Forecasting stock market indices using the recurrent neural network based hybrid models: CNN-LSTM, GRU-CNN, and ensemble models
H Song, H Choi - Applied Sciences, 2023 - mdpi.com
Various deep learning techniques have recently been developed in many fields due to the
rapid advancement of technology and computing power. These techniques have been …
rapid advancement of technology and computing power. These techniques have been …
Replicating a trading strategy by means of LSTM for financial industry applications
This paper investigates the possibility of learning a trading rule looking at the relationship
between market indicators and decisions undertaken regarding entering or quitting a …
between market indicators and decisions undertaken regarding entering or quitting a …
Deep learning models for the prediction of rainfall
S Aswin, P Geetha… - … on Communication and …, 2018 - ieeexplore.ieee.org
Rainfall is one of the major source of freshwater for all the organism around the world.
Rainfall prediction model provides the information regarding various climatological variables …
Rainfall prediction model provides the information regarding various climatological variables …
Surgical skill levels: Classification and analysis using deep neural network model and motion signals
Abstract Background and Objectives Currently, the assessment of surgical skills relies
primarily on the observations of expert surgeons. This may be time-consuming, non …
primarily on the observations of expert surgeons. This may be time-consuming, non …
CNN approaches for time series classification
L Sadouk - Time series analysis-data, methods, and applications, 2019 - books.google.com
Time series classification is an important field in time series data-mining which have covered
broad applications so far. Although it has attracted great interests during last decades, it …
broad applications so far. Although it has attracted great interests during last decades, it …
[HTML][HTML] Field test of neural-network based automatic bucket-filling algorithm for wheel-loaders
Automation of earth-moving industries (construction, mining and quarry) require automatic
bucket-filling algorithms for efficient operation of front-end loaders. Autonomous bucket …
bucket-filling algorithms for efficient operation of front-end loaders. Autonomous bucket …
Neural networks for metamodelling the hygrothermal behaviour of building components
When simulating the hygrothermal behaviour of a building component, there are many
inherently uncertain parameters. A probabilistic evaluation takes these uncertainties into …
inherently uncertain parameters. A probabilistic evaluation takes these uncertainties into …
Task runtime prediction in scientific workflows using an online incremental learning approach
Many algorithms in workflow scheduling and resource provisioning rely on the performance
estimation of tasks to produce a scheduling plan. A profiler that is capable of modeling the …
estimation of tasks to produce a scheduling plan. A profiler that is capable of modeling the …
Application of machine learning and time-series analysis for air pollution prediction
V Stojov, N Koteli, P Lameski, E Zdravevski - 2018 - repository.ukim.mk
Medical research studies show that low air quality can have a direct effect on the increased
number of diseases, especially respiratory defects, but also on the increased mortality rate in …
number of diseases, especially respiratory defects, but also on the increased mortality rate in …
Peak alignment of gas chromatography–mass spectrometry data with deep learning
M Li, XR Wang - Journal of Chromatography A, 2019 - Elsevier
We present ChromAlignNet, a deep learning model for alignment of peaks in Gas
Chromatography-Mass Spectrometry (GC–MS) data. In GC–MS data, a compound's …
Chromatography-Mass Spectrometry (GC–MS) data. In GC–MS data, a compound's …