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

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Dissertations / Theses on the topic 'Operations Research. Data mining. Machine learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Bonates, Tiberius. "Optimization in logical analysis of data." 2007. http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.15788.

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12

Sakr, Nourhan. "Data-Driven Combinatorial Optimization and Efficient Machine Learning Frameworks." Thesis, 2019. https://doi.org/10.7916/d8-z27f-xv84.

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Contemporary research in building optimization models and designing algorithms has become more data-centric and application-specific. While addressing three problems in the fields of combinatorial optimization and machine learning (ML), this work highlights the value of making data an important driver for building frameworks and designing solutions. In Chapter 2, we look into building frameworks for data-intensive applications, such as ML algorithms. We introduce a family of matrices, Structured Spinners, and use these to perform input data projections. This operation is commonly needed for

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13

Karnik, Shreyas. "Mining Biomedical Literature to Extract Pharmacokinetic Drug-Drug Interactions." Thesis, 2014. http://hdl.handle.net/1805/3912.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>Polypharmacy is a general clinical practice, there is a high chance that multiple administered drugs will interfere with each other, such phenomenon is called drug-drug interaction (DDI). DDI occurs when drugs administered change each other's pharmacokinetic (PK) or pharmacodynamic (PD) response. DDIs in many ways affect the overall effectiveness of the drug or at some times pose a risk of serious side effects to the patients thus, it becomes very challenging to for the successful drug development and clinical patient care. Biomedica

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14

(7036478), Aiyshwariya Paulvannan Kanmani. "A data-centric framework for assessing environmental sustainability." Thesis, 2019.

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Necessity to sustain resources has risen in recent years with significant number of people affected by lack of access to essential resources. Framing policies that support environmental sustainability is necessary for addressing the issue. Effective policies necessitate access to a framework which assesses and keeps track of sustainability. Conventional frameworks that support such policy-making involve ranking of countries based on a weighted sum of several environmental performance metrics. However, the selection and weighing of metrics is often biased. This study proposes a new framework to

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15

Akova, Ferit. "A nonparametric Bayesian perspective for machine learning in partially-observed settings." Thesis, 2014. http://hdl.handle.net/1805/4825.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>Robustness and generalizability of supervised learning algorithms depend on the quality of the labeled data set in representing the real-life problem. In many real-world domains, however, we may not have full knowledge of the underlying data-generating mechanism, which may even have an evolving nature introducing new classes continually. This constitutes a partially-observed setting, where it would be impractical to obtain a labeled data set exhaustively defined by a fixed set of classes. Traditional supervised learning algorithms, a

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16

(11210091), Prateek Jaiswal. "Variational Inference for Data-driven Stochastic Programming." Thesis, 2021.

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<div>Stochastic programs are standard models for decision-making under uncertainty and have been extensively studied in the operations research literature. In general, stochastic programming involves minimizing an expected cost function, where the expectation is with respect to fully specified stochastic models that quantify the aleatoric or `inherent' uncertainty in the decision-making problem. In practice, however, the stochastic models are unknown but can be estimated from data, introducing an additional epistemic uncertainty into the decision-making problem. The Bayesian framework provides

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17

Pandit, Yogesh. "Context specific text mining for annotating protein interactions with experimental evidence." Thesis, 2014. http://hdl.handle.net/1805/3809.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>Proteins are the building blocks in a biological system. They interact with other proteins to make unique biological phenomenon. Protein-protein interactions play a valuable role in understanding the molecular mechanisms occurring in any biological system. Protein interaction databases are a rich source on protein interaction related information. They gather large amounts of information from published literature to enrich their data. Expert curators put in most of these efforts manually. The amount of accessible and publicly availabl

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18

(11153640), Amir Daneshmand. "Parallel and Decentralized Algorithms for Big-data Optimization over Networks." Thesis, 2021.

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<p>Recent decades have witnessed the rise of data deluge generated by heterogeneous sources, e.g., social networks, streaming, marketing services etc., which has naturally created a surge of interests in theory and applications of large-scale convex and non-convex optimization. For example, real-world instances of statistical learning problems such as deep learning, recommendation systems, etc. can generate sheer volumes of spatially/temporally diverse data (up to Petabytes of data in commercial applications) with millions of decision variables to be optimized. Such problems are often referred

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19

"Managing a User's Vulnerability on a Social Networking Site." Doctoral diss., 2015. http://hdl.handle.net/2286/R.I.29638.

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abstract: Users often join an online social networking (OSN) site, like Facebook, to remain social, by either staying connected with friends or expanding social networks. On an OSN site, users generally share variety of personal information which is often expected to be visible to their friends, but sometimes vulnerable to unwarranted access from others. The recent study suggests that many personal attributes, including religious and political affiliations, sexual orientation, relationship status, age, and gender, are predictable using users' personal data from an OSN site. The majority of us

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20

Faustino, João Manuel Garcia. "Data Science: The state of the art." Master's thesis, 2017. http://hdl.handle.net/10071/16621.

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As organizações têm cada vez mais acesso a um maior volume de dados. A contribuir para este fenómeno está o desenvolvimento tecnológico e o conceito de internet das coisas, que permite cada vez mais interligar mecanismos e dispositivos, e consequentemente, diversificar as fontes de informação. Esta evolução tecnológica permite que os dados sejam retirados das mais diversas formas e plataformas, quer qualitativa quer quantitativamente. Este fenómeno que designamos por Big Data, está a tornar disruptivas muitas empresas, alterando desta forma modelos de negócio, inovando o marketing, produtos

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