- Does a Hybrid Neural Network based Feature Selection Model ...🔍
- [PDF] Does a Hybrid Neural Network based Feature Selection ...🔍
- Sequence based model using deep neural network and hybrid ...🔍
- Hybrid neural network|based metaheuristics for prediction of ...🔍
- Feature selection for neural network based defect classification of ...🔍
- Hybrid Neural Network Models for Detecting Fake News Articles🔍
- Hybrid deep models for parallel feature extraction and enhanced ...🔍
- Boosting feature selection for Neural Network based regression🔍
Does a Hybrid Neural Network based Feature Selection Model ...
Does a Hybrid Neural Network based Feature Selection Model ...
In this paper, we propose a hybrid feature selection method for obtaining relevant features by combining various filter-based feature selection methods and ...
Does a Hybrid Neural Network based Feature Selection Model ...
In this paper, we propose a hybrid feature selection method for obtaining relevant features by combining various filter-based feature selection methods and ...
[PDF] Does a Hybrid Neural Network based Feature Selection ...
Does a Hybrid Neural Network based Feature Selection Model Improve Text Classification? · Suman Dowlagar, R. Mamidi · Published in ICON 22 January 2021 · Computer ...
Does a Hybrid Neural Network based Feature Selection Model ...
Does a Hybrid Neural Network based Feature Selection Model Improve Text Classification? 01/22/2021. ∙. by Suman Dowlagar, et al. ∙. 0. ∙. share. Text ...
(Open Access) Does a Hybrid Neural Network based Feature ...
The use of dimensionality reduction methods with machine learning classifiers has achieved good results. In this paper, we propose a hybrid feature selection ...
Sequence based model using deep neural network and hybrid ...
For instance, the DNN model achieved an average success rate of 76.87% when utilizing hybrid features, whereas the highest success rate using ...
IGRF-RFE: a hybrid feature selection method for MLP-based ...
The effectiveness of machine learning models can be significantly averse to redundant and irrelevant features present in the large dataset ...
Hybrid neural network-based metaheuristics for prediction of ...
The moth-flame optimization (MFO) algorithm, a successful metaheuristic algorithm, has been utilized for feature selection because the use of all variables in ...
Feature selection for neural network based defect classification of ...
The experimental results confirm that features identified by Principal Component Analysis lead to improved performance in terms of classification percentage ...
Hybrid Neural Network Models for Detecting Fake News Articles
The two types of neural networks are convolutional and bi-directional long-short term memory. Robust features are extracted using two different ...
(PDF) A hybrid convolutional neural network approach for feature ...
Eventually, the experimental outcomes obtained from the presented model has also been compared with the recent existing feature selection and ...
Hybrid deep models for parallel feature extraction and enhanced ...
This research presents a hybrid deep learning model that extracts features using AlexNet and DenseNet models, followed by feature fusion and dimensionality ...
Boosting feature selection for Neural Network based regression
We propose a new method combining a boosting strategy for feature selection and a neural network for the regression. Potential features are a very large set of ...
A two-stage hybrid model by using artificial neural networks as ...
network structures create new features, which can help improve the model ... compared with modeling based on some feature selection algorithms, such as simulated.
A Hybrid Model Based on Convolutional Neural Network and Long ...
In the meantime, using single-label data algorithms could be very time-consuming. In MLTC, complexity costs should be reduced. Deep-learning ...
A hybrid neural network approach for classifying diabetic retinopathy ...
Improve prediction accuracy: the combination of hybrid models improves the model's prediction accuracy, which is better than a single model. The ...
(PDF) Hybrid Feature Selection Method Based on Neural Networks ...
The proposed method is compared with the one-way ANOVA method in terms of accuracy, number of features, and computing time to determine the feature set required ...
Multimodal hybrid convolutional neural network based brain tumor ...
Instead of these time-consuming methods, deep learning models are employed because they are less time-consuming, require less expensive ...
A Hybrid Feature Selection Method Based on Binary Differential ...
Obtaining essential genes from microarray data that can diagnose diseases can be very useful for researchers to understand diseases and ...
Proposing a hybrid technique of feature fusion and convolutional ...
The first one is the Hybrid Feature Extractor (HFE), and second one is the convolutional neural network VGG19-based CNN. The HFE combines 3 ...