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Dissertations / Theses on the topic 'Dati neurali'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

Avena, Anna. "Tecniche di data mining applicate alla decodifica di dati neurali." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14800/.

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Gli studi sulla decodifica dell'attività neuronale permettono di mappare gli impulsi elettrici della corteccia cerebrale in segnali da inviare a determinati dispositivi per poterli monitorare. È su questo tema che la ricerca scientifica si sta concentrando, al fine di aiutare le persone affette da gravi lesioni fisiche ad ottenere un maggiore grado di autonomia nelle piccole azioni di tutti i giorni. In questo elaborato, sono stati analizzati dati derivanti da attività neuronali raccolti da esperimenti effettuati su primati non umani, eseguiti dal gruppo di ricerca della professoressa Patrizi

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2

Borra, Davide. "Sviluppo ed applicazione di reti neurali convoluzionali con dati di neuroimaging." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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La malattia di Alzheimer (AD) è un disordine neurodegenerativo che rappresenta la forma più comune di demenza negli adulti sopra i 65 anni, mentre la compromissione cognitiva lieve (MCI) è una condizione che in alcuni casi può rappresentare una fase prodromica della malattia di Alzheimer, mentre in altri, è comune in pazienti con la malattia dei piccoli vasi cerebrali (SVD). In questo elaborato sono state sviluppate due reti neurali convoluzionali 2-D, NeuroNet-1 e NeuroNet-2 (o NeuroNet), ed applicate alla classificazione a 2 vie di: a) MCI con SVD (40 pazienti in totale) con dati di diffusio

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3

Di, Ielsi Luca. "Analisi di serie temporali riguardanti dati energetici mediante architetture neurali profonde." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/10504/.

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Il presente lavoro di tesi riguarda lo studio e l'impiego di architetture neurali profonde (nello specifico stacked denoising auto-encoder) per la definizione di un modello previsionale di serie temporali. Il modello implementato è stato applicato a dati industriali riguardanti un impianto fotovoltaico reale, per effettuare una predizione della produzione di energia elettrica sulla base della serie temporale che lo caratterizza. I risultati ottenuti hanno evidenziato come la struttura neurale profonda contribuisca a migliorare le prestazioni di previsione di strumenti statistici classici come

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4

Maffei, Nicola. "Reti neurali e modelli fisico-predittivi: Dati clinici e analisi di trattamenti in tomotherapy." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/6621/.

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Il lavoro è parte integrante di un progetto di ricerca del Ministero della Salute ed è stato sviluppato presso la Fisica Sanitaria ed il reparto di Radioterapia Oncologica dell’Azienda Ospedaliero Universitaria di Modena. L’obiettivo è la realizzazione di modelli predittivi e di reti neurali per tecniche di warping in ambito clinico. Modifiche volumetrico-spaziali di organi a rischio e target tumorali, durante trattamenti tomoterapici, possono alterare la distribuzione di dose rispetto ai constraints delineati in fase di pianificazione. Metodologie radioterapiche per la valutazione di organ

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5

Fabbri, Alessandro. "Reti neurali in ambito finanziario." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19593/.

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In particolare in questo lavoro cercherò di analizzare l’utilizzo di reti neurali in ambito economico-finanziario in quanto alcuni dei temi che si riscontrano in economia ben si prestano ad un’analisi attraverso le reti neurali. In particolare nel primo capitolo di questo elaborato descriverò le origini delle reti neurali e alcuni criteri attraverso i quali oggi si classificano le reti stesse. Nel secondo capitolo mi occuperò invece di approfondire quali sono i passaggi da seguire al fine di costruire una rete neurale concentrandomi sulla risoluzione di problemi legati all’ambito economico-fi

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6

Cesaroni, Maurizio. "Armonizzazione dei dati per l’addestramento di reti neurali ricorrenti: applicazione per la gestione delle promozioni nel settore retail." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23194/.

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All'interno dei dati processati da un'azienda è solito trovare errori dovuti a varie motivazioni, ad esempio cattive misurazioni. Questo può comportare interpretazioni errate e di conseguenza scelte future che non portano il profitto sperato. Il processo di correzione di tali errori in letteratura viene chiamato Data Harmonization, e permette alle aziende di poter ripulire i dati sfruttando regole euristiche proprie del business di competenza. Lavorare con set di dati coerenti alle dinamiche del mercato e allo stesso tempo solidi, è fondamentale per i modelli di tipo predittivo di cui sempre p

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Pallotti, Davide. "Integrazione di dati di disparità sparsi in algoritmi per la visione stereo basati su deep-learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16633/.

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La visione stereo consiste nell’estrarre informazioni di profondità da una scena a partire da una vista sinistra e una vista destra. Il problema si riduce a determinare punti corrispondenti nelle due immagini, che nel caso di immagini rettificate risultano traslati solo orizzontalmente, di una distanza detta disparità. Tra gli algoritmi stereo tradizionali spiccano SGM e la sua implementazione rSGM. SGM minimizza una funzione di costo definita su un volume dei costi, che misura la somiglianza degli intorni di potenziali punti omologhi per numerosi valori di disparità. L’abilità delle reti ne

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8

Portas, Daniela. "Studio, implementazione e confronto di diverse tecniche di deep learning per la valutazione morfologica dell'atrio sinistro da dati LGE-MRI." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/19938/.

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La definizione di algoritmi in grado di effettuare un’accurata segmentazione dell’atrio sinistro si è rilevata di fondamentale importanza nel trattamento di alcune aritmie cardiache come, per esempio, la fibrillazione atriale. I recenti sviluppi nel campo delle reti neurali convoluzionali hanno permesso lo studio di modelli che forniscono una segmentazione atriale automatica. Attualmente la rete U-Net sembra essere la più adatta a svolgere questo compito anche se presenta alcuni aspetti negativi che spingono a ricercare soluzioni alternative e a testare delle condizioni che possano migliorare

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9

Escuain, i. Poole Lara Sofia. "Data-driven neural mass modelling." Doctoral thesis, Universitat Politècnica de Catalunya, 2019. http://hdl.handle.net/10803/666615.

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The brain is a complex organ whose activity spans multiple scales, both spatial and temporal. The computational unit of the brain is thought to be the neurone. At the microscopic level, neurones communicate via action potentials. These may be observed experimentally by means of precise techniques that work with a small number of these cells and their interactions, and that can be modelled mathematically in a variety of ways. Other techniques consider the averaged activity of large groups of neurones in the mesoscale, or cortical columns; theoretical models of these signals also abound. The pr

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Rahman, Sardar Muhammad Monzurur, and mrahman99@yahoo com. "Data Mining Using Neural Networks." RMIT University. Electrical & Computer Engineering, 2006. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080813.094814.

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Data mining is about the search for relationships and global patterns in large databases that are increasing in size. Data mining is beneficial for anyone who has a huge amount of data, for example, customer and business data, transaction, marketing, financial, manufacturing and web data etc. The results of data mining are also referred to as knowledge in the form of rules, regularities and constraints. Rule mining is one of the popular data mining methods since rules provide concise statements of potentially important information that is easily understood by end users and also actionable pat

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11

Wang, Fengzhen. "Neural networks for data fusion." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ30179.pdf.

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12

Gómez, Juan Martínez. "Automatic classification of neural data." Thesis, University of Leicester, 2011. http://hdl.handle.net/2381/9696.

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In this thesis we present a new solution for an automatic classification of the single-neuron activity. The study of the computational role of individual neurons underlying different cognitive process is a gold standard in Neuroscience. This type of analysis is done first, by recording the extracellular spikes of the neurons near the tip of a microelectrode and second, by isolating the spikes of the recorded cells based on the similarity of their shapes using a method called spike sorting. In recent years, important advances in microelectrode technology allow us now to perform massive parallel

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13

Buarque, Hugo Leonardo de Brito. "Prediction of gasoline properties from composition data." Universidade Federal do CearÃ, 2006. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=172.

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AgÃncia Nacional do PetrÃleo<br>Commercial gasolines are normally produced by blending hydrocarbon fractions obtained from the distillation of crude oil or from other petrochemical or refining processes, and carried through in order to comply with a variety of legal and ambient specifications at minimum cost. The quality for the use and commercialization of gasolines is evaluated through certain characteristics specified by governmental regulation. Such characteristics are usually determined by different methodologies and experimental techniques, since those depend on their constituents and th

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14

Watkins, Bruce E. "Data compression using artificial neural networks." Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/25801.

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Approved for public release; distribution is unlimited<br>This thesis investigates the application of artificial neural networks for the compression of image data. An algorithm is developed using the competitive learning paradigm which takes advantage of the parallel processing and classification capability of neural networks to produce an efficient implementation of vector quantization. Multi-Stage, tree searched, and classification vector quantization codebook design techniques are adapted to the neural network design to reduce the computational cost and hardware requirements. The resu

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15

Roa, Rodríguez Rodrigo, and Robert Lundin. "Heatmap Visualization of Neural Frequency Data." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186860.

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Complex spatial relationships and patterns in multivariate data are relatively simple to identify visually but di cult to detect computation- ally. For this reason, Anivis, an interactive tool for visual exploration of multivariate quantitative pure serial periodic data was developed. The data has four dimensions depth, laterality, frequency and time. The data was visualized as an animated heatmap, by mapping depth and laterality to coordinates in a pixel grid and frequency to color. Transfer functions were devised to map a single variable to color through parametric curves. Anivis implemented

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16

Malfatti, Guilherme Meneguzzi. "Técnicas de agrupamento de dados para computação aproximativa." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/169096.

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Dois dos principais fatores do aumento da performance em aplicações single-thread – frequência de operação e exploração do paralelismo no nível das instruções – tiveram pouco avanço nos últimos anos devido a restrições de potência. Neste contexto, considerando a natureza tolerante a imprecisões (i.e.: suas saídas podem conter um nível aceitável de ruído sem comprometer o resultado final) de muitas aplicações atuais, como processamento de imagens e aprendizado de máquina, a computação aproximativa torna-se uma abordagem atrativa. Esta técnica baseia-se em computar valores aproximados ao invés d

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17

Alahakoon, Lakpriya Damminda 1968. "Data mining with structure adapting neural networks." Monash University, School of Computer Science and Software Engineering, 2000. http://arrow.monash.edu.au/hdl/1959.1/7987.

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18

Macdonald, Donald. "Unsupervised neural networks for visualisation of data." Thesis, University of the West of Scotland, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.395687.

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19

Ringienė, Laura. "Hybrid neural network for multidimensional data visualization." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2014~D_20140912_140117-42267.

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The area of research is data mining based on multidimensional data visual analysis. This allows researcher to participate in the process of data analysis directly, to understand the complex data better and to make the best decisions. The objective of the dissertation is to create a method for making a multidimensional data projection on the plane such that the researcher could see and assess the intergroup similarities and differences of multidimensional points. In order to achieve the target, a new hybrid neural network is proposed and investigated. This neural network integrates the ideas bo

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20

Daniell, Philip M. "A data frame architecture for neural networks." Thesis, University of Kent, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.293134.

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21

Berry, Ian Michael. "Data classification using unsupervised artificial neural networks." Thesis, University of Sussex, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390079.

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22

Antoniades, Andreas. "Interpreting biomedical data via deep neural networks." Thesis, University of Surrey, 2018. http://epubs.surrey.ac.uk/845765/.

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Machine learning technology has taken quantum leaps in the past few years. From the rise of voice recognition as an interface to interact with our computers, to self-organising photo albums and self-driving cars. Neural networks and deep learning contributed significantly to drive this revolution. Yet, biomedicine is one of the research areas that has yet to fully embrace the possibilities of deep learning. Engaged in a cross-disciplinary subject, researchers, and clinical experts are focused on machine learning and statistical signal processing techniques. The ability to learn hierarchical fe

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23

Sallander, Oscar. "Convolutional Neural Networks: Performance on Imbalanced Data." Thesis, Umeå universitet, Statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184920.

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Imbalanced data is a major problem in machine learning classification, since predictive performance can be hindered when one class occurs more frequently than the others. For example, in medical science, imbalanced data sets are very common. When searching for rare diseases in a population, the healthy proportion can be extremely large in comparison to the proportion with a disease.This raises a problem, because when a model is given only a few example observations of one class and a larger amount of observations of the other, the model tends to be biased towards the majority class. When the l

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24

Henning, Gustav. "Visualization of neural data : Dynamic representation and analysis of accumulated experimental data." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166770.

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The scientific method is an integral part of the investigation and exploration of hypotheses. Although procedures may vary from one field to the next, most have common identifiable stages. Today, there is no lack of tools that illustrate data in different graphical mediums. This thesis focuses instead on the type of tools that researchers use to investigate their hypotheses’ validity.When a sufficient amount of data is gathered, it can be presented for analysis in meaningful ways to illustrate patterns or abnormalities that would otherwise go unnoticed when only viewed in raw numbers. However

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25

Echtermeyer, Christoph. "Causal pattern inference from neural spike train data." Thesis, St Andrews, 2009. http://hdl.handle.net/10023/843.

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Kalkandara, Karolina. "Neural networks and classification trees for misclassified data." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.312187.

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27

Pagin, Matteo [Verfasser]. "Data compression of neural spike signals / Matteo Pagin." Ulm : Universität Ulm, 2021. http://d-nb.info/1232815179/34.

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28

Powers, John W. "Neural networks : an application to electrochemical noise data." Virtual Press, 1997. http://liblink.bsu.edu/uhtbin/catkey/1045629.

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Neural networks were applied to the analysis of electrochemical noise data. Electrochemical noise is defined as the fluctuations in either current or potential with time for a metal which is immersed in a conductive solution. This data is of interest because of its relationship to particular corrosion processes. Specifically, a system which is experiencing uniform corrosion will produce a different noise signal than one which is experiencing localized (perforation) corrosion. The economic effects of corrosion are significant and methods which improve the ability to detect, measure and predict

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29

Anastasiadis, Aristoklis. "Neural networks training and applications using biological data." Thesis, Birkbeck (University of London), 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.428055.

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30

Begum, Rushna. "Neural network processing of impact echo NDT data." Thesis, City University London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340456.

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31

Slone, Ambrose J. (Abrose Jay). "Improved remote sensing data analysis using neural networks." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/11461.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.<br>Includes bibliographical references (leaf 115).<br>by Ambrose J. Slone.<br>M.Eng.

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32

Marwala, Tshilidzi. "Fault identification using neural networks and vibration data." Thesis, University of Cambridge, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.621401.

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33

Wong, Helen. "A Bayesian neural network for censored survival data." Thesis, Liverpool John Moores University, 2001. http://researchonline.ljmu.ac.uk/4918/.

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34

Diffner, Fredrik, and Hovig Manjikian. "Training a Neural Network using Synthetically Generated Data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280334.

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A major challenge in training machine learning models is the gathering and labeling of a sufficiently large training data set. A common solution is the use of synthetically generated data set to expand or replace a real data set. This paper examines the performance of a machine learning model trained on synthetic data set versus the same model trained on real data. This approach was applied to the problem of character recognition using a machine learning model that implements convolutional neural networks. A synthetic data set of 1’240’000 images and two real data sets, Char74k and ICDAR 2003,

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35

Hadjiprocopis, Andreas. "Feed forward neural network entities." Thesis, City University London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340374.

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36

Danker-McDermot, Holly. "A Fuzzy/Neural Approach to Cost Prediction with Small Data Sets." ScholarWorks@UNO, 2004. http://scholarworks.uno.edu/td/86.

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The project objective in this work is to create an accurate cost estimate for NASA engine tests at the John C. Stennis Space Center testing facilities using various combinations of fuzzy and neural systems. The data set available for this cost prediction problem consists of variables such as test duration, thrust, and many other similar quantities, unfortunately it is small and incomplete. The first method implemented to perform this cost estimate uses the locally linear embedding (LLE) algorithm for a nonlinear reduction method that is then put through an adaptive network based fuzzy inferenc

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37

Kanerva, Anton, and Fredrik Helgesson. "On the Use of Model-Agnostic Interpretation Methods as Defense Against Adversarial Input Attacks on Tabular Data." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20085.

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Context. Machine learning is a constantly developing subfield within the artificial intelligence field. The number of domains in which we deploy machine learning models is constantly growing and the systems using these models spread almost unnoticeably in our daily lives through different devices. In previous years, lots of time and effort has been put into increasing the performance of these models, overshadowing the significant risks of attacks targeting the very core of the systems, the trained machine learning models themselves. A specific attack with the aim of fooling the decision-making

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Andersson, Joakim. "Evaluating Environmental Sensor Value Prediction using Machine Learning : Long Short-Term Memory Neural Networks for Smart Building Applications." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-42852.

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IoT har blivit en stor producent av big data. Big data kan användas för att optimera operationer, för att kunna göra det så måste man kunna extrahera användbar information från big data. Detta kan göras med hjälp av neurala nätverk och maskininlärning, vilket kan leda till nya typer av smarta applikationer. Den här rapporten fokuserar på att besvara frågan hur bra är neurala nätverk på att förutspå sensor värden och hur pålitliga är förutsägelserna och om dom kan användas i verkliga applikationer. Sensorlådor användes för att samla data från olika rum och olika neurala nätverksmodeller baserad

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Hoya, Tetsuya. "Graph theoretic methods for data partitioning." Thesis, Imperial College London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286542.

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McLean, David. "Improving generalisation in continuous data domains." Thesis, University of Manchester, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283816.

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Vignisson, Egill. "Anomaly Detection in Streaming Data from a Sensor Network." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-257507.

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In this thesis, the use of unsupervised and semi-supervised machine learning techniques was analyzed as potential tools for anomaly detection in the sensor network that the electrical system in a Scania truck is comprised of. The experimentation was designed to analyse the need for both point and contextual anomaly detection in this setting. For the point anomaly detection the method of Isolation Forest was experimented with and for contextual anomaly detection two different recurrent neural network architectures using Long Short Term Memory units was relied on. One model was simply a many to

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Lucke, Helmut. "On the representation of temporal data for connectionist word recognition." Thesis, University of Cambridge, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239520.

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43

Stigeborn, Patrik. "Generating 3D-objects using neural networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230668.

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Enabling a 2D- to 3D-reconstruction is an interesting future service for Mutate AB, where this thesis is conducted. Convolutional neural networks (CNNs) is examined in different aspects, in order to give a realistic perception of what this technology is capable of. The task conducted, is the creation of a CNN that can be used to predict how an object from a 2D image would look in 3D. The main areas that this CNN is optimized for are Quality, Speed, and Simplicity. Where Quality is the output resolution of the 3D object, Speed is measured by the number of seconds it takes to complete a reconstr

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Gousseau, Clément. "Hyperparameter Optimization for Convolutional Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-272107.

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Training algorithms for artificial neural networks depend on parameters called the hyperparameters. They can have a strong influence on the trained model but are often chosen manually with trial and error experiments. This thesis, conducted at Orange Labs Lannion, presents and evaluates three algorithms that aim at solving this task: a naive approach (random search), a Bayesian approach (Tree Parzen Estimator) and an evolutionary approach (Particle Swarm Optimization). A well-known dataset for handwritten digit recognition (MNIST) is used to compare these algorithms. These algorithms are also

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Zhang, Shuyuan. "AlphaZero with Input Convex Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281860.

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Modelling and solving real-life problems using reinforcement learning (RL) approaches is a typical and important branch in the world of artificial intelligence (AI). For playing board games, AlphaZero has been proved to be successful in games such as Go, Chess, and Shogi against professional human players or other AI counterparts. The very basic components of AlphaZero algorithm are MCTS tree search and deep neural networks for state value and policy prediction. These deep neural networks are designed to fit the mapping function between a state and its value/policy to make the initialization o

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46

Hebden, John Edward. "Acquisition and analysis of heart sound data." Thesis, University of Sussex, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.360518.

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47

Chow, Hsiao-Hui, Kristin M. Tolle, Denise J. Roe, Victor Elsberry, and Hsinchun Chen. "Application of Neural Networks to Population Pharmacokinetic Data Analysis." American Chemical Society and American Pharmaceutical Association, 1997. http://hdl.handle.net/10150/105273.

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Artificial Intelligence Lab, Department of MIS, University of Arizona<br>This research examined the applicability of using a neural network approach to analyze population pharmacokinetic data. Such data were collected retrospectively from pediatric patients who had received tobramycin for the treatment of bacterial infection. The information collected included patient-related demographic variables (age, weight, gender, and other underlying illness), the individualâ s dosing regimens (dose and dosing interval), time of blood drawn, and the resulting tobramycin concentration. Neural netw

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Tong, Dong Ling. "Genetic algorithm-neural network : feature extraction for bioinformatics data." Thesis, Bournemouth University, 2010. http://eprints.bournemouth.ac.uk/15788/.

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With the advance of gene expression data in the bioinformatics field, the questions which frequently arise, for both computer and medical scientists, are which genes are significantly involved in discriminating cancer classes and which genes are significant with respect to a specific cancer pathology. Numerous computational analysis models have been developed to identify informative genes from the microarray data, however, the integrity of the reported genes is still uncertain. This is mainly due to the misconception of the objectives of microarray study. Furthermore, the application of variou

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Dai, Hengchang. "Analysis of local earthquake data using artificial neural networks." Thesis, University of Edinburgh, 1995. http://hdl.handle.net/1842/12173.

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The aim of this thesis is to investigate the possibility of using artificial neural networks to develop automatic processing techniques for picking and identifying seismic arrivals. These two key procedures of an earthquake analysis system are extremely labour intensive, and any automation would allow further processing of larger datasets. Two approaches based on the back-propagation neural network (BPNN) are developed to pick and identify seismic arrivals for the dataset which includes 762 three component (3-C) recordings from stations DP and AY of a local earthquake network in Turkey. A BPNN

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Barton, Gabor J. "Visualisation of clinical gait analysis data using neural networks." Thesis, Liverpool John Moores University, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.436553.

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