Tootfinder

This https://arxiv.org/abs/2304.05805 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

GDP nowcasting with artificial neural networks: How much does long-term memory matter?
We apply artificial neural networks (ANNs) to nowcast quarterly GDP growth for the U.S. economy. Using the monthly FRED-MD database, we compare the nowcasting performance of five different ANN architectures: the multilayer perceptron (MLP), the one-dimensional convolutional neural network (1D CNN), the Elman recurrent neural network (RNN), the long short-term memory network (LSTM), and the gated recurrent unit (GRU). The empirical analysis presents results from two distinctively different evalu…

Enhancing Financial Data Visualization for Investment Decision-Making
Nisarg Patel, Harmit Shah, Kishan Mewada
https://arxiv.org/abs/2403.18822 https://

Enhancing Financial Data Visualization for Investment Decision-Making
Navigating the intricate landscape of financial markets requires adept forecasting of stock price movements. This paper delves into the potential of Long Short-Term Memory (LSTM) networks for predicting stock dynamics, with a focus on discerning nuanced rise and fall patterns. Leveraging a dataset from the New York Stock Exchange (NYSE), the study incorporates multiple features to enhance LSTM's capacity in capturing complex patterns. Visualization of key attributes, such as opening, closing, l…

Predicting Energy Budgets in Droplet Dynamics: A Recurrent Neural Network Approach
Diego A. de Aguiar, Hugo L. Fran\c{c}a, Cassio M. Oishi
https://arxiv.org/abs/2403.16144

Predicting Energy Budgets in Droplet Dynamics: A Recurrent Neural Network Approach
Neural networks in fluid mechanics offer an efficient approach for exploring complex flows, including multiphase and free surface flows. The recurrent neural network, particularly the Long Short-Term Memory (LSTM) model, proves attractive for learning mappings from transient inputs to dynamic outputs. This study applies LSTM to predict transient and static outputs for fluid flows under surface tension effects. Specifically, we explore two distinct droplet dynamic scenarios: droplets with divers…

LSTM-Based Text Generation: A Study on Historical Datasets
Mustafa Abbas Hussein Hussein, Serkan Sava\c{s}
https://arxiv.org/abs/2403.07087 https://…

LSTM-Based Text Generation: A Study on Historical Datasets
This paper presents an exploration of Long Short-Term Memory (LSTM) networks in the realm of text generation, focusing on the utilization of historical datasets for Shakespeare and Nietzsche. LSTMs, known for their effectiveness in handling sequential data, are applied here to model complex language patterns and structures inherent in historical texts. The study demonstrates that LSTM-based models, when trained on historical datasets, can not only generate text that is linguistically rich and c…

Machine Learning based Prediction of Ditching Loads
Henning Schwarz, Micha \"Uberr\"uck, Jens-Peter M. Zemke, Thomas Rung
https://arxiv.org/abs/2402.10724

Machine Learning based Prediction of Ditching Loads
We present approaches to predict dynamic ditching loads on aircraft fuselages using machine learning. The employed learning procedure is structured into two parts, the reconstruction of the spatial loads using a convolutional autoencoder (CAE) and the transient evolution of these loads in a subsequent part. Different CAE strategies are assessed and combined with either long short-term memory (LSTM) networks or Koopman-operator based methods to predict the transient behaviour. The training data …

Generative LSTM Models and Asset Hierarchy Creation in Industrial Facilities
Morgen Pronk
https://arxiv.org/abs/2403.06103 https://ar…

Generative LSTM Models and Asset Hierarchy Creation in Industrial Facilities
In the evolving field of maintenance and reliability engineering, the organization of equipment into hierarchical structures presents both a challenge and a necessity, directly impacting the operational integrity of industrial facilities. This paper introduces an innovative approach employing machine learning, specifically Long Short-Term Memory (LSTM) models, to automate and enhance the creation and management of these hierarchies. By adapting techniques commonly used in natural language proce…

Electrical Load Forecasting Model Using Hybrid LSTM Neural Networks with Online Correction
Nan Lu, Quan Ouyang, Yang Li, Changfu Zou
https://arxiv.org/abs/2403.03898

Electrical Load Forecasting Model Using Hybrid LSTM Neural Networks with Online Correction
Accurate electrical load forecasting is of great importance for the efficient operation and control of modern power systems. In this work, a hybrid long short-term memory (LSTM)-based model with online correction is developed for day-ahead electrical load forecasting. Firstly, four types of features are extracted from the original electrical load dataset, including the historical time series, time index features, historical statistical features, and similarity features. Then, a hybrid LSTM-base…

Beyond Gait: Learning Knee Angle for Seamless Prosthesis Control in Multiple Scenarios
Pengwei Wang, Yilong Chen, Wan Su, Jie Wang, Teng Ma, Haoyong Yu
https://arxiv.org/abs/2404.06772

Beyond Gait: Learning Knee Angle for Seamless Prosthesis Control in Multiple Scenarios
Deep learning models have become a powerful tool in knee angle estimation for lower limb prostheses, owing to their adaptability across various gait phases and locomotion modes. Current methods utilize Multi-Layer Perceptrons (MLP), Long-Short Term Memory Networks (LSTM), and Convolutional Neural Networks (CNN), predominantly analyzing motion information from the thigh. Contrary to these approaches, our study introduces a holistic perspective by integrating whole-body movements as inputs. We pr…

LSTM-Based Text Generation: A Study on Historical Datasets
Mustafa Abbas Hussein Hussein, Serkan Sava\c{s}
https://arxiv.org/abs/2403.07087 https://…

LSTM-Based Text Generation: A Study on Historical Datasets
This paper presents an exploration of Long Short-Term Memory (LSTM) networks in the realm of text generation, focusing on the utilization of historical datasets for Shakespeare and Nietzsche. LSTMs, known for their effectiveness in handling sequential data, are applied here to model complex language patterns and structures inherent in historical texts. The study demonstrates that LSTM-based models, when trained on historical datasets, can not only generate text that is linguistically rich and c…

GreenBytes: Intelligent Energy Estimation for Edge-Cloud
Kasra Kassai, Tasos Dagiuklas, Satwat Bashir, Muddesar Iqbal
https://arxiv.org/abs/2403.04665 http…

GreenBytes: Intelligent Energy Estimation for Edge-Cloud
This study investigates the application of advanced machine learning models, specifically Long Short-Term Memory (LSTM) networks and Gradient Booster models, for accurate energy consumption estimation within a Kubernetes cluster environment. It aims to enhance sustainable computing practices by providing precise predictions of energy usage across various computing nodes. Through meticulous analysis of model performance on both master and worker nodes, the research reveals the strengths and pote…

Generative LSTM Models and Asset Hierarchy Creation in Industrial Facilities
Morgen Pronk
https://arxiv.org/abs/2403.06103 https://ar…

Generative LSTM Models and Asset Hierarchy Creation in Industrial Facilities
In the evolving field of maintenance and reliability engineering, the organization of equipment into hierarchical structures presents both a challenge and a necessity, directly impacting the operational integrity of industrial facilities. This paper introduces an innovative approach employing machine learning, specifically Long Short-Term Memory (LSTM) models, to automate and enhance the creation and management of these hierarchies. By adapting techniques commonly used in natural language proce…

Using Neural Networks to Model Hysteretic Kinematics in Tendon-Actuated Continuum Robots
Yuan Wang, Max McCandless, Abdulhamit Donder, Giovanni Pittiglio, Behnam Moradkhani, Yash Chitalia, Pierre E. Dupont
https://arxiv.org/abs/2404.07168

Using Neural Networks to Model Hysteretic Kinematics in Tendon-Actuated Continuum Robots
The ability to accurately model mechanical hysteretic behavior in tendon-actuated continuum robots using deep learning approaches is a growing area of interest. In this paper, we investigate the hysteretic response of two types of tendon-actuated continuum robots and, ultimately, compare three types of neural network modeling approaches with both forward and inverse kinematic mappings: feedforward neural network (FNN), FNN with a history input buffer, and long short-term memory (LSTM) network. …

This https://arxiv.org/abs/2403.03434 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

An AI-enabled Agent-Based Model and Its Application in Measles Outbreak Simulation for New Zealand
Agent Based Models (ABMs) have emerged as a powerful tool for investigating complex social interactions, particularly in the context of public health and infectious disease investigation. In an effort to enhance the conventional ABM, enabling automated model calibration and reducing the computational resources needed for scaling up the model, we have developed a tensorized and differentiable agent-based model by coupling Graph Neural Network (GNN) and Long Short-Term Memory (LSTM) network. The …

GreenBytes: Intelligent Energy Estimation for Edge-Cloud
Kasra Kassai, Tasos Dagiuklas, Satwat Bashir, Muddesar Iqbal
https://arxiv.org/abs/2403.04665 http…

GreenBytes: Intelligent Energy Estimation for Edge-Cloud
This study investigates the application of advanced machine learning models, specifically Long Short-Term Memory (LSTM) networks and Gradient Booster models, for accurate energy consumption estimation within a Kubernetes cluster environment. It aims to enhance sustainable computing practices by providing precise predictions of energy usage across various computing nodes. Through meticulous analysis of model performance on both master and worker nodes, the research reveals the strengths and pote…

This https://arxiv.org/abs/2310.17571 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Inside the black box: Neural network-based real-time prediction of US recessions
A standard feedforward neural network (FFN) and two specific types of recurrent neural networks, long short-term memory (LSTM) and gated recurrent unit (GRU), are used for modeling US recessions in the period from 1967 to 2021. The estimated models are then employed to conduct real-time predictions of the Great Recession and the Covid-19 recession in the US. Their predictive performances are compared to those of the traditional linear models, the standard logit model and the ridge logit model. …

This https://arxiv.org/abs/2203.11155 has been replaced.
link: https://scholar.google.com/scholar?q=a

Quantum Neural Network with Density Matrix for Question Answering and Classical Image Classification
Quantum density matrix represents all the information of the entire quantum system, and novel models of meaning employing density matrices naturally model linguistic phenomena such as hyponymy and linguistic ambiguity, among others in quantum question answering tasks. Naturally, we argue that applying the quantum density matrix into classical Question Answering (QA) tasks can show more effective performance. Specifically, we (i) design a new mechanism based on Long Short-Term Memory (LSTM) to a…

An AI-enabled Agent-Based Model and Its Application in Measles Outbreak Simulation for New Zealand
Sijin Zhang, Alvaro Orsi, Richard Dean, Lei Chen, Rachel Qiu, Jiawei Zhao
https://arxiv.org/abs/2403.03434

An AI-enabled Agent-Based Model and Its Application in Measles Outbreak Simulation for New Zealand
Agent Based Models (ABMs) have emerged as a powerful tool for investigating complex social interactions, particularly in the context of public health and infectious disease investigation. In an effort to enhance the conventional ABM, enabling automated model calibration and reducing the computational resources needed for scaling up the model, we have developed a tensorized and differentiable agent-based model by coupling Graph Neural Network (GNN) and Long Short-Term Memory (LSTM) network. The …