Relevant bibliographies by topics / Data mining applications

Contents

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

Academic literature on the topic 'Data mining applications'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Data mining applications"

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Wang, Lidong, and Guanghui Wang. "Data Mining Applications in Big Data." Computer Engineering and Applications Journal 4, no. 3 (2015): 143–52. http://dx.doi.org/10.18495/comengapp.v4i3.155.

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Data mining is a process of extracting hidden, unknown, but potentially useful information from massive data. Big Data has great impacts on scientific discoveries and value creation. This paper introduces methods in data mining and technologies in Big Data. Challenges of data mining and data mining with big data are discussed. Some technology progress of data mining and data mining with big data are also presented.
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Kohli, Sankalp. "Data Mining and Its Applications in Higher Education." International Journal of Scientific Engineering and Research 11, no. 1 (2023): 51–60. https://doi.org/10.70729/se23125221124.

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Bhakare, Parimal, and Shaini Suraj. "Data Mining in Healthcare: Current Applications and Issues." International Journal of Science and Research (IJSR) 11, no. 11 (2022): 1129–31. http://dx.doi.org/10.21275/sr221118072136.

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Rohini, M., and Marrynal S. Eastaff Mrs. "DATA MINING KNOWLEDGE DISCOVERYANDITS APPLICATIONS." Volume 7 Issue 11 7, no. 11 (2021): 1–3. https://doi.org/10.5281/zenodo.5675585.

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Data Mining and Knowledge Discovery is a tested companion investigated logical diary zeroing in on datamining. It is distributed by Springer Science + Business Media. Starting at 2012, the proofreader in-boss is Geoffrey 1, Webb. The act of data mining or information revelation, in spite of profoundly perplexing methods and applications,is dependent on an extremely straightforward idea. Gathering data from a breath of hotspots for the motivations behind analysis.Generally, data mining of hard drive recuperation is done to gather data which would then be able to be used toimprove a cycle or met
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Rahman, Nayem. "Data Mining Techniques and Applications." International Journal of Strategic Information Technology and Applications 9, no. 1 (2018): 78–97. http://dx.doi.org/10.4018/ijsita.2018010104.

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Data mining has been gaining attention with the complex business environments, as a rapid increase of data volume and the ubiquitous nature of data in this age of the internet and social media. Organizations are interested in making informed decisions with a complete set of data including structured and unstructured data that originate both internally and externally. Different data mining techniques have evolved over the last two decades. To solve a wide variety of business problems, different data mining techniques are developed. Practitioners and researchers in industry and academia continuo
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Barai, Sudhir Kumar. "DATA MINING APPLICATIONS IN TRANSPORTATION ENGINEERING." TRANSPORT 18, no. 5 (2003): 216–23. http://dx.doi.org/10.3846/16483840.2003.10414100.

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Data mining is the extraction of implicit, previously unknown and potentially useful information from data. In recent time, data mining studies have been carried out in many engineering disciplines. In this paper the background of data mining and tools is introduced. Further applications of data mining to transportation engineering problems are reviewed. The application of data mining for typical example of ‘Vehicle Crash Study’ is demonstrated using commercially available data mining tool. The paper highlights the potential of data mining tool application in transportation engineering sector.
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Caby, Errol C. "Data Mining Using SAS Applications." Technometrics 46, no. 2 (2004): 260–61. http://dx.doi.org/10.1198/tech.2004.s805.

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Ghani, Rayid, and Carlos Soares. "Data mining for business applications." ACM SIGKDD Explorations Newsletter 8, no. 2 (2006): 79–81. http://dx.doi.org/10.1145/1233321.1233332.

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Apte, Chidanand, Bing Liu, Edwin P. D. Pednault, and Padhraic Smyth. "Business applications of data mining." Communications of the ACM 45, no. 8 (2002): 49–53. http://dx.doi.org/10.1145/545151.545178.

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Shortland, R., and R. Scarfe. "Data mining applications in BT." BT Technology Journal 25, no. 3-4 (2007): 272–77. http://dx.doi.org/10.1007/s10550-007-0084-7.

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Dissertations / Theses on the topic "Data mining applications"

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Eapen, Arun George. "Application of Data mining in Medical Applications." Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/772.

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Abstract Data mining is a relatively new field of research whose major objective is to acquire knowledge from large amounts of data. In medical and health care areas, due to regulations and due to the availability of computers, a large amount of data is becoming available. On the one hand, practitioners are expected to use all this data in their work but, at the same time, such a large amount of data cannot be processed by humans in a short time to make diagnosis, prognosis and treatment schedules. A major objective of this thesis is to evaluate data mining tools in medical and hea
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Vithal, Kadam Omkar. "Novel applications of Association Rule Mining- Data Stream Mining." AUT University, 2009. http://hdl.handle.net/10292/826.

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From the advent of association rule mining, it has become one of the most researched areas of data exploration schemes. In recent years, implementing association rule mining methods in extracting rules from a continuous flow of voluminous data, known as Data Stream has generated immense interest due to its emerging applications such as network-traffic analysis, sensor-network data analysis. For such typical kinds of application domains, the facility to process such enormous amount of stream data in a single pass is critical.
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Du, Sang. "Data Mining Applications to Brain Energy Metabolism." Case Western Reserve University School of Graduate Studies / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1323463827.

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Matyja, Dariusz. "Applications of data mining algorithms to analysis of medical data." Thesis, Blekinge Tekniska Högskola, Avdelningen för programvarusystem, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4253.

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Medical datasets have reached enormous capacities. This data may contain valuable information that awaits extraction. The knowledge may be encapsulated in various patterns and regularities that may be hidden in the data. Such knowledge may prove to be priceless in future medical decision making. The data which is analyzed comes from the Polish National Breast Cancer Prevention Program ran in Poland in 2006. The aim of this master's thesis is the evaluation of the analytical data from the Program to see if the domain can be a subject to data mining. The next step is to evaluate several dat
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Wang, Jie. "MATRIX DECOMPOSITION FOR DATA DISCLOSURE CONTROL AND DATA MINING APPLICATIONS." UKnowledge, 2008. http://uknowledge.uky.edu/gradschool_diss/677.

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Access to huge amounts of various data with private information brings out a dual demand for preservation of data privacy and correctness of knowledge discovery, which are two apparently contradictory tasks. Low-rank approximations generated by matrix decompositions are a fundamental element in this dissertation for the privacy preserving data mining (PPDM) applications. Two categories of PPDM are studied: data value hiding (DVH) and data pattern hiding (DPH). A matrix-decomposition-based framework is designed to incorporate matrix decomposition techniques into data preprocessing to distort or
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Qin, Xiao. "Sequential Data Mining and its Applications to Pharmacovigilance." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/515.

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With the phenomenal growth of digital devices coupled with their ever-increasing capabilities of data generation and storage, sequential data is becoming more and more ubiquitous in a wide spectrum of application scenarios. There are various embodiments of sequential data such as temporal database, time series and text (word sequence) where the first one is synchronous over time and the latter two often generated in an asynchronous fashion. In order to derive precious insights, it is critical to learn and understand the behavior dynamics as well as the causality relationships across sequences.
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Okafor, Anthony. "Entropy based techniques with applications in data mining." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0013113.

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You, Guangjing. "Two Data Mining Applications for Predicting Pre-Diabetes." Thesis, North Dakota State University, 2015. https://hdl.handle.net/10365/27638.

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Document incorrectly classified as a dissertation on title page (decision to classify as a thesis from NDSU Graduate School)<br>In this study, the performance of Logistic Regression and Decision Tree modeling is compared by using SAS Enterprise Miner for predicting pre-diabetes in US population by using several of the common factors from the type 2 diabetes screening criteria. From 17 variables of NHANES? three sets of dataset, a total of 13 risk factors were selected as predictors of pre-diabetes. A comparison of two data mining methodology showed that Decision Tree has a higher ROC index tha
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Bui, Dang Bach. "Mining complex structured data: Enhanced methods and applications." Thesis, Curtin University, 2015. http://hdl.handle.net/20.500.11937/480.

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Conventional approaches to analysing complex business data typically rely on process models, which are difficult to construct and use. This thesis addresses this issue by converting semi-structured event logs to a simpler flat representation without any loss of information, which then enables direct applications of classical data mining methods. The thesis also proposes an effective and scalable classification method which can identify distinct characteristics of a business process for further improvements.
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Better, Marco L. "Data mining techniques for prediction and classification in discrete data applications." Connect to online resource, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3273688.

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Books on the topic "Data mining applications"

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A, Zanasi, Ebecken N. F. F, and Brebbia C. A, eds. Data mining V: Data mining, text mining, and their business applications. WIT, 2004.

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Li, Bohan, Lin Yue, Jing Jiang, et al., eds. Advanced Data Mining and Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95408-6.

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Li, Bohan, Lin Yue, Jing Jiang, et al., eds. Advanced Data Mining and Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95405-5.

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Tang, Changjie, Charles X. Ling, Xiaofang Zhou, Nick J. Cercone, and Xue Li, eds. Advanced Data Mining and Applications. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88192-6.

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Gan, Guojun, Bohan Li, Xue Li, and Shuliang Wang, eds. Advanced Data Mining and Applications. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05090-0.

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Bitetta, Valerio, Ilaria Bordino, Andrea Ferretti, Francesco Gullo, Stefano Pascolutti, and Giovanni Ponti, eds. Mining Data for Financial Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37720-5.

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Cao, Longbing, Philip S. Yu, Chengqi Zhang, and Huaifeng Zhang, eds. Data Mining for Business Applications. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-79420-4.

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Motoda, Hiroshi, Zhaohui Wu, Longbing Cao, Osmar Zaiane, Min Yao, and Wei Wang, eds. Advanced Data Mining and Applications. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-53914-5.

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Motoda, Hiroshi, Zhaohui Wu, Longbing Cao, Osmar Zaiane, Min Yao, and Wei Wang, eds. Advanced Data Mining and Applications. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-53917-6.

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Huang, Ronghuai, Qiang Yang, Jian Pei, João Gama, Xiaofeng Meng, and Xue Li, eds. Advanced Data Mining and Applications. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03348-3.

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Book chapters on the topic "Data mining applications"

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Shortland, R., and R. Scarfe. "Data Mining Applications." In Computer Aided Decision Support in Telecommunications. Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-009-0081-3_2.

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Hadzic, Fedja, Henry Tan, and Tharam S. Dillon. "Tree Mining Applications." In Mining of Data with Complex Structures. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17557-2_9.

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Džeroski, Sašo. "Relational Data Mining Applications: An Overview." In Relational Data Mining. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04599-2_14.

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Grossmann, Wilfried, and Stefanie Rinderle-Ma. "Data Mining for Temporal Data." In Data-Centric Systems and Applications. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46531-8_6.

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Zaki, Mohammed. "Unified Approach to Rooted Tree Mining: Algorithms and Applications." In Mining Graph Data. John Wiley & Sons, Inc., 2006. http://dx.doi.org/10.1002/9780470073049.ch15.

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Chen, Qingfeng, Baoshan Chen, and Chengqi Zhang. "Data Resources and Applications." In Intelligent Strategies for Pathway Mining. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04172-8_2.

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Thuraisingham, Bhavani, Mohammad Mehedy Masud, Pallabi Parveen, and Latifur Khan. "Data Mining Techniques." In Big Data Analytics with Applications in Insider Threat Detection. Auerbach Publications, 2017. http://dx.doi.org/10.1201/9781315119458-4.

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Grossmann, Wilfried, and Stefanie Rinderle-Ma. "Data Mining for Cross-Sectional Data." In Data-Centric Systems and Applications. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46531-8_5.

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Ramakrishnan, Naren, and Ananth Y. Grama. "Data Mining Applications in Bioinformatics." In Data Mining for Scientific and Engineering Applications. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1733-7_8.

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Robbins, David E., and Marco Chiesa. "CLINICAL APPLICATIONS AND DATA MINING." In Biomedical Engineering. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0116-2_13.

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Conference papers on the topic "Data mining applications"

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Yuan, Yimeng. "Credit Card Applications Based on Data Mining." In 2024 6th International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI). IEEE, 2024. https://doi.org/10.1109/mlbdbi63974.2024.10823931.

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Holmes, Geoff. "Developing data mining applications." In the 18th ACM SIGKDD international conference. ACM Press, 2012. http://dx.doi.org/10.1145/2339530.2339569.

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Thuraisingham, Bhavani, Latifur Khan, Mohammad M. Masud, and Kevin W. Hamlen. "Data Mining for Security Applications." In 2008 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing (EUC). IEEE, 2008. http://dx.doi.org/10.1109/euc.2008.62.

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Haigh, Karen Zita, Wendy Foslien, and Valerie Guralnik. "Data Mining for Space Applications." In Space OPS 2004 Conference. American Institute of Aeronautics and Astronautics, 2004. http://dx.doi.org/10.2514/6.2004-255-106.

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Scarfe, R. T. "Data mining applications in BT." In IEE Colloquium on Knowledge Discovery in Databases. IEE, 1995. http://dx.doi.org/10.1049/ic:19950125.

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Wang, Xilei, Yongjun Ma, and Xin Li. "Data Mining in Inconsistent Data." In 2010 International Conference on Internet Technology and Applications (iTAP). IEEE, 2010. http://dx.doi.org/10.1109/itapp.2010.5566131.

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Liu, Miao, Hai-Feng Guo, and Zhengxin Chen. "On Multi-Relational Data Mining for Foundation of Data Mining." In 2007 IEEE/ACS International Conference on Computer Systems and Applications. IEEE, 2007. http://dx.doi.org/10.1109/aiccsa.2007.370911.

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Niimi, Ayahiko. "Nice data mining!" In 2021 IEEE 12th International Workshop on Computational Intelligence and Applications (IWCIA). IEEE, 2021. http://dx.doi.org/10.1109/iwcia52852.2021.9626025.

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Sobh, Karim M., Ahmed Rafea, and Amr El-Kadi. "Mining Cloud Environments Usage Data." In Artificial Intelligence and Applications. ACTAPRESS, 2013. http://dx.doi.org/10.2316/p.2013.795-031.

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Revesz, Peter Z. "Data Mining Citation Databases." In the 19th International Database Engineering & Applications Symposium. ACM Press, 2014. http://dx.doi.org/10.1145/2790755.2790763.

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Reports on the topic "Data mining applications"

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Bautista-Gomez, Leonardo, and Franck Cappello. Detecting Silent Data Corruption for Extreme-Scale Applications through Data Mining. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1177404.

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Nenci, Silvia, and Francesco Quatraro. Innovation and Competitiveness in Mining Value Chains in Latin America. Inter-American Development Bank, 2021. http://dx.doi.org/10.18235/0003805.

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This paper provides an international overview of the mining global value chain (GVC) and its most recent transformations and trends, focusing on Argentina, Brazil, and Peru. The study uses international trade data and patent and scientific publications data. By using trade in value added, we first investigate the role of those countries in the international mining trade, and their specialization, participation, and position in the mining GVC for the period 2005-15. The analysis is carried out for both mining products and mining-related services, and also looks at the contribution of services t
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Volkova, Nataliia P., Nina O. Rizun, and Maryna V. Nehrey. Data science: opportunities to transform education. [б. в.], 2019. http://dx.doi.org/10.31812/123456789/3241.

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The article concerns the issue of data science tools implementation, including the text mining and natural language processing algorithms for increasing the value of high education for development modern and technologically flexible society. Data science is the field of study that involves tools, algorithms, and knowledge of math and statistics to discover knowledge from the raw data. Data science is developing fast and penetrating all spheres of life. More people understand the importance of the science of data and the need for implementation in everyday life. Data science is used in business
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Yurica, Kevin, Rahul Pande, and Rajeev Motwani. Distributed Streams-Based Data-Mining for Application Intrusion Detection. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada424288.

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Rodriguez Muxica, Natalia. Open configuration options Bioinformatics for Researchers in Life Sciences: Tools and Learning Resources. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0003982.

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The COVID-19 pandemic has shown that bioinformatics--a multidisciplinary field that combines biological knowledge with computer programming concerned with the acquisition, storage, analysis, and dissemination of biological data--has a fundamental role in scientific research strategies in all disciplines involved in fighting the virus and its variants. It aids in sequencing and annotating genomes and their observed mutations; analyzing gene and protein expression; simulation and modeling of DNA, RNA, proteins and biomolecular interactions; and mining of biological literature, among many other c
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de Kemp, E. A., H. A. J. Russell, B. Brodaric, et al. Initiating transformative geoscience practice at the Geological Survey of Canada: Canada in 3D. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331097.

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Application of 3D technologies to the wide range of Geosciences knowledge domains is well underway. These have been operationalized in workflows of the hydrocarbon sector for a half-century, and now in mining for over two decades. In Geosciences, algorithms, structured workflows and data integration strategies can support compelling Earth models, however challenges remain to meet the standards of geological plausibility required for most geoscientific studies. There is also missing links in the institutional information infrastructure supporting operational multi-scale 3D data and model develo
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de Kemp, E. A., H. A. J. Russell, B. Brodaric, et al. Initiating transformative geoscience practice at the Geological Survey of Canada: Canada in 3D. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331871.

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Application of 3D technologies to the wide range of Geosciences knowledge domains is well underway. These have been operationalized in workflows of the hydrocarbon sector for a half-century, and now in mining for over two decades. In Geosciences, algorithms, structured workflows and data integration strategies can support compelling Earth models, however challenges remain to meet the standards of geological plausibility required for most geoscientific studies. There is also missing links in the institutional information infrastructure supporting operational multi-scale 3D data and model develo
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Kong, Zhihao, and Na Lu. Determining Optimal Traffic Opening Time Through Concrete Strength Monitoring: Wireless Sensing. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317613.

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Construction and concrete production are time-sensitive and fast-paced; as such, it is crucial to monitor the in-place strength development of concrete structures in real-time. Existing concrete strength testing methods, such as the traditional hydraulic compression method specified by ASTM C 39 and the maturity method specified by ASTM C 1074, are labor-intensive, time consuming, and difficult to implement in the field. INDOT’s previous research (SPR-4210) on the electromechanical impedance (EMI) technique has established its feasibility for monitoring in-situ concrete strength to determine t
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Allende López, Marcos, Diego López, Sergio Cerón, et al. Quantum-Resistance in Blockchain Networks. Inter-American Development Bank, 2021. http://dx.doi.org/10.18235/0003313.

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This paper describes the work carried out by the Inter-American Development Bank, the IDB Lab, LACChain, Cambridge Quantum Computing (CQC), and Tecnológico de Monterrey to identify and eliminate quantum threats in blockchain networks. The advent of quantum computing threatens internet protocols and blockchain networks because they utilize non-quantum resistant cryptographic algorithms. When quantum computers become robust enough to run Shor's algorithm on a large scale, the most used asymmetric algorithms, utilized for digital signatures and message encryption, such as RSA, (EC)DSA, and (EC)DH
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Boyer, A., and N. R. Billette. Orebodies and mining environment, links between geology and quantification. Natural Resources Canada/CMSS/Information Management, 1989. http://dx.doi.org/10.4095/331774.

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The application of quantitative techniques to solve geological and geoengineering problems is relatively recent. Often borrowed from other fields, models must be selected with care to ensure that they are appropriate. The introduction, increasing use and rapid development of these techniques follows the fast evolution of more powerful computer hardwares and softwares. Studies have been carried out to establish the usefulness of several quantitative techniques in solving typical geological/geotechnical problems using available exploration and production drillhole data. Results derived from thes
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