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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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Learning Velocity Model for Complex Media with Deep Convolutional Neural Networks
AbstractThe paper considers the problem of velocity model acquisition for a complex media based on boundary measurements. The acoustic model is used...
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Co-embedding of edges and nodes with deep graph convolutional neural networks
Graph neural networks (GNNs) have significant advantages in dealing with non-Euclidean data and have been widely used in various fields. However,...
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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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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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Enhancing Smart Home Security Using Deep Convolutional Neural Networks and Multiple Cameras
With the increasing use of smart homes and IoT devices, security has become a significant concern. This paper presents a method to enhance smart home...
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Approximation of functions from Korobov spaces by deep convolutional neural networks
The efficiency of deep convolutional neural networks (DCNNs) has been demonstrated empirically in many practical applications. In this paper, we...
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Flat-Field Correction of X-Ray Tomographic Images Using Deep Convolutional Neural Networks
AbstractIt is proposed that neural networks be used to solve the problem of flat-field correction. A process is described for selecting parameters of...
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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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Generalized Gradient Flow Based Saliency for Pruning Deep Convolutional Neural Networks
Model filter pruning has shown efficiency in compressing deep convolutional neural networks by removing unimportant filters without sacrificing the...
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Object detection and classification of butterflies using efficient CNN and pre-trained deep convolutional neural networks
With over 18,000 species, butterflies account for nearly one-quarter of all identified species on the planet. The images of different butterfly...
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Deep convolutional neural networks are not mechanistic explanations of object recognition
Given the extent of using deep convolutional neural networks to model the mechanism of object recognition, it becomes important to analyse the...
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Automatic Food Recognition Using Deep Convolutional Neural Networks with Self-attention Mechanism
The significance of food in human health and well-being cannot be overemphasized. Nowadays, in our dynamic life, people are increasingly concerned...
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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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Deep multi-scale convolutional neural networks for automated classification of multi-class leaf diseases in tomatoes
Deep learning techniques have gained immense popularity recently because of their remarkable capacity to learn complex patterns and features from...
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Classification of non-small cell lung cancers using deep convolutional neural networks
Lung cancer is a major cause of cancer-related deaths worldwide, and early detection is crucial in reducing mortality rates. To aid in this effort,...
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Unveiling the power of convolutional neural networks in melanoma diagnosis
BackgroundConvolutional neural networks are a type of deep learning algorithm. They are mostly applied in visual recognition and can be used for the...
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Discovery of a non-canonical GRHL1 binding site using deep convolutional and recurrent neural networks
BackgroundTranscription factors regulate gene expression by binding to transcription factor binding sites (TFBSs). Most models for predicting TFBSs...
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Downscaling Seasonal Precipitation Forecasts over East Africa with Deep Convolutional Neural Networks
This study assesses the suitability of convolutional neural networks (CNNs) for downscaling precipitation over East Africa in the context of seasonal...
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A Hybrid Deep Learning Framework to Predict Alzheimer’s Disease Progression Using Generative Adversarial Networks and Deep Convolutional Neural Networks
A major research subject in recent times is Alzheimer’s disease (AD) due to the growth and considerable societal impacts on health. So, the detection...
