Convolution neural networks
Plant Stress Detection Accuracy Using Deep Convolution Neural Networks
... Food security is achievable through application of deep convolution neural networks to detect diseases in plants at an early stage. Tomato image datasets captured over a six month period was used to ...
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Deep Convolution Neural Networks for Automatic Eyeglasses Removal
... Convolutional Neural Network (SRCNN) proposed by Dong [6] shows the great potential of an end-to-end DCN in image super- ...deep convolution neural networks ...
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An Approach to Classify Heritage Sites Architecture Using Convolution Neural Networks
... of Convolution Neural Networks for semantic classification of heritage site architectures like dome, bell tower, glass, and ...pre-trained networks that have been fine-tuned on a huge amount ...
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Deep Belief Networks Using Convolution Neural Networks Algorithm
... Convolutional Neural Networks (CNNs) and Deep Belief Networks (DBNs) together with their learning algorithms and their performances in different ...
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An Investigative Analysis on Mapping X Ray to Live Using Convolution Neural Networks for Detection of Genu Valgum
... Use of Adam : Adam is a very versatile algorithm for gradient descent when it comes to updating of weights. Research has shown that Adam is used as a bench marker for most neural networks. Paper [13] ...
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Vehicle Recognition based on Deep Convolution Neural Networks
... deep convolution neural network (DCNN) based on deep learning framework CAFFE and GPU which has the strong computing ...deep neural network with 13 layers was selected as the feature extraction ...
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Khmer handwritten text recognition with Convolution Neural Networks
... As mentioned in the data collection section, each of the six datasets is comprised of 561 (33X17) symbols. Division of each image is filed into an individual image of each syllable, cropping white space, and resizing to ...
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Machine Learning Perspectives for Dental Imaging
... The stochastic methodologies are used to identify the local information of the medical images, whether the image pixels are belongs to the desired region or not [11]. Methodologies like thresholding approaches, ...
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Human Behavior Recognition Based On CNN
... vision. Convolution neural networks provide an end-to- end learning model, in which parameters can be trained by traditional gradient descent ...trained convolution neural ...
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Survey on Unmanned Aerial Vehicle based Weeds Detection using Deep Neural Networks
... using Convolution Neural Networks (CNN) and the results are plotted on canvas representing the land with weeds are marked along with detailed ...
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HAND POSES DETECTION USING COVOLUTIONAL NEURAL NETWORK
... pooling convolution neural networks for vision based hand gesture ...deep convolution neural networks that incorporates convolution and max pooling to supervise feature ...
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Enterprise Chat Platform using Machine Learning Techniques
... Sentiment Analysis is implemented using Convolution Neural Networks (CNN). Twitter dataset was utilized for training CNN, as the content is similar to an enterprise chat data. CNN does not accept ...
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A Review on Indian Sign Language Recognition
... Artificial Neural Networks (ANN), Support Vector Machine (SVM), Hidden Markov Models (HMM), Deep Convolution Neural Networks (CNN, DCNN) ...
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Meta Learning for Graph Neural Networks
... Deep Learning has provided breakthrough research results in many fields. Deep learning network’s ability to extract its own features for classification has provided it an edge over other machine learning techniques. It ...
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Answer Sequence Learning with Neural Networks for Answer Selection in Community Question Answering
... In contrast to symbolic representation, Wang et al. (2010) proposed a deep belief nets (DBN) based semantic relevance model to learn the dis- tributed representation of QA pair. Recently, the convolutional neural ...
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A Novel Method for Remotely Sensed Hyperspectral Image Classification Based on Convolutional Neural Network
... In this study, a new eight layer HSI classification model based on deep convolutional neural network is proposed. The proposed CNN Model takes the input of both spectral and spatial data as a patch by considering ...
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Survey on Facial Expression Analysis and Recognition
... Field Convolution (VFC) technique, multiple Radial Bases Functions neural networks, active patches, histogram of oriented gradient (HOG) filter, diffeomorphic growth model, sparse group wise image ...
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Application research of convolution neural network in image classification of icing monitoring in power grid
... the convolution layer, it can theoretically be dir- ectly input into the full connection layer and classified by ...Although convolution layer uses local connection to reduce the number of connections in ...
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A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signal
... As shown in Figure 3, the input of the CNN is a segment of normalized bearing fault vibration temporal signal. The first convolutional layer extracts features from the input raw signal without any other transformation. ...
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Image Classification Using Android Things through Machine Learning
... Computer vision is an interdisciplinary intelligible field that attract with how computers can be made to get an unpredictable state of appreciation from mechanized pictures or records. Computer vision assignments ...
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