A hybrid deep learning model with 1DCNN|LSTM|Attention ...

A hybrid deep learning model with 1DCNN-LSTM-Attention ...

This paper considered the traffic flow data and weather conditions of the road section comprehensively, and proposed a short-term traffic flow prediction model

A Hybrid Deep Learning Model With Attention-Based Conv-LSTM ...

A Hybrid Deep Learning Model With Attention-Based Conv-LSTM Networks for Short-Term Traffic Flow Prediction. Abstract: Accurate short-time ...

A Hybrid Deep Learning Model with Attention-Based Conv-LSTM ...

In this paper, we propose a deep learning based model which uses hybrid and multiple-layer architectures to automatically extract inherent features of traffic ...

A Hybrid Deep Learning Model With Attention-Based Conv-LSTM ...

Firstly, built on the convolutional neural network (CNN) and the long short-term memory (LSTM) network, we develop an attention-based Conv-LSTM module to ...

A novel hybrid deep learning model with ARIMA Conv-LSTM ...

We propose a new hybrid deep learning model based on an attention mechanism that uses multi-layered hybrid architectures to extract spatial–temporal, nonlinear ...

A Deep Learning-Based Hybrid CNN-LSTM Model for Location ...

In this research, we developed a collaborative filtering-based hybrid CNN-LSTM model for recommending geographically relevant online services using deep neural ...

suprobe/AT-Conv-LSTM - GitHub

A Hybrid Deep Learning Model with Attention based ConvLSTM Networks for Short-Term Traffic Flow Prediction - suprobe/AT-Conv-LSTM.

A Hybrid Deep Learning Model With Attention-Based Conv-LSTM ...

Different from the existing deep learning models for traffic flow prediction, we propose a novel hybrid model integrating. CNN and Bi-LSTM ...

The Hybrid Deep Learning Model for Identification of Attention ...

The authors designed a hybrid deep learning model that combines long-short-term memory (LSTM) and CNN to simultaneously extract and learn the spatial features.

A Hybrid Deep Learning Approach with GCN and LSTM for Traffic ...

A hybrid deep learning approach, called graph and attention-based long short-term memory network (GLA) to efficiently capture the spatial-temporal features ...

RETRACTED ARTICLE: Hybrid CNN-LSTM model with efficient ...

The proposed hybrid model is compared with traditional NN and well-known machine learning-based CART and SVM & XGBoost models. The accuracy ...

An advanced hybrid deep learning model for accurate energy load ...

Even with their success, current DL models such as convolutional neural network (CNN), long short-term memory (LSTM), Gated Recurrent Unit (GRU) ...

A CNN–LSTM Hybrid Deep Learning Model for Detergent Products ...

In this paper, we present a hybrid Neural Network approach for accurate demand forecasts of the detergent manufacturing industry. It mainly consists of the ...

Hybrid deep learning approach to improve classification of low ...

[22] linearly combine the predictions of three models (neural network, support vector regression, and decision tree) to predict the outcomes of ...

Leveraging Hybrid Deep Learning Models for Enhanced Multivariate ...

In our research paper, we introduce hybrid Temporal Convolutional Network (TCN)-RNN models, such as TCN-LSTM, TCN-BiLSTM, and TCN-GRU, to tackle ...

A Hybrid Deep Learning Based Character Identification Model Using ...

A deep learning based handwritten text model is proposed that is capable of identifying both characters and numerals from an input image consisting of ...

Hybrid Deep Learning Network Intrusion Detection System Based ...

The hybrid NIDS model has been developed using. CNN, LSTM, and their combination by. Halbouni et al. [24]. The CNN algorithm is used to extract.

A hybrid CNN and LSTM-based deep learning model - ProQuest

Then, the convolutional neural network (CNN) is used to extract the action characteristics of each tracked trajectory, and the long short-term memory network ( ...

A Hybrid RNN based Deep Learning Approach for Text Classification

The F1 score is used to compare the performance of both models. The hybrid. RNN model has three LSTM layers and two GRU layers, whereas the RCNN model contains ...

RNN LSTM-based Deep Hybrid Learning Model for Text ...

In this paper, we build a hybrid deep learning model using Deep Learning (DL) and Machine learning (ML) models. This work combines two traditional neural ...