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Dissertations / Theses on the topic 'Machine learning-based approach'

Author: Grafiati

Published: 11 December 2022

Last updated: 31 July 2025

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1

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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Wang, Junlin. "Video based recognition of human dynamics : a machine learning approach." Thesis, University of Exeter, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.439806.

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3

Goodman, Genghis. "A Machine Learning Approach to Artificial Floorplan Generation." UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/89.

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The process of designing a floorplan is highly iterative and requires extensive human labor. Currently, there are a number of computer programs that aid humans in floorplan design. These programs, however, are limited in their inability to fully automate the creative process. Such automation would allow a professional to quickly generate many possible floorplan solutions, greatly expediting the process. However, automating this creative process is very difficult because of the many implicit and explicit rules a model must learn in order create viable floorplans. In this paper, we propose a met

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4

Ullmann, Thomas Daniel. "Automated detection of reflection in texts : a machine learning based approach." Thesis, Open University, 2015. http://oro.open.ac.uk/45402/.

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Promoting reflective thinking is an important educational goal. A common educational practice is to provide opportunities for learners to express their reflective thoughts in writing. The analysis of such text with regard to reflection is mainly a manual task that employs the principles of content analysis. Considering the amount of text produced by online learning systems, tools that automatically analyse text with regard to reflection would greatly benefit research and practice. Previous research has explored the potential of dictionary-based approaches that automatically map keywords to cat

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Balendran, Velupillai. "Cosmetic quality of surfaces : a computational approach." Thesis, Nottingham Trent University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295380.

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6

Westlinder, Simon. "Video Traffic Classification : A Machine Learning approach with Packet Based Features using Support Vector Machine." Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-43011.

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Internet traffic classification is an important field which several stakeholders are dependent on for a number of different reasons. Internet Service Providers (ISPs) and network operators benefit from knowing what type of traffic that propagates over their network in order to correctly treat different applications. Today Deep Packet Inspection (DPI) and port based classification are two of the more commonly used methods in order to classify Internet traffic. However, both of these techniques fail when the traffic is encrypted. This study explores a third method, classifying Internet traffic b

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Cheng, Jie. "Learning Bayesian networks from data : an information theory based approach." Thesis, University of Ulster, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.243621.

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8

Hill, Jerry L., and Randall P. Mora. "An Autonomous Machine Learning Approach for Global Terrorist Recognition." International Foundation for Telemetering, 2012. http://hdl.handle.net/10150/581675.

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ITC/USA 2012 Conference Proceedings / The Forty-Eighth Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2012 / Town and Country Resort & Convention Center, San Diego, California<br>A major intelligence challenge we face in today's national security environment is the threat of terrorist attack against our national assets, especially our citizens. This paper addresses global reconnaissance which incorporates an autonomous Intelligent Agent/Data Fusion solution for recognizing potential risk of terrorist attack through identifying and reporting imminent pers

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Màrquez, Lluís. "Part-of-speech Tagging: A Machine Learning Approach based on Decision Trees." Doctoral thesis, Universitat Politècnica de Catalunya, 1999. http://hdl.handle.net/10803/6663.

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The study and application of general Machine Learning (ML) algorithms to theclassical ambiguity problems in the area of Natural Language Processing (NLP) isa currently very active area of research. This trend is sometimes called NaturalLanguage Learning. Within this framework, the present work explores the applicationof a concrete machine-learning technique, namely decision-tree induction, toa very basic NLP problem, namely part-of-speech disambiguation (POS tagging).Its main contributions fall in the NLP field, while topics appearing are addressedfrom the artificial intelligence perspective,

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Lin, Xinyi. "A machine learning based approach for the link-to-system mapping problem." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214852.

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The quality of mobile communication is related to signal transmissions. Early detection of the errors in transmissions may reduce the time delay of communications. The traditional error detection methods are not accurate enough. Therefore, in this report, a machine learning based approach is proposed for the link-to-system mapping problem, which can predict the outcomes (received correctly or not) of the link-level simulations without knowing the exact signals that are being transmitted. In this method, the transmission state is assumed to be a function of the features of a channel environment

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11

Immaneni, Raghu Nandan. "An efficient approach to machine learning based text classification through distributed computing." Thesis, California State University, Long Beach, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1603338.

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<p> Text Classification is one of the classical problems in computer science, which is primarily used for categorizing data, spam detection, anonymization, information extraction, text summarization etc. Given the large amounts of data involved in the above applications, automated and accurate training models and approaches to classify data efficiently are needed. </p><p> In this thesis, an extensive study of the interaction between natural language processing, information retrieval and text classification has been performed. A case study named “keyword extraction” that deals wit

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Marcén, Terraza Ana Cristina. "Design of a Machine Learning-based Approach for Fragment Retrieval on Models." Doctoral thesis, Universitat Politècnica de València, 2021. http://hdl.handle.net/10251/158617.

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[ES] El aprendizaje automático (ML por sus siglas en inglés) es conocido como la rama de la inteligencia artificial que reúne algoritmos estadísticos, probabilísticos y de optimización, que aprenden empíricamente. ML puede aprovechar el conocimiento y la experiencia que se han generado durante años en las empresas para realizar automáticamente diferentes procesos. Por lo tanto, ML se ha aplicado a diversas áreas de investigación, que estudian desde la medicina hasta la ingeniería del software. De hecho, en el campo de la ingeniería del software, el mantenimiento y la evolución de un sistema

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Guo, Haipeng. "Algorithm selection for sorting and probabilistic inference : a machine learning-based approach /." Search for this dissertation online, 2003. http://wwwlib.umi.com/cr/ksu/main.

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Blank, Clas, and Tomas Hermansson. "A Machine Learning approach to churn prediction in a subscription-based service." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-240397.

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Prenumerationstjänster blir alltmer populära i dagens samhälle. En av nycklarna för att lyckas med en prenumerationsbaserad affärsmodell är att minimera kundbortfall (eng. churn), dvs. kunder som avslutar sin prenumeration inom en viss tidsperiod. I och med den ökande digitaliseringen, är det nu enklare att samla in data än någonsin tidigare. Samtidigt växer maskininlärning snabbt och blir alltmer lättillgängligt, vilket möjliggör nya infallsvinklar på problemlösning. Denna rapport kommer testa och utvärdera ett försök att förutsäga kundbortfall med hjälp av maskininlärning, baserat på kunddat

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Bagger, Toräng Malcolm, and Kasper Aldrin. "A machine learning approach to EEG based prediction of user's music preferences." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259625.

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Music has many benefits for our mood and feelings, especially so when we get to choose our own favorite music. However, accessing one's favorite music is not as easy for everyone. For motorically disabled and locked-in people, interacting with devices used for listening to music is challenging since it requires physical interaction. Machine learning classification methods used with EEG could prove useful for detecting individual musical preferences, extracted without any physical or verbal interaction. The two most common methods within EEG-based classification are Artificial neural networks (

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Awan, Faraz Malik. "Towards synthetic sensing for smart cities : a machine/deep learning-based approach." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAS006.

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Nous avons travaillé sur l'un des axes de recherche les plus importants de la ville intelligente, à savoir les systèmes de transport intelligents (STI). Les ITS englobent plusieurs domaines, tels que les systèmes de notification électronique des véhicules, les informations sur le trafic, le stationnement intelligent et l'environnement. Cependant, dans cette thèse, nous ciblons deux de ses domaines importants : i) le stationnement intelligent et ii) le trafic routier. Nous avons commencé notre recherche par le cas d'utilisation du stationnement intelligent. En effectuant une revue de la littéra

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Lam, Ho-Yu. "A learning approach to spam detection based on social networks /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?CSED%202007%20LAM.

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Qader, Aso, and William Shiver. "Developing an Advanced Internal Ratings-Based Model by Applying Machine Learning." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273418.

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Since the regulatory framework Basel II was implemented in 2007, banks have been allowed to develop internal risk models for quantifying the capital requirement. By using data on retail non-performing loans from Hoist Finance, the thesis assesses the Advanced Internal Ratings-Based approach. In particular, it focuses on how banks active in the non-performing loan industry, can risk-classify their loans despite limited data availability of the debtors. Moreover, the thesis analyses the effect of the maximum-recovery period on the capital requirement. In short, a comparison of five different mat

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Mestres, Sugrañes Albert. "Knowledge-defined networking : a machine learning based approach for network and traffic modeling." Doctoral thesis, Universitat Politècnica de Catalunya, 2017. http://hdl.handle.net/10803/461831.

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The research community has considered in the past the application of Machine Learning (ML) techniques to control and operate networks. A notable example is the Knowledge Plane proposed by D.Clark et al. However, such techniques have not been extensively prototyped or deployed in the field yet. In this thesis, we explore the reasons for the lack of adoption and posit that the rise of two recent paradigms: Software-Defined Networking (SDN) and Network Analytics (NA), will facilitate the adoption of ML techniques in the context of network operation and control. We describe a new paradigm that acc

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Tribus, Hannes. "Static Code Features for a Machine Learning based Inspection : An approach for C." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2550.

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Delivering fault free code is the clear goal of each devel- oper, however the best method to achieve this aim is still an open question. Despite that several approaches have been proposed in literature there exists no overall best way. One possible solution proposed recently is to combine static source code analysis with the discipline of machine learn- ing. An approach in this direction has been defined within this work, implemented as a prototype and validated subse- quently. It shows a possible translation of a piece of source code into a machine learning algorithm’s input and further- more

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Nguyen, Thi Thu Thuy. "A novel approach for practical real-time, machine learning based ip traffic classification." Swinburne Research Bank, 2009. http://hdl.handle.net/1959.3/61268.

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Thesis (PhD) - Swinburne University of Technology, Faculty of Engineering and Industrial Sciences, Centre for Advanced Internet Architectures, 2009.<br>A thesis submitted for the degree of Doctor of Philosophy, Centre for Advanced Internet Architectures, Faculty of Engineering and Industrial Sciences, Swinburne University of Technology, 2009. Typescript. Bibliography: p. 218-240.

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Asber, Johnny. "A Machine Learning-Based Approach for Fault Detection of Railway Track and its Components." Thesis, Luleå tekniska universitet, Drift, underhåll och akustik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-81275.

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The hard equation of railway safety versus the high commercial profits can only be achieved through the use of new inspection methods supported by modern technologies. The track and its components can have different types of troubles, such as rail surface defects, broken sleepers, missing fasteners, and irregular ballast levels. Each component of the track infrastructure plays a significant role, where the failure or the absence of any of them can pave the way to undesired situations. The rail is designed to carry and direct the train, the sleepers are meant to maintain the level of the rail,

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O'Leary, Brian. "A Vertex-Based Approach to the Statistical and Machine Learning Analyses of Brain Structure." University of Toledo / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1576254162111087.

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Allworth, James William. "A Machine Learning Approach to Space Debris Characterisation and Classification using Ground Based Optical Observations." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29185.

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Space debris is becoming an increasingly prevalent issue through a combination of the recent rise in the accessibility of space and the difficulty in actively removing space debris from orbit. The high relative velocity between orbital objects and the difficulty in maintaining their state, results in space debris posing a significant collision risk to active satellites. Risk mitigation strategies rely on space situational awareness, which focuses on tracking space objects and predicting their future states to then inform satellite operators of potential future conjunctions. However, the accura

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Chai, Kevin Eng Kwong. "A machine learning-based approach for automated quality assessment of user generated content in web forums." Thesis, Curtin University, 2011. http://hdl.handle.net/20.500.11937/107.

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Web 2.0 platforms such as forums, blogs and wikis allow users from its community to contribute content. However, users often received little if any professional training in content creation and content is commonly published without peer review. Excessive low quality user contributions can lead to information overload, which describes the situation when a user feels overwhelmed with unwanted information. Information overload can cause users to withdraw from using a website therefore decreasing a website's overall sustainability through the loss of users from its community.Many Web 2.0 websites

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Nordlund, Fredrik Hans. "Enabling Network-Aware Cloud Networked Robots with Robot Operating System : A machine learning-based approach." Thesis, KTH, Radio Systems Laboratory (RS Lab), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-160877.

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During the recent years, a new area called Cloud Networked Robotics (CNR) has evolved from conventional robotics, thanks to the increasing availability of cheap robot systems and steady improvements in the area of cloud computing. Cloud networked robots refers to robots with the ability to offload computation heavy modules to a cloud, in order to make use of storage, scalable computation power, and other functionalities enabled by a cloud such as shared knowledge between robots on a global level. However, these cloud robots face a problem with reachability and QoS of crucial modules that are o

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Hanna, Peter, and Erik Swartling. "Anomaly Detection in Time Series Data using Unsupervised Machine Learning Methods: A Clustering-Based Approach." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273630.

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For many companies in the manufacturing industry, attempts to find damages in their products is a vital process, especially during the production phase. Since applying different machine learning techniques can further aid the process of damage identification, it becomes a popular choice among companies to make use of these methods to enhance the production process even further. For some industries, damage identification can be heavily linked with anomaly detection of different measurements. In this thesis, the aim is to construct unsupervised machine learning models to identify anomalies on un

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Geyer, Joseph Michael. "Identification of Candidate Concepts in a Learning-Based Approach to Reverse Engineering." Miami University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=miami1272036566.

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Javed, Kamran. "A robust & reliable Data-driven prognostics approach based on extreme learning machine and fuzzy clustering." Phd thesis, Université de Franche-Comté, 2014. http://tel.archives-ouvertes.fr/tel-01025295.

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Le Pronostic et l'étude de l'état de santé (en anglais Prognostics and Health Management (PHM)) vise à étendre le cycle de vie d'un actif physique, tout en réduisant les coûts d'exploitation et de maintenance. Pour cette raison, le pronostic est considéré comme un processus clé avec des capacités de prédictions. En effet, des estimations précises de la durée de vie avant défaillance d'un équipement, Remaining Useful Life (RUL), permettent de mieux définir un plan d'actions visant à accroître la sécurité, réduire les temps d'arrêt, assurer l'achèvement de la mission et l'efficacité de la produc

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Hanselowski, Andreas Verfasser], Iryna [Akademischer Betreuer] Gurevych, and Chris [Akademischer Betreuer] [Reed. "A Machine-Learning-Based Pipeline Approach to Automated Fact-Checking / Andreas Hanselowski ; Iryna Gurevych, Chris Reed." Darmstadt : Universitäts- und Landesbibliothek, 2020. http://d-nb.info/1224048164/34.

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Javed, kamran. "A robust and reliable data-driven prognostics approach based on Extreme Learning Machine and Fuzzy Clustering." Thesis, Besançon, 2014. http://www.theses.fr/2014BESA2021/document.

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Le pronostic industriel vise à étendre le cycle de vie d’un dispositif physique, tout en réduisant les couts d’exploitation et de maintenance. Pour cette raison, le pronostic est considéré comme un processus clé avec des capacités de prédiction. En effet, des estimations précises de la durée de vie avant défaillance d’un équipement, Remaining Useful Life (RUL), permettent de mieux définir un plan d’action visant à accroitre la sécurité, réduire les temps d’arrêt, assurer l’achèvement de la mission et l’efficacité de la production.Des études récentes montrent que les approches guidées par les d

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Hanselowski, Andreas [Verfasser], Iryna [Akademischer Betreuer] Gurevych, and Chris [Akademischer Betreuer] Reed. "A Machine-Learning-Based Pipeline Approach to Automated Fact-Checking / Andreas Hanselowski ; Iryna Gurevych, Chris Reed." Darmstadt : Universitäts- und Landesbibliothek, 2020. http://d-nb.info/1224048164/34.

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Cirella, Riccardo. "The use of subspace-based methods for damage detection in civili structures: a machine learning approach." Doctoral thesis, Università degli Studi dell'Aquila, 2022. https://hdl.handle.net/11697/198068.

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Events in recent years have shown how important it is to monitor the structural health of existing civil works. Structural Health Monitoring systems are a useful tool to provide an objective and automatic valuation of the state of health of a structure, in order to detect the emergence of anomalies in its behavior. They are also an auxiliary tool in the decision-making phase for maintenance work or after extraordinary events. The Thesis work explores the topic of damage detection based on the analysis of subspaces of dynamical systems matrices. The aim of the research was to investigate a meth

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Simeoni, Christian <1994&gt. "A Machine Learning-based approach for the assessment of water quality variation in the Venice Lagoon." Master's Degree Thesis, Università Ca' Foscari Venezia, 2020. http://hdl.handle.net/10579/16759.

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Water quality (WQ) is one of the most critical issues in lakes, estuaries, marine and costal water management, affecting not only the socio-economic systems, but also the sustainability of natural processes. As a consequence of the complex interplay between climate and human-induced pressures, changes in marine WQ are observed (e.g. higher turbidity with resulting reduced water clarity, acidification) with cascading effects on the environmental status of natural ecosystems and their capacity to flow services for human wellbeing. To evaluate such effects, a continuous monitoring of WQ parameter

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Shafi, Kamran Information Technology &amp Electrical Engineering Australian Defence Force Academy UNSW. "An online and adaptive signature-based approach for intrusion detection using learning classifier systems." Awarded by:University of New South Wales - Australian Defence Force Academy, 2008. http://handle.unsw.edu.au/1959.4/38991.

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This thesis presents the case of dynamically and adaptively learning signatures for network intrusion detection using genetic based machine learning techniques. The two major criticisms of the signature based intrusion detection systems are their i) reliance on domain experts to handcraft intrusion signatures and ii) inability to detect previously unknown attacks or the attacks for which no signatures are available at the time. In this thesis, we present a biologically-inspired computational approach to address these two issues. This is done by adaptively learning maximally general rules, whic

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Bonakdar, Sakhi Omid. "Segmentation of heterogeneous document images : an approach based on machine learning, connected components analysis, and texture analysis." Phd thesis, Université Paris-Est, 2012. http://tel.archives-ouvertes.fr/tel-00912566.

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Document page segmentation is one of the most crucial steps in document image analysis. It ideally aims to explain the full structure of any document page, distinguishing text zones, graphics, photographs, halftones, figures, tables, etc. Although to date, there have been made several attempts of achieving correct page segmentation results, there are still many difficulties. The leader of the project in the framework of which this PhD work has been funded (*) uses a complete processing chain in which page segmentation mistakes are manually corrected by human operators. Aside of the costs it re

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Ruffa, Giorgio. "Towards unification of organ labeling in radiation therapy using a machine learning approach based on 3D geometries." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-256075.

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In radiation therapy, it is important to control the radiation dose absorbed by Organs at Risk (OARs). The OARs are represented as 3D volumes delineated by medical experts, typically using computed tomography images of the patient. The OARs are identified using user-provided text labels, which, due to a lack of enforcement of existing naming standards, are subject to a great level of heterogeneity. This condition negatively impacts the development of procedures that require vast amounts of standardized data, like organ segmentation algorithms and inter-institutional clinical studies. Previous

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Stacchio, Lorenzo. "Detecting social patterns within 20th century documentary photos: a deep learning based approach." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21552/.

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The job of a historian is to understand what happened in the past, resorting in many cases to written documents as a firsthand source of information. Text, however, does not amount to the only source of knowledge. Pictorial representations, in fact, have also accompanied the main events of the historical timeline. In particular, the opportunity of visually representing circumstances has bloomed since the invention of photography, with the possibility of capturing in real-time the occurrence of a specific events. Thanks to the widespread use of digital technologies (e.g. smartphones and dig

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Johansson, Simon. "A machine-learning based approach to pre-impact fall detection with wearable devices : MOTION MONITORING USING SENSOR FUSION AND THE SUPPORT VECTOR MACHINE." Thesis, KTH, Maskinkonstruktion (Inst.), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-170804.

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Falling accidents represents a major threat and is considered as a major source of morbidity and mortality among the elderly. As a consequence of fall related injuries, three people dies in Sweden every day. Additional factors, such as fear of falling further impacts the quality of life for the elderly. Due to the demographic change, which results in an increasing amount of elderly in the population, the costrelated to fall accidents is increasing. In order to the reduce the cost, preventivemethods and tools are believed to be a feasible approach. This report is the resultof a conceptual study

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Rastegari, Samaneh. "Intelligent network intrusion detection using an evolutionary computation approach." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2015. https://ro.ecu.edu.au/theses/1760.

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With the enormous growth of users' reliance on the Internet, the need for secure and reliable computer networks also increases. Availability of effective automatic tools for carrying out different types of network attacks raises the need for effective intrusion detection systems. Generally, a comprehensive defence mechanism consists of three phases, namely, preparation, detection and reaction. In the preparation phase, network administrators aim to find and fix security vulnerabilities (e.g., insecure protocol and vulnerable computer systems or firewalls), that can be exploited to launch attac

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Sendi, Naziha. "Transparent approach based on deep learning and multiagent argumentation for hypertension management." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASG036.

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L'hypertension est connue pour être l'une des principales causes de maladies cardiaques et d'accidents vasculaires cérébraux, tuant environ 7,5 millions de personnes dans le monde chaque année, principalement en raison de son diagnostic tardif.Afin de confirmer le diagnostic d'hypertension, il est nécessaire de collecter des mesures médicales répétées. Une solution consiste à exploiter ces mesures et à les intégrer dans les dossiers électroniques de santé par des algorithmes d'apprentissage automatique.Dans ce travail, nous nous sommes concentrés sur les méthodes d'ensemble qui combinent plusi

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Lahza, Hassan Fareed M. "Designing a feature construction and selection approach for machine learning-based intrusion detection in industrial control system networks." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/132657/1/Hassan%20Fareed%20M_Lahza_Thesis.pdf.

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This thesis presents an approach for constructing new accurate and efficient advanced features to improve the accuracy of detecting cyber-attacks using machine learning on the critical infrastructure networks. The empirical results indicate that our feature construction approach not only outperforms other methods in term of detection rate and performance but also provides automation to the entire construction processes. This thesis also proposes a framework for constructing advanced features for various critical infrastructure communication protocols by adopting and improving the window-based

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Patel, Jiten. "Enhanced classification approach with semi-supervised learning for reliability-based system design." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44872.

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Traditionally design engineers have used the Factor of Safety method for ensuring that designs do not fail in the field. Access to advanced computational tools and resources have made this process obsolete and new methods to introduce higher levels of reliability in an engineering systems are currently being investigated. However, even though high computational resources are available the computational resources required by reliability analysis procedures leave much to be desired. Furthermore, the regression based surrogate modeling techniques fail when there is discontinuity in the design spa

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Herzig, Sebastian J. I. "A Bayesian learning approach to inconsistency identification in model-based systems engineering." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53576.

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Designing and developing complex engineering systems is a collaborative effort. In Model-Based Systems Engineering (MBSE), this collaboration is supported through the use of formal, computer-interpretable models, allowing stakeholders to address concerns using well-defined modeling languages. However, because concerns cannot be separated completely, implicit relationships and dependencies among the various models describing a system are unavoidable. Given that models are typically co-evolved and only weakly integrated, inconsistencies in the agglomeration of the information and knowledge encod

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Tamaddoni, Nezhad Alireza. "Logic-based machine learning using a bounded hypothesis space : the lattice structure, refinement operators and a genetic algorithm approach." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/29849.

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Rich representation inherited from computational logic makes logic-based machine learning a competent method for application domains involving relational background knowledge and structured data. There is however a trade-off between the expressive power of the representation and the computational costs. Inductive Logic Programming (ILP) systems employ different kind of biases and heuristics to cope with the complexity of the search, which otherwise is intractable. Searching the hypothesis space bounded below by a bottom clause is the basis of several state-of-the-art ILP systems (e.g. Progol a

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Antony, Bhavna Josephine. "A combined machine-learning and graph-based framework for the 3-D automated segmentation of retinal structures in SD-OCT images." Diss., University of Iowa, 2013. https://ir.uiowa.edu/etd/4944.

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Spectral-domain optical coherence tomography (SD-OCT) is a non-invasive imaging modality that allows for the quantitative study of retinal structures. SD-OCT has begun to find widespread use in the diagnosis and management of various ocular diseases. While commercial scanners provide limited analysis of a small number of retinal layers, the automated segmentation of retinal layers and other structures within these volumetric images is quite a challenging problem, especially in the presence of disease-induced changes. The incorporation of a priori information, ranging from qualitative assessmen

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Miri, Mohammad Saleh. "A multimodal machine-learning graph-based approach for segmenting glaucomatous optic nerve head structures from SD-OCT volumes and fundus photographs." Diss., University of Iowa, 2016. https://ir.uiowa.edu/etd/5574.

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Glaucoma is the second leading cause of blindness worldwide. The clinical standard for monitoring the functional deficits in the retina that are caused by glaucoma is the visual field test. In addition to monitoring the functional loss, evaluating the disease-related structural changes in the human retina also helps with diagnosis and management of this progressive disease. The characteristic changes of retinal structures such as the optic nerve head (ONH) are monitored utilizing imaging modalities such as color (stereo) fundus photography and, more recently, spectral-domain optical coherence

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Jayaram, Vinay [Verfasser], and Moritz [Akademischer Betreuer] Grosse-Wentrup. "A machine learning approach to taking EEG-based brain-computer interfaces out of the lab / Vinay Jayaram ; Betreuer: Moritz Grosse-Wentrup." Tübingen : Universitätsbibliothek Tübingen, 2018. http://d-nb.info/1173699996/34.

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Morales-Tirado, Lizdabel. "An Approach to Using Cognition in Wireless Networks." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/37185.

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Third Generation (3G) wireless networks have been well studied and optimized with traditional radio resource management techniques, but still there is room for improvement. Cognitive radio technology can bring significantcant network improvements by providing awareness to the surrounding radio environment, exploiting previous network knowledge and optimizing the use of resources using machine learning and artificial intelligence techniques. Cognitive radio can also co-exist with legacy equipment thus acting as a bridge among heterogeneous communication systems. In this work, an approach for ap

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Velka, Elina. "Loss Given Default Estimation with Machine Learning Ensemble Methods." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279846.

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This thesis evaluates the performance of three machine learning methods in prediction of the Loss Given Default (LGD). LGD can be seen as the opposite of the recovery rate, i.e. the ratio of an outstanding loan that the loan issuer would not be able to recover in case the customer would default. The methods investigated are decision trees, random forest and boosted methods. All of the methods investigated performed well in predicting the cases were the loan is not recovered, LGD = 1 (100%), or the loan is totally recovered, LGD = 0 (0% ). When the performance of the models was evaluated on a d

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