Convolutional Neural Network
Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network
... deep convolutional neural network (CNN) models have demon- strated extraordinary performance in medical image recognition tasks ...residual network [30]) and a cost-sensitive data-balancing ...
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Convolutional Neural Network for Paraphrase Identification
... using convolutional neural network (CNN) and model interaction features at each ...the network in a novel way using a language mod- eling ...
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Vehicle Recognition Based On Convolutional Neural Network
... Transportation problem is one of the crucial issues faced by many big cities.With the development of computer technology and artificial intelligence technology, intelligent transportation will become a hot research ...
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DNA Sequence Classification by Convolutional Neural Network
... As shown in Table 2, our proposed model has significant improvements in all validation datasets. The lowest improvement is nearly 1% of accuracy and the highest improvement is over 6% of accuracy. These improve- ments ...
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A Detection algorithm based on Convolutional Neural Network
... In this paper we proposes novel algorithm to detect station logo, which is based on deep learning convolutional neural network and target detection tool SSD. Firstly, construct an experimental ...
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Identification Of Weeds From Crops Using Convolutional Neural Network
... of network Architecture YannLeCun uses a new architecture which is good at object recognition in image dataset called the Convolutional Neural Network ...The convolutional technique is ...
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Fixed layer Convolutional Neural Network
... At present times, biometric recognition, like face recog- nition, is being used as the main security barrier in various projects, making the need for it to be as fast and accurate as possible obvious. The state of the ...
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Convolutional Neural Network in Medical Diagnosis
... Abstract- Convolutional Neural Network (CNN) is transforming the field of medical diagnosis. CNN can help doctors make faster, more accurate diagnosis by providing automatic learning techniques for ...
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Prediction of Rice Diseases Using Convolutional Neural Network (in Rstudio)
... Abstract:- Detection of rice pest and diseases, and proper management and control of pest infested rice fields may result to a higher rice crop production. Using modern technologies, like smart phones, farmers can be ...
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Garments Texture Design Class Identification Using Deep Convolutional Neural Network
... Garments Texture Design Class Identification Using Deep Convolutional Neural Network.. S.M.[r] ...
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A Multithreaded CGRA for Convolutional Neural Network Processing
... Convolutional neural network (CNN) is an essential model to achieve high accuracy in various machine learning applications, such as image recognition and natural language ...
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A Convolutional Neural Network for Handwritten Digit Recognition
... a Convolutional Neural Network (CNN) to classify them, also, a proposed extra preprocessing technique is applied to the images of one of the data ...
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Image Segmentation Using Convolutional Neural Network
... to evaluate and improve the accuracy and efficiency of the model. Jie Chang Et al. [18] have done segmentation of MR images of the brain using CNN for developing a method of automatic brain tumor detection. They have ...
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Research on road extraction of remote sensing image based on convolutional neural network
... on convolutional neural network is proposed in this ...Firstly, convolutional neural network is used to classify the high-resolution remote sensing images into two classes, which ...
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A Convolutional Neural Network for Modelling Sentences
... Of the other sentence models, the NBoW is a shallow model and the RNN has a linear chain structure. The subgraphs induced in the Max- TDNN model have a single fixed-range feature ob- tained through max pooling. The ...
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3D Convolutional Neural Network for Object Recognition
... Object Recognition is an important part of modern intelligent machines and systems. It is used in many applica- tions related to multiple fields such as character recognition for mail sorting service [1], traffic ...
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A Linguistically Informed Convolutional Neural Network
... powerful for unbounded dependencies, but tweets are short; the sentiment of a tweet is usually de- termined by one part of it and unlike RNN/LSTM, convolution plus max pooling can learn to focus on that. Recursive ...
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Video Summarization using Convolutional Neural Network
... The present work aims to address this ever increasing gap between the volumes of actual data generated and the volume that can be reasonably inspected manually. It is laborious and time consuming to scrutinize the ...
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Waste Segregation using Convolutional Neural Network
... Abstract: Looking at the population increase in India, waste generation and segregation is a major issue in the current scenario. Tonnes of mixed waste is dumped without segregating it properly which leads to problem in ...
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Firearm Recognition Using Convolutional Neural Network
... During training, the only pre-processing step is to get the mean value of RGB, which is on the computed training data. After that the images are moved to the stack of convolutional layers. Three fully connected ...
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