Results 61 to 70 of about 3,768 (183)

Cryptocurrency Price Prediction Model Using GRU, LSTM and Bi-LSTM Machine Learning Algorithms

Proceedings of the International Conference on Applied Innovations in IT
The rapid rise of cryptocurrencies has indeed created both investment opportunities and forecasting challenges. Accurate predictions of cryptocurrency prices are crucial for traders and financial planners to make informed decisions.
Laila Suwaid Said
doaj   +1 more source

A New Hybrid Deep Learning Method with Neutrosophic Sets for Social Media Sentiment Analysis of the COVID-19 Vaccine [PDF]

Neutrosophic Sets and Systems
The COVID-19 pandemic has launched historic public debates on vaccines, and more significantly, on social media posts regarding vaccines. In this study, we investigate how the public thinks about COVID19 vaccinations using state-of-the-art machine ...
Tasneem Abdelrahman, Mohamed El-Rashidy, Mohamed Marie   +2 more
doaj   +1 more source

Spectral Representation of Neurochemicals With Phase, Frequency Offset, and Lineshape Invariance: Application to JPRESS for In Vivo Concentration and T2 Mapping by Deep Learning

Magnetic Resonance in Medicine, Volume 95, Issue 6, Page 3052-3064, June 2026.
ABSTRACT Purpose Using artificial intelligence neural networks to generate a representation that maps the input directly to neurochemical concentrations and metabolite‐level average transverse relaxation times (T2). Methods The proposed model used time‐domain JPRESS data as input and was trained to be invariant to phase shifts, frequency offsets, and ...
Yan Zhang, Jun Shen
wiley   +1 more source

Physics‐Informed Recurrent Neural Networks for Predicting Elasto‐Plastic Behavior in Hierarchical Finite Element Modeling

Proceedings in Applied Mathematics and Mechanics, Volume 26, Issue 2, June 2026.
ABSTRACT Nanoporous gold, with its hierarchical structure comprising interconnected networks on multiple length scales, poses significant computational challenges for traditional modeling methods. To solve this challenge, this study introduces a physics‐informed recurrent neural network (RNN) to model the homogenized material response of a diamond beam‐
Lena Dyckhoff, Norbert Huber
wiley   +1 more source

CNN-GRU for Drowsiness Detection from Electrocardiogram Signal

ComTech
Drowsiness is a problem that needs to be addressed to improve road safety. To minimize this safety issue, driving-monitoring systems have been implemented in current car models, and electrocardiography (ECG) is one of the most commonly used driving ...
Setiawan Hendratno, Nico Surantha
doaj   +1 more source

An electroencephalography sleep staging model based on bidirectional Mamba combining synergistic attention with interactive convolution

Sleep Research, Volume 3, Issue 2, Page 135-149, June 2026.
Abstract Background This study presents SIMSleepSM, a novel single‐channel electroencephalography (EEG) sleep staging model. It addresses two primary challenges: insufficient modeling of long‐range temporal dependencies combined with limited multi‐scale feature extraction, and poor accuracy in identifying the N1 stage.
Ya‐mei Xu, Ding‐Yuan An
wiley   +1 more source

Forecasting Shifts in Europe's Renewable and Fossil Fuel Markets Using Deep Learning Methods

Energy Science & Engineering
Accurate forecasts of renewable and nonrenewable energy output are essential for meeting global energy needs and resolving environmental issues. Energy sources like the sun and wind are variable, making forecasting difficult.
Yonghong Liu, Muhammad S. Saleem, Javed Rashid, Sajjad Ahmad, Muhammad Faheem   +4 more
doaj   +1 more source

Research on network traffic prediction based on Bi-GRU model

Dianzi Jishu Yingyong, 2022
At present, there are some problems such as lag and low prediction accuracy when using gated recurrent units(GRU) neural network to predict traffic. This paper proposes an improved GRU model for traffic prediction. Firstly, based on GRU neural network, a network model integrating Bi-GRU neural network and artificial neural network is proposed, which ...
Xu Haibing, Guo Jiuming
openaire   +1 more source

Three Hour Ahead PV Power Forecasting with Bidirectional Recurrent Networks: Insights into Monthly Variability

Revue des Énergies Renouvelables
Despite extensive research into PV power forecast models, their monthly performance is rarely thoroughly examined, creating gaps in our understanding of their accuracy and applicability across different times of the year. This paper focuses on evaluating
Ferial El Robrini, Badia Amrouche
doaj   +1 more source

A Bi-GRU-DSA-based social network rumor detection approach

Open Computer Science
AbstractIn the rumor detection based on crowd intelligence, the crowd behavior is constructed as a graph model or probability mode. The detection of rumors is achieved through the collaborative utilization of data and knowledge. Aiming at the problems of insufficient feature extraction ability and data redundancy of current rumor detection methods ...
Huang, Xiang, Liu, Yan
openaire   +2 more sources