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Dissertations / Theses on the topic 'Classification used machine learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Testi, Enrico. "Machine Learning for User Traffic Classification in Wireless Systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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With the advent of Internet of Things telecommunications will play a crucial role in every day life. The rapidly growing demand for radio services by millions of user all over the world will make the radio spectrum an increasingly valuable resource. The modern standards of communications provide a static utilization of the radio spectrum resources, which results in its under-utilization. Therefore let us imagine a dynamic sharing of the radio resources, where every device can use a portion of such resources if and only if they are not utilized yet. In this regard, the Federal Communicati

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Feng, Zao. "Condition Classification in Underground Pipes Based on Acoustical Characteristics. Acoustical characteristics are used to classify the structural and operational conditions in underground pipes with advanced signal classification methods." Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/9463.

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Axén, Maja, and Jennifer Karlberg. "Binary Classification for Predicting Customer Churn." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-171892.

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Predicting when a customer is about to turn to a competitor can be difficult, yet extremely valuable from a business perspective. The moment a customer stops being considered a customer is known as churn, a widely researched topic in several industries when dealing with subscription-services. However, in industries with non-subscription services and products, defining churn can be a daunting task and the existing literature does not fully cover this field. Therefore, this thesis can be seen as a contribution to current research, specially when not having a set definition for churn. A definitio

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Olofsson, Nina, and Nivin Fakih. "A Machine Learning Approach to Dialogue Act Classification in Human-Robot Conversations : Evaluation of dialogue act classification with the robot Furhat and an analysis of the market for social robots used for education." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175705.

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The interest in social robots has grown dramatically in the last decade. Several studies have investigated the potential markets for such robots and how to enhance their human-like abilities. Both of these subjects have been investigated in this thesis using the company Furhat Robotics, and their robot Furhat, as a case study. This paper explores how machine learning could be used to classify dialogue acts in human-robot conversations, which could help Furhat interact in a more human-like way. Dialogue acts are acts of natural speech, such as questions or statements. Several variables and thei

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Sousa, Beatriz Fernandes SimplÃcio. "Remote sensing and machine learning applied to soil use detection in caatinga bioma." Universidade Federal do CearÃ, 2009. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=5201.

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Conselho Nacional de Desenvolvimento CientÃfico e TecnolÃgico<br>In order to manage adequately natural resources inside a fragile environment, just like Caatinga, one should know its properties and spatial distribution. This work proposes an approach to classify LANDSAT-5 satellite images. These images, corresponding to a semiarid environment located in Iguatu country, Ceara, Brazil, were classified aiming at detecting the Caatinga biome by two type of classifiers based on machinery learning: Multi Layer Perceptron (MLP) and Support Vector Machine (SVM). The static classifier of Maximum Likeli

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Brown, Ryan Charles. "Development of Ground-Level Hyperspectral Image Datasets and Analysis Tools, and their use towards a Feature Selection based Sensor Design Method for Material Classification." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/84944.

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Visual sensing in robotics, especially in the context of autonomous vehicles, has advanced quickly and many important contributions have been made in the areas of target classification. Typical to these studies is the use of the Red-Green-Blue (RGB) camera. Separately, in the field of remote sensing, the hyperspectral camera has been used to perform classification tasks on natural and man-made objects from typically aerial or satellite platforms. Hyperspectral data is characterized by a very fine spectral resolution, resulting in a significant increase in the ability to identify materials in t

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Tang, Danny M. Eng Massachusetts Institute of Technology. "Empowering novices to understand and use machine learning with personalized image classification models, intuitive analysis tools, and MIT App Inventor." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123130.

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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: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 129-131).<br>As machine learning permeates our society and manifests itself through commonplace technologies such as autonomous vehicles, facial recognition, and online store recommendations, it is necessary that the increasin

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Zaman, Bushra. "Remotely Sensed Data Assimilation Technique to Develop Machine Learning Models for Use in Water Management." DigitalCommons@USU, 2010. https://digitalcommons.usu.edu/etd/584.

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Increasing population and water conflicts are making water management one of the most important issues of the present world. It has become absolutely necessary to find ways to manage water more efficiently. Technological advancement has introduced various techniques for data acquisition and analysis, and these tools can be used to address some of the critical issues that challenge water resource management. This research used learning machine techniques and information acquired through remote sensing, to solve problems related to soil moisture estimation and crop identification on large spat

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Marquez, Astrid. "Use of multispectral data to identify farm intensification levels by applying emergent computing techniques." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/6232.

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Concern about feeding an ever increasing population has long been one of humankind’s most pressing problems. This has been addressed throughout history by introducing into farming systems changes allowing them to produce more per unit of land area. However, these changes have also been linked to negative effects on the socio economic and environmental sphere, that have created the need for an integral understanding of this phenomenon. This thesis describes the application of learning machine methods to induct a relationship between the spectral response of farms’ land cover and their intensifi

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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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Ligier, Damien. "Functional encryption applied to privacy-preserving classification : practical use, performances and security." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2018. http://www.theses.fr/2018IMTA0040/document.

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L'apprentissage automatique (en anglais machine learning) ou apprentissage statistique, a prouvé être un ensemble de techniques très puissantes. La classification automatique en particulier, permettant d'identifier efficacement des informations contenues dans des gros ensembles de données. Cependant, cela lève le souci de la confidentialité des données. C'est pour cela que le besoin de créer des algorithmes d'apprentissage automatique capable de garantir la confidentialité a été mis en avant. Cette thèse propose une façon de combiner certains systèmes cryptographiques avec des algorithmes de c

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Sarmadi, Soheil. "On the Feasibility of Profiling, Forecasting and Authenticating Internet Usage Based on Privacy Preserving NetFlow Logs." Scholar Commons, 2018. https://scholarcommons.usf.edu/etd/7568.

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Understanding Internet user behavior and Internet usage patterns is fundamental in developing future access networks and services that meet technical as well as Internet user needs. User behavior is routinely studied and measured, but with different methods depending on the research discipline of the investigator, and these disciplines rarely cross. We tackle this challenge by developing frameworks that the Internet usage statistics used as the main features in understanding Internet user behaviors, with the purpose of finding a complete picture of the user behavior and working towards a unifi

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Cosa, Liñán Alejandro. "Analytical fusion of multimodal magnetic resonance imaging to identify pathological states in genetically selected Marchigian Sardinian alcohol-preferring (msP) rats." Doctoral thesis, Universitat Politècnica de València, 2017. http://hdl.handle.net/10251/90523.

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[EN] Alcohol abuse is one of the most alarming issues for the health authorities. It is estimated that at least 23 million of European citizens are affected by alcoholism causing a cost around 270 million euros. Excessive alcohol consumption is related with physical harm and, although it damages the most of body organs, liver, pancreas, and brain are more severally affected. Not only physical harm is associated to alcohol-related disorders, but also other psychiatric disorders such as depression are often comorbiding. As well, alcohol is present in many of violent behaviors and traffic injures

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Joshi, Apoorva. "Trajectory-based methods to predict user churn in online health communities." Thesis, University of Iowa, 2018. https://ir.uiowa.edu/etd/6152.

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Online Health Communities (OHCs) have positively disrupted the modern global healthcare system as patients and caregivers are interacting online with similar peers to improve quality of their life. Social support is the pillar of OHCs and, hence, analyzing the different types of social support activities contributes to a better understanding and prediction of future user engagement in OHCs. This thesis used data from a popular OHC, called Breastcancer.org, to first classify user posts in the community into the diff

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Albanwan, Hessah AMYM. "Remote Sensing Image Enhancement through Spatiotemporal Filtering." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492011122078055.

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Kindbom, Hannes. "LSTM vs Random Forest for Binary Classification of Insurance Related Text." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252748.

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The field of natural language processing has received increased attention lately, but less focus is put on comparing models, which differ in complexity. This thesis compares Random Forest to LSTM, for the task of classifying a message as question or non-question. The comparison was done by training and optimizing the models on historic chat data from the Swedish insurance company Hedvig. Different types of word embedding were also tested, such as Word2vec and Bag of Words. The results demonstrated that LSTM achieved slightly higher scores than Random Forest, in terms of F1 and accuracy. The mo

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Koseler, Kaan Tamer. "Realization of Model-Driven Engineering for Big Data: A Baseball Analytics Use Case." Miami University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=miami1524832924255132.

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18

Magnier, Caroline. "Production acoustique d'une flottille côtière : Application au suivi environnemental et à l'identification automatisée de sources sonores anthropiques." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAU040/document.

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Le trafic maritime est le principal contributeur des bruits sous-marins anthropique : depuis les années 1970, l’augmentation du trafic maritime hauturier a provoqué dans certaines zones une augmentation du bruit ambiant de plus de 10 dB. En réponse à cette préoccupation, la Directive Cadre pour la Stratégie pour le Milieu Marin (DCSMM) recommande un suivi acoustique. Peu d’études s’intéressent à l’activité côtière et aux bruits rayonnés par les petites embarcations ainsi qu’à leurs conséquences sur la faune marine alors que ces environnements côtiers sont les pourvoyeurs de 41.7 % des services

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OGURI, PEDRO. "MACHINE LEARNING FOR SENTIMENT CLASSIFICATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2006. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=9947@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>Sentiment Analysis é um problema de categorização de texto no qual deseja-se identificar opiniões favoráveis e desfavoráveis com relação a um tópico. Um exemplo destes tópicos de interesse são organizações e seus produtos. Neste problema, documentos são classificados pelo sentimento, conotação, atitudes e opiniões ao invés de se restringir aos fatos descritos neste. O principal desafio em Sentiment Classification é identificar como sentimentos são expressados em textos e se tais sentimentos indicam uma opinião positiva (favoráv

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Patel, Darshan D. "Vehicle classification using machine learning algorithm." Thesis, California State University, Long Beach, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1604876.

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<p> Increasing traffic on roadways requires some real-time system that can collect traffic data and helps us to manage existing road infrastructure. For this purpose, we need a state of art system that can detect and classify vehicles into different categories. We developed an in-node microprocessor-based vehicle classification system to analyze and determine the types of vehicles passing over a 3-axis magnetometer sensor. Our approach for vehicle classification utilizes J48 classification algorithm, which is implemented in machine learning software Weka. J48 is a Quinlan's C4.5 algorithm, an

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Meng, Li. "Pattern classification using enhanced machine learning." Thesis, University of Liverpool, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399037.

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Jabali, Aghyad, and Husein Abdelkadir Mohammedbrhan. "Tyre sound classification with machine learning." Thesis, Högskolan i Gävle, Datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-36209.

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Having enough data about the usage of tyre types on the road can lead to a better understanding of the consequences of studded tyres on the environment. This paper is focused on training and testing a machine learning model which can be further integrated into a larger system for automation of the data collection process. Different machine learning algorithms, namely CNN, SVM, and Random Forest, were compared in this experiment. The method used in this paper is an empirical method. First, sound data for studded and none-studded tyres was collected from three different locations in the city of

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Amerineni, Rajesh. "BRAIN-INSPIRED MACHINE LEARNING CLASSIFICATION MODELS." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/dissertations/1806.

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This dissertation focuses on the development of three classes of brain-inspired machine learning classification models. The models attempt to emulate (a) multi-sensory integration, (b) context-integration, and (c) visual information processing in the brain.The multi-sensory integration models are aimed at enhancing object classification through the integration of semantically congruent unimodal stimuli. Two multimodal classification models are introduced: the feature integrating (FI) model and the decision integrating (DI) model. The FI model, inspired by multisensory integration in the subc

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Felldin, Markus. "Machine Learning Methods for Fault Classification." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-183132.

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This project, conducted at Ericsson AB, investigates the feasibility of implementing machine learning techniques in order to classify dump files for more effi cient trouble report routing. The project focuses on supervised machine learning methods and in particular Bayesian statistics. It shows that a program utilizing Bayesian methods can achieve well above random prediction accuracy. It is therefore concluded that machine learning methods may indeed become a viable alternative to human classification of trouble reports in the near future.<br>Detta examensarbete, utfört på Ericsson AB, ämnar

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Niemi, Mikael. "Machine Learning for Rapid Image Classification." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-97375.

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In this thesis project techniques for training a rapid image classifier that can recognize an object of a predefined type has been studied. Classifiers have been trained with the AdaBoost algorithm, with and without the use of Viola-Jones cascades. The use of Weight trimming in the classifier training has been evaluated and resulted in a significant speed up of the training, as well as improving the performance of the trained classifier. Different preprocessings of the images have also been tested, but resulted for the most part in worse performance for the classifiers when used individually.

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Zhou, Xujuan. "Rough set-based reasoning and pattern mining for information filtering." Queensland University of Technology, 2008. http://eprints.qut.edu.au/29350/.

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An information filtering (IF) system monitors an incoming document stream to find the documents that match the information needs specified by the user profiles. To learn to use the user profiles effectively is one of the most challenging tasks when developing an IF system. With the document selection criteria better defined based on the users’ needs, filtering large streams of information can be more efficient and effective. To learn the user profiles, term-based approaches have been widely used in the IF community because of their simplicity and directness. Term-based approaches are relativel

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SpasicÌ, Irena. "A machine learning approach to term classification." Thesis, University of Salford, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.419298.

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Botsch, Michael-Felix. "Machine learning techniques for time series classification." Göttingen Cuvillier, 2009. http://d-nb.info/994721455/04.

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Wong, Chi Man. "Extreme learning machine for multi-class classification." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3948432.

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Kostopouls, Theodore P. "A Machine Learning approach to Febrile Classification." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1173.

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General health screening is needed to decrease the risk of pandemic in high volume areas. Thermal characterization, via infrared imaging, is an effective technique for fever detection, however, strict use requirements in combination with highly controlled environmental conditions compromise the practicality of such a system. Combining advanced processing techniques to thermograms of individuals can remove some of these requirements allowing for more flexible classification algorithms. The purpose of this research was to identify individuals who had febrile status utilizing modern thermal imagi

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Bhattarya, Rahul. "In-Node Machine Learning-based Vehicle Classification." Thesis, California State University, Long Beach, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10837826.

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<p> Over the last few decades, our nation has experienced a significant increase in the amount of traffic congestion on freeways and intra-city roads. The current technology designed to control and improve this national problem has met with some real success. Based on inductive loops, that system’s ability to detect the presence of cars and, therefore, the frequency of use of roads has been a great boon. However, the system is not perfect; moreover, based on projections for future use of road infrastructure, the inductive loops solution will not be adequate for long. Continued smooth ope

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Covella, Vito Vincenzo. "Multi-node Fault Classification using Machine Learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22867/.

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An HPC system, a system with much more computational power than general computing systems, is a complex system made up of different sections and many computing nodes. In such systems failures can arise for different reasons: because of the interactions among the components, because of the specific technologies used or because of bugs in the software. In order to reach Exascale performances and guarantee availability and reliability it is important to detect and recover from these anomalies. In this thesis we propose a fault classification method based on machine learning. Other researchers

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Teatini, Alex. "Movement trajectory classification using supervised machine learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-265009.

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Anything that moves can be tracked, and hence its trajectory analysed. The trajectory of a moving object can carry a lot of useful information depending on what is sought. In this work, the aim is to exploit machine learning to be able to classify finite trajectories based on their shape. In a clinical environment, a set of trajectory classes have been defined based on relevance to particular pathologies. Furthermore, several trajectories have been collected using a depth sensor from a number of subjects. The problem to address is to evaluate whether it is possible to classify these trajectori

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Pehrson, Jakob, and Sara Lindstrand. "Support Unit Classification through Supervised Machine Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281537.

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The purpose of this article is to evaluate the impact a supervised machine learning classification model can have on the process of internal customer support within a large digitized company. Chatbots are becoming a frequently used utility among digital services, though the true general impact is not always clear. The research is separated into the following two questions: (1) Which supervised machine learning algorithm of naïve Bayes, logistic regression, and neural networks can best predict the correct support a user needs and with what accuracy? And (2) What is the effect on the productivit

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Sooknunan, Kimeel. "Classification of multiwavelength transients with machine learning." Master's thesis, Faculty of Science, 2019. http://hdl.handle.net/11427/31271.

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With the advent of powerful telescopes such as the Square Kilometre Array (SKA), its precursor MeerKAT and the Large Synoptic Survey Telescope (LSST), we are entering a golden era of multiwavelength transient astronomy. The large MeerKAT science project ThunderKAT may dramatically increase the detected number of radio transients. Currently radio transient datasets are still very small, allowing spectroscopic classification of all objects of interest. As the event rate increases, follow-up resources must be prioritised by making use of early classification of the radio data. Machine learning al

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Brossa, Dachs Núria. "Machine learning in classification of latex gloves." Thesis, Högskolan i Gävle, Avdelningen för elektronik, matematik och naturvetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-26043.

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The project consists in develop a tool that make a classification of latex gloves taking into account the different features that make the difference between them using a Matlab code in machine learning. This project has the purpose to have a tool that classify those gloves in order to know in which market of the world they could be sold and at what price. So as to achieve this purpose, it is necessary to collect the data and prepare them to introduce in the code. The project can be divided in three different parts; the first one is to make a research of all the theory about latex gloves, achi

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Babbar, Rohit. "Machine Learning Strategies for Large-scale Taxonomies." Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENM064/document.

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À l'ère de Big Data, le développement de modèles d'apprentissage machine efficaces et évolutifs opérant sur des Tera-Octets de données est une nécessité. Dans cette thèse, nous étudions un cadre d'apprentissage machine pour la classification hiérarchique à large échelle. Cette analyse comprend l'étude des défis comme la complexité d'entraînement des modèles ainsi que leur temps de prédiction. Dans la première partie de la thèse, nous étudions la distribution des lois de puissance sous-jacente à la création des taxonomies à grande échelle. Cette étude permet de dériver des bornes sur la complex

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Berisha, Sebastian. "Image classification using Gabor filters and machine learning." Winston-Salem, NC : Wake Forest University, 2009. http://dspace.zsr.wfu.edu/jspui/handle/10339/42584.

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Thesis (M.S.)--Wake Forest University. Dept. of Computer Science, 2009.<br>Title from electronic thesis title page. Thesis advisor: Victor Paúl Pauca. Vita. Includes bibliographical references (p. 78-80).

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Wiens, Jenna Marleau. "Machine learning for patient-adaptive ectopic beat classification." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/60823.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 83-85).<br>Physicians require automated techniques to accurately analyze the vast amount of physiological data collected by continuous monitoring devices. In this thesis, we consider one analysis task in particular, the classification of heartbeats from electrocardiographic recordings (ECG). This problem is made challenging by the inter-patient differences present in ECG morphology and timing charac

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Pektaş, Abdurrahman. "Behavior based malware classification using online machine learning." Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAM065/document.

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Les malwares, autrement dit programmes malicieux ont grandement évolué ces derniers temps et sont devenus une menace majeure pour les utilisateurs grand public, les entreprises et même le gouvernement. Malgré la présence et l'utilisation intensive de divers outils anti-malwares comme les anti-virus, systèmes de détection d'intrusions, pare-feux etc ; les concepteurs de malwares peuvent significativement contourner ses protections en utilisant les techniques d'offuscation. Afin de limiter ces problèmes, les chercheurs spécialisés dans les malwares ont proposé différentes approches comme l'explo

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Byrén, Filip. "Machine Learning for Traffic Classification in Industrial Environments." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-234933.

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The consumption has increased drastically over the years,where consumers have high demands on the quality of theproducts, the time it takes to receive the products and thepersonalization options. Factories try to scale with the consumersdemands by removing human labour and deployingautomation devices that can produce products more rapidlyand with higher precision.Wireless communication in the factories would help to achievethis goal, by enabling mobility as well as reducing cablereconfiguration/troubleshooting and increasing the utilizationof the factories resources.This report is investigatin

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Norgren, Eric. "Pulse Repetition Interval Modulation Classification using Machine Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-241152.

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Radar signals are used for estimating location, speed and direction of an object. Some radars emit pulses, while others emit a continuous wave. Both types of radars emit signals according to some pattern; a pulse radar, for example, emits pulses with a specific time interval between pulses. This time interval may either be stable, change linearly, or follow some other pattern. The interval between two emitted pulses is often referred to as the pulse repetition interval (PRI), and the pattern that defines the PRI is often referred to as the modulation. Classifying which PRI modulation is used i

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Sîrbu, Adela-Maria. "Dynamic machine learning for supervised and unsupervised classification." Thesis, Rouen, INSA, 2016. http://www.theses.fr/2016ISAM0002/document.

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La direction de recherche que nous abordons dans la thèse est l'application des modèles dynamiques d'apprentissage automatique pour résoudre les problèmes de classification supervisée et non supervisée. Les problèmes particuliers que nous avons décidé d'aborder dans la thèse sont la reconnaissance des piétons (un problème de classification supervisée) et le groupement des données d'expression génétique (un problème de classification non supervisée). Les problèmes abordés sont représentatifs pour les deux principaux types de classification et sont très difficiles, ayant une grande importance da

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Stenekap, Daniel. "Classification of Gear-shift data using machine learning." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-53445.

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Today, automatic transmissions are the industrial standard in heavy-duty vehicles. However, tolerances and component wear can cause factory calibrated gearshifts to have deviations that have a negative impact on clutch durability and driver comfort. An adaptive shift process could solve this problem by recognizing when pre-calibrated values are out-dated. The purpose of this thesis is to examine the classification of shift types using machine learning for the future goal of an adaptive gearshift process. Recent papers concerning machine learning on time-series are reviewed. Adata set is collec

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Fujino, Akinori. "Machine Learning with Heterogeneous Data for Classification Problems." 京都大学 (Kyoto University), 2009. http://hdl.handle.net/2433/123832.

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Bhaskar, Dhananjay. "Morphology based cell classification : unsupervised machine learning approach." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/61342.

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Individual cells adapt their morphology as a function of their differentiation status and in response to environmental cues and selective pressures. While it known that the great majority of these cues and pressures are mediated by changes in intracellular signal transduction, the precise regulatory mechanisms that govern cell shape, size and polarity are not well understood. Systematic investigation of cell morphology involves experimentally perturbing biochemical pathways and observing changes in phenotype. In order to facilitate this work, experimental biologists need software capable of an

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Isaksson, Ola. "Classification of Flying Qualities with Machine Learning Methods." Thesis, KTH, Flygdynamik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302145.

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The primary objective of this thesis is to evaluate the prospect of machine learning methods being used to classify flying qualities based on simulator data (with the focus being on pitch maneuvers). If critical flying qualities could be identified earlier in the verification process, they can be further invested in and focused on with less cost for design changes of the flight control system. Information from manned simulations with given flying quality levels are used to create a replication of the performed pitch maneuver in a desktop simulator. The generated flight data is represented by d

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Montiel, López Jacob. "Fast and slow machine learning." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT014/document.

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L'ère du Big Data a révolutionné la manière dont les données sont créées et traitées. Dans ce contexte, de nombreux défis se posent, compte tenu de la quantité énorme de données disponibles qui doivent être efficacement gérées et traitées afin d’extraire des connaissances. Cette thèse explore la symbiose de l'apprentissage en mode batch et en flux, traditionnellement considérés dans la littérature comme antagonistes, sur le problème de la classification à partir de flux de données en évolution. L'apprentissage en mode batch est une approche bien établie basée sur une séquence finie: d'abord le

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Sendur, Zeynel. "Text Document Categorization by Machine Learning." Scholarly Repository, 2008. http://scholarlyrepository.miami.edu/oa_theses/209.

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Because of the explosion of digital and online text information, automatic organization of documents has become a very important research area. There are mainly two machine learning approaches to enhance the organization task of the digital documents. One of them is the supervised approach, where pre-defined category labels are assigned to documents based on the likelihood suggested by a training set of labeled documents; and the other one is the unsupervised approach, where there is no need for human intervention or labeled documents at any point in the whole process. In this thesis, we conce

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Wan, Fengkai. "Deep Learning Method used in Skin Lesions Segmentation and Classification." Thesis, KTH, Medicinsk teknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233467.

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Malignant melanoma (MM) is a type of skin cancer that is associated with a very poor prognosis and can often lead to death. Early detection is crucial in order to administer the right treatment successfully but currently requires the expertise of a dermatologist. In the past years, studies have shown that automatic detection of MM is possible through computer vision and machine learning methods. Skin lesion segmentation and classification are the key methods in supporting automatic detection of different skin lesions. Compared with traditional computer vision as well as other machine learning

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