596/219/4/042057.

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17

Mathaikutty, Deepak A., and Sandeep K. Shukla. "Mining metadata for composability of IPs from SystemC IP library." Design Automation for Embedded Systems 12, no. 1-2 (April 19, 2008): 63–94. http://dx.doi.org/10.1007/s10617-008-9013-3.

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18

Tolwinska, Anna. "Participation Reports help Crossref members drive research further." Science Editing 8, no. 2 (August 20, 2021): 180–85. http://dx.doi.org/10.6087/kcse.253.

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Анотація:

This article aims to explain the key metadata elements listed in Participation Reports, why it’s important to check them regularly, and how Crossref members can improve their scores. Crossref members register a lot of metadata in Crossref. That metadata is machine-readable, standardized, and then shared across discovery services and author tools. This is important because richer metadata makes content more discoverable and useful to the scholarly community. It’s not always easy to know what metadata Crossref members register in Crossref. This is why Crossref created an easy-to-use tool called

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19

Rasaiah, B., C. Bellman, R. D. Hewson, S. D. Jones, and T. J. Malthus. "ENHANCED DATA DISCOVERABILITY FOR IN SITU HYPERSPECTRAL DATASETS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-4 (June 3, 2016): 49–52. http://dx.doi.org/10.5194/isprsannals-iii-4-49-2016.

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Field spectroscopic metadata is a central component in the quality assurance, reliability, and discoverability of hyperspectral data and the products derived from it. Cataloguing, mining, and interoperability of these datasets rely upon the robustness of metadata protocols for field spectroscopy, and on the software architecture to support the exchange of these datasets. Currently no standard for in situ spectroscopy data or metadata protocols exist. This inhibits the effective sharing of growing volumes of in situ spectroscopy datasets, to exploit the benefits of integrating with the evolving

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20

Rasaiah, B., C. Bellman, R. D. Hewson, S. D. Jones, and T. J. Malthus. "ENHANCED DATA DISCOVERABILITY FOR IN SITU HYPERSPECTRAL DATASETS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-4 (June 3, 2016): 49–52. http://dx.doi.org/10.5194/isprs-annals-iii-4-49-2016.

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Field spectroscopic metadata is a central component in the quality assurance, reliability, and discoverability of hyperspectral data and the products derived from it. Cataloguing, mining, and interoperability of these datasets rely upon the robustness of metadata protocols for field spectroscopy, and on the software architecture to support the exchange of these datasets. Currently no standard for in situ spectroscopy data or metadata protocols exist. This inhibits the effective sharing of growing volumes of in situ spectroscopy datasets, to exploit the benefits of integrating with the evolving

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21

Ivanov, Boris V., Pavel N. Sviashchennikov, Danila M. Zhuravskiy, Alexey K. Pavlov, Eirik J. Frland, and Ketil Isaksen. "Sea ice metadata for Billefjorden and Grnfjorden, Svalbard." Czech Polar Reports 4, no. 2 (June 1, 2014): 129–39. http://dx.doi.org/10.5817/cpr2014-2-13.

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Description of sea ice conditions in the fjords of Svalbard is crucial for sea transport as well as studies of local climate and climate change. Old observations from the Russian Hydrometeorological stations in the mining settlements Barentsburg (Grnfjorden) and Pyramiden (Billefjorden) have now been digitized. These visual and instrumental observations are archived in the State Archive of Arctic and Antarctic Research Institute (AARI) and Murmansk Branch of the Russian Hydrometeorological Service. In this paper, we bring an overview of the sea ice metadata with few examples of yearly changes

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22

Fong, J., H. K. Wong, and S. M. Huang. "Continuous and incremental data mining association rules using frame metadata model." Knowledge-Based Systems 16, no. 2 (March 2003): 91–100. http://dx.doi.org/10.1016/s0950-7051(02)00076-x.

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23

Csurka, Gabriela, and Katerina Pastra. "Introduction to the special issue on “metadata mining for image understanding”." Multimedia Tools and Applications 42, no. 1 (November 12, 2008): 1–4. http://dx.doi.org/10.1007/s11042-008-0248-6.

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24

Li, Zhi Gang, Hui Liu, and Wu Nian Yang. "Service-Oriented Sharing Architecture for Mining Area Spatial Information and Key Techniques." Advanced Materials Research 230-232 (May 2011): 501–5. http://dx.doi.org/10.4028/www.scientific.net/amr.230-232.501.

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Mining spatial information sharing platform, as a new type of mining information management systems, will greatly enhance the level of the existing mine information management and their ability to support production operations. Based on the analysis of the current information sharing framework, a new mining information sharing platform which is service-oriented GIS is introduced. Then, this article describes three key techniques to achieve: the SOA-based GIS technology, metadata technology and spatial database technology. Finally, the paper talks about the research way for the development of m

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25

Tosaka, Yuji, and Cathy Weng. "Reexamining Content-Enriched Access: Its Effect on Usage and Discovery." College & Research Libraries 72, no. 5 (September 1, 2011): 412–27. http://dx.doi.org/10.5860/crl-137.

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Content-enriched metadata in bibliographic records is considered helpful to library users in identifying and selecting library materials for their needs. The paper presents a study, using circulation data from a medium-sized academic library, of the effect of content-enriched records on library materials usage. The study also examines OPAC search transactions of circulated items to learn how enriched metadata is used. The findings show that enhanced records were overall associated with higher circulation rates and that keyword search was the most frequently used search option directly associat

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26

Sinclair, Lucas, Umer Z. Ijaz, Lars Juhl Jensen, Marco J. L. Coolen, Cecile Gubry-Rangin, Alica Chroňáková, Anastasis Oulas, et al. "Seqenv: linking sequences to environments through text mining." PeerJ 4 (December 20, 2016): e2690. http://dx.doi.org/10.7717/peerj.2690.

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Understanding the distribution of taxa and associated traits across different environments is one of the central questions in microbial ecology. High-throughput sequencing (HTS) studies are presently generating huge volumes of data to address this biogeographical topic. However, these studies are often focused on specific environment types or processes leading to the production of individual, unconnected datasets. The large amounts of legacy sequence data with associated metadata that exist can be harnessed to better place the genetic information found in these surveys into a wider environment

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27

Chen Huayue. "A Novel Data Mining Metadata Constructing Algorithm based on Formal Logic DLRDM." Journal of Convergence Information Technology 7, no. 11 (June 30, 2012): 132–40. http://dx.doi.org/10.4156/jcit.vol7.issue11.17.

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28

T. "Significant Term List Based Metadata Conceptual Mining Model for Effective Text Clustering." Journal of Computer Science 8, no. 10 (October 1, 2012): 1660–66. http://dx.doi.org/10.3844/jcssp.2012.1660.1666.

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29

Sun, Li, Li Guo, and Huan Tian. "Research on Distributed Vertical Frequent Pattern Mining Method Based on Metadata Integration." Journal of Physics: Conference Series 1449 (January 2020): 012062. http://dx.doi.org/10.1088/1742-6596/1449/1/012062.

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30

Malon, D., J. Cranshaw, and Q. Zhang. "An extensible infrastructure for querying and mining event-level metadata in ATLAS." Journal of Physics: Conference Series 396, no. 5 (December 13, 2012): 052053. http://dx.doi.org/10.1088/1742-6596/396/5/052053.

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31

Guerrero, Juan I., Antonio García, Enrique Personal, Joaquín Luque, and Carlos León. "Heterogeneous data source integration for smart grid ecosystems based on metadata mining." Expert Systems with Applications 79 (August 2017): 254–68. http://dx.doi.org/10.1016/j.eswa.2017.03.007.

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32

Moreels, Dries. "Mining the databases of the Vlaams Theater Instituut." Art Libraries Journal 33, no. 3 (2008): 39–43. http://dx.doi.org/10.1017/s0307472200015479.

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In 1987 the Vlaams Theater Instituut (VTi) was born, as a result of the need to support and identify the ambitions of a new generation of performing artists in Flanders and Brussels, to document and investigate the context of this turbulent but artistically exceptional period, and to develop appropriate policy instruments for this burgeoning practice. Twenty years on, and the artistic and social context has changed radically. Initiatives that were played out on the fringes ‘back then’ we now see right at the centre of things. Today the need to keep documenting, investigating and reflecting is

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33

Jalal, Ahmed Adeeb. "ENGINEERING MINING A LARGE SCALE DATA BASED ON FEATURE ENGINEERING, METADATA, AND ONTOLOGIES." International Journal of Digital Information and Wireless Communications 6, no. 4 (2016): 219–29. http://dx.doi.org/10.17781/p002091.

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34

Klavans, Judith L., Carolyn Sheffield, Eileen Abels, Jimmy Lin, Rebecca Passonneau, Tandeep Sidhu, and Dagobert Soergel. "Computational linguistics for metadata building (CLiMB): using text mining for the automatic identification, categorization, and disambiguation of subject terms for image metadata." Multimedia Tools and Applications 42, no. 1 (November 8, 2008): 115–38. http://dx.doi.org/10.1007/s11042-008-0253-9.

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35

Maiah, Lax, DR A. GOVARDHAN DR.A.GOVARDHAN, and DR C. SUNIL KUMAR. "A FRAMEWORK FOR SPATIO-TEMPORAL DATA WAREHOUSE." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 1 (February 1, 2013): 146–50. http://dx.doi.org/10.24297/ijct.v4i1c.3114.

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Анотація:

Data Warehouse (DW) is topic-oriented, integrated, static datasets which are used to support decision-making. Driven by the constraint of mass spatio-temporal data management and application, Spatio-Temporal Data Warehouse (STDW) was put forward, and many researchers scattered all over the world focused their energy on it.Although the research on STDW is going in depth , there are still many key difficulties to be solved, such as the design principle, system framework, spatio-temporal data model (STDM), spatio-temporal data process (STDP), spatial data mining (SDM) and etc. In this paper, the

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36

Ivanova, Svetlana, Elena Sant’eva, Maxim Bakanov, Leszek Sobik, and Leonid Lopukhinsky. "Integration of Environmental Information in a Mining Region Using a Geoportal." E3S Web of Conferences 278 (2021): 01013. http://dx.doi.org/10.1051/e3sconf/202127801013.

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At present, the complex nature of the impact on the ecosystem in regions with intensive mining creates a multidimensional information “plume” consisting of data on mineral reserves, the state of mining operations, accumulated, current and future environmental pollution. The transition to the lean use of the subsoil and the reasonable disposal of mining waste requires fundamentally new forms of environmental information accumulation and processing during designing new enterprises and regulating the activities of existing ones. The most promising form of information support for the greening of m

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37

Wang, Xiao Bin, and Qing Jun Wang. "Study on Personalized Recommendation Technology of Digital TV Programs." Applied Mechanics and Materials 347-350 (August 2013): 3035–38. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3035.

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This paper aims at one of key technologies in digital television development ---intelligent personalized recommendation technology of digital TV programs for study. This paper proposes to take advantage of ample TV-Anytime to describe metadata so as to perform specific plans of guide service for TV programs based on TV-Anytime metadata specification. It combines technology such as data mining and artificial intelligence etc with a view of building a personalized TV program recommendation system on the framework of the multi-agent. Besides, a hybrid algorithm with content filtering and collabor

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38

Han, Jun, Yu Huang, Kuldeep Kumar, and Sukanto Bhattacharya. "Time-Varying Dynamic Topic Model." Journal of Global Information Management 26, no. 1 (January 2018): 104–19. http://dx.doi.org/10.4018/jgim.2018010106.

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In this paper the authors build on prior literature to develop an adaptive and time-varying metadata-enabled dynamic topic model (mDTM) and apply it to a large Weibo dataset using an online Gibbs sampler for parameter estimation. Their approach simultaneously captures the maximum number of inherent dynamic features of microblogs thereby setting it apart from other online document mining methods in the extant literature. In summary, the authors' results show a better performance of mDTM in terms of the quality of the mined information compared to prior research and showcases mDTM as a promising

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39

D.A., Olubukola, Stephen O.M., Funmilayo A.K., Ayokunle O., Oyebola A., Oduroye A., Wumi A., and Yaw M. "Movie Success Prediction Using Data Mining." British Journal of Computer, Networking and Information Technology 4, no. 2 (September 22, 2021): 22–30. http://dx.doi.org/10.52589/bjcnit-cqocirec.

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Анотація:

The movie industry is arguably one of the biggest entertainment sectors. Nollywood, the Nigerian movie industry produces tons of movies for public consumption, but only a few make it to box-office or end up becoming blockbusters. The introduction of movie success prediction can play an important role in the industry not only to predict movie success but to help directors and producers make better decisions for the purpose of profit. This study proposes a movie prediction model that applies data mining techniques and machine learning algorithms to predict the success or failure of an upcoming m

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40

Pika, Anastasiia, Moe T. Wynn, Stephanus Budiono, Arthur H. M. ter Hofstede, Wil M. P. van der Aalst, and Hajo A. Reijers. "Privacy-Preserving Process Mining in Healthcare." International Journal of Environmental Research and Public Health 17, no. 5 (March 2, 2020): 1612. http://dx.doi.org/10.3390/ijerph17051612.

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Process mining has been successfully applied in the healthcare domain and has helped to uncover various insights for improving healthcare processes. While the benefits of process mining are widely acknowledged, many people rightfully have concerns about irresponsible uses of personal data. Healthcare information systems contain highly sensitive information and healthcare regulations often require protection of data privacy. The need to comply with strict privacy requirements may result in a decreased data utility for analysis. Until recently, data privacy issues did not get much attention in t

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41

Ryberg, Martin, R. Henrik Nilsson, Erik Kristiansson, Mats Töpel, Stig Jacobsson, and Ellen Larsson. "Mining metadata from unidentified ITS sequences in GenBank: A case study in Inocybe (Basidiomycota)." BMC Evolutionary Biology 8, no. 1 (2008): 50. http://dx.doi.org/10.1186/1471-2148-8-50.

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42

Djordjevic, Djordje, Joshua Y. S. Tang, Yun Xin Chen, Shu Lun Shannon Kwan, Raymond W. K. Ling, Gordon Qian, Chelsea Y. Y. Woo, Samuel J. Ellis, and Joshua W. K. Ho. "Discovery of perturbation gene targets via free text metadata mining in Gene Expression Omnibus." Computational Biology and Chemistry 80 (June 2019): 152–58. http://dx.doi.org/10.1016/j.compbiolchem.2019.03.014.

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43

Li, Yun, Yongyao Jiang, Juan Gu, Mingyue Lu, Manzhu Yu, Edward Armstrong, Thomas Huang, et al. "A Cloud-Based Framework for Large-Scale Log Mining through Apache Spark and Elasticsearch." Applied Sciences 9, no. 6 (March 16, 2019): 1114. http://dx.doi.org/10.3390/app9061114.

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Анотація:

The volume, variety, and velocity of different data, e.g., simulation data, observation data, and social media data, are growing ever faster, posing grand challenges for data discovery. An increasing trend in data discovery is to mine hidden relationships among users and metadata from the web usage logs to support the data discovery process. Web usage log mining is the process of reconstructing sessions from raw logs and finding interesting patterns or implicit linkages. The mining results play an important role in improving quality of search-related components, e.g., ranking, query suggestion

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Shrestha, Sushil, and Manish Pokharel. "Data Mining Applications Used in Education Sector." Journal of Education and Research 10, no. 2 (November 6, 2020): 27–51. http://dx.doi.org/10.3126/jer.v10i2.32721.

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Анотація:

The purpose of this work is to study the usage trends of Data Mining (DM) methods in education. It discusses different data mining techniques used for different types of educational data. The related papers were initially selected from the metadata containing words like Online Learning (OL) and Educational Data Mining (EDM). The papers were then filtered on the basis of DM algorithms, the purpose of study, and the types of data used. The findings suggested that EDM is the most commonly used technique for the prediction of students’ academic success, and the most used purpose is classification,

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45

Barbosa, Flávio, Arthur Vidal, and Flávio Mello. "Machine Learning for Cryptographic Algorithm Identification." Journal of Information Security and Cryptography (Enigma) 3, no. 1 (September 3, 2016): 3. http://dx.doi.org/10.17648/enig.v3i1.55.

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This paper aims to study encrypted text files in order to identify their encoding algorithm. Plain texts were encoded with distinct cryptographic algorithms and then some metadata were extracted from these codifications. Afterward, the algorithm identification is obtained by using data mining techniques. Firstly, texts in Portuguese, English and Spanish were encrypted using DES, Blowfish, RSA, and RC4 algorithms. Secondly, the encrypted files were submitted to data mining techniques such as J48, FT, PART, Complement Naive Bayes, and Multilayer Perceptron classifiers. Charts were created using

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46

Alshameri, Faleh, and Abdul Karim Bangura. "Generating metadata to study and teach about African issues." Information Technology & People 27, no. 3 (July 29, 2014): 341–65. http://dx.doi.org/10.1108/itp-06-2013-0112.

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Purpose – After almost three centuries of employing western educational approaches, many African societies are still characterized by low western literacy rates, civil conflicts, and underdevelopment. It is obvious that these western educational paradigms, which are not indigenous to Africans, have done relatively little good for Africans. Thus, the purpose of this paper is to argue that the salvation for Africans hinges upon employing indigenous African educational paradigms which can be subsumed under the rubric of ubuntugogy, which the authors define as the art and science of teaching and l

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Algur, Siddu P., and Prashant Bhat. "Web Video Object Mining: Expectation Maximization and Density Based Clustering of Web Video Metadata Objects." International Journal of Information Engineering and Electronic Business 8, no. 1 (January 8, 2016): 69–77. http://dx.doi.org/10.5815/ijieeb.2016.01.08.

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Yang, Haishui, Yajun Dai, Mingmin Xu, Qian Zhang, Xinmin Bian, Jianjun Tang, and Xin Chen. "Metadata-mining of 18S rDNA sequences reveals that “everything is not everywhere” for glomeromycotan fungi." Annals of Microbiology 66, no. 1 (June 25, 2015): 361–71. http://dx.doi.org/10.1007/s13213-015-1116-z.

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Wang, Jiangping. "Extracting Value from Unstructured Data – Implementing Text Analytics on the Voice of Student." Transactions on Machine Learning and Artificial Intelligence 8, no. 4 (August 1, 2020): 14–22. http://dx.doi.org/10.14738/tmlai.84.8456.

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Unstructured data is chaotic and messy with little or no metadata and lacks of traditional organization structure. However, same as any structured data, unstructured data is also part of valuable business asset. Many times, it is text heavy and needs extensive preprocessing before data mining algorithm can apply for building models in order to reveal value hidden in the data. Text as a form of data is widely used in business operations as a major way of communication, generating increasing volumes of data. Text data in its raw form is relatively dirty. The embedded business value can be extrac

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Forghani, M., and F. Karimipour. "EXTRACTING HUMAN BEHAVIORAL PATTERNS BY MINING GEO-SOCIAL NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-2/W3 (October 22, 2014): 115–20. http://dx.doi.org/10.5194/isprsarchives-xl-2-w3-115-2014.

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Accessibility of positioning technologies such as GPS offer the opportunity to store one’s travel experience and publish it on the web. Using this feature in web-based social networks and considering location information shared by users as a bridge connecting the users’ network to location information layer leads to the formation of Geo-Social Networks. The availability of large amounts of geographical and social data on these networks provides rich sources of information that can be utilized for studying human behavior through data analysis in a spatial-temporal-social context. This paper att

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