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Relevant bibliographies by topics / Machine Learning,Artificial Neural Networks,Spiking neural networks / Dissertations / Theses

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Dissertations / Theses on the topic 'Machine Learning,Artificial Neural Networks,Spiking neural networks'

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Published: 18 April 2022

Last updated: 30 July 2025

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1

De, Pasquale Daniele. "Modelli di neural network ispirati alla biofisica." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Il machine learning è stata una delle discipline più rivoluzionarie dello scorso secolo: ha permesso di affrontare problemi al di là della portata dei normali algoritmi computazionali, ed ha dato nuove prospettive per lo studio dell’intelligenza in natura. Grazie all’avvento dei big data e dei moderni calcolatori, il deep learning in particolare ha dal 2006 ricevuto una grande attenzione e riportato risultati straordinari in applicazioni cognitive e commerciali. Lo scopo di questa tesi è quello di riportare i principali risultati storici in queste discipline, partendo da una veloce anali

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2

Simões, Alexandre da Silva. "Aprendizado não-supervisionado em redes neurais pulsadas de base radial." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-15092006-153353/.

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Redes neurais pulsadas - redes que utilizam uma codificação temporal da informação - têm despontado como uma nova e promissora abordagem dentro do paradigma conexionista emergente da ciência cognitiva. Um desses novos modelos é a rede neural pulsada de base radial, capaz de armazenar informação nos tempos de atraso axonais dos neurônios e que comporta algoritmos explícitos de treinamento. A recente proposição de uma sistemática para a codificação temporal dos dados de entrada utilizando campos receptivos gaussianos tem apresentado interessantes resultados na tarefa do agrupamento de dados (clu

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Tavanaei, Amirhossein. "Spiking Neural Networks and Sparse Deep Learning." Thesis, University of Louisiana at Lafayette, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=10807940.

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<p> This document proposes new methods for training multi-layer and deep spiking neural networks (SNNs), specifically, spiking convolutional neural networks (CNNs). Training a multi-layer spiking network poses difficulties because the output spikes do not have derivatives and the commonly used backpropagation method for non-spiking networks is not easily applied. Our methods use novel versions of the brain-like, local learning rule named spike-timing-dependent plasticity (STDP) that incorporates supervised and unsupervised components. Our method starts with conventional learning methods and co

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4

Louis, Thomas. "Conventionnel ou bio-inspiré ? Stratégies d'optimisation de l'efficacité énergétique des réseaux de neurones pour environnements à ressources limitées." Electronic Thesis or Diss., Université Côte d'Azur, 2025. http://www.theses.fr/2025COAZ4001.

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Intégrer des algorithmes d'intelligence artificielle (IA) directement dans des satellites présente de nombreux défis. Ces systèmes embarqués, fortement limités en consommation d'énergie et en empreinte mémoire, doivent également résister aux interférences. Cela nécessite systématiquement l'utilisation de systèmes sur puce (SoC) afin de combiner deux systèmes dits « hétérogènes » : un microcontrôleur polyvalent et un accélérateur de calcul économe en énergie (comme un FPGA ou un ASIC). Pour relever les défis liés au portage de telles architectures, cette thèse se concentre sur l'optimisation et

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5

Kalchbrenner, Nal. "Encoder-decoder neural networks." Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:d56e48db-008b-4814-bd82-a5d612000de9.

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This thesis introduces the concept of an encoder-decoder neural network and develops architectures for the construction of such networks. Encoder-decoder neural networks are probabilistic conditional generative models of high-dimensional structured items such as natural language utterances and natural images. Encoder-decoder neural networks estimate a probability distribution over structured items belonging to a target set conditioned on structured items belonging to a source set. The distribution over structured items is factorized into a product of tractable conditional distributions over in

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6

Lind, Benjamin. "Artificial Neural Networks for Image Improvement." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-137661.

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After a digital photo has been taken by a camera, it can be manipulated to be more appealing. Two ways of doing that are to reduce noise and to increase the saturation. With time and skills in an image manipulating program, this is usually done by hand. In this thesis, automatic image improvement based on artificial neural networks is explored and evaluated qualitatively and quantitatively. A new approach, which builds on an existing method for colorizing gray scale images is presented and its performance compared both to simpler methods and the state of the art in image denoising. Saturation

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Fraticelli, Chiara. "Λc reconstruction with artificial neural networks". Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/19985/.

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Il rivelatore ALICE studia collisioni di ioni pesanti ultrarelativistici in modo da creare e di conseguenza studiare lo stato della materia chiamato plasma di quark e gluoni. Questo obiettivo risulta difficoltoso data la sua vita breve, quindi facciamo riferimento a misure indirette per la prova della sua esistenza. In questa tesi abbiamo sfruttato tecniche di machine learning per studiare il decadimento del barione charmato Λc per dedurre alcune sue proprietà. In particolare abbiamo usato il metodo delle reti neurali per ricavare tutte le informazioni possibili con la tecninca di un'analisi m

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Fredrik, Schlyter. "Predicting Personal Taxi Destinations Using Artificial Neural Networks." Thesis, Linköpings universitet, Statistik och maskininlärning, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148427.

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Taxi Stockholm is a Swedish taxi company which would like to improve their mobile phone application with a destination prediction feature. This thesis has created an algo- rithm which predicts a destination to which a taxi customer would like to go. The problem is approached using the KDD process and data mining methods. A dataset consisting of previous taxi rides is cleaned, transformed, and then used to evaluate the performance of three machine learning models. More specifically a neural network model paired with K- Means clustering, a random forest model, and a k-nearest neighbour model. Th

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9

Chen, Hsinchun. "Machine Learning for Information Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms." Wiley Periodicals, Inc, 1995. http://hdl.handle.net/10150/106427.

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Artificial Intelligence Lab, Department of MIS, University of Arizona<br>Information retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge-based techniques also made an impressive contribution to “intelligent” information retrieval and indexing. More recently, information science researchers have turned to other newer artificial-intelligence- based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms. These newe

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10

Åkesson, Mattias. "Learning Phantom Dose Distribution using Regression Artificial Neural Networks." Thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-380767.

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Before a radiation treatment on a cancer patient can get accomplished the treatment planning system (TPS) needs to undergo a quality assurance (QA). The QA consists of a pre-treatment (PT-QA) on a synthetic phantom body. During the PT-QA, data is collected from the phantom detectors, a set of monitors (transmission detectors) and the angular state of the machine. The outcome of this thesis project is to investigate if it is possible to predict the radiation dose distribution on the phantom body based on the data from the transmission detectors and the angular state of the machine. The motive f

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11

Bergsten, John, and Konrad Öhman. "Player Analysis in Computer Games Using Artificial Neural Networks." Thesis, Blekinge Tekniska Högskola, Institutionen för kreativa teknologier, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14812.

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Star Vault AB is a video game development company that has developed the video game Mortal Online. The company has stated that they believe that players new to the game repeatedly find themselves being lost in the game. The objective of this study is to evaluate whether or not an Artificial Neural Network can be used to evaluate when a player is lost in the game Mortal Online. This is done using the free open source library Fast Artifical Neural Network Library. People are invited to a data collection event where they play a tweaked version of the game to facilitate data collection. Players sp

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Squadrani, Lorenzo. "Deep neural networks and thermodynamics." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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Deep learning is the most effective and used approach to artificial intelligence, and yet it is far from being properly understood. The understanding of it is the way to go to further improve its effectiveness and in the best case to gain some understanding of the "natural" intelligence. We attempt a step in this direction with the aim of physics. We describe a convolutional neural network for image classification (trained on CIFAR-10) within the descriptive framework of Thermodynamics. In particular we define and study the temperature of each component of the network. Our results provides a n

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Carey, Howard J. III. "EEG Interictal Spike Detection Using Artificial Neural Networks." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4648.

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Epilepsy is a neurological disease causing seizures in its victims and affects approximately 50 million people worldwide. Successful treatment is dependent upon correct identification of the origin of the seizures within the brain. To achieve this, electroencephalograms (EEGs) are used to measure a patient’s brainwaves. This EEG data must be manually analyzed to identify interictal spikes that emanate from the afflicted region of the brain. This process can take a neurologist more than a week and a half per patient. This thesis presents a method to extract and process the interictal spikes in

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Gelin, Martin, and Skogsberg Rikard Fridsén. "Water Contamination Detection With Artificial Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-295605.

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Drinking water is one of our most important re- sources, so the ability to reliably monitor harmful contaminations in our water distribution network is vital. In order to minimize false alarms for water monitoring, while keeping a high sensitivity, a machine learning approach was evaluated in this project. Measurement data captured with a new kind of sensor, an electronic tongue, was provided by Linköping university. The solution was an artificial neural network, in the structure of an Autoencoder, which could learn the dynamic behaviour of natural deviations and with a false alarm rate of app

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Chavez, Wesley. "An Exploration of Linear Classifiers for Unsupervised Spiking Neural Networks with Event-Driven Data." PDXScholar, 2018. https://pdxscholar.library.pdx.edu/open_access_etds/4439.

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Object recognition in video has seen giant strides in accuracy improvements in the last few years, a testament to the computational capacity of deep convolutional neural networks. However, this computational capacity of software-based neural networks coincides with high power consumption compared to that of some spiking neural networks (SNNs), up to 300,000 times more energy per synaptic event in IBM's TrueNorth chip, for example. SNNs are also well-suited to exploit the precise timing of event-driven image sensors, which transmit asynchronous "events" only when the luminance of a pixel change

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Freire, Duarte Vaz. "Forecasting U.S. REIT index prices with artificial neural networks." Master's thesis, Instituto Superior de Economia e Gestão, 2021. http://hdl.handle.net/10400.5/22765.

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Mestrado Bolonha em Mathematical Finance<br>Artiﬁcial Neural Networks are innovative mathematical models that have re- cently gathered much attention as a new tool for forecasting in economics and ﬁnance. These algorithms are characterized by being able to handle vast amounts of data and solve complex problems, without the assumption of linearity often made by traditional models. This thesis investigates the use of this Machine Learning method for forecast- ing Real Estate Investment Trusts (REIT) prices and their movement. In this experiment, we make use of a 20-year data sample related to fo

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Lundström, Christoffer, and Camilla Heiding. "Predictive Autoscaling of Systems using Artificial Neural Networks." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-104714.

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Autoscalers handle the scaling of instances in a system automatically based on specified thresholds such as CPU utilization. Reactive autoscalers do not take the delay of initiating a new instance into account, which may lead to overutilization. By applying machine learning methodology to predict future loads and the desired number of instances, it is possible to preemptively initiate scaling such that new instances are available before demand occurs. Leveraging efficient scaling policies keeps the costs and energy consumption low while ensuring the availability of the system. In this thesis,

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18

Dahlberg, Emil, Mattias Mineur, Linus Shoravi, and Holger Swartling. "Replacing Setpoint Control with Machine Learning : Model Predictive Control Using Artificial Neural Networks." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-413003.

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Indoor climate control is responsible for a substantial amount of the world's total energy expenditure. In a time of climate crisis where a reduction of energy consumption is crucial to avoid climate disaster, indoor climate control is a ripe target for eliminating energy waste. The conventional method of adjusting the indoor climate with the use of setpoint curves, based solely on outdoor temperature, may lead to notable inefficiencies. This project evaluates the possibility to replace this method of regulation with a system based on model predictive control (MPC) in one of Uppsala University

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19

Sabo, Juraj. "Gradient Boosting Machine and Artificial Neural Networks in R and H2O." Master's thesis, Vysoká škola ekonomická v Praze, 2016. http://www.nusl.cz/ntk/nusl-264614.

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Artificial neural networks are fascinating machine learning algorithms. They used to be considered unreliable and computationally very expensive. Now it is known that modern neural networks can be quite useful, but their computational expensiveness unfortunately remains. Statistical boosting is considered to be one of the most important machine learning ideas. It is based on an ensemble of weak models that together create a powerful learning system. The goal of this thesis is the comparison of these machine learning models on three use cases. The first use case deals with modeling the probabil

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Tigreat, Philippe. "Sparsity, redundancy and robustness in artificial neural networks for learning and memory." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2017. http://www.theses.fr/2017IMTA0046/document.

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L'objectif de la recherche en Intelligence Artificielle (IA) est de répliquer les capacités cognitives humaines au moyen des ordinateurs modernes. Les résultats de ces dernières années semblent annoncer une révolution technologique qui pourrait changer profondément la société. Nous focalisons notre intérêt sur deux aspects cognitifs fondamentaux, l'apprentissage et la mémoire. Les mémoires associatives offrent la possibilité de stocker des éléments d'information et de les récupérer à partir d'une partie de leur contenu, et imitent ainsi la mémoire cérébrale. L'apprentissage profond permet de p

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Nayak, Richi. "GYAN: A methodology for rule extraction from artificial neural networks." Thesis, Queensland University of Technology, 1999. https://eprints.qut.edu.au/36857/6/Richi%20Nayak_Digitised%20Thesis.pdf.

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Artificial neural network (ANN) learning methods provide a robust and non-linear approach to approximating the target function for many classification, regression and clustering problems. ANNs have demonstrated good predictive performance in a wide variety of practical problems. However, there are strong arguments as to why ANNs are not sufficient for the general representation of knowledge. The arguments are the poor comprehensibility of the learned ANN, and the inability to represent explanation structures. The overall objective of this thesis is to address these issues by: (1) explanatio

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Amarasinghe, Kasun. "Explainable Neural Networks based Anomaly Detection for Cyber-Physical Systems." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/6091.

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Cyber-Physical Systems (CPSs) are the core of modern critical infrastructure (e.g. power-grids) and securing them is of paramount importance. Anomaly detection in data is crucial for CPS security. While Artificial Neural Networks (ANNs) are strong candidates for the task, they are seldom deployed in safety-critical domains due to the perception that ANNs are black-boxes. Therefore, to leverage ANNs in CPSs, cracking open the black box through explanation is essential. The main objective of this dissertation is developing explainable ANN-based Anomaly Detection Systems for Cyber-Physical System

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Flaherty, Drew. "Artistic approaches to machine learning." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/200191/1/Drew_Flaherty_Thesis.pdf.

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This research is about how Artificial Intelligence and Machine Learning may impact creative practice. The thesis looks at various implementations and models related to the subject from different cultural and technical viewpoints. The project also provides experimental creative outcomes from my personal practice along with a qualitative study into attitudes and perspectives from other creative practitioners.

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Lombardi, Alessandro. "Multiple time series forecasting with Graph Neural Networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24729/.

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Time series forecasting aims to predict future values to support organizations making strategic decisions. This problem has been studied for decades due to its relevance in almost all industries and areas, ranging from financial data to product demand. Recently, modern solutions based on deep learning have gained popularity among academia and industry, mainly due to the necessity to automatize the forecasting of multiple time series and exploit external explanatory variables. Considering the recent successes of Graph Neural Networks (GNNs) in modelling graph data, this study extends previous w

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Ribeiro, André Mauric Frossard. "Application of artificial neural networks to genome-enabled prediction in Nellore cattle /." Jaboticabal, 2019. http://hdl.handle.net/11449/190702.

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Orientador: Henrique Nunes de [UNESP] Oliveira<br>Resumo: Nos últimos anos, o rápido desenvolvimento de tecnologias de sequenciamento de alto rendimento permitiu a genotipagem em larga escala de milhares de marcadores genéticos. Diversos modelos estatísticos foram desenvolvidos para predizer os valores genéticos para traços complexos usando as informações de marcadores moleculares em alta densidade, pedigrees ou ambos. Esses modelos incluem, entre outros, as redes neurais artificiais (RNA) que têm sido amplamente utilizadas em problemas de previsão em outros campos de aplicação e, mais recente

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Galanis, Ioannis. "RESOURCE MANAGEMENT IN EDGE COMPUTING FOR INTERNET OF THINGS APPLICATIONS." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/dissertations/1871.

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The Internet of Things (IoT) computing paradigm has connected smart objects “things” and has brought new services at the proximity of the user. Edge Computing, a natural evolution of the traditional IoT, has been proposed to deal with the ever-increasing (i) number of IoT devices and (ii) the amount of data traffic that is produced by the IoT endpoints. EC promises to significantly reduce the unwanted latency that is imposed by the multi-hop communication delays and suggests that instead of uploading all the data to the remote cloud for further processing, it is beneficial to perform computati

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FALCIONELLI, NICOLA. "From Symbolic Artificial Intelligence to Neural Networks Universality with Event-based Modeling." Doctoral thesis, Università Politecnica delle Marche, 2020. http://hdl.handle.net/11566/274620.

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Rappresentare la conoscenza, modellare il ragionamento umano e comprendere i processi di pensiero sono sempre state parti centrali delle attività intellettuali, fin dai primi tentativi dei filosofi greci. Non è solo un caso che, non appena i computer hanno iniziato a diffondersi, scienziati e matematici straordinari come John McCarthy, Marvin Minsky e Claude Shannon hanno iniziato a creare sistemi Artificialmente Intelligenti con una prospettiva orientata al simbolismo. Anche se questo è stato un percorso parzialmente forzato a causa delle capacità di calcolo molto limitate dell'epoca, ha se

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Knutsson, Adam. "Hand Detection and Pose Estimation using Convolutional Neural Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-174197.

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This thesis examines how convolutional neural networks can applied to the problem of hand detection and hand pose estimation. Two families of convolutional neural networks are trained, aimed at performing the task of classification or regression. The networks are trained on specialized data generated from publicly available datasets. The algorithms used to generate the specialized data are also disclosed. The main focus has been to investigate the different structural properties of convolutional neural networks, not building optimized hand detection, or hand pose estimation, systems. Experimen

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Vikström, Filip. "A recurrent neural network approach to quantification of risks surrounding the Swedish property market." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-126192.

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As the real estate market plays a central role in a countries financial situation, as a life insurer, a bank and a property developer, Skandia wants a method for better assessing the risks connected to the real estate market. The goal of this paper is to increase the understanding of property market risk and its covariate risks and to conduct an analysis of how a fall in real estate prices could affect Skandia’s exposed assets.This paper explores a recurrent neural network model with the aim of quantifying identified risk factors using exogenous data. The recurrent neural network model is comp

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Manne, Mihira. "MACHINE VISION FOR AUTOMATICVISUAL INSPECTION OF WOODENRAILWAY SLEEPERS USING UNSUPERVISED NEURAL NETWORKS." Thesis, Högskolan Dalarna, Datateknik, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3977.

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The motivation for this thesis work is the need for improving reliability of equipment and quality of service to railway passengers as well as a requirement for cost-effective and efficient condition maintenance management for rail transportation. This thesis work develops a fusion of various machine vision analysis methods to achieve high performance in automation of wooden rail track inspection.The condition monitoring in rail transport is done manually by a human operator where people rely on inference systems and assumptions to develop conclusions. The use of conditional monitoring allows

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Sun, Chang. "Scalability Analysis of Synchronous Data-Parallel Artificial Neural Network (ANN) Learners." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/85020.

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Artificial Neural Networks (ANNs) have been established as one of the most important algorithmic tools in the Machine Learning (ML) toolbox over the past few decades. ANNs' recent rise to widespread acceptance can be attributed to two developments: (1) the availability of large-scale training and testing datasets; and (2) the availability of new computer architectures for which ANN implementations are orders of magnitude more efficient. In this thesis, I present research on two aspects of the second development. First, I present a portable, open source implementation of ANNs in OpenCL and MPI.

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Linn, Hanna. "Detecting quantum speedup for random walks with artificial neural networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289347.

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Random walks on graphs are an essential base for crucial algorithms for solving problems, like the boolean satisfiability problem. A speedup of random walks could improve these algorithms. The quantum version of the random walk, quantum walk, is faster than random walks in specific cases, e.g., on some linear graphs. An analysis of when the quantum walk is faster than the random walk can be accomplished analytically or by simulating both the walks on the graph. The problem arises when the graphs grow in size and connectivity. There are no known general rules for what an arbitrary graph not hav

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Tarullo, Viviana. "Artificial Neural Networks for classification of EMG data in hand myoelectric control." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19195/.

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This thesis studies the state-of-the-art in myoelectric control of active hand prostheses for people with trans-radial amputation using pattern recognition and machine learning techniques. Our work is supported by Centro Protesi INAIL in Vigorso di Budrio (BO). We studied the control system developed by INAIL consisting in acquiring EMG signals from amputee subjects and using pattern recognition methods for the classifcation of acquired signals, associating them with specifc gestures and consequently commanding the prosthesis. Our work consisted in improving classifcation methods used in the l

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Li, Zhengrong. "Aerial image analysis using spiking neural networks with application to power line corridor monitoring." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/46161/1/Zhengrong_Li_Thesis.pdf.

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Trees, shrubs and other vegetation are of continued importance to the environment and our daily life. They provide shade around our roads and houses, offer a habitat for birds and wildlife, and absorb air pollutants. However, vegetation touching power lines is a risk to public safety and the environment, and one of the main causes of power supply problems. Vegetation management, which includes tree trimming and vegetation control, is a significant cost component of the maintenance of electrical infrastructure. For example, Ergon Energy, the Australia’s largest geographic footprint energy distr

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Rimer, Michael Edwin. "Improving Neural Network Classification Training." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2094.pdf.

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Tickle, Alan Barry. "Machine learning, neural networks and information security : techniques for extracting rules from trained feedforward Artificial Neural Networks and their application in an information security problem domain." Thesis, Queensland University of Technology, 1997.

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Shi, Shaohuai. "Communication optimizations for distributed deep learning." HKBU Institutional Repository, 2020. https://repository.hkbu.edu.hk/etd_oa/813.

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With the increasing amount of data and the growing computing power, deep learning techniques using deep neural networks (DNNs) have been successfully applied in many practical artificial intelligence applications. The mini-batch stochastic gradient descent (SGD) algorithm and its variants are the most widely used algorithms in training deep models. The SGD algorithm is an iterative algorithm that needs to update the model parameters many times by traversing the training data, which is very time-consuming even using the single powerful GPU or TPU. Therefore, it becomes a common practice to expl

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Mendez, Kevin M. "Deriving statistical inference from the application of artificial neural networks to clinical metabolomics data." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2020. https://ro.ecu.edu.au/theses/2296.

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Metabolomics data are complex with a high degree of multicollinearity. As such, multivariate linear projection methods, such as partial least squares discriminant analysis (PLS-DA) have become standard. Non-linear projections methods, typified by Artificial Neural Networks (ANNs) may be more appropriate to model potential nonlinear latent covariance; however, they are not widely used due to difficulty in deriving statistical inference, and thus biological interpretation. Herein, we illustrate the utility of ANNs for clinical metabolomics using publicly available data sets and develop an open f

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Xu, Jin. "Machine Learning – Based Dynamic Response Prediction of High – Speed Railway Bridges." Thesis, KTH, Bro- och stålbyggnad, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278538.

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Targeting heavier freights and transporting passengers with higher speeds became the strategic railway development during the past decades significantly increasing interests on railway networks. Among different components of a railway network, bridges constitute a major portion imposing considerable construction and maintenance costs. On the other hand, heavier axle loads and higher trains speeds may cause resonance occurrence on bridges; which consequently limits operational train speed and lines. Therefore, satisfaction of new expectations requires conducting a large number of dynamic assess

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Ferroni, Nicola. "Exact Combinatorial Optimization with Graph Convolutional Neural Networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/17502/.

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Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm. We propose to learn a variable selection policy for branch-and-bound in mixed-integer linear programming, by imitation learning on a diversified variant of the strong branching expert rule. We encode states as bipartite graphs and parameterize the policy as a graph convolutional neural network. Experiments on a series of synthetic problems demonstrate that our approach produces policies that can improve upon expert-designed branching rules on large problems, and generalize to instances significantly lar

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Hammond, Alec Michael. "Machine Learning Methods for Nanophotonic Design, Simulation, and Operation." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7131.

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Interest in nanophotonics continues to grow as integrated optics provides an affordable platform for areas like telecommunications, quantum information processing, and biosensing. Designing and characterizing integrated photonics components and circuits, however, remains a major bottleneck. This is especially true when complex circuits or devices are required to study a particular phenomenon.To address this challenge, this work develops and experimentally validates a novel machine learning design framework for nanophotonic devices that is both practical and intuitive. As case studies, artifici

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BINDER, THOMAS. "Gland Segmentation with Convolutional Neural Networks : Validity of Stroma Segmentation as a General Approach." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-246134.

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The analysis of glandular morphology within histopathology images is a crucial step in determining the stage of cancer. Manual annotation is a very laborious task. It is time consuming and suffers from the subjectivity of the specialists that label the glands. One of the aims of computational pathology is developing tools to automate gland segmentation. Such an algorithm would improve the efficiency of cancer diag- nosis. This is a complex task as there is a large variability in glandular morphologies and staining techniques. So far, specialised models have given promising results focusing on

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Bligh, W. O. M. "Application of machine learning and connectionist modeling to an Australian dairy database." Thesis, Queensland University of Technology, 2000. https://eprints.qut.edu.au/36851/1/36851_Bligh_2000.pdf.

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The Australian Dairy Herd Improvement Scheme (ADIDS) provides a database containing both raw and processed data relating to milk production in Australia. This thesis provides estimations of potential milk production for dairy breeding using dairy animal data and artificial neural networks (ANNs). By predicting daughter milk production from data representative of dams in a herd and artificial insemination sires, an evaluation of those potential daughter results can lead to the selection of a breeding sire for that herd. Relevant data fields and derived attributes from the dairy database that si

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Svebrant, Henrik. "Latent variable neural click models for web search." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-232311.

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User click modeling in web search is most commonly done through probabilistic graphical models. Due to the successful use of machine learning techniques in other fields of research, it is interesting to evaluate how machine learning can be applied to click modeling. In this thesis, modeling is done using recurrent neural networks trained on a distributed representation of the state of the art user browsing model (UBM). It is further evaluated how extending this representation with a set of latent variables that are easily derivable from click logs, can affect the model's prediction performance

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Truzzi, Stefano. "Event classification in MAGIC through Convolutional Neural Networks." Doctoral thesis, Università di Siena, 2022. http://hdl.handle.net/11365/1216295.

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The Major Atmospheric Gamma Imaging Cherenkov (MAGIC) telescopes are able to detect gamma rays from the ground with energies beyond several tens of GeV emitted by the most energetic known objects, including Pulsar Wind Nebulae, Active Galactic Nuclei, and Gamma-Ray Bursts. Gamma rays and cosmic rays are detected by imaging the Cherenkov light produced by the charged superluminal leptons in the extended air shower originated when the primary particle interacts with the atmosphere. These Cherenkov flashes brighten the night sky for short times in the nanosecond scale. From the image topology an

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Singh, Y., and M. Mars. "A pilot study to integrate HIV drug resistance gold standard interpretation algorithms using neural networks." Journal for New Generation Sciences, Vol 11, Issue 2: Central University of Technology, Free State, Bloemfontein, 2013. http://hdl.handle.net/11462/639.

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Published Article<br>There are several HIV drug resistant interpretation algorithms which produce different resistance measures even if applied to the same resistance profile. This discrepancy leads to confusion in the mind of the physician when choosing the best ARV therapy.

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Bonnell, Jeffrey A. "Implementation of a New Sigmoid Function in Backpropagation Neural Networks." Digital Commons @ East Tennessee State University, 2011. https://dc.etsu.edu/etd/1342.

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This thesis presents the use of a new sigmoid activation function in backpropagation artificial neural networks (ANNs). ANNs using conventional activation functions may generalize poorly when trained on a set which includes quirky, mislabeled, unbalanced, or otherwise complicated data. This new activation function is an attempt to improve generalization and reduce overtraining on mislabeled or irrelevant data by restricting training when inputs to the hidden neurons are sufficiently small. This activation function includes a flattened, low-training region which grows or shrinks during back-pro

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Andersson, Viktor. "Semantic Segmentation : Using Convolutional Neural Networks and Sparse dictionaries." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139367.

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The two main bottlenecks using deep neural networks are data dependency and training time. This thesis proposes a novel method for weight initialization of the convolutional layers in a convolutional neural network. This thesis introduces the usage of sparse dictionaries. A sparse dictionary optimized on domain specific data can be seen as a set of intelligent feature extracting filters. This thesis investigates the effect of using such filters as kernels in the convolutional layers in the neural network. How do they affect the training time and final performance? The dataset used here is the

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Foster, Roland. "A comparison of machine learning techniques for hand shape recognition." University of the Western Cape, 2015. http://hdl.handle.net/11394/4388.

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>Magister Scientiae - MSc<br>There are five fundamental parameters that characterize any sign language gesture. They are hand shape, orientation, motion and location, and facial expressions. The SASL group at the University of the Western Cape has created systems to recognize each of these parameters in an input video stream. Most of these systems make use of the Support Vector Machine technique for the classification of data due to its high accuracy. It is, however, unknown how other machine learning techniques compare to Support Vector Machines in the recognition of each of these parameters.

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Ayodele, Taiwo Oladipupo. "An integrated framework for solving email management problems with unsupervised machine learning techniques and artificial neural networks." Thesis, University of Portsmouth, 2010. https://researchportal.port.ac.uk/portal/en/theses/an-integrated-framework-for-solving-email-management-problems-with-unsupervised-machine-learning-techniques-and-artificial-neural-networks(7bb647da-3759-47e2-812a-e1adc5e36af0).html.

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The volume of email being received by email users nowadays is enormous. Email users spend significant amount of time to manage their emails, which tends to be tedious. The task of grouping emails for further processing often discourages users from filing their mail, resulting in unmanageable mailboxes that contain hundreds or even thousands of unsorted messages. The present work starts by redeveloping a systematic framework of email management. Major email managing tasks were identified, investigated and classified into categories, namely, email summarisation, email grouping and email urgency

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