two-step neural network
LSCE-FFNN-v1: a two-step neural network model for the reconstruction of surface ocean pCO2 over the global ocean
... Results of the LSCE-FFNN mapping model were com- pared to three published mapping methods which partic- ipated in the “Surface Ocean pCO2 Mapping Intercom- parison” (SOCOM) exercise presented in Rödenbeck et al. (2015) ...
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An ensemble CNN method for biomedical entity normalization
... a two-step neural network- based ensemble method that links free text pre- annotated microbiology-related entities to stand- ard concepts using semantic information from pre-trained word ...
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Microaneurysm detection in fundus images using a two-step convolutional neural network
... used two standard publicly available datasets, Retinopathy Online Challenge [35] and E-Ophtha-MA [36] databases to train and test the proposed method for the detec- tion of MA in retinal ...into two parts ...
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Review on Classification of Genes and Biomarker Identification
... Artificial Neural Network (ANN) classifier ...(RBF) neural network for cancer classification using expression of very few ...RBF neural networked used only 9 genes for the lymphoma ...
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A Step Forward to Revolutionise IntrusionDetection System Using Deep Convolution Neural Network
... computer, network, program and data from unauthorized ...are two main categories of cyber security, designed to identify any suspicious activities present in inbound and outbound network packets and ...
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Understanding Pictograph with Facial Features: End-to-End Sentence-Level Lip Reading of Chinese
... a two-step end-to-end architecture (LipCH-Net), in which two deep neural network models are employed to perform the recognition of Picture- to-Pinyin (mouth motion pictures to ...
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A Neural Network Model Deployed in the Cloud for Prediction of Network Traffic
... (BP) neural network which is then used by the source node to adjust the sent-out rate ...(FIR) neural network and controlled congestion by throttling the input arrival ...on ...
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Spatial and Textural Aspects for Arabic Handwritten Characters Recognition
... the two aspects: spatial and textural. In the first step, a modified Bitmap Sampling method is proposed, which converts the character’s images into a binary Matrix and then constructs a Mask for each ...
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Hybrid computing algorithm in representing solid model
... contains two steps namely reconstruction and ...reconstruction step, neural network with back propagation has been applied to derive the depth values of solid model that was represented by the ...
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Improved Study of Side-Channel Attacks Using Recurrent Neural Networks
... Recurrent neural network has a state and it basically receives input (input vectors) through time so that at every single time step we can feed an input vector into the network and it has some ...
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Bidirectional Recurrent Convolutional Neural Network for Relation Classification
... 2.3 Two-Channel Recurrent Neural Network with Long Short Term Memory Units The recurrent neural network is suitable for mod- eling sequential data, as it keeps hidden state vec- tor h, ...
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Joint Multitask Learning for Community Question Answering Using Task Specific Embeddings
... of two community Question Answering problems: question-question relatedness and an- swer ...a two- step framework based on deep neural networks and structured conditional models, with a feed- ...
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DEVELOPMENT AND APPLICATION OF A STAGE GATE PROCESS TO REDUCE THE UNERLYING RISKS OF IT SERVICE PROJECTS
... Deep Neural Network model and Bi-propagation Deep Neural Network algorithm separately for matching step, which are two more biologically inspired neural networks for ...
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Survey Report on Cryptography Based on Neural Network
... using neural cryptography, based on synchronization of Tree Parity Machines (TPMs) by mutual ...has two identical dynamical systems, which starting from different initial conditions and synchronized by a ...
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Short Term Load Forecasting With Feed Forward Neural Network Algorithm
... The network weights are adjusted by training the ...the network learns through examples. The idea is to give the network input signals and desired ...the network produces an output signal, and ...
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Two Step
... It was that afternoon that he learned that she had grown up a dancer—that she was in ballet performances by the time she was four, that she had gone to dance school for ten years in a city two states from her ...
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A Radial Basis Function Approach to Retrieve Soil Moisture and Crop Variables from X-Band Scatterometer Observations
... when network was trained with VV-polarization ...regression network, the optimized spread constant was ...the network for the optimized spread constant of ...the network was trained with ...
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3D Firework Reconstruction from a Given Videos
... The convolution kernel is usually initialized in the form of a random decimal matrix [7] whose value are usually very small and the bias should set to zero. The convolution kernel will gradually learn reasonable weights ...
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ASSOCIATION RULE MINING BASED VIDEO CLASSIFIER WITH LATE ACCEPTANCE HILL CLIMBING APPROACH
... From the table 3, it can be seen that the single network may cause over-fitting problem which make the prediction ability decline. By comparison, ECPSO-NNE, which need only simple design process, has obtained ...
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Local System Voting Feature for Machine Translation System Combination
... confusion network system combination approach with an additional model trained by a neural ...a neural network which is based on the words themselves and the combinatorial occurrences of the ...
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