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Dissertations / Theses on the topic 'LST1'

Author: Grafiati

Published: 9 March 2023

Last updated: 31 July 2025

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1

Schiller, Christian. "Funktion und Expression der transmembranen Isoformen des HLA-Klasse-III-Gens LST1." Diss., lmu, 2009. http://nbn-resolving.de/urn:nbn:de:bvb:19-126674.

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D'ALOIA, ALESSIA. "RalGPS2 interacts with LST1 and supports tunneling nanotubes formation in human bladder cancer cells." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2017. http://hdl.handle.net/10281/158357.

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RalGPS2 è uno scambiatore appartenente alla famiglia RalGPS, composto da un dominio catalitico Cdc25-like nella regione N-terminale, un motivo PxxP nella regione centrale, e un dominio di omologia alla Pleckstrina (PH) nella regione C-terminale. E’ stato precedentemente dimostrato che RalGPS2 attiva in “vivo” la GTPasi RalA, mentre la regione PH-PxxP si comporta da dominante negativo per l’attività di RalA in cellule NIH3T3 e PC12. Inoltre, se è overespresso RalGPS2 causa cambiamenti morfologici consistenti nelle cellule HEK293, suggerendo che esso possa avere effetti sul citoscheletro. Tutto

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Pacitto, Angela. "Towards structural and functional understanding of the Flcn/Fnip complex through its yeast orthologue Lst7/Lst4." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708934.

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Edholm, Gustav, and Xuechen Zuo. "A comparison between aconventional LSTM network and agrid LSTM network applied onspeech recognition." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230173.

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In this paper, a comparision between the conventional LSTM network and the one-dimensionalgrid LSTM network applied on single word speech recognition is conducted. The performanceof the networks are measured in terms of accuracy and training time. The conventional LSTMmodel is the current state of the art method to model speech recognition. However, thegrid LSTM architecture has proven to be successful in solving other emperical tasks such astranslation and handwriting recognition. When implementing the two networks in the sametraining framework with the same training data of single word audio

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Fu, Reid J. "CCG Realization with LSTM Hypertagging." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1534236955413883.

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Borrello, Maria Teresa. "Reversible and irreversible LSD1 inhibitors." Thesis, University of East Anglia, 2016. https://ueaeprints.uea.ac.uk/59682/.

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Environmental factors and lifestyle can alter the way our genes are expressed influencing a network of chemical switches within our cells collectively known as the Epigenome. Among the epigenetic mechanisms orchestrating the gene expression, methylation is of foremost importance and probably fair to say, still incompletely decoded. Dysregulations of histone methylation patterns lead to the repression or activation of signalling pathways that often promote the genesis and progression of disease states. Lysine specific demethylase 1 (LSD1) oxidatively removes methyl groups from histone H3 and it

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Nordin, Stensö Isak. "Predicting Tropical Thunderstorm Trajectories Using LSTM." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231613.

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Thunderstorms are both dangerous as well as important rain-bearing structures for large parts of the world. The prediction of thunderstorm trajectories is however difficult, especially in tropical regions. This is largely due to their smaller size and shorter lifespan. To overcome this issue, this thesis investigates how well a neural network composed of long short-term memory (LSTM) units can predict the trajectories of thunderstorms, based on several years of lightning strike data. The data is first clustered, and important features are extracted from it. These are used to predict the mean p

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Rogers, Joseph. "Effects of an LSTM Composite Prefetcher." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-396842.

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Recent work in computer architecture and machine learning has seen various groups begin exploring the viability of using neural networks to augment conventional processor designs. Of particular interest is using the predictive capabilities of techniques in natural language processing to assist traditional CPU memory prefetching methods. This work demonstrates one of these proposed techniques, and examines some of the challenges associated with producing satisfactory and consistently reproducible results. Special attention is given to data acquisition and preprocessing as different methods. Thi

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Schelhaas, Wietze. "Predicting network performancein IoT environments using LSTM." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-454062.

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There are still many problems that need to be solved with Internet of Things (IoT) technology, one of them being performance assurance. To ensure a certain quality of service in an IoT environment, the network has to be monitored and actively measured. However, Due to the limited computational recourses Internet of things nodes have, active measurement is difficult to achieve without also inducing energy and network overhead. A potential solution to this problem is to apply a machine-learning algorithm to predict network performance metrics such as round- trip time or packet loss. By substitut

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Nilson, Erik, and Arvid Renström. "LSTM-nätverk för generellt Atari 2600 spelande." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17174.

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I detta arbete jämfördes ett LSTM-nätverk med ett feedforward-nätverk för generellt Atari 2600 spelande. Prestandan definierades som poängen agenten får för ett visst spel. Hypotesen var att LSTM skulle prestera minst lika bra som feedforward och förhoppningsvis mycket bättre. För att svara på frågeställningen skapades två olika agenter, en med ett LSTM-nätverk och en med ett feedforward-nätverk. Experimenten utfördes på Stella emulatorn med hjälp av ramverket the Arcade Learning Environment (ALE). Hänsyn togs till Machado råd om inställningar för användning av ALE och hur agenter borde tränas

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Paschou, Michail. "ASIC implementation of LSTM neural network algorithm." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254290.

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LSTM neural networks have been used for speech recognition, image recognition and other artificial intelligence applications for many years. Most applications perform the LSTM algorithm and the required calculations on cloud computers. Off-line solutions include the use of FPGAs and GPUs but the most promising solutions include ASIC accelerators designed for this purpose only. This report presents an ASIC design capable of performing the multiple iterations of the LSTM algorithm on a unidirectional and without peepholes neural network architecture. The proposed design provides arithmetic level

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Valluru, Aravind-Deshikh. "Realization of LSTM Based Cognitive Radio Network." Thesis, University of North Texas, 2019. https://digital.library.unt.edu/ark:/67531/metadc1538697/.

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This thesis presents the realization of an intelligent cognitive radio network that uses long short term memory (LSTM) neural network for sensing and predicting the spectrum activity at each instant of time. The simulation is done using Python and GNU Radio. The implementation is done using GNU Radio and Universal Software Radio Peripherals (USRP). Simulation results show that the confidence factor of opportunistic users not causing interference to licensed users of the spectrum is 98.75%. The implementation results demonstrate high reliability of the LSTM based cognitive radio network.

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HOSSEINI, SEYED AMIR. "DISSECTING THE ROLE OF LYSINE-SPECIFIC DEMETHYLASE1 (LSD1): IDENTIFICATION OF MARKERS/EFFECTORS OF SENSITIVITY TO LSD1 INHIBITORS IN CANCER." Doctoral thesis, Università degli Studi di Milano, 2018. http://hdl.handle.net/2434/561514.

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Post-translational modification of histone tails plays a critical role in chromatin regulation, gene activity and nuclear architecture. The addition or removal of post-translational modifications from histone tails is fairly dynamic and is achieved by a number of different histone modifying enzymes. Given the fundamental roles of histone modifications in gene regulation and expression, it is not surprising that aberrant patterns of histone marks are found in cancer. Such modifications include histone lysine methylation, which can either promote or repress gene activity depending on the extent

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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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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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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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Andréasson, David, and Blomquist Jesper Mortensen. "Forecasting the OMXS30 - a comparison between ARIMA and LSTM." Thesis, Uppsala universitet, Statistiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-413793.

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Machine learning is a rapidly growing field with more and more applications being proposed every year, including but not limited to the financial sector. In this thesis, historical adjusted closing prices from the OMXS30 index are used to forecast the corresponding future values using two different approaches; one using an ARIMA model and the other using an LSTM neural network. The forecasts are made on three different time intervals: 90, 30 and 7 days ahead. The results showed that the LSTM model performs slightly better when forecasting 90 and 30 days ahead, whereas the ARIMA model has compa

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Cavallie, Mester Jon William. "Using LSTM Neural Networks To Predict Daily Stock Returns." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-106124.

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Long short-term memory (LSTM) neural networks have been proven to be effective for time series prediction, even in some instances where the data is non-stationary. This lead us to examine their predictive ability of stock market returns, as the development of stock prices and returns tend to be a non-stationary time series. We used daily stock trading data to let an LSTM train models at predicting daily returns for 60 stocks from the OMX30 and Nasdaq-100 indices. Subsequently, we measured their accuracy, precision, and recall. The mean accuracy was 49.75 percent, meaning that the observed accu

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Carnesecchi, Julie. "Régulation réciproque et coopération transcriptionnelle du complexe ERRalpha-LSD1." Thesis, Lyon, École normale supérieure, 2014. http://www.theses.fr/2014ENSL0935.

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Les récepteurs nucléaires sont des facteurs de transcription qui exercent leur fonction via le contrôle de la transcription de leurs gènes cibles, une régulation qui est dépendante de cofacteurs associés. Les complexes transcriptionnels ainsi formés dialogueront avec l’environnement chromatinien (méthylation de l’ADN, remodelage des nucléosomes, modifications post-traductionnelles des histones) afin de promouvoir la répression ou l’activation transcriptionnelle des cibles géniques de ces récepteurs. Ce projet a identifié une interaction entre la lysine déméthylase LSD1 et le récepteur nucléair

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Pokhrel, Abhishek <1996&gt. "Stock Returns Prediction using Recurrent Neural Networks with LSTM." Master's Degree Thesis, Università Ca' Foscari Venezia, 2022. http://hdl.handle.net/10579/22038.

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Research in asset pricing has, until recently, side-stepped the high dimensionality problem by focusing on low-dimensional models. Work on cross-sectional stock return prediction, for example, has focused on regressions with a small number of characteristics. Given the background of an enormously large number of variables that could potentially be relevant for predicting returns, focusing on such a small number of factors effectively means that the researchers are imposing a very high degree of sparsity on these models. This research studies the use of the recurrent neural network (RNN) method

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Gualandi, Giacomo. "Analisi di dataset in campo finanziario mediante reti neurali LSTM." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19623/.

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Con il presente elaborato si è esplorato il campo della data analytics. È stato analizzato un dataset relativo all' andamento storico del titolo di borsa di una società, i cui dati sono stati manipolati in modo tale da renderli compatibili per un loro utilizzo in una applicazione di Machine Learning. Si sono approfondite le reti neurali artificiali LSTM e con esse si è creato un modello che permettesse di effettuare delle predizioni sui valori futuri del titolo. Infine sono state valutate le differenze tra i valori predetti e quelli reali assunti dal titolo di borsa.

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Wang, Jianxun. "LSD1 complex controls cell type terminal differentiation during mammalian organogenesis." Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2006. http://wwwlib.umi.com/cr/ucsd/fullcit?p3220377.

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Thesis (Ph. D.)--University of California, San Diego, 2006.<br>Title from first page of PDF file (viewed September 8, 2006). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 59-71).

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Larsson, Joel. "Optimizing text-independent speaker recognition using an LSTM neural network." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-26312.

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In this paper a novel speaker recognition system is introduced. Automated speaker recognition has become increasingly popular to aid in crime investigations and authorization processes with the advances in computer science. Here, a recurrent neural network approach is used to learn to identify ten speakers within a set of 21 audio books. Audio signals are processed via spectral analysis into Mel Frequency Cepstral Coefficients that serve as speaker specific features, which are input to the neural network. The Long Short-Term Memory algorithm is examined for the first time within this area, wit

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Wolpher, Maxim. "Anomaly Detection in Unstructured Time Series Datausing an LSTM Autoencoder." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231368.

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An exploration of anomaly detection. Much work has been done on the topic of anomalyd etection, but what seems to be lacking is a dive into anomaly detection of unstructuredand unlabeled data. This thesis aims to determine the efctiveness of combining recurrentneural networks with autoencoder structures for sequential anomaly detection. The use of an LSTM autoencoder will be detailed, but along the way there will also be backgroundon time-independent anomaly detection using Isolation Forests and Replicator Neural Networks on the benchmark DARPA dataset. The empirical results in this thesis sho

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Berenji, Ardestani Sarah. "Time Series Anomaly Detection and Uncertainty Estimation using LSTM Autoencoders." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281354.

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The goal of this thesis is to implement an anomaly detection tool using LSTM autoencoder and apply a novel method for uncertainty estimation using Bayesian NeuralNetworks (BNNs) based on a paper from Uber research group [1]. Having a reliable anomaly detection tool and accurate uncertainty estimation is critical in many fields. At Telia, such a tool can be used in many different data domains like device logs to detect abnormal behaviours. Our method uses an autoencoder to extract important features and learn the encoded representation of the time series. This approach helps to capture testing

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Singh, J. P., A. Kumar, Nripendra P. Rana, and Y. K. Dwivedi. "Attention-based LSTM network for rumor veracity estimation of tweets." Springer, 2020. http://hdl.handle.net/10454/17942.

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Yes<br>Twitter has become a fertile place for rumors, as information can spread to a large number of people immediately. Rumors can mislead public opinion, weaken social order, decrease the legitimacy of government, and lead to a significant threat to social stability. Therefore, timely detection and debunking rumor are urgently needed. In this work, we proposed an Attention-based Long-Short Term Memory (LSTM) network that uses tweet text with thirteen different linguistic and user features to distinguish rumor and non-rumor tweets. The performance of the proposed Attention-based LSTM mo

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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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Ärlemalm, Filip. "Harbour Porpoise Click Train Classification with LSTM Recurrent Neural Networks." Thesis, KTH, Teknisk informationsvetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215088.

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The harbour porpoise is a toothed whale whose presence is threatened in Scandinavia. Onestep towards preserving the species in critical areas is to study and observe the harbourporpoise population growth or decline in these areas. Today this is done by using underwateraudio recorders, so called hydrophones, and manual analyzing tools. This report describes amethod that modernizes the process of harbour porpoise detection with machine learning. Thedetection method is based on data collected by the hydrophone AQUAclick 100. The data isprocessed and classified automatically with a stacked long sh

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Bergström, Carl, and Oscar Hjelm. "Impact of Time Steps on Stock Market Prediction with LSTM." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-262221.

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Machine learning models as tools for predicting time series have in recent years proven to perform exceptionally well. With financial time series in the form of stock indices being inherently complex and subject to noise and volatility, the prediction of stock market movements has proven to be especially difficult throughout extensive research. The objective of this study is to thoroughly analyze the LSTM architecture for neural networks and its performance when applied to the S&P 500 stock index. The main research question revolves around quantifying the impact of varying the number of ti

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Paganini, L. "NEUROSPECIFIC LSD1 SPLICING ISOFORM LINKS EPIGENETICS TO MAMMALIAN BRAIN PHYSIOLOGY." Doctoral thesis, Università degli Studi di Milano, 2013. http://hdl.handle.net/2434/215885.

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LSD1, the first identified Lysine specific demethylase that removes methyl groups from mono- or di-methylated Histone 3 Lys4 (H3K4), has a mammalian-restricted neuronal isoform (LSD1-E8a), generated by the alternative inclusion of the 12-bp neurospecific exon E8a. LSD1 general function is to inhibit the expression of neuronal genes in non-neuronal cells, but LSD1-E8a isoform is characterized by a less gene repressing action. Indeed, the 4 aa coded by the exon E8a, which form a protruding loop in the LSD1 catalytic domain, contain a Threonine residue that can be phosphorylated and that is requi

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GRILLO, BARBARA. "PARTNERS, TARGETS AND MODULATORS OF LSD1 IN STRESS-RESPONSE REGULATION." Doctoral thesis, Università degli Studi di Milano, 2019. http://hdl.handle.net/2434/612975.

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In mammals, different forms of stress, including psychosocial stress, can affect various aspects of human health, promoting mood and anxiety disorders. However, very little is known about the mechanisms underlying the brain physiology of stress response, hindering the development of new therapeutic strategies. We uncover a role for the transcriptional corepressor Lysine Specific Demethylase-1 (LSD1) and its dominant negative splicing isoform neuroLSD1, in the modulation of emotional behavior. In the mouse hippocampus, LSD1 and neuroLSD1 interacting with the transcription factor Serum Response

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Poormehdi, Ghaemmaghami Masoumeh. "Tracking of Humans in Video Stream Using LSTM Recurrent Neural Network." Thesis, KTH, Teoretisk datalogi, TCS, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217495.

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In this master thesis, the problem of tracking humans in video streams by using Deep Learning is examined. We use spatially supervised recurrent convolutional neural networks for visual human tracking. In this method, the recurrent convolutional network uses both the history of locations and the visual features from the deep neural networks. This method is used for tracking, based on the detection results. We concatenate the location of detected bounding boxes with high-level visual features produced by convolutional networks and then predict the tracking bounding box for next frames. Because

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Singh, Akash. "Anomaly Detection for Temporal Data using Long Short-Term Memory (LSTM)." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215723.

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We explore the use of Long short-term memory (LSTM) for anomaly detection in temporal data. Due to the challenges in obtaining labeled anomaly datasets, an unsupervised approach is employed. We train recurrent neural networks (RNNs) with LSTM units to learn the normal time series patterns and predict future values. The resulting prediction errors are modeled to give anomaly scores. We investigate different ways of maintaining LSTM state, and the effect of using a fixed number of time steps on LSTM prediction and detection performance. LSTMs are also compared to feed-forward neural networks wit

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Hau, Mirjam [Verfasser], and Manfred [Akademischer Betreuer] Jung. "Zielgerichtete Inhibition der Lysin-spezifischen Demethylase 1 (LSD1) mittels Nitroreduktase-Prodrugs." Freiburg : Universität, 2020. http://d-nb.info/1220631469/34.

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Zambezi, Samantha. "Predicting social unrest events in South Africa using LSTM neural networks." Master's thesis, Faculty of Science, 2021. http://hdl.handle.net/11427/33986.

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This thesis demonstrates an approach to predict the count of social unrest events in South Africa. A comparison is made between traditional forecasting approaches and neural networks; the traditional forecast method selected being the Autoregressive Integrated Moving Average (ARIMA model). The type of neural network implemented was the Long Short-Term Memory (LSTM) neural network. The basic theoretical concepts of ARIMA and LSTM neural networks are explained and subsequently, the patterns of the social unrest time series were analysed using time series exploratory techniques. The social unrest

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Holm, Noah, and Emil Plynning. "Spatio-temporal prediction of residential burglaries using convolutional LSTM neural networks." Thesis, KTH, Geoinformatik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229952.

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The low amount solved residential burglary crimes calls for new and innovative methods in the prevention and investigation of the cases. There were 22 600 reported residential burglaries in Sweden 2017 but only four to five percent of these will ever be solved. There are many initiatives in both Sweden and abroad for decreasing the amount of occurring residential burglaries and one of the areas that are being tested is the use of prediction methods for more efficient preventive actions. This thesis is an investigation of a potential method of prediction by using neural networks to identify are

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Sarika, Pawan Kumar. "Comparing LSTM and GRU for Multiclass Sentiment Analysis of Movie Reviews." Thesis, Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20213.

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Today, we are living in a data-driven world. Due to a surge in data generation, there is a need for efficient and accurate techniques to analyze data. One such kind of data which is needed to be analyzed are text reviews given for movies. Rather than classifying the reviews as positive or negative, we will classify the sentiment of the reviews on the scale of one to ten. In doing so, we will compare two recurrent neural network algorithms Long short term memory(LSTM) and Gated recurrent unit(GRU). The main objective of this study is to compare the accuracies of LSTM and GRU models. For trainin

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Kindbom, Hannes. "LSTM vs Random Forest for Binary Classification of Insurance Related Text." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252748.

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The field of natural language processing has received increased attention lately, but less focus is put on comparing models, which differ in complexity. This thesis compares Random Forest to LSTM, for the task of classifying a message as question or non-question. The comparison was done by training and optimizing the models on historic chat data from the Swedish insurance company Hedvig. Different types of word embedding were also tested, such as Word2vec and Bag of Words. The results demonstrated that LSTM achieved slightly higher scores than Random Forest, in terms of F1 and accuracy. The mo

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Gessle, Gabriel, and Simon Åkesson. "A comparative analysis of CNN and LSTM for music genre classification." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260138.

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The music industry has seen a great influx of new channels to browse and distribute music. This does not come without drawbacks. As the data rapidly increases, manual curation becomes a much more difficult task. Audio files have a plethora of features that could be used to make parts of this process a lot easier. It is possible to extract these features, but the best way to handle these for different tasks is not always known. This thesis compares the two deep learning models, convolutional neural network (CNN) and long short-term memory (LSTM), for music genre classification when trained usin

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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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Toffolo, E. "POST-TRANSCRIPTIONAL AND POST-TRANSLATIONAL REGULATION OF LSD1 IN MAMMALIAN BRAIN." Doctoral thesis, Università degli Studi di Milano, 2015. http://hdl.handle.net/2434/286318.

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Epigenetic mechanisms play important roles in brain development, orchestrating proliferation, differentiation, and morphogenesis. Lysine-Specific Demethylase 1 (LSD1 also known as KDM1A and AOF2) is a histone modifier involved in transcriptional repression, forming a stable core complex with the corepressors corepressor of REST (CoREST) and histone deacetylases (HDAC1/2). Importantly, in the mammalian CNS, neuronal neuroLSD1, an alternative splicing isoform of LSD1 including the microexon E8a, sets alongside LSD1 and is capable of enhancing neurite growth and morphogenesis. Here, we describe t

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Khaghani, Farnaz. "A Deep Learning Approach to Predict Accident Occurrence Based on Traffic Dynamics." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/98801.

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Traffic accidents are of concern for traffic safety; 1.25 million deaths are reported each year. Hence, it is crucial to have access to real-time data and rapidly detect or predict accidents. Predicting the occurrence of a highway car accident accurately any significant length of time into the future is not feasible since the vast majority of crashes occur due to unpredictable human negligence and/or error. However, rapid traffic incident detection could reduce incident-related congestion and secondary crashes, alleviate the waste of vehicles’ fuel and passengers’ time, and provide appropriate

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Turková, Linda. "Struktura a funkce nového transmembránového adaptorového proteinu LST1." Master's thesis, 2006. http://www.nusl.cz/ntk/nusl-380543.

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Chmátal, Lukáš. "Biochemická a funkční charakterizace transmembránového adaptorového proteinu LST1/A." Master's thesis, 2008. http://www.nusl.cz/ntk/nusl-290775.

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Schiller, Christian [Verfasser]. "Funktion und Expression der transmembranen Isoformen des HLA-Klasse-III-Gens LST1 / vorgelegt von Christian Schiller." 2009. http://d-nb.info/1010537091/34.

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Kuo, Shih-Chun, and 郭士鈞. "LSTM-Based Vehicle Trajectory Prediction." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/q7qwdc.

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碩士<br>國立清華大學<br>通訊工程研究所<br>107<br>Future trajectory prediction of objects is a very important technical link for self-driving cars and navigation systems. In order to be safe, efficient, and to avoid collisions, self-driving cars should be able to anticipate what will happen in a changeable environment and predict the future location of surrounding objects in advance. There have been many significant technical advances in autonomous cars, such as Google’s self-driving cars and Tesla’s Autopilot. In the past research of object trajectory prediction, the interaction between objects was simulated

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XIAO, HUNG-JIE, and 蕭宏杰. "LSTM-based Parking Space Detection." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/z88u44.

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碩士<br>國立中正大學<br>電機工程研究所<br>107<br>In this research, we propose the LSTM-based parking lot detection method architecture. This framework divides to two parts, one is “Status ConvNet”, and another is “Action ConvNet”. Frist, we will separate individual frame from sequence of image to become the spatial stream. And then, we will calculate optical flow to be moving information to become the temporal stream. For spatial stream, we input an image to Convolutional Neural Network (CNN) to detect the status of parking space, called the network “Status ConvNet”. At the same time, input extracted high-le

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Mendes, João Filipe Batista. "Forecasting bitcoin prices: ARIMA vs LSTM." Master's thesis, 2019. http://hdl.handle.net/10071/19724.

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Bitcoin has recently received special attention in economics and finance as the most popular blockchain technology. This dissertation aims to discuss whether newly machine-leaning models perform better than traditional models in forecasting. Particularly, this study compares the accuracy of the prediction of bitcoin prices using two different models: Long-Short Term Memory (LSTM) versus Auto Regressive Integrated Moving Average (ARIMA), in terms of forecasting errors, and Python routines were used for such purpose. Bitcoin price time series ranges from 2017-06-18 to 2019-08-07, in a daily basi

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Chen, Wei-Rui, and 陳維睿. "Applying LSTM to Bitcoin price prediction." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/67y8s7.

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碩士<br>國立政治大學<br>資訊科學系<br>106<br>This thesis focuses on applying Long Short-Term Memory (LSTM) technique to predict Bitcoin price direction. Features including internal and external features are extracted from Bitcoin blockchain and exchange center respectively. Cryptocurrency is a new type of currency that is traded over the infrastructure of Internet. Bitcoin (BTC) is the first cryptocurrency and ranks first in the market capitalization among all the other cryptocurrencies. Predicting Bitcoin price is a novel topic because of its differences with traditional financial assets and its volatili

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Fonseca, José Pedro Castro. "FPGA implementation of a LSTM Neural Network." Master's thesis, 2016. https://repositorio-aberto.up.pt/handle/10216/90359.

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Este trabalho pretende fazer uma implementação customizada, em Hardware, duma Rede Neuronal Long Short-Term Memory. O modelo python, assim como a descrição Verilog, e síntese RTL, encontram-se terminadas. Falta apenas fazer o benchmarking e a integração de um sistema de aprendizagem.

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