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Dissertations / Theses on the topic 'LSTM ALGORITHM'

Author: Grafiati

Published: 11 September 2023

Last updated: 26 July 2025

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1

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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Shaif, Ayad. "Predictive Maintenance in Smart Agriculture Using Machine Learning : A Novel Algorithm for Drift Fault Detection in Hydroponic Sensors." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-42270.

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The success of Internet of Things solutions allowed the establishment of new applications such as smart hydroponic agriculture. One typical problem in such an application is the rapid degradation of the deployed sensors. Traditionally, this problem is resolved by frequent manual maintenance, which is considered to be ineffective and may harm the crops in the long run. The main purpose of this thesis was to propose a machine learning approach for automating the detection of sensor fault drifts. In addition, the solution’s operability was investigated in a cloud computing environment in terms of

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3

Hambarek, Djamel Eddine. "Développement d'une méthodologie d'essais dynamiques appliquée à la mise au point moteur." Electronic Thesis or Diss., Ecole centrale de Nantes, 2023. http://www.theses.fr/2023ECDN0035.

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Les travaux de cette thèse de doctorat s’inscrivent dans le contexte d’évolution desnormes de dépollution des moteurs thermiquescouplée aux exigences de baisse de la consommation des véhicules. La méthodologie développée tente de répondre avec un processus industriel efficace aux exigences d’émissions en roulage réel, dites RDE (Real Driving Emissions). La méthode proposée est basée sur la technique des plans d’expériences dynamiques utilisant les suites à faible discrépance : les résultats d’essais sont utilisés afin d’entraîner un modèle de réseau de neurones type LSTM capable de prédire l’h

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Malina, Ondřej. "Detekce začátku a konce komplexu QRS s využitím hlubokého učení." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442595.

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This thesis deals with the issue of automatic measurement of the duration of QRS complexes in ECG signals. Special emphasis is then placed on the possibility of automatic detection of QRS complexes while exciting cardiac tissue with a pacemaker. The content of this work is divided into four logical units, where the first part deals with the heart as an organ. It describes the origin and spread of excitement in the heart, its possible pathologies and their manifestations in ECG recording, it also deals with pacing and measuring ECG recording during simultaneous pacing. The second part of the th

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Olsson, Charlie, and David Hurtig. "An approach to evaluate machine learning algorithms for appliance classification." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20217.

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A cheap and powerful solution to lower the electricity usage and making the residents more energy aware in a home is to simply make the residents aware of what appliances that are consuming electricity. Meaning the residents can then take decisions to turn them off in order to save energy. Non-intrusive load monitoring (NILM) is a cost-effective solution to identify different appliances based on their unique load signatures by only measuring the energy consumption at a single sensing point. In this thesis, a low-cost hardware platform is developed with the help of an Arduino to collect consump

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Freberg, Daniel. "Evaluating Statistical MachineLearning and Deep Learning Algorithms for Anomaly Detection in Chat Messages." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235957.

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Automatically detecting anomalies in text is of great interest for surveillance entities as vast amounts of data can be analysed to find suspicious activity. In this thesis, three distinct machine learning algorithms are evaluated as a chat message classifier is being implemented for the purpose of market surveillance. Naive Bayes and Support Vector Machine belong to the statistical class of machine learning algorithms being evaluated in this thesis and both require feature selection, a side objective of the thesis is thus to find a suitable feature selection technique to ensure mentioned algo

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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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Blanco, Martínez Alejandro. "Study and design of classification algorithms for diagnosis and prognosis of failures in wind turbines from SCADA data." Doctoral thesis, Universitat de Vic - Universitat Central de Catalunya, 2018. http://hdl.handle.net/10803/586097.

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Actualmente las operaciones de mantenimiento preventivo de los parques eólicos se soportan sobre técnicas de Machine Learning para reducir los costes de las paradas no planificadas. Por eso se necesita una predicción de fallos con cierta anticipación que funcione sobre los datos de SCADA. Estos datos necesitan ser procesados en distintas etapas descritas en esta tesis, con resultados publicados en cada una de ellas. En una primera fase se limpian los valores extremos (Outliers), indicando cómo deben ser tratados para no eliminar la información sobre los fallos. En una segunda, las distintas va

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Arvidsson, Philip, and Tobias Ånhed. "Sequence-to-sequence learning of financial time series in algorithmic trading." Thesis, Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-12602.

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Predicting the behavior of financial markets is largely an unsolved problem. The problem hasbeen approached with many different methods ranging from binary logic, statisticalcalculations and genetic algorithms. In this thesis, the problem is approached with a machinelearning method, namely the Long Short-Term Memory (LSTM) variant of Recurrent NeuralNetworks (RNNs). Recurrent neural networks are artificial neural networks (ANNs)—amachine learning algorithm mimicking the neural processing of the mammalian nervoussystem—specifically designed for time series sequences. The thesis investigates the

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10

Nitz, Pettersson Hannes, and Samuel Vikström. "VISION-BASED ROBOT CONTROLLER FOR HUMAN-ROBOT INTERACTION USING PREDICTIVE ALGORITHMS." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-54609.

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The demand for robots to work in environments together with humans is growing. This calls for new requirements on robots systems, such as the need to be perceived as responsive and accurate in human interactions. This thesis explores the possibility of using AI methods to predict the movement of a human and evaluating if that information can assist a robot with human interactions. The AI methods that were used is a Long Short Term Memory(LSTM) network and an artificial neural network(ANN). Both networks were trained on data from a motion capture dataset and on four different prediction times:

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11

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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12

Mohammadisohrabi, Ali. "Design and implementation of a Recurrent Neural Network for Remaining Useful Life prediction." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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A key idea underlying many Predictive Maintenance solutions is Remaining Useful Life (RUL) of machine parts, and it simply involves a prediction on the time remaining before a machine part is likely to require repair or replacement. Nowadays, with respect to fact that the systems are getting more complex, the innovative Machine Learning and Deep Learning algorithms can be deployed to study the more sophisticated correlations in complex systems. The exponential increase in both data accumulation and processing power make the Deep Learning algorithms more desirable that before. In this paper a L

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Lousseief, Elias. "MahlerNet : Unbounded Orchestral Music with Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264993.

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Modelling music with mathematical and statistical methods in general, and with neural networks in particular, has a long history and has been well explored in the last decades. Exactly when the first attempt at strictly systematic music took place is hard to say; some would say in the days of Mozart, others would say even earlier, but it is safe to say that the field of algorithmic composition has a long history. Even though composers have always had structure and rules as part of the writing process, implicitly or explicitly, following rules at a stricter level was well investigated in the mi

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Liu, Szu-Yu, and 劉思妤. "Using LSTM algorithm to improve network management in SDN." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/qqh5m3.

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碩士<br>國立交通大學<br>資訊管理研究所<br>107<br>There are a lot of network monitoring technologies existed so far. Network administrators must have accurate monitoring to operate efficiently. In this paper, we propose a dynamic adjustment threshold method – Long short term memory network. In a resource-constrained network, SDN traffic engineering (SDN TE) can improve network utilization and service quality. we use a minimum bandwidth utilization routing mechanism to avoid congestion. The controller periodically monitors the traffic utilization of each link in the network. The overused links are identified a

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AGGARWAL, TUSHAR. "IMAGE DESCRIPTIVE SUMMARIZATION BY DEEP LEARNING AND ADVANCED LSTM MODEL ARCHITECTURE." Thesis, 2019. http://dspace.dtu.ac.in:8080/jspui/handle/repository/17084.

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Auto Image Descriptor is becoming a trending point of interest in current era of research among researchers. Being a great community, which is proposing a continuous and enhanced Iist of intuitive algorithms which is solving to its problems. However, still there are lot of improvement to this field. Therefore, it’s becoming a field of attraction for many researchers and industries and reliable to this digital world. Of these various image descriptive algorithms, some outperform others in terms of basic descriptors requirements like robustness, invisibility, processing cost, etc.

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Tsai, Jia-Ling, and 蔡佳陵. "A LSTM-Based Algorithm for the Estimation of Plantar Pressure Dynamics Using Inertial Sensors." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/f9dsxf.

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碩士<br>國立清華大學<br>資訊工程學系所<br>106<br>Gait analysis has become prevalent in many fields such as sports biomechanics, medical diagnostics, and injury prevention. For the plantar pressure dynamic estimation in gait analysis, the vertical component of ground reaction force (vGRF) and center of pressure (CoP) trajectories are vital parameters about human locomotion and balance. Three main approaches are used in measuring the gait parameters, namely computer vision, floor sensors, and wearables. Though the first two techniques are accurate, they are expensive and limited in the sensing area. The weara

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Oguntala, George A., Yim Fun Hu, Ali A. S. Alabdullah, Raed A. Abd-Alhameed, Muhammad Ali, and D. K. Luong. "Passive RFID Module with LSTM Recurrent Neural Network Activity Classification Algorithm for Ambient Assisted Living." 2021. http://hdl.handle.net/10454/18418.

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Yes<br>IEEE Human activity recognition from sensor data is a critical research topic to achieve remote health monitoring and ambient assisted living (AAL). In AAL, sensors are integrated into conventional objects aimed to support targets capabilities through digital environments that are sensitive, responsive and adaptive to human activities. Emerging technological paradigms to support AAL within the home or community setting offers people the prospect of a more individually focused care and improved quality of living. In the present work, an ambient human activity classification framework tha

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Tseng, Xian-Hong, and 曾憲泓. "Using LSTM algorithm to establish an Evaluation System for Child with Autistic Disorder during Autism Diagnostic Observation Schedule Interview." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/nr83hf.

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碩士<br>國立清華大學<br>電機工程學系所<br>106<br>Autism spectrum disorder (ASD) is a highly-prevalent neuraldevelopmental disorder. In medical research often characterized by social communicative deficits and restricted repetitive interest. The heterogeneous nature of ASD in its behavior manifestations encompasses broad syndromes such as, Classical Autism (AD), Asperger syndrome (AS), and High functioning Autism (HFA). To evaluate the degree and there syndromes in ASD, doctor will diagnose through clinical observation and auxiliary diagnostic tools, one of them is Autism Diagnostic Observation Schedule (ADOS

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19

CHU, YI-JUI, and 朱奕叡. "A Study of PCA dimensionality reduction technique and news articles for the Prediction of Stock Price - Using LSTM algorithm as modeling technology." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/nz4at6.

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碩士<br>輔仁大學<br>資訊管理學系碩士在職專班<br>107<br>The main purpose of this study is to examine the influence of different data structure attributes, the number of eigenvalues, and the length of the training period on the accuracy of individual stock price trend prediction. In this study, the deep learning technique, of the LSTM algorithm, is used as the predictive model, and the PCA algorithm is adopted to screen the technical indicators for dimensionality reduction, moreover, Word2Vect text search technology is used to process the non-structural data of stock news, which is adopted as the predicted featur

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Chen, Brian, and 陳柏穎. "AUC oriented Bidirectional LSTM-CRF Models to Identify Algorithms Described in an Abstract." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/p3grat.

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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>105<br>In this thesis, we attempt to identify algorithms mentioned in the paper abstract. We further want to discriminate the algorithm proposed in this paper from algorithms only mentioned or compared, since we are more interested in the former. We model this task as a sequential labeled task and propose to use a state-of-the-art deep learning model LSTM-CRF as our solution. However, the data or labels are generally imbalanced since not all the sentence in the abstract is describing its algorithm. That is, the ratio between different labels is skewed. As a result,

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Kurach, Karol. "Deep Neural Architectures for Algorithms and Sequential Data." Doctoral thesis, 2016. https://depotuw.ceon.pl/handle/item/1860.

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The first part of the dissertation describes two deep neural architectures with external memories: Neural Random-Access Machine (NRAM) and Hierarchical Attentive Memory (HAM). The NRAM architecture is inspired by Neural Turing Machines, but the crucial difference is that it can manipulate and dereference pointers to its random-access memory. This allows it to learn concepts that require pointers chasing, such as “linked list” or “binary tree”. The HAM architecture is based on a binary tree with leaves corresponding to memory cells. This enables the memory access in Θ(log n), which is a signifi

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JHA, ROMAN KUMAR. "FORECASTING OF SOLAR IRRADIATION USING DEEP LEARNING ALGORITHMS." Thesis, 2022. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19274.

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This dissertation presents one of many application of Machine Learning (ML) and Deep Learning in the field of forecasting. ML algorithms used Multivariate Linear Regression(MLR), Support Vector Regression (SVR), Feed Forward Neural Network(FFNN) and Layered Recurrent Neural Network(RNN) to make solar irradiation forecasting. The forecasting has been done for the period of ten months in 2021 based on the historical data available for the year 2019 and 2020.MATLAB has been used to develop the ML model. The model developed using the above mentioned algorithms have been compared on t

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Peterson, Cole. "Generating rhyming poetry using LSTM recurrent neural networks." Thesis, 2019. http://hdl.handle.net/1828/10801.

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Current approaches to generating rhyming English poetry with a neural network involve constraining output to enforce the condition of rhyme. We investigate whether this approach is necessary, or if recurrent neural networks can learn rhyme patterns on their own. We compile a new dataset of amateur poetry which allows rhyme to be learned without external constraints because of the dataset’s size and high frequency of rhymes. We then evaluate models trained on the new dataset using a novel framework that automatically measures the system’s knowledge of poetic form and generalizability. We

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Lopes, Tiago Miguel Dias da Gama Lobo de Sousa. "Como construir um modelo híbrido de previsão para o S&P500 usando um modelo VECM com um algoritmo LSTM?" Master's thesis, 2021. http://hdl.handle.net/10071/23512.

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A previsão de séries financeiras faz parte do processo de decisão das políticas monetárias por parte dos bancos centrais. Mendes, Ferreira e Mendes (2020) propõem um modelo híbrido que junta um VECM (modelo vetorial corretor de erro) com um algoritmo de aprendizagem profunda o LSTM (memória de longo curto-prazo) para uma previsão multivariada do índice acionista norte-americano S&P500, utilizando-se as séries do Nasdaq, Dow Jones e as taxas de juro dos bilhetes do tesouro americano a 3 meses no mercado secundário, com dados semanais, entre 19/04/2019 e 17/04/2020. Nesta dissertação, replicou-s

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Rohovets, Taras. "Machine learning algorithms to predict stocks movements with Python language and dedicated libraries." Master's thesis, 2019. http://hdl.handle.net/10400.26/30163.

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This research work focuses on machine learning algorithms in order to make predictions in financial markets. The foremost objective is to test whether the two machine learning algorithms: SVM and LSTM are capable of predicting the price movement in different time-frames and then develop a comparison analysis. In this research work, it is applied supervised machine learning with different input features. The practical and software component of this thesis applies Python programming language to test the hypothesis and act as proof of concept. The financial data quotes were obtained through onlin

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Rita, Nicole Oliveira. "Machine learning techniques for predicting the stock market using daily market variables." Master's thesis, 2020. http://hdl.handle.net/10362/94992.

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Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence<br>Predicting the stock market was never seen as an easy task. The complexity of the financial systems makes it extremely difficult for anything or anyone to predict what the future of prices holds, let it be a day, a week, a month or even a year. Many variables influence the market’s volatility and some of these may even be the gut feeling of an investor on a specific day. Several machine learning techniques were already

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