"long short-term memory network" · 14 results

Biological applications, visualizations, and extensions of the long short-term memory network

van der Westhuizen, JosUniversity of Cambridge2018

… techniques for analysing sequences is the long short-term memory (LSTM) network. Owing to significant barriers to adoption in biology, focussed efforts are required to realize the use of LSTMs in practice. Thus, the aim of this work is to improve the state of LSTMs for biology, and we focus …

cambridge Repository record for Biological applications, visualizations, and extensions of the long short-term memory network (opens in a new tab)

Percussion Based Detection Method for Localization of Pipe Inspection Gauge using Advanced Machine Learning Classification and Clustering Techniques.

Cohen, Carter PatrickUniversity of Houston2025

… (Support Vector Machine, Convolutional Neural Network + Long-Short Term Memory Network, Feedforward Neural Network, gradient boosting, Gaussian Mixed Model and K-means clustering) to find cost effective localization techniques. Results showed that MFCC feature extraction and Convolutional …

houston Repository record for Percussion Based Detection Method for Localization of Pipe Inspection Gauge using Advanced Machine Learning Classification and Clustering Techniques. (opens in a new tab)

Towards federated learning over large-scale streaming data

Pereira, Aaron, author, Pallickara, Sangmi, advisor, Pallickara, Shrideep, committee member, Zahran, Sammy, committee memberColorado State University. Libraries2020

… (DSPEs) have seen significant deployment growth along with an increase in streaming data sources such as sensor networks. These DSPEs enable processing large amounts of streaming data in a cluster of commodity machines to extract knowledge and insights in real-time. Due to fluctuating data arrival …

colostate Repository record for Towards federated learning over large-scale streaming data (opens in a new tab)

Fall Prediction Model for a Reconfigurable Mobile Support Robot

Kamienski, EmilyMassachusetts Institute of Technology2021

… support. The fall prediction model is based on a Long Short Term Memory network. A predicted fall will inform a reconfigurable robot to expand its base of support to avoid possible tipping induced by the fall. A wearable support interface consisting of an instrumented harness and auto retracting …

mit Repository record for Fall Prediction Model for a Reconfigurable Mobile Support Robot (opens in a new tab)

Forecasting the lift of a randomly maneuvering airfoil under dynamic stall conditions, Re ∼ 10⁵

Kim, DonghyunMassachusetts Institute of Technology2025

… large eddy simulations, we demonstrate that a long short-term memory network, fed with raw surface pressures, delivers accurate predictions. Also, a new method introduced here, IdDM, conclusively links the characteristic frequency range of pressure fluctuations that emerges during the dynamic …

mit Repository record for Forecasting the lift of a randomly maneuvering airfoil under dynamic stall conditions, Re ∼ 10⁵ (opens in a new tab)

Predicting blood pressure response to fluid bolus therapy in the ICU using attention-based stacked neural networks for clinical interpretability

Girkar, Uma M.Massachusetts Institute of Technology2019

… room physicians are constantly challenged in determining whether administering FBT will result in a corresponding increase in blood pressure. In this thesis, we utilized regression models and attention-based recurrent neural network (RNN) algorithms to predict the response of hypotensive …

mit Repository record for Predicting blood pressure response to fluid bolus therapy in the ICU using attention-based stacked neural networks for clinical interpretability (opens in a new tab)

Context-Aware Credit Card Fraud Detection

Jurgovsky, JohannesUniversität Passau

… to identify fraudulent transactions which, in terms of their attribute values, are globally distinguishable from genuine transactions. We provide an empirical study of the influence of class imbalance and forecasting horizons on the classification performance of a random forest classifier. We …

passau-thes Repository record for Context-Aware Credit Card Fraud Detection (opens in a new tab)

Towards Multiclass Damage Detection and Localization using Limited Vibration Measurements

Sony, SandeepThe University of Western Ontario2021

… one-dimensional convolutional neural network is explored to classify sequential time-series of vibration measurements with only one hidden layer. The robustness of the proposed method is further evaluated by a suite of parametric and sensitivity analysis. Improvement of this method is …

uwo Repository record for Towards Multiclass Damage Detection and Localization using Limited Vibration Measurements (opens in a new tab)

Vision systems in agriculture – Lagrangian particle tracking and field robotics

Zhang, ZhongzhongUniversity of Illinois at Urbana-Champaign2018

… of circular and semi-circular jets in the intermediate and far fields. Grid-interpolated velocity are used to validate the measurements, and confirmed the negligible effect of the pipe shape on the mean flow in the intermediate field. Several volumetric regions are defined to get Lagrangian …

uiuc Repository record for Vision systems in agriculture – Lagrangian particle tracking and field robotics (opens in a new tab)

Evaluation of Snow and Streamflow in the National Water Model with Analysis using Machine Learning

Sthapit, Engela2022

… approaches such as artificial recurrent neural networks that are able to learn the non-linear input-output hydrological relationships in a catchment without explicit physical representation of the processes. This will help in understanding the vices and virtues of each method. The second …

cuny Repository record for Evaluation of Snow and Streamflow in the National Water Model with Analysis using Machine Learning (opens in a new tab)

Multi-label/multi-class Deep Learning Classification Of Spatiotemporal Data

Sommer, Natalie

… senses. This is not a factor with machines. As long as a system is given a signal and instructed how to analyze this signal and extract useful information, it will be able to complete this task repeatedly with enough processing power.Automated and simultaneous detection of activity in …

syracuse-diss Repository record for Multi-label/multi-class Deep Learning Classification Of Spatiotemporal Data (opens in a new tab)

Multi-Label/Multi-Class Deep Learning Classification of Spatiotemporal Data

Sommer, Natalie

… senses. This is not a factor with machines. As long as a system is given a signal and instructed how to analyze this signal and extract useful information, it will be able to complete this task repeatedly with enough processing power. Automated and simultaneous detection of activity in …

syracuse-diss Repository record for Multi-Label/Multi-Class Deep Learning Classification of Spatiotemporal Data (opens in a new tab)

Inferring travel activity pattern from smartphone sensing data using deep learning

Ghorpade, Ajinkya (Ajinkya Ranjeet)Massachusetts Institute of Technology2018

… from raw data. The convolutional neural networks have been particularly effective in learning feature representations on many datasets. These models have achieved significant improvement on many complex problems over other machine learning approaches. For the sequential classification …

mit Repository record for Inferring travel activity pattern from smartphone sensing data using deep learning (opens in a new tab)

Development of Wearable Technologies to Titrate Rehabilitation Interventions for Individuals with Knee Osteoarthritis

PUGLIESE, BENITO LORENZOPolitecnico di Torino2025

L'abstract è presente nell'allegato / the abstract is in the attachment

poli-torino Repository record for Development of Wearable Technologies to Titrate Rehabilitation Interventions for Individuals with Knee Osteoarthritis (opens in a new tab)

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