Relevant bibliographies by topics / Hybrid machine learning models / Dissertations / Theses
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Dissertations / Theses on the topic 'Hybrid machine learning models'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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1

Sayin, Günel Burcu. "Towards Reliable Hybrid Human-Machine Classifiers." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/349843.

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In this thesis, we focus on building reliable hybrid human-machine classifiers to be deployed in cost-sensitive classification tasks. The objective is to assess ML quality in hybrid classification contexts and design the appropriate metrics, thereby knowing whether we can trust the model predictions and identifying the subset of items on which the model is well-calibrated and trustworthy. We start by discussing the key concepts, research questions, challenges, and architecture to design and implement an effective hybrid classification service. We then present a deeper investigation of each ser
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Abdullah, Siti Norbaiti binti. "Machine learning approach for crude oil price prediction." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/machine-learning-approach-for-crude-oil-price-prediction(949fa2d5-1a4d-416a-8e7c-dd66da95398e).html.

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Crude oil prices impact the world economy and are thus of interest to economic experts and politicians. Oil price’s volatile behaviour, which has moulded today’s world economy, society and politics, has motivated and continues to excite researchers for further study. This volatile behaviour is predicted to prompt more new and interesting research challenges. In the present research, machine learning and computational intelligence utilising historical quantitative data, with the linguistic element of online news services, are used to predict crude oil prices via five different models: (1) the H
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ISAKSSON, LARS JOHANNES. "HYBRID DEEP LEARNING AND RADIOMICS MODELS FOR ASSESSMENT OF CLINICALLY RELEVANT PROSTATE CANCER." Doctoral thesis, Università degli Studi di Milano, 2022. https://hdl.handle.net/2434/946529.

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Precision medicine holds the potential to revolutionize healthcare by providing every patient with personalized treatments and decisions tailored to his or her individual needs. This might be enabled by the large influx of potentially diagnostic information from new sources such as genetics and modern imaging techniques, provided the relevant information can be extracted. One such framework that has started to demonstrate promise in radiology, especially in the assessment of cancer, is radiomics; the practice of characterizing images by extracting a substantial amount of quantitative mathemati
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Tian, Xiaoguang. "Hybrid Models in Automobile Insurance: Technology Adoption and Customer Relations." Thesis, University of North Texas, 2019. https://digital.library.unt.edu/ark:/67531/metadc1538717/.

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Customer relationship management (CRM), a primary activity in the business value chain to relate to the customer, involves solicitation, analysis, and the use of the knowledge about the customer to provide goods and services through effective and efficient methods. It is a wise strategy and source of competitive advantage for customer behavior understanding and business performance management. The use of information technology (IT) in CRM allows companies to simplify their processes, to integrate product or service related decision making with the business strategies, and to optimize their ope
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Ramachandra, Rao Sanjay Kamath. "Question Answering with Hybrid Data and Models." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASS024.

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La recherche de réponses à des questions relève de deux disciplines : le traitement du langage naturel et la recherche d’information. L’émergence de l’apprentissage profond dans plusieurs domaines de recherche tels que la vision par ordinateur, le traitement du langage naturel etc. a conduit à l’émergence de modèles de bout en bout. Les travaux actuels de l’état de l’art en question-réponse (QR) visent à mettre en oeuvre de tels modèles. Dans le cadre du projet GoASQ, l’objectif est d’étudier, comparer et combiner différentes approches pour répondre à des questions formulées en langage naturel
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Chowdhury, Ziaul Islam, and Iskanter Bensenousi. "Evaluation of different machine learning models for the prediction of electric or hybrid vehicle buyers and identification of the characteristics of the buyers in the EU." Thesis, Blekinge Tekniska Högskola, Institutionen för industriell ekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20712.

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The main goal of this thesis is to evaluate different machine learning models in order to classify buyers of an electric or a hybrid vehicle and to identify the characteristics of the buyers in the European Union. Machine learning algorithms and techniques were adopted to analyze the dataset and to create models that could predict, with a certain accuracy, the customer’s willingness to buy an EV. Identification of the characteristics of the buyers were based on the identified most important features from the machine learning models and statistical analysis. The research consisted of explorator
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Black, Kevin P. "Interactive Machine Assistance: A Case Study in Linking Corpora and Dictionaries." BYU ScholarsArchive, 2015. https://scholarsarchive.byu.edu/etd/5620.

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Machine learning can provide assistance to humans in making decisions, including linguistic decisions such as determining the part of speech of a word. Supervised machine learning methods derive patterns indicative of possible labels (decisions) from annotated example data. For many problems, including most language analysis problems, acquiring annotated data requires human annotators who are trained to understand the problem and to disambiguate among multiple possible labels. Hence, the availability of experts can limit the scope and quantity of annotated data. Machine-learned pre-annotation
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Torregrosa, jordan Sergio. "Approches Hybrides et Méthodes d'Intelligence Artificielle Basées sur la Simulation Numérique pour l'Optimisation des Systèmes Aérodynamiques Complexes." Electronic Thesis or Diss., Paris, HESAM, 2024. http://www.theses.fr/2024HESAE002.

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La conception industrielle d'un composant est un processus complexe, long et coûteux, contraint par des spécifications physiques, stylistiques et de développement précises en fonction de ses conditions et de son environnement d'utilisation futurs. En effet, un composant industriel est défini et caractérisé par de nombreux paramètres qui doivent être optimisés pour satisfaire au mieux toutes ces spécifications. Cependant, la complexité de ce problème d'optimisation multiparamétrique sous contraintes est telle que sa résolution analytique est compromise.Dans le passé, un tel problème était résol
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Johansson, Åke, and Joel Wikner. "Learning-Based Motion Planning and Control of a UGV With Unknown and Changing Dynamics." Thesis, Linköpings universitet, Reglerteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176923.

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Research about unmanned ground vehicles (UGVs) has received an increased amount of attention in recent years, partly due to the many applications of UGVs in areas where it is inconvenient or impossible to have human operators, such as in mines or urban search and rescue. Two closely linked problems that arise when developing such vehicles are motion planning and control of the UGV. This thesis explores these subjects for a UGV with an unknown, and possibly time-variant, dynamical model. A framework is developed that includes three components: a machine learning algorithm to estimate the unknow
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Vasquez, Capacho John William. "Chronicle Based Alarm Management." Thesis, Toulouse, INSA, 2017. http://www.theses.fr/2017ISAT0032/document.

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La sécurité des installations industrielles implique une gestion intégrée de tous les facteurs pouvant causer des incidents. La gestion d’alarmes est une condition qui peut être formulée comme un problème de reconnaissance de motifs pour lequel les motifs temporels sont utilisés pour caractériser différentes situations typiques, en particulier liées au phases de démarrage et d'arrêt. Dans cette thèse, nous proposons une nouvelle approche de gestion des alarmes basée sur un processus de diagnostic. En considérant les alarmes et les actions des procédures d'exploitation standard comme des événem
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Reategui, Eliseo Berni. "Um modelo para sistemas especialistas conexionistas híbridos." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 1993. http://hdl.handle.net/10183/25522.

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O presente trabalho descreve um modelo híbrido para sistemas especialistas classificatórios que Integra os mecanismos de representação de conhecimento de frames e de redes neurais, assimilando qualidades inerentes de cada um dos dois paradigmas. O mecanismo de frames fornece tipos construtores flexíveis para a modelagem do conhecimento do domínio, enquanto as redes neurais possibilitam tanto a automação da aquisição de conhecimento, a partir de uma base de casos, quanto a implementação de aprendizado indutivo e dedutivo. Empregam-se dois tipos de frames para representar o conhecimento relativo
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Lundström, Robin. "Machine Learning for Air Flow Characterization : An application of Theory-Guided Data Science for Air Fow characterization in an Industrial Foundry." Thesis, Karlstads universitet, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-72782.

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In industrial environments, operators are exposed to polluted air which after constant exposure can cause irreversible lethal diseases such as lung cancer. The current air monitoring techniques are carried out sparely in either a single day annually or at few measurement positions for a few days.In this thesis a theory-guided data science (TGDS) model is presented. This hybrid model combines a steady state Computational Fluid Dynamics (CFD) model with a machine learning model. Both the CFD model and the machine learning algorithm was developed in Matlab. The CFD model serves as a basis for the
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Lounici, Sofiane. "Watermarking machine learning models." Electronic Thesis or Diss., Sorbonne université, 2022. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2022SORUS282.pdf.

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La protection de la propriété intellectuelle des modèles d’apprentissage automatique apparaît de plus en plus nécessaire, au vu des investissements et de leur impact sur la société. Dans cette thèse, nous proposons d’étudier le tatouage de modèles d’apprentissage automatique. Nous fournissons un état de l’art sur les techniques de tatouage actuelles, puis nous le complétons en considérant le tatouage de modèles au-delà des tâches de classification d’images. Nous définissons ensuite les attaques de contrefaçon contre le tatouage pour les plateformes d’hébergement de modèles, et nous présentons
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Hardoon, David Roi. "Semantic models for machine learning." Thesis, University of Southampton, 2006. https://eprints.soton.ac.uk/262019/.

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In this thesis we present approaches to the creation and usage of semantic models by the analysis of the data spread in the feature space. We aim to introduce the general notion of using feature selection techniques in machine learning applications. The applied approaches obtain new feature directions on data, such that machine learning applications would show an increase in performance. We review three principle methods that are used throughout the thesis. Firstly Canonical Correlation Analysis (CCA), which is a method of correlating linear relationships between two multidimensional variables
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Gil, Stephanie Ph D. Massachusetts Institute of Technology. "Robust learning of probabilistic hybrid models." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/46562.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2008.<br>Includes bibliographical references (p. 125-127).<br>Advances in autonomy, in the fields of control, estimation, and diagnosis, have improved immensely, as seen by spacecraft that navigate toward pinpoint landings, or speech recognition enabled in hand-held devices. Arguably the most important step to controlling and improving a system, is to understand that system. For this reason, accurate models are essential for continued advancements in the field of autonomy. Hybrid stochastic models, suc
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Bone, Nicholas. "Models of programs and machine learning." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244565.

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Zhu, Xiaodan. "On Cross-Series Machine Learning Models." W&M ScholarWorks, 2020. https://scholarworks.wm.edu/etd/1616444550.

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Sparse high dimensional time series are common in industry, such as in supply chain demand and retail sales. Accurate and reliable forecasting of high dimensional time series is essential for supply chain planning and business management. In practical applications, sparse high dimensional time series prediction faces three challenges: (1) simple models cannot capture complex patterns, (2) insufficient data prevents us from pursuing more advanced models, and (3) time series in the same dataset may have widely different properties. These challenges prevent the currently prevalent models and theo
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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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MARRAS, MIRKO. "Machine Learning Models for Educational Platforms." Doctoral thesis, Università degli Studi di Cagliari, 2020. http://hdl.handle.net/11584/285377.

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Scaling up education online and onlife is presenting numerous key challenges, such as hardly manageable classes, overwhelming content alternatives, and academic dishonesty while interacting remotely. However, thanks to the wider availability of learning-related data and increasingly higher performance computing, Artificial Intelligence has the potential to turn such challenges into an unparalleled opportunity. One of its sub-fields, namely Machine Learning, is enabling machines to receive data and learn for themselves, without being programmed with rules. Bringing this intelligent support to e
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Caccin, Marco. "Oxide chemomechanics by hybrid atomistic machine learning methods." Thesis, King's College London (University of London), 2017. https://kclpure.kcl.ac.uk/portal/en/theses/oxide-chemomechanics-by-hybrid-atomistic-machine-learning-methods(e69ed02a-5b80-4353-aa55-e5186e8cc36f).html.

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Atomic scale phenomena concurring to atomic bond ruptures at a crack tip determine the chemomechanical properties of oxide materials, thus a better understanding of them is instrumental in addressing engineering issues related to the brittleness of oxides. In a fracturing material, the macroscopic stress field couples with the chemical reactions occurring at the crack tip in a bidirectional interplay requiring a concurrent multiscale (QM/MM) computational approach. Due to long range electrostatic interactions, the dynamics of chemically accurate description of the neighbourhood of a breaking b
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Kim, Been. "Interactive and interpretable machine learning models for human machine collaboration." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/98680.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2015.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 135-143).<br>I envision a system that enables successful collaborations between humans and machine learning models by harnessing the relative strength to accomplish what neither can do alone. Machine learning techniques and humans have skills that complement each other - machine learning techniques are good at computation on data at the lowest level of granularity, whereas people are better at abstracting
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Shen, Chenyang. "Regularized models and algorithms for machine learning." HKBU Institutional Repository, 2015. https://repository.hkbu.edu.hk/etd_oa/195.

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Multi-lable learning (ML), multi-instance multi-label learning (MIML), large network learning and random under-sampling system are four active research topics in machine learning which have been studied intensively recently. So far, there are still a lot of open problems to be figured out in these topics which attract worldwide attention of researchers. This thesis mainly focuses on several novel methods designed for these research tasks respectively. Then main difference between ML learning and traditional classification task is that in ML learning, one object can be characterized by several
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Ahlin, Mikael, and Felix Ranby. "Predicting Marketing Churn Using Machine Learning Models." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-161408.

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For any organisation that engages in marketing actions there is a need to understand how people react to communication messages that are sent. Since the introduction of General Data Protection Regulation, the requirements for personal data usage have increased and people are able to effect the way their personal information is used by companies. For instance people have the possibility to unsubscribe from communication that is sent, this is called Opt-Out and can be viewed as churning from communication channels. When a customer Opt-Out the organisation loses the opportunity to send personalis
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BALLANTE, ELENA. "Statistical and Machine Learning models for Neurosciences." Doctoral thesis, Università degli studi di Pavia, 2021. http://hdl.handle.net/11571/1447634.

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This thesis addresses several problems encountered in the field of statistical and machine learning methods for data analysis in neurosciences. The thesis is divided into three parts. The first part of the thesis is related to classification tree models. In the research field of polarization measures, a new polarization measure is defined. The function is incorporated in the decision tree algorithm as a splitting function in order to tackle some weaknesses of classical impurity measures. The new algorithm is called Polarized Classification Tree model. The model is tested on simulated and rea
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GUIDOTTI, DARIO. "Verification and Repair of Machine Learning Models." Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1082694.

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In these last few years, machine learning (ML) has gained incredible traction in the Artificial Intelligence community, and ML models have found successful applications in many different domains across computer science. However, it is hard to provide any formal guarantee on the behavior of ML models, and therefore their reliability is still in doubt, especially concerning their deployment in safety and security-critical applications. Verification and repair emerged as promising solutions to address some of these problems. In this dissertation, we present our contributions to these two lines
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Tripathi, Nandita. "Two-level text classification using hybrid machine learning techniques." Thesis, University of Sunderland, 2012. http://sure.sunderland.ac.uk/3305/.

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Nowadays, documents are increasingly being associated with multi-level category hierarchies rather than a flat category scheme. To access these documents in real time, we need fast automatic methods to navigate these hierarchies. Today’s vast data repositories such as the web also contain many broad domains of data which are quite distinct from each other e.g. medicine, education, sports and politics. Each domain constitutes a subspace of the data within which the documents are similar to each other but quite distinct from the documents in another subspace. The data within these domains is fre
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Markou, Markos N. "Models of novelty detection based on machine learning." Thesis, University of Exeter, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.426165.

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Shepherd, T. "Dynamical models and machine learning for supervised segmentation." Thesis, University College London (University of London), 2009. http://discovery.ucl.ac.uk/18729/.

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This thesis is concerned with the problem of how to outline regions of interest in medical images, when the boundaries are weak or ambiguous and the region shapes are irregular. The focus on machine learning and interactivity leads to a common theme of the need to balance conflicting requirements. First, any machine learning method must strike a balance between how much it can learn and how well it generalises. Second, interactive methods must balance minimal user demand with maximal user control. To address the problem of weak boundaries,methods of supervised texture classification are invest
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Liu, Xiaoyang. "Machine Learning Models in Fullerene/Metallofullerene Chromatography Studies." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/93737.

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Machine learning methods are now extensively applied in various scientific research areas to make models. Unlike regular models, machine learning based models use a data-driven approach. Machine learning algorithms can learn knowledge that are hard to be recognized, from available data. The data-driven approaches enhance the role of algorithms and computers and then accelerate the computation using alternative views. In this thesis, we explore the possibility of applying machine learning models in the prediction of chromatographic retention behaviors. Chromatographic separation is a key techni
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Gosch, Aron. "Exploration of 5G Traffic Models using Machine Learning." Thesis, Linköpings universitet, Databas och informationsteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-168160.

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The Internet is a major communication tool that handles massive information exchanges, sees a rapidly increasing usage, and offers an increasingly wide variety of services.   In addition to these trends, the services themselves have highly varying quality of service (QoS), requirements and the network providers must take into account the frequent releases of new network standards like 5G. This has resulted in a significant need for new theoretical models that can capture different network traffic characteristics. Such models are important both for understanding the existing traffic in networks
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Awaysheh, Abdullah Mamdouh. "Data Standardization and Machine Learning Models for Histopathology." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/85040.

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Machine learning can provide insight and support for a variety of decisions. In some areas of medicine, decision-support models are capable of assisting healthcare practitioners in making accurate diagnoses. In this work we explored the application of these techniques to distinguish between two diseases in veterinary medicine; inflammatory bowel disease (IBD) and alimentary lymphoma (ALA). Both disorders are common gastrointestinal (GI) diseases in humans and animals that share very similar clinical and pathological outcomes. Because of these similarities, distinguishing between these two dis
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Aryasomayajula, Naga Srinivasa Baradwaj. "Machine Learning Models for Categorizing Privacy Policy Text." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535633397362514.

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IGUIDER, WALID. "Machine Learning Models for Sports Remote Coaching Platforms." Doctoral thesis, Università degli Studi di Cagliari, 2022. http://hdl.handle.net/11584/326530.

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Offering timely support to users in eCoaching systems is a crucial factor to keep them engaged. However, coaches usually follow many users, so it is hard to prioritize those they should interact with first. Timeliness is especially needed when health implications might be the consequence of a lack of support. Thanks to the data provided by U4FIT (an eCoaching platform for runners we will describe in Chapter 1) and the rise of high-performance computing, Artificial Intelligence can turn such challenges into unparalleled opportunities. One of its sub-fields, namely Machine Learning, enables mach
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Morettin, Paolo. "Learning and Reasoning in Hybrid Structured Spaces." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/264203.

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Many real world AI applications involve reasoning on both continuous and discrete variables, while requiring some level of symbolic reasoning that can provide guarantees on the system's behaviour. Unfortunately, most of the existing probabilistic models do not efficiently support hard constraints or they are limited to purely discrete or continuous scenarios. Weighted Model Integration (WMI) is a recent and general formalism that enables probabilistic modeling and inference in hybrid structured domains. A difference of WMI-based inference algorithms with respect to most alternatives
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Morettin, Paolo. "Learning and Reasoning in Hybrid Structured Spaces." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/264203.

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Many real world AI applications involve reasoning on both continuous and discrete variables, while requiring some level of symbolic reasoning that can provide guarantees on the system's behaviour. Unfortunately, most of the existing probabilistic models do not efficiently support hard constraints or they are limited to purely discrete or continuous scenarios. Weighted Model Integration (WMI) is a recent and general formalism that enables probabilistic modeling and inference in hybrid structured domains. A difference of WMI-based inference algorithms with respect to most alternatives
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Rado, Omesaad A. M. "Contributions to evaluation of machine learning models. Applicability domain of classification models." Thesis, University of Bradford, 2019. http://hdl.handle.net/10454/18447.

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Artificial intelligence (AI) and machine learning (ML) present some application opportunities and challenges that can be framed as learning problems. The performance of machine learning models depends on algorithms and the data. Moreover, learning algorithms create a model of reality through learning and testing with data processes, and their performance shows an agreement degree of their assumed model with reality. ML algorithms have been successfully used in numerous classification problems. With the developing popularity of using ML models for many purposes in different domains,
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Vantzelfde, Nathan Hans. "Prognostic models for mesothelioma : variable selection and machine learning." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33370.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.<br>Includes bibliographical references (leaves 103-107).<br>Malignant pleural mesothelioma is a rare and lethal form of cancer affecting the external lining of the lungs. Extrapleural pneumonectomy (EPP), which involves the removal of the affected lung, is one of the few treatments that has been shown to have some effectiveness in treatment of the disease [39], but this procedure carries with it a high risk of mortality and morbidity [8]. This paper is concerned with building mo
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Ebbesson, Markus. "Mail Volume Forecasting an Evaluation of Machine Learning Models." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-301333.

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This study applies machine learning models to mail volumes with the goal of making sufficiently accurate forecasts to minimise the problem of under- and overstaffing at a mail operating company. A most suitable model appraisal in the context is found by evaluating input features and three different models, Auto Regression (AR), Random Forest (RF) and Neural Network (NN) (Multilayer Perceptron (MLP)). The results provide exceedingly improved forecasting accuracies compared to the model that is currently in use. The RF model is recommended as the most practically applicable model for the company
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Wissel, Benjamin D. "Generalizability of Electronic Health Record-Based Machine Learning Models." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627659161796896.

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Pirgul, Khalid, та Jonathan Svensson. "Verification of Powertrain Simulation Models Using Machine Learning Methods". Thesis, Linköpings universitet, Fordonssystem, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166290.

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This thesis is providing an insight into the verification of a quasi-static simulation model based on the estimation of fuel consumption using machine learning methods. Traditional verification using real test data is not always available. Therefore, a methodology consisting of verification analysis based on estimation methods was developed together with an improving process of a quasi-static simulation model. The modelling of the simulation model mainly consists of designing and implementing a gear selection strategy together with the gearbox itself for a dual clutch transmission dedicated to
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Elf, Sebastian, and Christopher Öqvist. "Comparison of supervised machine learning models forpredicting TV-ratings." Thesis, KTH, Hälsoinformatik och logistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278054.

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Abstract Manual prediction of TV-ratings to use for program and advertisement placement can be costly if they are wrong, as well as time-consuming. This thesis evaluates different supervised machine learning models to see if the process of predicting TV-ratings can be automated with better accuracy than the manual process. The results show that of the two tested supervised machine learning models, Random Forest and Support Vector Regression, Random Forest was the better model. Random Forest was better on both measurements, mean absolute error and root mean squared error, used to compare the mo
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Lanka, Venkata Raghava Ravi Teja Lanka. "VEHICLE RESPONSE PREDICTION USING PHYSICAL AND MACHINE LEARNING MODELS." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1511891682062084.

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Hugo, Linsey Sledge. "A Comparison of Machine Learning Models Predicting Student Employment." Ohio University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1544127100472053.

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Snelson, Edward Lloyd. "Flexible and efficient Gaussian process models for machine learning." Thesis, University College London (University of London), 2007. http://discovery.ucl.ac.uk/1445855/.

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Gaussian process (GP) models are widely used to perform Bayesian nonlinear regression and classification tasks that are central to many machine learning problems. A GP is nonparametric, meaning that the complexity of the model grows as more data points are received. Another attractive feature is the behaviour of the error bars. They naturally grow in regions away from training data where we have high uncertainty about the interpolating function. In their standard form GPs have several limitations, which can be divided into two broad categories: computational difficulties for large data sets, a
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Zeng, Haoyang Ph D. Massachusetts Institute of Technology. "Machine learning models for functional genomics and therapeutic design." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122689.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 213-230).<br>Due to the limited size of training data available, machine learning models for biology have remained rudimentary and inaccurate despite the significant advance in machine learning research. With the recent advent
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Olubeko, Olasubomi O. "Machine learning models for screening and diagnosis of infections." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123039.

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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 71-74).<br>Millions of people around the globe die or are severely burdened every year at the hands of infections. These infections can occur in wounds on the surface of the body, often after surgery. They also occur inside th
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Macis, Ambra. "Statistical Models and Machine Learning for Survival Data Analysis." Doctoral thesis, Università degli studi di Brescia, 2023. https://hdl.handle.net/11379/568945.

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L'argomento principale di questa tesi è l'analisi della sopravvivenza, un insieme di metodi utilizzati negli studi longitudinali in cui l'interesse non è solo nel verificarsi (o meno) di un particolare evento, ma anche nel tempo necessario per osservarlo. Negli anni sono stati inizialmente proposti dei modelli statistici e, in seguito, sono stati introdotti anche metodi di machine learning per affrontare studi di analisi di sopravvivenza. La prima parte del lavoro fornisce un'introduzione ai concetti di base dell'analisi di sopravvivenza e un'ampia rassegna della letteratura esistente. Nello
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MARZIALI, ANDREA. "Machine learning models applied to energy time-series forecasting." Doctoral thesis, Università degli studi di Pavia, 2020. http://hdl.handle.net/11571/1326207.

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BARDELLI, CHIARA. "Machine Learning and Statistical models in real world applications." Doctoral thesis, Università degli studi di Pavia, 2021. http://hdl.handle.net/11571/1447635.

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Machine Learning and Statistical models are nowadays widely used in different fields of application thanks to their flexibility and adaptation to specific type of data and problem domain. Each part of this thesis presents a domain of application and proposes a specific approach to solve the problem described. A Bayesian framework is applied in the First and Third part of this thesis, while a Machine Learning approach is preferred in the Second part. The first part of this thesis proposes an algorithm for a new ensemble decision tree procedure based on Proper Bayesian bootstrap. The introductio
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Darwaish, Asim. "Adversary-aware machine learning models for malware detection systems." Electronic Thesis or Diss., Université Paris Cité, 2022. http://www.theses.fr/2022UNIP7283.

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La popularisation des smartphones et leur caractère indispensable les rendent aujourd'hui indéniables. Leur croissance exponentielle est également à l'origine de l'apparition de nombreux logiciels malveillants et fait trembler le prospère écosystème mobile. Parmi tous les systèmes d'exploitation des smartphones, Android est le plus ciblé par les auteurs de logiciels malveillants en raison de sa popularité, de sa disponibilité en tant que logiciel libre, et de sa capacité intrinsèque à accéder aux ressources internes. Les approches basées sur l'apprentissage automatique ont été déployées avec s
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