Advanced hybrid attention|based deep learning network with ...

Advanced hybrid attention-based deep learning network ... - PubMed

An image fusion-based detection model is proposed for lung cancer detection using an improved heuristic algorithm of the deep learning model.

Advanced hybrid attention-based deep learning network with ...

An image fusion-based detection model is proposed for lung cancer detection using an improved heuristic algorithm of the deep learning model.

Advanced Hybrid Attention-Based Deep Learning Network with ...

Conclusions: Applying a deep learning-based slice thickness reduction technique significantly enhances CAD performance in lung nodule detection on chest CT ...

HADLN: Hybrid Attention-Based Deep Learning Network ... - Frontiers

The hybrid attention-based deep learning network (HADLN) method is proposed to implement arrhythmia classification.

Hybrid attention-based deep neural networks for short-term wind ...

... advanced deep learning techniques in enhancing wind energy forecasting accuracy, particularly in challenging desert environments. The hybrid ...

Advanced hybrid attention-based deep learning network with ... - OUCI

Advanced hybrid attention-based deep learning network with heuristic algorithm for adaptive CT and PET image fusion in lung cancer detection.

Attention-Based Hybrid Deep Learning Network for Human Activity ...

Over the last decade, human activity recognition (HAR) research has advanced significantly. It has proven successful in several areas, such ...

Attention-Based Deep Learning Frameworks for Network Intrusion ...

... Attention Mechanism, Hybrid Models, Cybersecurity, Anomaly Detection, Machine Learning, Data Mining, Real-Time Detection, Model Comparison.

HADLN: Hybrid Attention-Based Deep Learning Network for ...

In recent years, with the development of artificial intelligence, deep learning model has achieved initial success in ECG data analysis, ...

A hybrid transformer and attention based recurrent neural network ...

A novel hybrid sentiment analysis framework that integrates transformer-based architecture, attention mechanism, and recurrent neural networks like BiLSTM.

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

Khan N, Khan SU, Baik SW (2024) Deep autoencoder-based hybrid network for building energy consumption forecasting. Computer Systems Science ...

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 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 ...

An advanced hybrid deep learning model for predicting total ...

[25] used radial basis function neural networks (RBFNNs) and support vector machine (SVM) models to predict the water quality index. Based on ...

A Hybrid Deep Learning Approach for Advanced Persistent Threat ...

Xuan & Dao proposed a new analysis method based on the network traffic datasets of some APT attack malware to detect abnormal behavior of APT attacks [22]. The ...

A Hybrid RNN based Deep Learning Approach for Text Classification

The authors have proposed a new framework called. Hierarchical Attention-based Recurrent Neural Network. (HARNN) for categorizing documents into the ...

A Hybrid Deep Learning‐Based Forecasting Model for the Peak ...

A hybrid deep-learning neural network model is proposed to improve the forecasting accuracy of ionospheric hmF2 This model improves the ...

A Hybrid Deep Learning Approach for Advanced Persistent Threat ...

To analyze huge network traffic, a hybrid deep learning approach that builds two models is used: Stacked Autoencoder with Long Short-Term Memory (SAE-LSTM) ...

Advanced Hybrid Model for Multi Paddy diseases detection using ...

METHODS: The Proposed System have used Deep Learning Image Processing algorithm and neural Network Like DCNN ,SVM and Transfer Learning .The ...

Multimodal hybrid convolutional neural network based brain tumor ...

... attention, with convolutional neural ... For brain tumor-related tasks, reliable advanced artificial intelligence and neural network ...