Deep CNN
Image Retrieval Using Features From Pre-Trained Deep CNN
... VGG16 deep CNN features achieves an average precision of ...VGG16 CNN model performs better than traditional CBIR using handcrafted features (Color, texture and ...
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Text Extraction and a Deep CNN Based Model for Character Classification in Kannada Documents
... lightweight CNN based architecture was developed to classify these ...Different CNN models like Resnet, VGG and Densenet are used to check the performance analysis of Bangla ...
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Deep CNN with Residual Connections and Range Normalization for Clinical Text Classification
... Abstract Deep learning has achieved remarkable performance in many classification tasks such as image processing and computer ...performance, deep learning techniques have found their way into natural ...
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Facial Emotion Recognition by Deep CNN and HAAR Cascade
... The facial emotion recognition is done through the usage of two algorithms. The dataset used is Cohn-Kanade (CK/CK+) dataset. The facial recognition is done by using the HAAR cascade classifier. The emotion recognition ...
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A general purpose intelligent surveillance system for mobile devices using deep learning
... called Deep Convolutional Neural Networks [13] (CNN). A Deep CNN is composed of stacked convolutional layers that are used for feature training and extraction, followed by additional fully ...
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DEVELOPMENT OF SUSTAINABILITY SYSTEMS FOR OPEN GOVERNMENT DATA (OGD) MANAGEMENT BY COMBINING THE SHEL MODEL AND SOFT SYSTEMS METHODOLOGY ANALYSIS
... 4) The Deep CNN-based Classifier: The deep CNN-based classifier is the final stage in our license plate detection framework. After extracting features for each of proposals via RoI Pooling, we ...
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Glioblastoma Multiforme Classification by Deep Learning Techniques on Histopathology Images
... not deep enough to capture the properties of our complex Glioma histopathology images ...Developed deep CNN architecture which consists of 3 convolution layers, 3 pooling layers and 3 ...proposed ...
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Learning Compact Spatio Temporal Features for Fast Content based Video Retrieval
... VI. CONCLUSION AND FUTURE WORK This paper presents a framework of fast content based video retrieval via deep learning with hashing. Specifically, deep CNN along with LSTM is deployed to learn ...
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Fault Diagnosis of Gearbox with a Transferable Deep Neural Network
... used Deep Neural Network for extracting features from the spectrum instead of the raw data and only used labelled source domain data and normal category data from the target domain to accomplish DA ...a CNN ...
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CHEST X-RAY IMAGE CLASSIFICATION USING FASTER R-CNN
... applied Deep CNN to classify those images and predict the related eight ...of CNN, and Yao et ...Guided CNN which learns from a global CNN branch, Gundel et ...on CNN which can ...
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Joint Embedding of Words and Labels for Text Classification
... Testing accuracy Simple compositional meth- ods indeed achieve comparable performance as the sophisticated deep CNN/RNN models. On the other hand, deep hierarchical attention model can improve the ...
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Discerning Facial Expressions Using CK+
... using deep CNN. In this paper, they have employed a Deep Convolutional Neural Network (CNN) to devise a facial expression recognition system, which is capable to discover deeper feature ...
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Convolutional Neural Networks and Hash Learning for Feature Extraction and of Fast Retrieval of Pulmonary Nodules
... Deep learning is based on artificial neural networks, trying to mimic the way that the human brain ...a deep architecture possessing multiple processing layers, having linear and nonlinear transformation ...
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A Proposed Framework: Face Recognition With Deep Learning
... for CNN has emerged to obtain optimal accuracy such as CelebFaces+ [26], VGG face dataset, MS-Celeb-1M, VGGFace2, CASIA-Web Face and UMDFace and many more ...Lightened CNN showcase a computational ...
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A comparison between two semantic deep learning frameworks for the autosomal dominant polycystic kidney disease segmentation based on magnetic resonance images
... on Deep Learning architectures using Convolutional Neural Networks (CNN) for the semantic segmentation of images, without needing to extract any hand-crafted ...first CNN automatically detects ...
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Survey on Unmanned Aerial Vehicle based Weeds Detection using Deep Neural Networks
... This paper is focused on detection of weeds in the crop through UAV. Usually weeds control consists in spraying herbicides all over the agricultural field. This practice involves significant waste and cost of herbicide ...
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A Convolution Neural Network Based Deep Learning System for Brain Tumor Detection towards MRI Image Segmentation
... . CNN : A convolutional neural network (CNN, or ConvNet) is a type of feed-forwardartificial neural network in which the connectivity pattern between its ...is deep learning convolution neural ...
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Online/offline score informed music signal decomposition: application to minus one
... novel deep learning (DL) strategies have been developed combining deep neural networks (DNN) with score infor- mation to estimate soft masks for specific instrument classes ...
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Disease Detection in the Leaves of Multiple Plants
... The deep algorithms can be made useful in plant disease ...a deep learning based method for the detection of diseases effected in the leaves of ...the deep Convolutional Neural Network (CNN) ...
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The Understanding of Convolutional Neuron Network Family
... Along with the development of the computing speed and Artificial Intelligence, more and more jobs have been done by computers. Convolutional Neural Network (CNN) is one of the most popular algorithms in the field ...
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