random neural network model
Neural Factor Graph Models for Cross lingual Morphological Tagging
... proposed model uses factorial conditional random fields with neural network potentials, mak- ing it possible to (1) utilize the expres- sive power of neural network represen- ...
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BAT ALGORITHM FOR ROUGH SET ATTRIBUTE REDUCTION
... Grey neural network model combines the advantages of grey GM(1,1) model and neural network model, which suits for few sample data and volatile random ...grey ...
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Risk Prediction Assessment In Life Insurance Company Through Dimensionality
... the model can be ...Artificial Neural Network, Multiple Linear Regression, Random Tree and the proposed Random Forest are applied on the dataset to predict the risk level of ...the ...
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Comparative Study of Static and Dynamic Artificial Neural Network Models in Forecasting of Tehran Stock Exchange
... a random walk ...the random appearance of the stock indexes, a specific nonlinear process can be ...and random walk model (Scheinkman and Lebaron, 1989; Hou et al, 2005; Abhyankar et al, ...
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Investigating the Relationship between the Reflected Near Infrared Light and the Internal Quality of Pineapples Using Neural Network
... artificial neural network to predict the internal quality of ...the random seed in the neural network, the predictive model could classify the near infrared light from LED data ...
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On predicting the outcomes of chemotherapy treatments in Breast cancer
... Abstract. Chemotherapy is the main treatment commonly used for treating cancer patients. However, chemotherapy usually causes side ef- fects some of which can be severe. The effects depend on a variety of factors ...
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Comparative study of static and dynamic neural network models for nonlinear time series forecasting
... this model is that it is able to make more accurate long-term forecasts under similar conditions in comparison with the ANN model (Taskaya and Caseym ...using random initial weights (Matkovskyy, ...
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Random Walks and Neural Network Language Models on Knowledge Bases
... Neural Network Language Models have become a useful tool in NLP on the last years, specially in se- ...feedforward Neural Network Language Model, but instead of a hidden layer it has a ...
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Wavelet Neural Network with Random Wavelet Function Parameters
... wavelet neural network. A wavelet neural network has two types of parameters, namely wavelet function parameters (translation, dilation) and the weights between the hidden layer and output ...
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The Stock Price Crash Risk Prediction by Neural Network
... a neural network utilizing variables constructed from Compustat and ...use neural network to forecast crash risk, I choose eight widely used financial ratios as explanatory ...for ...
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An Ensemble Classification Approach for Intrusion Detection
... artificial neural network and random forest are used for ...Ensemble model is formed for producing better result. The model shows promising result for all classes of ...
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The Genetic Algorithm based Recommender Systems
... linear model, decision tree, random forest, neural network of existing system with proposed system which uses genetic algorithm that is it will gives best values in comparison to ...
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Investigating electrochemical drilling (ECD) using statistical and soft computing techniques
... (modular neural network), GFF (generalized feed forward), and CANFIS (coactive neuro-fuzzy inference system) models were compared with the intention of identifying the model with higher ...
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Blind Navigation System using Artificial Intelligence
... We are using CIFAR-10 classification to classify RGB 32x32 pixel images. The reason CIFAR-10 was selected was that it is complex enough to exercise much of TensorFlow's ability to scale to large models. At the same time, ...
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Glyph aware Embedding of Chinese Characters
... Due to the lack of improvement of the proposed mixed embedder over the ID embedder in the lan- guage modeling task, we suspect that the CNN embedder is under-trained. Unlike a digit class in MNIST (LeCun et al., 2010) ...
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Human-level Moving Object Recognition from Traffic Video
... Abstract. Video preserves valuable raw information. Understanding these data and then recognizing objects and tagging them are crucial to intelligent planning and decision making. Deep learning provides us an effective ...
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Osmotic Drying Rate Estimation for Dehydration of Beetroot Slices using Artificial Neural Network
... Artificial Neural Network model that will correlate the dependent parameters, the weight loss and drying rate, with temperature, concentration of salt solution and time for osmotic dehydration of ...
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A machine learning approach to voice separation in lute tablature
... Temperley [17] adopts an approach based on four ‘preference rules,’ i.e., criteria to evaluate a possible analysis. Two of these match the abovementioned principles; the other two prescribe to minimise the number of ...
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Neural Network based Software Effort Estimation: A Survey
... the neural network model trained using previous data has good generalization capabilities and is able to successfully predict the effort closely matching the experimental observations with less error ...
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Data Authentication Based on Discrete Random Convolutional Neural Network
... With the popularity of multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) in edge computing transmission, one-dimensional (1D) channel estimation vectors has become two-dimensional (2D) ...
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