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Dissertations / Theses on the topic 'LSTM modeli'

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Published: 7 June 2025

Last updated: 2 August 2025

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1

Li, Edwin. "LSTM Neural Network Models for Market Movement Prediction." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231627.

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Interpreting time varying phenomena is a key challenge in the capital markets. Time series analysis using autoregressive methods has been carried out over the last couple of decades, often with reassuring results. However, such methods sometimes fail to explain trends and cyclical fluctuations, which may be characterized by long-range dependencies or even dependencies between the input features. The purpose of this thesis is to investigate whether recurrent neural networks with LSTM-cells can be used to capture these dependencies, and ultimately be used as a complement for index trading decisi

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Tang, Hao. "Bidirectional LSTM-CNNs-CRF Models for POS Tagging." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-362823.

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In order to achieve state-of-the-art performance for part-of-speech(POS) tagging, the traditional systems require a significant amount of hand-crafted features and data pre-processing. In this thesis, we present a discriminative word embedding, character embedding and byte pair encoding (BPE) hybrid neural network architecture to implement a true end-to-end system without feature engineering and data pre-processing. The neural network architecture is a combination of bidirectional LSTM, CNNs, and CRF, which can achieve a state-of-the-art performance for a wide range of sequence labeling tasks.

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Backer-Meurke, Henrik, and Marcus Polland. "Predicting Road Rut with a Multi-time-series LSTM Model." Thesis, Högskolan Dalarna, Institutionen för information och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:du-37599.

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Road ruts are depressions or grooves worn into a road. Increases in rut depth are highly undesirable due to the heightened risk of hydroplaning. Accurately predicting increases in road rut depth is important for maintenance planning within the Swedish Transport Administration. At the time of writing this paper, the agency utilizes a linear regression model and is developing a feed-forward neural network for road rut predictions. The aim of the study was to evaluate the possibility of using a Recurrent Neural Network to predict road rut. Through design science research, an artefact in the form

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Vitali, Greta <1995&gt. "“Forecasting Stock Index Volatility: A comparison between GARCH and LSTM models”." Master's Degree Thesis, Università Ca' Foscari Venezia, 2019. http://hdl.handle.net/10579/15933.

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The financial world is characterized by the uncertainty of events and this phenomenon can expose operators to huge financial risks. Thus, there is a need to measure this uncertainty, with the aim to predict it and to make adequate plans of action. The concept of uncertainty is often associated with the definition of volatility, which is a measure of the variation of stock prices of a financial instrument during the time. But modelling volatility is not a trivial task, because of the essence of financial stock prices, which usually present volatility clusters, fat tails, nonnormality and struct

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Wang, Nancy. "Spectral Portfolio Optimisation with LSTM Stock Price Prediction." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273611.

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Nobel Prize-winning modern portfolio theory (MPT) has been considered to be one of the most important and influential economic theories within finance and investment management. MPT assumes investors to be riskaverse and uses the variance of asset returns as a proxy of risk to maximise the performance of a portfolio. Successful portfolio management reply, thus on accurate risk estimate and asset return prediction. Risk estimates are commonly obtained through traditional asset pricing factor models, which allow the systematic risk to vary over time domain but not in the frequency space. This ap

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Nääs, Starberg Filip, and Axel Rooth. "Predicting a business application's cloud server CPU utilization using the machine learning model LSTM." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301247.

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Cloud Computing sees increased adoption as companies seek to increase flexibility and reduce cost. Although the large cloud service providers employ a pay-as-you-go pricing model and enable customers to scale up and down quickly, there is still room for improvement. Workload in the form of CPU utilization often fluctuates which leads to unnecessary cost and environmental impact for companies. To help mitigate this issue, the aim of this paper is to predict future CPU utilization using a long short-term memory (LSTM) machine learning model. By predicting utilization up to 30 minutes into the fu

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Werngren, Simon. "Comparison of different machine learning models for wind turbine power predictions." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-362332.

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The goal of this project is to compare different machine learning algorithms ability to predict wind power output 48 hours in advance from earlier power data and meteorological wind speed predictions. Three different models were tested, two autoregressive integrated moving average (ARIMA) models one with exogenous regressors one without and one simple LSTM neural net model. It was found that the ARIMA model with exogenous regressors was the most accurate while also beingrelatively easy to interpret and at 1h 45min 32s had a comparatively short training time. The LSTM was less accurate, harder

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Shojaee, Ali B. S. "Bacteria Growth Modeling using Long-Short-Term-Memory Networks." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617105038908441.

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Anese, Gianluca <1995&gt. "Explanatory power of GARCH models using news-based investor sentiment: Applications of LSTM networks for text classification." Master's Degree Thesis, Università Ca' Foscari Venezia, 2020. http://hdl.handle.net/10579/16940.

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Many authors have shown that investors are not fully rational, as the traditional Efficient Markets Hypothesis suggests, and that investor sentiment can have an impact on stock prices. As investor sentiment is not directly measurable, different proxies have been used by researchers. In addition, progress in natural language processing has contributed to the development of new sentiment measures based on text sources obtained by news providers and social media. This work deals with a classification problem on financial news data and defines a reliable proxy for investor sentiment using both dic

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Suresh, Sreerag. "An Analysis of Short-Term Load Forecasting on Residential Buildings Using Deep Learning Models." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/99287.

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Building energy load forecasting is becoming an increasingly important task with the rapid deployment of smart homes, integration of renewables into the grid and the advent of decentralized energy systems. Residential load forecasting has been a challenging task since the residential load is highly stochastic. Deep learning models have showed tremendous promise in the fields of time-series and sequential data and have been successfully used in the field of short-term load forecasting at the building level. Although, other studies have looked at using deep learning models for building energy f

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Parthiban, Dwarak Govind. "On the Softmax Bottleneck of Word-Level Recurrent Language Models." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41412.

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For different input contexts (sequence of previous words), to predict the next word, a neural word-level language model outputs a probability distribution over all the words in the vocabulary using a softmax function. When the log of probability outputs for all such contexts are stacked together, the resulting matrix is a log probability matrix which can be denoted as Q_theta, where theta denotes the model parameters. When language modeling is formulated as a matrix factorization problem, the matrix to be factorized Q_theta is expected to be high-rank as natural language is highly context-depe

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Hellstenius, Sasha. "Model comparison of patient volume prediction in digital health care." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229908.

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Accurate predictions of patient volume are an essential tool to improve resource allocation and doctor utilization in the traditional, as well as the digital health care domain. Varying methods for patient volume prediction within the traditional health care domain has been studied in contemporary research, while the concept remains underexplored within the digital health care domain. In this paper, an evaluation of how two different non-linear state-of-the-art time series prediction models compare when predicting patient volume within the digital health care domain is presented. The models co

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Cissoko, Mamadou Ben Hamidou. "Adaptive time-aware LSTM for predicting and interpreting ICU patient trajectories from irregular data." Electronic Thesis or Diss., Strasbourg, 2024. http://www.theses.fr/2024STRAD012.

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En médecine prédictive personnalisée, modéliser avec précision la maladie et les processus de soins d'un patient est crucial en raison des dépendances temporelles à long terme inhérentes. Cependant, les dossiers de santé électroniques (DSE) se composent souvent de données épisodiques et irrégulières, issues des admissions hospitalières sporadiques, créant des schémas uniques pour chaque séjour hospitalier.Par conséquent, la construction d'un modèle prédictif personnalisé nécessite une considération attentive de ces facteurs pour capturer avec précision le parcours de santé du patient et aider

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Vera, Barberán José María. "Adding external factors in Time Series Forecasting : Case study: Ethereum price forecasting." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289187.

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The main thrust of time-series forecasting models in recent years has gone in the direction of pattern-based learning, in which the input variable for the models is a vector of past observations of the variable itself to predict. The most used models based on this traditional pattern-based approach are the autoregressive integrated moving average model (ARIMA) and long short-term memory neural networks (LSTM). The main drawback of the mentioned approaches is their inability to react when the underlying relationships in the data change resulting in a degrading predictive performance of the mode

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Ahmed, War, and Mehrdad Bahador. "The accuracy of the LSTM model for predicting the S&P 500 index and the difference between prediction and backtesting." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229415.

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In this paper the question of the accuracy of the LSTM algorithm for predicting stock prices is being researched. The LSTM algorithm is a form of deep learning algorithm. The algorithm takes in a set of data as inputs and finds a pattern to dissolve an output. Our results point to that using backtesting as the sole method to verify the accuracy of a model can fallible. For the future, researchers should take a fresh approach by using real-time testing. We completed this by letting the algorithm make predictions on future data. For the accuracy of the model we reached the conclusion that having

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Rostami, Jako, and Fredrik Hansson. "Time Series Forecasting of House Prices: An evaluation of a Support Vector Machine and a Recurrent Neural Network with LSTM cells." Thesis, Uppsala universitet, Statistiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385823.

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In this thesis, we examine the performance of different forecasting methods. We use dataof monthly house prices from the larger Stockholm area and the municipality of Uppsalabetween 2005 and early 2019 as the time series to be forecast. Firstly, we compare theperformance of two machine learning methods, the Long Short-Term Memory, and theSupport Vector Machine methods. The two methods forecasts are compared, and themodel with the lowest forecasting error measured by three metrics is chosen to be comparedwith a classic seasonal ARIMA model. We find that the Long Short-Term Memorymethod is the b

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Anstensrud, Ole-Petter Bård. "Pricing a Bermudan Swaption using the LIBOR Market Model : A LSM approach." Thesis, Norwegian University of Science and Technology, Department of Mathematical Sciences, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9787.

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<p>This study will focus on the pricing of interest rate derivatives within the framework of the LIBOR Market Model. First we introduce the mathematical and financial foundations behind the basic theory. Then we give a rather rigouros introduction to the LIBOR Market Model and show how to calibrate the model to a real data set. We use the model to price a basic swaption contract before we choose to concentrate on a more exotic Bermudan swaption. We use the Least Squares Monte Carlo (LSM) algorithm to handle the early exercise features of the Bermuda swaption. All major results are vizualised a

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Goncalves, Juliana Bittencourt. "Calibração do modelo de superfície noah lsm: aplicação em uma região agrícola no sul do Brasil." Universidade Federal de Santa Maria, 2016. http://repositorio.ufsm.br/handle/1/3934.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico<br>In this study, simulated to net radiation and energy flux in a region with rotation of crops, for two distinct periods: Period 1 (01 / Feb / 2009 to 31 / Jan / 2010) and period 2 (14 / Dec / 2009 to 28 / Apr / 2010). In these simulations we used the NOAH LSM surface model. For the period 1 initially, without any calibration simulations were performed only with the input of the local weather conditions, an adjustment of an experiment controlfile file and spin up for the stabilization of the initial conditions. In these simulations

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Cheng, Kun. "Deformable models for adaptive radiotherapy planning." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/22893.

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Radiotherapy is the most widely used treatment for cancer, with 4 out of 10 cancer patients receiving radiotherapy as part of their treatment. The delineation of gross tumour volume (GTV) is crucial in the treatment of radiotherapy. An automatic contouring system would be beneficial in radiotherapy planning in order to generate objective, accurate and reproducible GTV contours. Image guided radiotherapy (IGRT) acquires patient images just before treatment delivery to allow any necessary positional correction. Consequently, real-time contouring system provides an opportunity to adopt radiothera

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Van, den Bergh F., Wyk MA Van, Wyk BJ Van, and G. Udahemuka. "A comparison of data-driven and model-driven approaches to brightness temperature diurnal cycle interpolation." SAIEE Africa Research Journal, 2007. http://encore.tut.ac.za/iii/cpro/DigitalItemViewPage.external?sp=1001082.

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This paper presents two new schemes for interpolating missing samples in satellite diurnal temperature cycles (DTCs). The first scheme, referred to here as the cosine model, is an improvement of the model proposed in [2] and combines a cosine and exponential function for modelling the DTC. The second scheme uses the notion of a Reproducing Kernel Hilbert Space (RKHS) interpolator [1] for interpolating the missing samples. The application of RKHS interpolators to the DTC interpolation problem is novel. Results obtained by means of computer experiments are presented.

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Mukhedkar, Dhananjay. "Polyphonic Music Instrument Detection on Weakly Labelled Data using Sequence Learning Models." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279060.

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Polyphonic or multiple music instrument detection is a difficult problem compared to detecting single or solo instruments in an audio recording. As music is time series data it be can modelled using sequence learning methods within deep learning. Recently, temporal convolutional networks (TCN) have shown to outperform conventional recurrent neural networks (RNN) on various sequence modelling tasks. Though there have been significant improvements in deep learning methods, data scarcity becomes a problem in training large scale models. Weakly labelled data is an alternative where a clip is annot

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Ujihara, Rintaro. "Multi-objective optimization for model selection in music classification." Thesis, KTH, Optimeringslära och systemteori, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-298370.

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With the breakthrough of machine learning techniques, the research concerning music emotion classification has been getting notable progress combining various audio features and state-of-the-art machine learning models. Still, it is known that the way to preprocess music samples and to choose which machine classification algorithm to use depends on data sets and the objective of each project work. The collaborating company of this thesis, Ichigoichie AB, is currently developing a system to categorize music data into positive/negative classes. To enhance the accuracy of the existing system, thi

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Almqvist, Olof. "A comparative study between algorithms for time series forecasting on customer prediction : An investigation into the performance of ARIMA, RNN, LSTM, TCN and HMM." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-16974.

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Time series prediction is one of the main areas of statistics and machine learning. In 2018 the two new algorithms higher order hidden Markov model and temporal convolutional network were proposed and emerged as challengers to the more traditional recurrent neural network and long-short term memory network as well as the autoregressive integrated moving average (ARIMA). In this study most major algorithms together with recent innovations for time series forecasting is trained and evaluated on two datasets from the theme park industry with the aim of predicting future number of visitors. To dev

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Mirshekarianbabaki, Sadegh. "Blood Glucose Level Prediction via Seamless Incorporation of Raw Features Using RNNs." Ohio University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1523988526094778.

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Sibelius, Parmbäck Sebastian. "HMMs and LSTMs for On-line Gesture Recognition on the Stylaero Board : Evaluating and Comparing Two Methods." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-162237.

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In this thesis, methods of implementing an online gesture recognition system for the novel Stylaero Board device are investigated. Two methods are evaluated - one based on LSTMs and one based on HMMs - on three kinds of gestures: Tap, circle, and flick motions. A method’s performance was measured in its accuracy in determining both whether any of the above listed gestures were performed and, if so, which gesture, in an online single-pass scenario. Insight was acquired regarding the technical challenges and possible solutions to the online aspect of the problem. Poor performance was, however, o

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Annecchini, Andrea. "Valutazione di derivati Americani in modelli multi-dimensionali." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/20743/.

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La valutazione del prezzo di opzioni americane è un problema molto diffuso in ambito finanziario. In questo elaborato viene presentato l'algoritmo LSM basato sulla simulazione e una sua modifica che permette di eliminare un tipo di bias. Viene studiata la convergenza dei coefficienti di regressione che compaiono nell'algoritmo e vengono fatti diversi esperimenti numerici per valutare alcuni tipi di opzioni americane.

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Santos, Julio Cesar Grimalt dos. "Cálculo do Value at Risk (VaR) para o Ibovespa, pós crise de 2008, por meio dos modelos de heterocedasticidade condicional (GARCH) e de volatilidade estocástica (Local Scale Model - LSM)." reponame:Repositório Institucional do FGV, 2015. http://hdl.handle.net/10438/13521.

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Submitted by JULIO CESAR GRIMALT DOS SANTOS (grimbil@hotmail.com) on 2015-02-23T21:08:49Z No. of bitstreams: 1 Dissertação Final.pdf: 1416129 bytes, checksum: fcbac3f948355bac6f5b59569bf2610a (MD5)<br>Approved for entry into archive by Janete de Oliveira Feitosa (janete.feitosa@fgv.br) on 2015-03-04T16:04:21Z (GMT) No. of bitstreams: 1 Dissertação Final.pdf: 1416129 bytes, checksum: fcbac3f948355bac6f5b59569bf2610a (MD5)<br>Approved for entry into archive by Marcia Bacha (marcia.bacha@fgv.br) on 2015-03-12T19:06:25Z (GMT) No. of bitstreams: 1 Dissertação Final.pdf: 1416129 bytes, checksum

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Albert, Florea George, and Filip Weilid. "Deep Learning Models for Human Activity Recognition." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20201.

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AMI Meeting Corpus (AMI) -databasen används för att undersöka igenkännande av gruppaktivitet. AMI Meeting Corpus (AMI) -databasen ger forskare fjärrstyrda möten och naturliga möten i en kontorsmiljö; mötescenario i ett fyra personers stort kontorsrum. För attuppnågruppaktivitetsigenkänninganvändesbildsekvenserfrånvideosoch2-dimensionella audiospektrogram från AMI-databasen. Bildsekvenserna är RGB-färgade bilder och ljudspektrogram har en färgkanal. Bildsekvenserna producerades i batcher så att temporala funktioner kunde utvärderas tillsammans med ljudspektrogrammen. Det har visats att inkluder

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Keisala, Simon. "Using a Character-Based Language Model for Caption Generation." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-163001.

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Using AI to automatically describe images is a challenging task. The aim of this study has been to compare the use of character-based language models with one of the current state-of-the-art token-based language models, im2txt, to generate image captions, with focus on morphological correctness. Previous work has shown that character-based language models are able to outperform token-based language models in morphologically rich languages. Other studies show that simple multi-layered LSTM-blocks are able to learn to replicate the syntax of its training data. To study the usability of character

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Boukachaba, Niama. "Apport des observations satellitaires hyperspectrales infrarouges IASI au-dessus des continents dans le modèle météorologique à échelle convective AROME." Phd thesis, Toulouse, INPT, 2017. http://oatao.univ-toulouse.fr/19257/1/BOUKACHABA_Niama.pdf.

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Le sondeur infrarouge hyperspectral IASI (Interféromètre Atmosphérique de Sondage Infrarouge, développé conjointement par le CNES et EUMETSAT et embarqué à bord des satellites défilants Metop A, Metop B et très prochainement Metop C (2006, 2012 et 2018, respectivement)) apporte une très grande quantité d’informations permettant, entre autres, de décrire finement les paramètres de surface (température et émissivité sur une large gamme de longueurs d’onde). Néanmoins, les prévisions de température des surfaces continentales ne sont pas encore suffisamment réalistes pour utiliser l’information in

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Jain, Monika. "Regularized ensemble correlation filter tracking." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/229266/1/Monika_Jain_Thesis.pdf.

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Visual Object Tracking is the task of tracking an object within a video. Broadly, most tracking algorithms can be classified into neural network based, correlation filter based, and hybrid. This thesis investigates various methods to improve tracking using correlation filters. The thesis contributes four novel trackers. The first tracker uses an appearance model pool to avoid faulty filter updates. Next, the appearance feature channel weights are learned using the graph-based similarity followed by modelling sparse spatio-temporal variations. At last, non-linearity of the appearance features i

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Ridhagen, Markus, and Petter Lind. "A comparative study of Neural Network Forecasting models on the M4 competition data." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-445568.

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The development of machine learning research has provided statistical innovations and further developments within the field of time series analysis. This study seeks to investigate two different approaches on artificial neural network models based on different learning techniques, and answering how well the neural network approach compares with a basic autoregressive approach, as well as how the artificial neural network models compare to each other. The models were compared and analyzed in regards to the univariate forecast accuracy on 20 randomly drawn time series from two different time fre

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Mealey, Thomas C. "Binary Recurrent Unit: Using FPGA Hardware to Accelerate Inference in Long Short-Term Memory Neural Networks." University of Dayton / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1524402925375566.

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Li, Yuntao. "Federated Learning for Time Series Forecasting Using Hybrid Model." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254677.

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Time Series data has become ubiquitous thanks to affordable edge devices and sensors. Much of this data is valuable for decision making. In order to use these data for the forecasting task, the conventional centralized approach has shown deficiencies regarding large data communication and data privacy issues. Furthermore, Neural Network models cannot make use of the extra information from the time series, thus they usually fail to provide time series specific results. Both issues expose a challenge to large-scale Time Series Forecasting with Neural Network models. All these limitations lead to

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Nováčik, Tomáš. "Rekurentní neuronové sítě pro rozpoznávání řeči." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-255371.

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This master thesis deals with the implementation of various types of recurrent neural networks via programming language lua using torch library. It focuses on finding optimal strategy for training recurrent neural networks and also tries to minimize the duration of the training. Furthermore various types of regularization techniques are investigated and implemented into the recurrent neural network architecture. Implemented recurrent neural networks are compared on the speech recognition task using AMI dataset, where they model the acustic information. Their performance is also compared to sta

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Burg, Antoine. "Multivariate extensions for mortality modelling." Electronic Thesis or Diss., Université Paris sciences et lettres, 2025. http://www.theses.fr/2025UPSLD002.

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Au cours des deux derniers siècles, l’espérance de vie tout autour du globe a connu un accroissement considérable. Si la tendance sur le long terme est plutôt régulière, l’amélioration de la longévité peut être décomposée sur le court-terme en plusieurs phases, que l’on peut relier le plus souvent aux progrès médicaux et à la diminution de causes de mortalité particulières. L’année 2020 marque un tournant du fait de l’ampleur de la pandémie Covid-19 et de ses conséquences. Ses effets directs et indirects sur l’économie et les systèmes de santé se manifestent également au travers des autres cau

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Soares, Leonardo dos Reis Leano. "Estudo do comportamento de sinais OSL de BeO e Al2O3:C usando o Modelo OTOR Simplificado e Método dos Mínimos Quadrados." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/43/43134/tde-02112018-132426/.

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A dosimetria das radiações alfa, beta e gama é importante para diversas áreas aplicadas, sendo utilizada na proteção radiológica de pacientes e profissionais que se expõem a esses tipos de radiações. Com estudos dosimétricos pode-se obter melhores estimativas de dose absorvida, e ter mais precisão na estimativa de riscos populacionais. As técnicas de Termoluminescência (TL) e Luminescência Oticamente Estimulada (OSL) são utilizadas para essas aplicações dosimétricas. Estudos recentes têm mostrado que alguns materiais dosimétricos conhecidos como óxido de alumínio dopado com carbono (Al$_2$O$_3

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Lund, Max. "Duplicate Detection and Text Classification on Simplified Technical English." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158714.

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This thesis investigates the most effective way of performing classification of text labels and clustering of duplicate texts in technical documentation written in Simplified Technical English. Pre-trained language models from transformers (BERT) were tested against traditional methods such as tf-idf with cosine similarity (kNN) and SVMs on the classification task. For detecting duplicate texts, vector representations from pre-trained transformer and LSTM models were tested against tf-idf using the density-based clustering algorithms DBSCAN and HDBSCAN. The results show that traditional method

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Alsulami, Khalil Ibrahim D. "Application-Based Network Traffic Generator for Networking AI Model Development." University of Dayton / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1619387614152354.

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Holcner, Jonáš. "Strojový překlad pomocí umělých neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2018. http://www.nusl.cz/ntk/nusl-386020.

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The goal of this thesis is to describe and build a system for neural machine translation. System is built with recurrent neural networks - encoder-decoder architecture in particular. The result is a nmt library used to conduct experiments with different model parameters. Results of the experiments are compared with system built with the statistical tool Moses.

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Eriksson, Henrik. "Federated Learning in Large Scale Networks : Exploring Hierarchical Federated Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-292744.

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Federated learning faces a challenge when dealing with highly heterogeneous data and it can sometimes be inadequate to adopt an approach where a single model is trained for usage at all nodes in the network. Different approaches have been investigated to succumb this issue such as adapting the trained model to each node and clustering the nodes in the network and train a different model for each cluster where the data is less heterogeneous. In this work we study the possibilities to improve the local model performance utilizing the hierarchical setup that comes with clustering the participatin

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Ramesh, Chandra D. S. "Turbulent Mixed Convection." Thesis, Indian Institute of Science, 2000. https://etd.iisc.ac.in/handle/2005/236.

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Turbulent mixed convection is a complicated flow where the buoyancy and shear forces compete with each other in affecting the flow dynamics. This thesis deals with the near wall dynamics in a turbulent mixed convection flow over an isothermal horizontal heated plate. We distinguish between two types of mixed convection ; low-speed mixed convection (LSM) and high-speed mixed convection (HSM). In LSM the entire boundary layer, including the near-wall region, is dominated by buoyancy; in HSM the near-wall region, is dominated by shear and the outer region by buoyancy. We show that the value of th

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Ramesh, Chandra D. S. "Turbulent Mixed Convection." Thesis, Indian Institute of Science, 2000. http://hdl.handle.net/2005/236.

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Turbulent mixed convection is a complicated flow where the buoyancy and shear forces compete with each other in affecting the flow dynamics. This thesis deals with the near wall dynamics in a turbulent mixed convection flow over an isothermal horizontal heated plate. We distinguish between two types of mixed convection ; low-speed mixed convection (LSM) and high-speed mixed convection (HSM). In LSM the entire boundary layer, including the near-wall region, is dominated by buoyancy; in HSM the near-wall region, is dominated by shear and the outer region by buoyancy. We show that the value of th

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Elistratova, Vera. "Conception optimale d’une gamme de moteurs synchrones à démarrage direct à haute performance énergétique." Thesis, Ecole centrale de Lille, 2015. http://www.theses.fr/2015ECLI0022/document.

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Ce travail a pour objectif de développer un outil analytique multi-physiques de dimensionnement d’une gamme de moteurs « hybrides » à démarrage direct, intégrant les avantages des deux technologies : l’auto-démarrage de la technologie asynchrone et les bonnes performances énergétique en régime permanent de la technologie synchrone à aimants permanents en répondant aux nouveaux enjeux d’efficacité énergétique et en ajoutant à cela les aspects économiques.La validation de cet outil est effectuée par des modèles éléments finis créés avec un logiciel commercial ANSYS/Maxwell et par des essais expé

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Kau, Wei-Hao, and 高偉豪. "Time series prediction using LSTM Network Models." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/ey3638.

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碩士<br>國立中山大學<br>應用數學系研究所<br>106<br>As recent computing hardware technology has undergone rapid and significant advances, complex methods that require a lot of computing power have been realized, which has led to the development of more machine learning methods and neural network models. This paper discusses the Long short-term memory (LSTM) network model of recurrent neural networks. The first part of this paper introduces the basic concept of LSTM and its training method. The second part discusses short- and long-term prediction, and compares their differences with the conventional time serie

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Yen-LiangLin and 林彥良. "PM 2.5 Prediction based on LSTM Model." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/8gayas.

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碩士<br>國立成功大學<br>工程科學系<br>106<br>Recently, pollution conditions of particulate matter 2.5 in Taiwan have become more severe day by day. Several other cities in Asia such as Beijing and Delhi are also facing the same pollution problem, which draws attention to government and experts. Due to the human activities in Asia such as industrialization and animal husbandry, air pollution condition has been getting worse, increases the possibility of population suffering from cardiovascular disease. Particular matter pollution has become a problem we cannot ignore in modern society. Currently, official

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TSAI, YI-TING, and 蔡宜廷. "Air Pollution Forecasting using LSTM with Aggregation Model." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/5p88t6.

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碩士<br>國立臺北大學<br>資訊工程學系<br>107<br>In developed countries or developing countries, the effects of air pollutants on the health of the public are consistent. PM2.5 is a suspended particle In the airborne particulate pollutants. There is no impact on the human body for the concentration threshold of suspended particulates. The concentration of suspended particulates is for humans with diseases of the respiratory system. The impact is also different, so no standard or regulation can completely protect the public. Therefore, predicting the value of PM2.5 in the future is an important issue. This pap

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TSAI, CHENG-HAN, and 蔡承翰. "Application of LSTM Model to Water Stage Forecasting." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/mzv86q.

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碩士<br>逢甲大學<br>水利工程與資源保育學系<br>107<br>In recent years, abnormal weather conditions are observed and the heavy rainfall events are increasing, which is different from the previous disaster characteristics. The river stage rises rapidly and various areas have suffered from floods, resulting in losses of life and property. In previous studies, neural network was often used for prediction but ordinary neural networks cannot preserve the previous information during the prediction which limits the long-term prediction ability. Recurrent Neural Network (RNN) is a suitable choice to overcome this limita

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Tsung-LingHsu and 許琮苓. "Stock Market Trend Prediction Based on LSTM Model." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/9zca24.

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碩士<br>國立成功大學<br>工程科學系<br>107<br>The stock market has always been an important economic indicator for the society. It is believed that the fluctuation of the stock market seems changed according to some cyclic regularity. For a stock investor, how to find the cyclic regularity of a stock market is a most important issue. However, due to many factors affecting the stock market, it is difficult to obtain accurate predictions. At present, many algorithms have been applied for predicting stock market trends. Due to both local/regional and global economic performance will affect the stock market. Th

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Hsu, Yi-Kuan, and 許以觀. "A Factory-aware Attentional LSTM Model for PM2.5 Prediction." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/fcx28w.

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碩士<br>國立交通大學<br>資訊管理研究所<br>107<br>With air quality issues becoming a global concern, many countries is facing lot of air pollution problems. While monitoring stations have been established to collect air quality information, and scientists have been committed to the study of air quality predictions, but few studies have taken the different monitoring areas and industrial features into account. In this paper, we propose a deep neural network for PM2.5 predictions, named FAA-LSTM, collecting air quality data from three types of monitors and factory data that is highly related to air quality. A s

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