[PDF] Top 20 Detection of Sarcasm in Text Data using Deep Convolutional Neural Networks
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Detection of Sarcasm in Text Data using Deep Convolutional Neural Networks
... and sarcasm as strongly related and sometimes equate these two terms in their ...that sarcasm is not a discrete logic or part of any linguistic phenomenon but the irony is part of the verbal ...of ... See full document
10
Disease Detection of Plants using Deep Learning and Convolutional Neural Networks
... object detection have been taken care by Convolutional Neural Networks, which is a type of Deep Neural Network which was developed as similar as Human Visual system, Many CNN ... See full document
5
Aggression Detection on Social Media Text Using Deep Neural Networks
... There have been several studies on computa- tional methods to detect abusive/aggressive lan- guage published on social media in the last few years (Razavi et al., 2010) (Watanabe et al., 2018). The first thing to observe ... See full document
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Deep Pyramid Convolutional Neural Networks for Text Categorization
... of data (either the original data such as text or image or the output of the pre- vious layer) at every location ...to text, a recurrent unit takes words one by one as well as its own output ... See full document
9
Tweet Sarcasm Detection Using Deep Neural Network
... on using neural networks for sarcasm detection, neural models have seen increasing applications in sentiment analysis, which is a closely-related ...Different neural ... See full document
12
Captioning for Motion Detection for video surveillance Applications using Deep Learning
... model neural network which automatically learns to features from the images and generate the ...trained using COCO dataset where the dataset is split into test, train and validation ...A ... See full document
6
Fungus Detection using Convolutional Neural Networks
... fungus detection using convolutional neural networks in order to identify whether the given classes of fungus is normal or ...websites. Using this dataset, we are applying ... See full document
5
Pneumonia Detection Using Convolutional Neural Networks
... the deep learning function in detecting pneumonia through computer vision using five convolutional neural network ...other convolutional neural network architectures like ... See full document
6
Breast cancer detection using deep convolutional neural networks and support vector machines
... artificial neural networks (ANN) for classifying normal, abnormal tissues, benign and malignant MCs tumors was ...achieved detection rate was 96% for ANN and 98% for SVM (Ragab, Sharkas & ... See full document
23
UAV based slope failure detection using deep learning convolutional neural networks
... training data set composites, different sample patch window sizes, and multiple-layer depth ...study, using only optical data resulted in a larger number of FPs than using a combination of ... See full document
24
Facial Keypoints Detection with Deep Learning
... keypoints detection is a challenging task due to the large variation of facial features, the change in 3D viewing angle, and difference in size and position of the ...the detection accuracy and to ... See full document
8
Brain Tumor Classification Using Convolutional Neural Networks
... of data generated by MRI scan thwarts manual classification of tumor vs non-tumor in a particular ...tumor detection is proposed by using Convolutional Neural Networks (CNN) ... See full document
5
Spam detection in im images using convolutional neural networks
... we check our WhatsApp, we are usually bombarded with many messages. Some of them are from our loved ones, while others are of professional importance. But unfortunately, among these messages are many unwanted messages, ... See full document
6
Unified Framework For Deep Learning Based Text Classification
... Abstract: Deep learning has emerged as a very popular approach for solving large scale pattern recognition ...various text mining problems with improved accuracy as compared to pre-existing ...are ... See full document
5
Resiliency in Deep Convolutional Neural Networks
... used directly in Keras (with TensorFlow in the background). Luckily there is a tool [47] available online which can be used to convert this weight file to .h5 file which is compatible with Tensorflow. This tool maps the ... See full document
109
Deep Learning Techniques for Object Detection
... A neural network comprises interconnected neurons each having inputs and outputs, with the output of one layer becoming the input to the next layer; except the final layer whose output is the ultimate outcome of ... See full document
8
Integrated Management System For Sugarcane Disease Using Deep Learning Techniques-A Review
... disease detection techniques for this they used ...two deep learning techniques for visual object counting task, demonstrating their efficiency on the CVPPP 2017 leaf counting challenge ...performed ... See full document
5
Denoising of Images using Deep Convolutional Neural Networks (DCNN)
... of convolutional networks as an image processing architecture and an unsupervised learning procedure that synthesizes training samples from specific noise ...denoising. Using a test set with a ... See full document
7
Phone recognition with hierarchical convolutional deep maxout networks
... In the so-called tandem model, the ANN outputs are used as features of a conventional HMM, virtually stack- ing a GMM on top of the ANN [44]. Perhaps the success of this model motivated the idea of stacking two ... See full document
13
Human emotion recognition in video using subtraction pre-processing
... The whole system is combined with a new pre-processing method and a well-trained CNN. The CNN structure is chosen from Alex- net, google-net and different deep Res-net structure. The best results of these three ... See full document
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