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Centrosymmetric constrained Convolutional Neural Networks
Complex signals can be viewed as compositions of numerous sine waves with different frequencies and amplitudes. As the fundamental unit of perceiving...
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Rule extraction from convolutional neural networks for heart disease prediction
The accurate prediction of heart disease is crucial in the field of medicine. While convolutional neural networks have shown remarkable precision in...
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Review of Lightweight Deep Convolutional Neural Networks
Lightweight deep convolutional neural networks (LDCNNs) are vital components of mobile intelligence, particularly in mobile vision. Although various...
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Convolutional Neural Networks
This chapter is concerned with the convolutional neural networks as another type of deep neural networks. We studied the MLP which is expanded into...
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Quantum convolutional neural networks for multi-channel supervised learning
As the rapidly evolving field of machine learning continues to produce incredibly useful tools and models, the potential for quantum computing to...
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Gaussian mixture models for training Bayesian convolutional neural networks
Bayes by Backprop is a variational inference method based on the reparametrization trick to assure backpropagation in Bayesian neural networks....
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Prediction of Froth Flotation Performance Using Convolutional Neural Networks
Deep learning is a subset of machine learning that uses artificial neural networks for extracting high-level features from image data. In the present...
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APT Attack Detection Based on Graph Convolutional Neural Networks
Advanced persistent threat (APT) attacks are malicious and targeted forms of cyberattacks that pose significant challenges to the information...
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Drowsiness detection in real-time via convolutional neural networks and transfer learning
Drowsiness detection is a critical aspect of ensuring safety in various domains, including transportation, online learning, and multimedia...
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Deep Convolutional Neural Networks
Over the last decade, deep learning frameworks have revolutionized the field of computer vision, delivering state-of-the-art performance across...
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Quantum convolutional neural networks with interaction layers for classification of classical data
Quantum machine learning (QML) has come into the limelight due to the exceptional computational abilities of quantum computers. With the promises of...
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Automated micro aneurysm classification using deep convolutional spike neural networks
One of the common diseases in people with micro aneurysms is diabetic retinopathy (DR). Due to a lack of early diagnosis, diabetic retinopathy poses...
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Convolutional Neural Networks
In the previous chapters, we have seen how to construct neural networks using fully-connected layers. We will now look at a different class of...
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Object detection using convolutional neural networks and transformer-based models: a review
Transformer models are evolving rapidly in standard natural language processing tasks; however, their application is drastically proliferating in...
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Vehicle detection systems for intelligent driving using deep convolutional neural networks
In the paper, a vision-based vehicle identification system is proposed for autonomous intelligent car driving. The accurate detection of obstacles...
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Fpar: filter pruning via attention and rank enhancement for deep convolutional neural networks acceleration
Pruning deep neural networks is crucial for enabling their deployment on resource-constrained edge devices, where the vast number of parameters and...
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Enhancing human behavior recognition with spatiotemporal graph convolutional neural networks and skeleton sequences
ObjectivesThis study aims to enhance supervised human activity recognition based on spatiotemporal graph convolutional neural networks by addressing...
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A Novel Feature Selection Approach-Based Sampling Theory on Grapevine Images Using Convolutional Neural Networks
Feature selection, reducing number of input variables to develop classification model, is an important process to reduce computational and modeling...
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Image segmentation using convolutional neural networks in multi-sensor information fusion
In recent years, the rapid advancement of artificial intelligence (AI) has revolutionized various industries, with image processing playing an...
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Abnormal nodes sensing model in regional wireless networks based on convolutional neural network
There are some problems in abnormal node sensing in regional wireless networks, such as low sensing accuracy and poor judgment results of abnormal...
