Relevant bibliographies by topics / Operations Research. Data mining. Machine learning

Contents

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

Academic literature on the topic 'Operations Research. Data mining. Machine learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Operations Research. Data mining. Machine learning"

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Zelinska, Snizhana. "Machine learning: technologies and potential application at mining companies." E3S Web of Conferences 166 (2020): 03007. http://dx.doi.org/10.1051/e3sconf/202016603007.

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Implementation of machine learning systems is currently one of the most sought-after spheres of human activities at the interface of information technologies, mathematical analysis and statistics. Machine learning technologies are penetrating into our life through applied software created with the help of artificial intelligence algorithms. It is obvious that machine learning technologies will be developing fast and becoming part of the human information space both in our everyday life and in professional activities. However, building of machine learning systems requires great labour contribut
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Percha, Bethany. "Modern Clinical Text Mining: A Guide and Review." Annual Review of Biomedical Data Science 4, no. 1 (2021): 165–87. http://dx.doi.org/10.1146/annurev-biodatasci-030421-030931.

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Electronic health records (EHRs) are becoming a vital source of data for healthcare quality improvement, research, and operations. However, much of the most valuable information contained in EHRs remains buried in unstructured text. The field of clinical text mining has advanced rapidly in recent years, transitioning from rule-based approaches to machine learning and, more recently, deep learning. With new methods come new challenges, however, especially for those new to the field. This review provides an overview of clinical text mining for those who are encountering it for the first time (e.
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Fedushko, Solomia, Taras Ustyianovych, and Michal Gregus. "Real-Time High-Load Infrastructure Transaction Status Output Prediction Using Operational Intelligence and Big Data Technologies." Electronics 9, no. 4 (2020): 668. http://dx.doi.org/10.3390/electronics9040668.

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An approach to use Operational Intelligence with mathematical modeling and Machine Learning to solve industrial technology projects problems are very crucial for today’s IT (information technology) processes and operations, taking into account the exponential growth of information and the growing trend of Big Data-based projects. Monitoring and managing high-load data projects require new approaches to infrastructure, risk management, and data-driven decision support. Key difficulties that might arise when performing IT Operations are high error rates, unplanned downtimes, poor infrastructure
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Troncoso Espinosa, Fredy Humberto, Yamil Gerard Avello Betancur, and Luis Andres Martinez Flores. "Prediction of cellulose sheet cutting using Machine Learning." Universidad Ciencia y Tecnología 25, no. 110 (2021): 109–18. http://dx.doi.org/10.47460/uct.v25i110.481.

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Cellulose is the main raw material for the production of paper. Companies that produce it present in their production line the cutting of the cellulose sheet. This failure is sporadic and has a high economic impact since it paralyzes the production line for several hours, incurring unproductive hours and a large deployment of human and financial resources. In this research, the use of Data Mining is proposed to define a machine learning algorithm that allows predicting the cutting of the cellulose sheet in a production line of a cellulose plant in Chile. The results show that by applying this
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Qiu, Yongtao, Weixi Ji, and Chaoyang Zhang. "A Hybrid Machine Learning and Population Knowledge Mining Method to Minimize Makespan and Total Tardiness of Multi-Variety Products." Applied Sciences 9, no. 24 (2019): 5286. http://dx.doi.org/10.3390/app9245286.

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Nowadays, the production model of many enterprises is multi-variety customized production, and the makespan and total tardiness are the main metrics for enterprises to make production plans. This requires us to develop a more effective production plan promptly with limited resources. Previous research focuses on dispatching rules and algorithms, but the application of the knowledge mining method for multi-variety products is limited. In this paper, a hybrid machine learning and population knowledge mining method to minimize makespan and total tardiness for multi-variety products is proposed. F
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Esmaeilzadeh, Ehsan, and Seyedmirsajad Mokhtarimousavi. "Machine Learning Approach for Flight Departure Delay Prediction and Analysis." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 8 (2020): 145–59. http://dx.doi.org/10.1177/0361198120930014.

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The expected growth in air travel demand and the positive correlation with the economic factors highlight the significant contribution of the aviation community to the U.S. economy. On‐time operations play a key role in airline performance and passenger satisfaction. Thus, an accurate investigation of the variables that cause delays is of major importance. The application of machine learning techniques in data mining has seen explosive growth in recent years and has garnered interest from a broadening variety of research domains including aviation. This study employed a support vector machine
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Lucero, Robert, and Ragnhildur Bjarnadottir. "ADVANCING AN INTERDISCIPLINARY SCIENCE OF AGING THROUGH A PRACTICE-BASED DATA SCIENCE APPROACH." Innovation in Aging 3, Supplement_1 (2019): S480. http://dx.doi.org/10.1093/geroni/igz038.1786.

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Abstract Two hundred and fifty thousand older adults die annually in United States hospitals because of iatrogenic conditions (ICs). Clinicians, aging experts, patient advocates and federal policy makers agree that there is a need to enhance the safety of hospitalized older adults through improved identification and prevention of ICs. To this end, we are building a research program with the goal of enhancing the safety of hospitalized older adults by reducing ICs through an effective learning health system. Leveraging unique electronic data and healthcare system and human resources at the Univ
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Witulska, Justyna, Paweł Stefaniak, Bartosz Jachnik, Artur Skoczylas, Paweł Śliwiński, and Marek Dudzik. "Recognition of LHD Position and Maneuvers in Underground Mining Excavations—Identification and Parametrization of Turns." Applied Sciences 11, no. 13 (2021): 6075. http://dx.doi.org/10.3390/app11136075.

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The Inertial Measurement Unit (IMU) is widely used in the monitoring of mining assets. A good example is the Polish underground copper ore mines of KGHM, where research work with the use of the IMU has been carried out for several years. The potential of inertial sensors was ensured by the development of advanced analytics using machine learning methods to support the maintenance management of an extensive machine park and machine manufacturer in adapting various construction elements to mining conditions. The key algorithms developed in the field of inertial data concern: identification of cy
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Kopeć, Anna, Paweł Trybała, Dariusz Głąbicki, et al. "Application of Remote Sensing, GIS and Machine Learning with Geographically Weighted Regression in Assessing the Impact of Hard Coal Mining on the Natural Environment." Sustainability 12, no. 22 (2020): 9338. http://dx.doi.org/10.3390/su12229338.

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Mining operations cause negative changes in the environment. Therefore, such areas require constant monitoring, which can benefit from remote sensing data. In this article, research was carried out on the environmental impact of underground hard coal mining in the Bogdanka mine, located in the southeastern Poland. For this purpose, spectral indexes, satellite radar interferometry, Geographic Information System (GIS) tools and machine learning algorithms were utilized. Based on optical, radar, geological, hydrological and meteorological data, a spatial model was developed to determine the stati
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Lammatha, Kranthi K. "Data Mining on 5G Technology IOT." International Journal of Engineering and Computer Science 8, no. 05 (2019): 24655–60. http://dx.doi.org/10.18535/ijecs/v8i05.4291.

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Data Mining on 5G Technology IOT Currently, data mining is regarded as one of the essential factors for the next generation of mobile networks. Through research and data analysis, there are expectations that complexity of these networks will be overcome and it will be possible to carry out dynamic management and operation activities. In order to full comprehend the particulars of 5G network, there are certain kind of information that should be gathered by network components in order to be analyzed by a data mining scheme. The recent years have seen a tremendous effort put in the course of desi
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Dissertations / Theses on the topic "Operations Research. Data mining. Machine learning"

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Cooper, Heather. "Comparison of Classification Algorithms and Undersampling Methods on Employee Churn Prediction: A Case Study of a Tech Company." DigitalCommons@CalPoly, 2020. https://digitalcommons.calpoly.edu/theses/2260.

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Churn prediction is a common data mining problem that many companies face across industries. More commonly, customer churn has been studied extensively within the telecommunications industry where there is low customer retention due to high market competition. Similar to customer churn, employee churn is very costly to a company and by not deploying proper risk mitigation strategies, profits cannot be maximized, and valuable employees may leave the company. The cost to replace an employee is exponentially higher than finding a replacement, so it is in any company’s best interest to prioritize
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Pawlowski, Colin. "Machine learning for problems with missing and uncertain data with applications to personalized medicine." Thesis, Massachusetts Institute of Technology, 2019.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2019<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 205-215).<br>When we try to apply statistical learning in real-world applications, we frequently encounter data which include missing and uncertain values. This thesis explores the problem of learning from missing and uncertain da
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McKeague-McFadden, Ikaika A. "Identifying Students at Risk of Not Passing Introductory Physics Using Data Mining and Machine Learning." Miami University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=miami1596214863294544.

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Li, Rui [Verfasser], Burkhard [Akademischer Betreuer] [Gutachter] Rost, and Stefan [Gutachter] Kramer. "Data Mining and Machine Learning Methods for High-dimensional Patient Data in Dementia Research: Voxel Features Mining, Subgroup Discovery and Multi-view Learning / Rui Li ; Gutachter: Burkhard Rost, Stefan Kramer ; Betreuer: Burkhard Rost." München : Universitätsbibliothek der TU München, 2017. http://d-nb.info/1125018224/34.

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Kurmapu, Dhruva. "A Methodology to Measure and Improve U.S. States Highway Sustainability Using Data Envelopment Analysis and Self Organizing Maps." Ohio University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1344475693.

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Kanashiro, Augusto. "Um data warehouse de publicações científicas: indexação automática da dimensão tópicos de pesquisa dos data marts." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-13092007-094903/.

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Este trabalho de mestrado insere-se no contexto do projeto de uma Ferramenta Inteligente de Apoio à Pesquisa (FIP), sendo desenvolvida no Laboratório de Inteligência Computacional do ICMC-USP. A ferramenta foi proposta para recuperar, organizar e minerar grandes conjuntos de documentos científicos (na área de computação). Nesse contexto, faz-se necessário um repositório de artigos para a FIP. Ou seja, um Data Warehouse que armazene e integre todas as informações extraídas dos documentos recuperados de diferentes páginas pessoais, institucionais e de repositórios de artigos da Web. Para suporta
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Solihu, Gaffar. "Applying Deep Learning to the Ice Cream Vendor Problem: An Extension of the Newsvendor Problem." Digital Commons @ East Tennessee State University, 2021. https://dc.etsu.edu/etd/3945.

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The Newsvendor problem is a classical supply chain problem used to develop strategies for inventory optimization. The goal of the newsvendor problem is to predict the optimal order quantity of a product to meet an uncertain demand in the future, given that the demand distribution itself is known. The Ice Cream Vendor Problem extends the classical newsvendor problem to an uncertain demand with unknown distribution, albeit a distribution that is known to depend on exogenous features. The goal is thus to estimate the order quantity that minimizes the total cost when demand does not follow any kno
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Otine, Charles. "HIV Patient Monitoring Framework Through Knowledge Engineering." Doctoral thesis, Blekinge Tekniska Högskola [bth.se], School of Planning and Media Design, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-00540.

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Uganda has registered more than a million deaths since the HIV virus was first offi¬cially reported in the country over 3 decades ago. The governments in partnership with different groups have implemented different programmes to address the epidemic. The support from different donors and reduction in prices of treatment resulted in the focus on antiretroviral therapy access to those affected. Presently only a quarter of the approximately 1 million infected by HIV in Uganda are undergoing antiretroviral therapy. The number of patients pause a challenge in monitoring of therapy given the overall
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Augier, Sébastien. "Apprentissage Supervisé Relationnel par Algorithmes d'Évolution." Phd thesis, Université Paris Sud - Paris XI, 2000. http://tel.archives-ouvertes.fr/tel-00947322.

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Cette thèse concerne l'apprentissage de règles relationnelles à partir d'exemples et de contre-exemples, à l'aide d'algorithmes évolutionnaires. Nous étudions tout d'abord un biais de langage offrant une expressivité suffisamment riche pour permettre de couvrir à la fois le cadre de l'apprentissage relationnel par interprétations et les formalismes propositionnels classiques. Bien que le coût de l'induction soit caractérisé par la complexité NP-difficile du test de subsomption pour cette classe de langages, une solution capable de traiter en pratique les problèmes réels complexes est proposée.
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Milliet, De Faverges Marie. "Développement et implémentation de modèles apprenants pour l’exploitation des grandes gares." Electronic Thesis or Diss., Paris, CNAM, 2020. http://www.theses.fr/2020CNAM1283.

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Cette thèse traite de l’incertitude et de la robustesse dans les problèmes de d´décision, avec le cas d’application des affectations de quais en gare en cas de retards. Une m´méthodologie en deux parties est proposée pour aborder ce problème. Dans un premier temps, les archives de données de retards sont utilisées pour construire des modèles de prédiction de distribution de probabilités conditionnellement aux valeurs d’un ensemble de variables explicatives. Une m´méthodologie de validation et d’évaluation de ces prédictions est mise en place afin d’assurer leur fiabilité pour de la prise de d´
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Books on the topic "Operations Research. Data mining. Machine learning"

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Philippe, Lenca, Petit Jean-Marc, and SpringerLink (Online service), eds. Discovery Science: 15th International Conference, DS 2012, Lyon, France, October 29-31, 2012. Proceedings. Springer Berlin Heidelberg, 2012.

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Data Science. MIT Press, 2018.

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Kelleher, John D., and Brendan Tierney. Data Science. MIT Press, 2018.

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Kelleher, John D., and Brendan Tierney. Data Science. MIT Press, 2018.

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author, Tierney Brendan 1970, ed. Data science. MIT Press, 2018.

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Poon, Josiah, and Simon K. Poon. Data Analytics for Traditional Chinese Medicine Research. Springer, 2014.

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Support Vector Machines Chapman HallCRC Data Mining and Knowledge Discovery Serie. CRC Press, 2012.

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Petit, Jean-Marc, Philippe Lenca, and Jean-Gabriel Ganascia. Discovery Science: 15th International Conference, DS 2012, Lyon, France, October 29-31, 2012, Proceedings. Springer, 2012.

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Dobson, James E. Critical Digital Humanities. University of Illinois Press, 2019. http://dx.doi.org/10.5622/illinois/9780252042270.001.0001.

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This book seeks to develop an answer to the major question arising from the adoption of sophisticated data-science approaches within humanities research: are existing humanities methods compatible with computational thinking? Data-based and algorithmically powered methods present both new opportunities and new complications for humanists. This book takes as its founding assumption that the exploration and investigation of texts and data with sophisticated computational tools can serve the interpretative goals of humanists. At the same time, it assumes that these approaches cannot and will not
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Fridlund, Mats, Mila Oiva, and Petri Paju, eds. Digital Histories: Emergent Approaches within the New Digital History. Helsinki University Press, 2020. http://dx.doi.org/10.33134/hup-5.

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Historical scholarship is currently undergoing a digital turn. All historians have experienced this change in one way or another, by writing on word processors, applying quantitative methods on digitalized source materials, or using internet resources and digital tools. Digital Histories showcases this emerging wave of digital history research. It presents work by historians who – on their own or through collaborations with e.g. information technology specialists – have uncovered new, empirical historical knowledge through digital and computational methods. The topics of the volume range from
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Book chapters on the topic "Operations Research. Data mining. Machine learning"

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Hauck, Florian, and Natalia Kliewer. "Data Analytics in Railway Operations: Using Machine Learning to Predict Train Delays." In Operations Research Proceedings. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48439-2_90.

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Bhattacharya, Sweta, and Sombuddha Sengupta. "Application of Data Mining Techniques in Autoimmune Diseases Research and Treatment." In Machine Learning and IoT. CRC Press, 2018. http://dx.doi.org/10.1201/9781351029940-7.

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Cornuéjols, Antoine, and Christel Vrain. "Designing Algorithms for Machine Learning and Data Mining." In A Guided Tour of Artificial Intelligence Research. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-06167-8_12.

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Schneckenreither, Manuel, and Stefan Haeussler. "Reinforcement Learning Methods for Operations Research Applications: The Order Release Problem." In Machine Learning, Optimization, and Data Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13709-0_46.

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Fernández-Aguirre, Karmele, María I. Landaluce, Ana Martín, and Juan I. Modroño. "Data Mining of an On-line Survey – A Market Research Application." In Data Analysis, Machine Learning and Applications. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78246-9_22.

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Mei, Yuan, Ting Hu, and Li Chun Yang. "Research on Short-Term Urban Traffic Congestion Based on Fuzzy Comprehensive Evaluation and Machine Learning." In Data Mining and Big Data. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7205-0_9.

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Li, Rui. "Data Mining and Machine Learning Methods for Dementia Research." In Biomarkers for Alzheimer’s Disease Drug Development. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7704-8_25.

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Qin, Danyang, Ruixue Liu, Jiaqi Zhen, Songxiang Yang, and Erfu Wang. "Research on Decentralized Group Replication Strategy Based on Correlated Patterns Mining in Data Grids." In Machine Learning and Intelligent Communications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52730-7_30.

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Li, Jian-qiang, Cheng-lin Niu, Ji-zhen Liu, and Luan-ying Zhang. "Research and Application of Data Mining in Power Plant Process Control and Optimization." In Advances in Machine Learning and Cybernetics. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11739685_16.

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Singh, Tajinder, and Madhu Kumari. "Machine Learning-Based Text Mining in Social Media for COVID-19." In Computational Modeling and Data Analysis in COVID-19 Research. CRC Press, 2021. http://dx.doi.org/10.1201/9781003137481-6.

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Conference papers on the topic "Operations Research. Data mining. Machine learning"

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Jian-Qiang Li, Ji-Zhen Liu, Cheng-Lin Niu, and Luan-Ying Zhang. "The research and application of data mining in power plant operation optimization." In Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527208.

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Sharma, Seema, Jitendra Agrawal, Shikha Agarwal, and Sanjeev Sharma. "Machine learning techniques for data mining: A survey." In 2013 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2013. http://dx.doi.org/10.1109/iccic.2013.6724149.

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Wang, Zhan-quan, Hai-bo Chen, and Hui-qun Yu. "Spatial Co-Location Rule Mining Research in Continuous Data." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258705.

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Zhu-xi Chen, Kong-fa Hu, Yan Sun, and Ling Chen. "Research on mining frequency path in compressed path data." In 2008 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2008. http://dx.doi.org/10.1109/icmlc.2008.4620423.

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Lin, Xu-Dong, Shang-Wei Yan, Pi-Yuan Lin, and Pei-Jie Huang. "Research on examination of broiler price periods in data mining." In 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5580750.

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Bin Dong, Xiu-Ling Liu, and Hong-Rui Wang. "Research on three dimension data mining based on visualization technology." In 2009 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2009. http://dx.doi.org/10.1109/icmlc.2009.5212476.

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Jin, Shimei, Wei Chen, and Jiarui Han. "Graph-based machine learning algorithm with application in data mining." In 2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN). IEEE, 2017. http://dx.doi.org/10.1109/icrcicn.2017.8234519.

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Huang, Lan, Chun-guang Zhou, Yu-qin Zhou, and Zhe Wang. "Research on Data Mining Algorithms for Automotive Customers' Behavior Prediction Problem." In 2008 Seventh International Conference on Machine Learning and Applications. IEEE, 2008. http://dx.doi.org/10.1109/icmla.2008.23.

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Zhang, Rong-Mei, and Ling-Ling Liu. "Research on Internet Intelligent Tutoring System based on MAS and data mining." In 2009 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2009. http://dx.doi.org/10.1109/icmlc.2009.5212562.

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Fang, Ying-wu, Yi Wang, Peng-yang Li, Yan-jun Lu, Xiu-bin Zhao, and Hui Xu. "Research on Dynamic Generating Algorithms of Large Itemsets of Distributive Data Mining Architecture." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258659.

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Reports on the topic "Operations Research. Data mining. Machine learning"

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Becker, Sarah, Megan Maloney, and Andrew Griffin. A multi-biome study of tree cover detection using the Forest Cover Index. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42003.

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Tree cover maps derived from satellite and aerial imagery directly support civil and military operations. However, distinguishing tree cover from other vegetative land covers is an analytical challenge. While the commonly used Normalized Difference Vegetation Index (NDVI) can identify vegetative cover, it does not consistently distinguish between tree and low-stature vegetation. The Forest Cover Index (FCI) algorithm was developed to take the multiplicative product of the red and near infrared bands and apply a threshold to separate tree cover from non-tree cover in multispectral imagery (MSI)
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