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Relevant bibliographies by topics / Statistics|Artificial intelligence / Dissertations / Theses

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Dissertations / Theses on the topic 'Statistics|Artificial intelligence'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Jutras, Pierre. "Modeling of urban tree growth with artificial intelligence and multivariate statistics." Thesis, McGill University, 2008. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=21963.

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The urban environment induces severe ecological conditions that impair tree growth. This situation is of major concern to municipal administrations that devote large budgets to arboricultural programmes. To adequately preserve arboreal heritage, three main issues must be resolved. First, biotic and abiotic inventory parameters that can express the complexity of street tree growth have to be assessed. Second, for an enhanced understanding of tree health and related environmental conditions, an analytical methodology should be defined to cluster street trees with similar growth patterns. Third,

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2

Valenzuela, Russell. "Predicting National Basketball Association Game Outcomes Using Ensemble Learning Techniques." Thesis, California State University, Long Beach, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=10980443.

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<p> There have been a number of studies that try to predict sporting event outcomes. Most previous research has involved results in football and college basketball. Recent years has seen similar approaches carried out in professional basketball. This thesis attempts to build upon existing statistical techniques and apply them to the National Basketball Association using a synthesis of algorithms as motivation. A number of ensemble learning methods will be utilized and compared in hopes of improving the accuracy of single models. Individual models used in this thesis will be derived from Logist

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3

Tolle, Kristin M. "Domain-independent semantic concept extraction using corpus linguistics, statistics and artificial intelligence techniques." Diss., The University of Arizona, 2003. http://hdl.handle.net/10150/280502.

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For this dissertation two software applications were developed and three experiments were conducted to evaluate the viability of a unique approach to medical information extraction. The first system, the AZ Noun Phraser, was designed as a concept extraction tool. The second application, ANNEE, is a neural net-based entity extraction (EE) system. These two systems were combined to perform concept extraction and semantic classification specifically for use in medical document retrieval systems. The goal of this research was to create a system that automatically (without human interaction) enable

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4

Nyongesa, Denis Barasa. "Various considerations on performance measures for a classification of ordinal data." Thesis, California State University, Long Beach, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10133995.

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<p> The technological advancement and the escalating interest in personalized medicine has resulted in increased ordinal classification problems. The most commonly used performance metrics for evaluating the effectiveness of a multi-class ordinal classifier include; predictive accuracy, Kendall's tau-b rank correlation, and the average mean absolute error (AMAE). These metrics are beneficial in the quest to classify multi-class ordinal data, but no single performance metric incorporates the misclassification cost. Recently, distance, which finds the optimal trade-off between the predictive ac

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5

Costa, Thiago. "A Non-Parametric Perspective on the Analysis of Massive Networks." Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:17467341.

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This dissertation develops an inferential framework for a highly non-parametric class of network models called graphons, which are the limit objects of converging sequences in the theory of dense graph limits. The theory, introduced by Lovász and co-authors, uses structural properties of very large networks to describe a notion of convergence for sequences of dense graphs. Converging sequences define a limit which can be represented by a class of random graphs that preserve many properties of the networks in the sequence. These random graphs are intuitive and have a relatively simple mathem

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6

Palitawanont, Nanta. "An Investigation into the Effectiveness of Intelligent Tutoring on Learning of College Level Statistics." Thesis, University of North Texas, 1989. https://digital.library.unt.edu/ark:/67531/metadc331166/.

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The present research incorporated the content of basic statistics into the Artificial Intelligence Physics Tutor (ARPHY), which was used as the expert system shell, and investigated the effects of the Artificial Intelligent Statistics Tutor (ARSTAT) as a supplement to learning statistics at the college level. Two classes of an introductory educational statistics course in the Department of Educational Foundations, University of North Texas, were used in the study. The daytime class was used as the experimental group and the evening class was used as the control group. The experimental group's

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Navaroli, Nicholas Martin. "Generative Probabilistic Models for Analysis of Communication Event Data with Applications to Email Behavior." Thesis, University of California, Irvine, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3668831.

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<p> Our daily lives increasingly involve interactions with others via different communication channels, such as email, text messaging, and social media. In this context, the ability to analyze and understand our communication patterns is becoming increasingly important. This dissertation focuses on generative probabilistic models for describing different characteristics of communication behavior, focusing primarily on email communication. </p><p> First, we present a two-parameter kernel density estimator for estimating the probability density over recipients of an email (or, more generally,

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8

Wu, Tao. "Higher-order Random Walk Methods for Data Analysis." Thesis, Purdue University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10790747.

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<p> Markov random walk models are powerful analytical tools for multiple areas in machine learning, numerical optimizations and data mining tasks. The key assumption of a first-order Markov chain is memorylessness, which restricts the dependence of the transition distribution to the current state only. However in many applications, this assumption is not appropriate. We propose a set of higher-order random walk techniques and discuss their applications to tensor co-clustering, user trails modeling, and solving linear systems. First, we develop a new random walk model that we call the super-spa

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9

Ross, Stephane. "Interactive Learning for Sequential Decisions and Predictions." Thesis, Carnegie Mellon University, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3575525.

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<p>Sequential prediction problems arise commonly in many areas of robotics and information processing: e.g., predicting a sequence of actions over time to achieve a goal in a control task, interpreting an image through a sequence of local image patch classifications, or translating speech to text through an iterative decoding procedure. </p><p> Learning predictors that can reliably perform such sequential tasks is challenging. Specifically, as predictions influence future inputs in the sequence, the data-generation process and executed predictor are inextricably intertwined. This can often l

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10

Ramsahai, Roland Ryan. "Causal inference with instruments and other supplementary variables." Thesis, University of Oxford, 2008. http://ora.ox.ac.uk/objects/uuid:df2961da-0843-421f-8be4-66a92e6b0d13.

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Instrumental variables have been used for a long time in the econometrics literature for the identification of the causal effect of one random variable, B, on another, C, in the presence of unobserved confounders. In the classical continuous linear model, the causal effect can be point identified by studying the regression of C on A and B on A, where A is the instrument. An instrument is an instance of a supplementary variable which is not of interest in itself but aids identification of causal effects. The method of instrumental variables is extended here to generalised linear models, for whi

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11

Jaitha, Anant. "An Introduction to the Theory and Applications of Bayesian Networks." Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/cmc_theses/1638.

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Bayesian networks are a means to study data. A Bayesian network gives structure to data by creating a graphical system to model the data. It then develops probability distributions over these variables. It explores variables in the problem space and examines the probability distributions related to those variables. It conducts statistical inference over those probability distributions to draw meaning from them. They are good means to explore a large set of data efficiently to make inferences. There are a number of real world applications that already exist and are being actively researched. Th

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12

Yekollu, Srikar. "Graph Based Regularization of Large Covariance Matrices." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1237243768.

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13

Natale, James. "A Strategy for Reducing Congestive Heart Failure Readmissions Through the Use of Interventions Targeted by Machine Learning." University of Akron / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=akron1428233380.

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14

Calderon-Vilca, Hugo D., William I. Wun-Rafael, and Roberto Miranda-Loarte. "Simulation of suicide tendency by using machine learning." IEEE Computer Society, 2018. http://hdl.handle.net/10757/624720.

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.<br>Suicide is one of the most distinguished causes of death on the news worldwide. There are several factors and variables that can lead a person to commit this act, for example, stress, self-esteem, depression, among others. The causes and profiles of suicide cases are not revealed in detail by the competent institutions. We propose a simulation with a systematically generated dataset; such data reflect the adolescent population with suicidal ten

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15

Elshamy, Wesam Samy. "Continuous-time infinite dynamic topic models." Diss., Kansas State University, 2012. http://hdl.handle.net/2097/15176.

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Doctor of Philosophy<br>Department of Computing and Information Sciences<br>William Henry Hsu<br>Topic models are probabilistic models for discovering topical themes in collections of documents. In real world applications, these models provide us with the means of organizing what would otherwise be unstructured collections. They can help us cluster a huge collection into different topics or find a subset of the collection that resembles the topical theme found in an article at hand. The first wave of topic models developed were able to discover the prevailing topics in a big collection of

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16

Curnalia, James W. "The Impact of Training Epoch Size on the Accuracy of Collaborative Filtering Models in GraphChi Utilizing a Multi-Cyclic Training Regimen." Youngstown State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1370016838.

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17

Abdulgader, Musbah M. "Bio Inspired Evolutionary Fuzzy System for Data Classification." University of Toledo / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1575563281684676.

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18

Shewinvanakitkul, Prapan. "Automated Detection and Prediction of Sleep Apnea Events." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1486490112558014.

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19

Parker, Lonnie Thomas. "Science-centric sampling approaches of geo-physical environments for realistic robot navigation." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44813.

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The objective of this research effort is to provide a methodology for assessing the effectiveness of sampling techniques used to gather different types of geo-physical information by a robotic agent. We focus on assessing how well unique real-time sampling strategies acquire information that is, otherwise, too dangerous or costly to collect by human scientists. Traditional sampling strategies and informed search tech- niques provide the underlying structure for a navigating robotic surveyor whose goal is to collect samples that yield an accurate representation of the measured phenomena under r

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20

Yu, Daoping. "Early Stopping of a Neural Network via the Receiver Operating Curve." Digital Commons @ East Tennessee State University, 2010. https://dc.etsu.edu/etd/1732.

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This thesis presents the area under the ROC (Receiver Operating Characteristics) curve, or abbreviated AUC, as an alternate measure for evaluating the predictive performance of ANNs (Artificial Neural Networks) classifiers. Conventionally, neural networks are trained to have total error converge to zero which may give rise to over-fitting problems. To ensure that they do not over fit the training data and then fail to generalize well in new data, it appears effective to stop training as early as possible once getting AUC sufficiently large via integrating ROC/AUC analysis into the training pro

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Ghodsi, Boushehri Ali. "Nonlinear Dimensionality Reduction with Side Information." Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/1020.

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In this thesis, I look at three problems with important applications in data processing. Incorporating side information, provided by the user or derived from data, is a main theme of each of these problems. <br /><br /> This thesis makes a number of contributions. The first is a technique for combining different embedding objectives, which is then exploited to incorporate side information expressed in terms of transformation invariants known to hold in the data. It also introduces two different ways of incorporating transformation invariants in order to make new similarity meas

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22

Hazarika, Subhashis. "Statistical and Machine Learning Approaches For Visualizing and Analyzing Large-Scale Simulation Data." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1574692702479196.

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23

Åkerström, Otto. "Multi-Agent System for Coordinated Defence." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273582.

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Today defence systems are becoming more complex as technology advances and it is of great importance to explore new ways of solving problems and keep national defence current. In particular, Artificial Intelligence (AI) is used in an increasing number of industries such as logistic solutions, inventory management and defence. This thesis will evaluate the possibility to use Reinforcement Learning (RL) in an Air Defence Coordination(ADC) scenario at Saab AB. To evaluate RL, a simplified ADC-scenario is considered and solved using two different methods, Q-learning and Deep Q-learning (DQL). The

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Groves, Adrian R. "Bayesian learning methods for modelling functional MRI." Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:fe46e696-a1a6-4a9d-9dfe-861b05b1ed33.

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Bayesian learning methods are the basis of many powerful analysis techniques in neuroimaging, permitting probabilistic inference on hierarchical, generative models of data. This thesis primarily develops Bayesian analysis techniques for magnetic resonance imaging (MRI), which is a noninvasive neuroimaging tool for probing function, perfusion, and structure in the human brain. The first part of this work fits nonlinear biophysical models to multimodal functional MRI data within a variational Bayes framework. Simultaneously-acquired multimodal data contains mixtures of different signals and ther

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25

Eldridge, Justin Eldridge. "Clustering Consistently." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1512070374903249.

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Hugueny, Samuel Y. "Novelty detection with extreme value theory in vital-sign monitoring." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:804a226c-a298-4764-9bc8-b191d2b852cd.

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Every year in the UK, tens of thousands of hospital patients suffer adverse events, such as un-planned transfers to Intensive Therapy Units or unexpected cardiac arrests. Studies have shown that in a large majority of cases, significant physiological abnormalities can be observed within the 24-hour period preceding such events. Such warning signs may go unnoticed, if they occur between observations by the nursing staff, or are simply not identified as such. Timely detection of these warning signs and appropriate escalation schemes have been shown to improve both patient outcomes and the use of

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Costa, Alexandre Fructuoso da [UNESP]. "Previsão de séries temporais financeiras por meio de redes neurais dinâmicas e processos de transformação de dados: uma abordagem empírico-comparativa." Universidade Estadual Paulista (UNESP), 2012. http://hdl.handle.net/11449/92999.

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Made available in DSpace on 2014-06-11T19:26:16Z (GMT). No. of bitstreams: 0 Previous issue date: 2012-12-21Bitstream added on 2014-06-13T19:13:20Z : No. of bitstreams: 1 costa_af_me_bauru.pdf: 1184483 bytes, checksum: 1783e7ab1d2b2b0b7dab05babd2093e8 (MD5)<br>Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)<br>A previsão de séries temporais financeiras é uma das questões mais pesquisadas no campo das finanças, sobretudo, no que diz respeito ao mercado acionário e à análise de riscos. Para tanto, essas

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Costa, Alexandre Fructuoso da. "Previsão de séries temporais financeiras por meio de redes neurais dinâmicas e processos de transformação de dados : uma abordagem empírico-comparativa /." Bauru : [s.n.], 2012. http://hdl.handle.net/11449/92999.

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Orientador: Antonio Fernando Crepaldi<br>Banca: Rogério Andrade Flauzino<br>Banca: José Alfredo Colovan Ulson<br>Resumo: A previsão de séries temporais financeiras é uma das questões mais pesquisadas no campo das finanças, sobretudo, no que diz respeito ao mercado acionário e à análise de riscos. Para tanto, essas pesquisas envolvem desde modelos estatísticos e econométricos até modelos de inteligência artificial, como as redes neurais dinâmicas. Nesse sentido este estudo tem o propósito de desenvolver e aplicar dois modelos de redes neurais artificiais dinâmicas, a rede neural focada atrasada

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Gaumer, Madelyn. "Using Neural Networks to Classify Discrete Circular Probability Distributions." Scholarship @ Claremont, 2019. https://scholarship.claremont.edu/hmc_theses/226.

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Given the rise in the application of neural networks to all sorts of interesting problems, it seems natural to apply them to statistical tests. This senior thesis studies whether neural networks built to classify discrete circular probability distributions can outperform a class of well-known statistical tests for uniformity for discrete circular data that includes the Rayleigh Test1, the Watson Test2, and the Ajne Test3. Each neural network used is relatively small with no more than 3 layers: an input layer taking in discrete data sets on a circle, a hidden layer, and an output layer output

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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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Geltz, Rebecca L. "Using Data Mining to Model Student Success." Youngstown State University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1264697709.

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32

Wang, Xiangxue. "A PROGNOSTIC AND PREDICTIVE COMPUTATIONAL PATHOLOGY BASED COMPANION DIAGNOSTIC APPROACH: PRECISION MEDICINE FOR LUNG CANCER." Case Western Reserve University School of Graduate Studies / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1574125440501667.

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33

Chaibva, Faith Anesu. "The use of response surface methodology and artificial neural networks for the establishment of a design space for a sustained release salbutamol sulphate formulation." Thesis, Rhodes University, 2010. http://hdl.handle.net/10962/d1010432.

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Quality by Design (QbD) is a systematic approach that has been recommended as suitable for the development of quality pharmaceutical products. The QbD approach commences with the definition of a quality target drug profile and predetermined objectives that are then used to direct the formulation development process with an emphasis on understanding the pharmaceutical science and manufacturing principles that apply to a product. The design space is directly linked to the use of QbD for formulation development and is a multidimensional combination and interaction of input variables and process p

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Hamraz, Hamid. "AUTOMATED TREE-LEVEL FOREST QUANTIFICATION USING AIRBORNE LIDAR." UKnowledge, 2018. https://uknowledge.uky.edu/cs_etds/69.

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Traditional forest management relies on a small field sample and interpretation of aerial photography that not only are costly to execute but also yield inaccurate estimates of the entire forest in question. Airborne light detection and ranging (LiDAR) is a remote sensing technology that records point clouds representing the 3D structure of a forest canopy and the terrain underneath. We present a method for segmenting individual trees from the LiDAR point clouds without making prior assumptions about tree crown shapes and sizes. We then present a method that vertically stratifies the point clo

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Bessinger, Zachary. "Modeling and Mapping Location-Dependent Human Appearance." UKnowledge, 2018. https://uknowledge.uky.edu/cs_etds/75.

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Human appearance is highly variable and depends on individual preferences, such as fashion, facial expression, and makeup. These preferences depend on many factors including a person's sense of style, what they are doing, and the weather. These factors, in turn, are dependent upon geographic location and time. In our work, we build computational models to learn the relationship between human appearance, geographic location, and time. The primary contributions are a framework for collecting and processing geotagged imagery of people, a large dataset collected by our framework, and several gener

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Ghareeb, Ahmed. "Data mining for University of Dayton campus buildings to predict future demand." University of Dayton / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1490472227466522.

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Hassanniakalager, Arman. "Four essays in quantitative analysis : artificial intelligence and statistical inference." Thesis, University of Glasgow, 2018. http://theses.gla.ac.uk/30934/.

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This thesis consists of four essays exploring quantitative methods for investment analysis. Chapter 1 is an introduction to the topic where the backgrounds, motivations and contributions of the thesis are discussed. This Chapter proposes an expert system paradigm which accommodates the methodology for all four empirical studies presented in Chapters 2 to 5. In Chapter 2 the profitability of technical analysis and Bayesian Statistics in trading the EUR/USD, GBP/USD, and USD/JPY exchange rates are examined. For this purpose, seven thousand eight hundred forty-six technical rules are generated, a

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Widing, Härje. "Business analytics tools for data collection and analysis of COVID-19." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176514.

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The pandemic that struck the entire world 2020 caused by the SARS-CoV-2 (COVID-19) virus, will have an enormous interest for statistical and economical analytics for a long time. While the pandemic of 2020 is not the first that struck the entire world, it is the first pandemic in history where the data were gathered to this extent. Most countries have collected and shared its numbers of cases, tests and deaths related to the COVID-19 virus using different storage methods and different data types. Gaining quality data from the COVID-19 pandemic is a problem most countries had during the pandemi

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Hamed, Ahmed A. "An Exploratory Analysis of Twitter Keyword-Hashtag Networks and Knowledge Discovery Applications." ScholarWorks @ UVM, 2014. http://scholarworks.uvm.edu/graddis/325.

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The emergence of social media has impacted the way people think, communicate, behave, learn, and conduct research. In recent years, a large number of studies have analyzed and modeled this social phenomena. Driven by commercial and social interests, social media has become an attractive subject for researchers. Accordingly, new models, algorithms, and applications to address specific domains and solve distinct problems have erupted. In this thesis, we propose a novel network model and a path mining algorithm called HashnetMiner to discover implicit knowledge that is not easily exposed using ot

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Hadidi, Tareq. "Modélisation et simulation des déplacements de la vie quotidienne dans un habitat intelligent pour la santé." Thesis, Grenoble, 2011. http://www.theses.fr/2011GRENS005.

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Pour répondre au besoin de 14 millions de personnes, dont près de 10% vit en situation de perte d'autonomie ou de grande dépendance, notre société va devoir trouver rapidement des solutions de prise en charge. La télémédecine, et plus particulièrement la télésurveillance médicale à domicile, constitue aujourd'hui une solution pour pallier le manque de professionnels de santé face au fort accroissement de la population âgée en Europe. Dans ce contexte, nous nous intéressons au HIS -« Habitat intelligent pour la Santé ». Le travail de la thèse a été de développer un outil informatique de simulat

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Sublemontier, Jacques-Henri. "Classification non supervisée : de la multiplicité des données à la multiplicité des analyses." Phd thesis, Université d'Orléans, 2012. http://tel.archives-ouvertes.fr/tel-00801555.

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La classification automatique non supervisée est un problème majeur, aux frontières de multiples communautés issues de l'Intelligence Artificielle, de l'Analyse de Données et des Sciences de la Cognition. Elle vise à formaliser et mécaniser la tâche cognitive de classification, afin de l'automatiser pour la rendre applicable à un grand nombre d'objets (ou individus) à classer. Des visées plus applicatives s'intéressent à l'organisation automatique de grands ensembles d'objets en différents groupes partageant des caractéristiques communes. La présente thèse propose des méthodes de classificatio

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Leite, Daniel Saraiva. "Um estudo comparativo de modelos baseados em estatísticas textuais, grafos e aprendizado de máquina para sumarização automática de textos em português." Universidade Federal de São Carlos, 2010. https://repositorio.ufscar.br/handle/ufscar/459.

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Made available in DSpace on 2016-06-02T19:05:48Z (GMT). No. of bitstreams: 1 3512.pdf: 1897835 bytes, checksum: 598f309a846cb201fe8f13be0f2e37da (MD5) Previous issue date: 2010-12-21<br>Automatic text summarization has been of great interest in Natural Language Processing due to the need of processing a huge amount of information in short time, which is usually delivered through distinct media. Thus, large-scale methods are of utmost importance for synthesizing and making access to information simpler. They aim at preserving relevant content of the sources with little or no human interventio

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Val, Petran. "BINOCULAR DEPTH PERCEPTION, PROBABILITY, FUZZY LOGIC, AND CONTINUOUS QUANTIFICATION OF UNIQUENESS." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1504749439893027.

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Islam, Md Samsul, Lin Zhou, and Fei Li. "Application of Artificial Intelligence (Artificial Neural Network) to Assess Credit Risk : A Predictive Model For Credit Card Scoring." Thesis, Blekinge Tekniska Högskola, Sektionen för management, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2099.

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Credit Decisions are extremely vital for any type of financial institution because it can stimulate huge financial losses generated from defaulters. A number of banks use judgmental decisions, means credit analysts go through every application separately and other banks use credit scoring system or combination of both. Credit scoring system uses many types of statistical models. But recently, professionals started looking for alternative algorithms that can provide better accuracy regarding classification. Neural network can be a suitable alternative. It is apparent from the classification out

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Tsanas, Athanasios. "Accurate telemonitoring of Parkinson's disease symptom severity using nonlinear speech signal processing and statistical machine learning." Thesis, University of Oxford, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.572585.

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This study focuses on the development of an objective, automated method to extract clinically useful information from sustained vowel phonations in the context of Parkinson’s disease (PD). The aim is twofold: (a) differentiate PD subjects from healthy controls, and (b) replicate the Unified Parkinson’s Disease Rating Scale (UPDRS) metric which provides a clinical impression of PD symptom severity. This metric spans the range 0 to 176, where 0 denotes a healthy person and 176 total disability. Currently, UPDRS assessment requires the physical presence of the subject in the clinic, is subjective

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Andersson, Martin, and Marcus Mazouch. "Binary classification for predicting propensity to buy flight tickets. : A study on whether binary classification can be used to predict Scandinavian Airlines customers’ propensity to buy a flight ticket within the next seven days." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-160855.

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A customers propensity to buy a certain product is a widely researched field and is applied in multiple industries. In this thesis it is showed that using binary classification on data from Scandinavian Airlines can predict their customers propensity to book a flight within the next coming seven days. A comparison between logistic regression and support vector machine is presented and logistic regression with reduced number of variables is chosen as the final model, due to it’s simplicity and accuracy. The explanatory variables contains exclusively booking history, whilst customer demographics

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Medina, Oliva Gabriela. "Modélisation conjointe des connaissances multi-points de vue d'un système industriel et de son système de soutien pour l'évaluation des stratégies de maintenance." Thesis, Nancy 1, 2011. http://www.theses.fr/2011NAN10092/document.

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Par rapport aux exigences de plus en plus importantes relatives au Maintien en Condition Opérationnelle d'un système industriel, le processus de maintenance joue un rôle fondamental pour l'amélioration de la disponibilité, de la productivité, etc. Pour essayer de contrôler au mieux ces performances, les responsables de maintenance doivent donc être capables de choisir les stratégies de maintenance et les ressources à mettre en oeuvre les plus adaptées aux besoins. Dans un objectif d'aide à la prise de décisions en maintenance, les travaux présentés dans ce mémoire ont pour objet de proposer un

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Burk, Ieva. "Snap Scholar: The User Experience of Engaging with Academic Research Through a Tappable Stories Medium." Scholarship @ Claremont, 2019. https://scholarship.claremont.edu/cmc_theses/2156.

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With the shift to learn and consume information through our mobile devices, most academic research is still only presented in long-form text. The Stanford Scholar Initiative has explored the segment of content creation and consumption of academic research through video. However, there has been another popular shift in presenting information from various social media platforms and media outlets in the past few years. Snapchat and Instagram have introduced the concept of tappable “Stories” that have gained popularity in the realm of content consumption. To accelerate the growth of the creation o

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Segal, Aleksandr V. "Iterative Local Model Selection for tracking and mapping." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:8690e0e0-33c5-403e-afdf-e5538e5d304f.

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The past decade has seen great progress in research on large scale mapping and perception in static environments. Real world perception requires handling uncertain situations with multiple possible interpretations: e.g. changing appearances, dynamic objects, and varying motion models. These aspects of perception have been largely avoided through the use of heuristics and preprocessing. This thesis is motivated by the challenge of including discrete reasoning directly into the estimation process. We approach the problem by using Conditional Linear Gaussian Networks (CLGNs) as a generalization o

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Campbell, Benjamin W. "Supervised and Unsupervised Machine Learning Strategies for Modeling Military Alliances." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1558024695617708.

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