Alzheimer Disease Prediction Model Based on Decision Fusion of CNN-BiLSTM Deep Neural Networks

2020 ◽  
pp. 482-492
Author(s):  
Shaker El-Sappagh ◽  
Tamer Abuhmed ◽  
Kyung Sup Kwak
Keyword(s):  
Neural Networks ◽  
Alzheimer Disease ◽  
Prediction Model ◽  
Deep Neural Networks ◽  
Decision Fusion ◽  
Disease Prediction ◽  
Model Based

An effective daily box office prediction model based on deep neural networks

2018 ◽  
Vol 52 ◽  
pp. 182-191 ◽  
Author(s):  
Yunian Ru ◽  
Bo Li ◽  
Jianbo Liu ◽  
Jianping Chai
Keyword(s):  
Neural Networks ◽  
Prediction Model ◽  
Deep Neural Networks ◽  
Model Based ◽  
Box Office

scholarly journals Eigen Solution of Neural Networks and Its Application in Prediction and Analysis of Controller Parameters of Grinding Robot in Complex Environments

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Shixi Tang ◽  
Jinan Gu ◽  
Keming Tang ◽  
Wei Ding ◽  
Zhengyang Shang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Prediction Model ◽  
Deep Neural Networks ◽  
Complete Solution ◽  
Partial Solution ◽  
Complex Environments ◽  
Solution Theory ◽  
Analysis Model ◽  
Neural Network Structure

The robot dynamic model is often rarely known due to various uncertainties such as parametric uncertainties or modeling errors existing in complex environments. It is a key problem to find the relationship between the changes of neural network structure and the changes of input and output environments and their mutual influences. Firstly, this paper defined the conceptions of neural network solution, neural network eigen solution, neural network complete solution, and neural network partial solution and the conceptions of input environments, output environments, and macrostructure of neural networks. Secondly, an eigen solution theory of general neural networks was proposed and proven including consistent approximation theorem, eigen solution existence theorem, consistency theorem of complete solution, the partial solution, and none solution theorem of neural networks. Lastly, to verify the eigen solution theory of neural networks, the proposed theory was applied to a novel prediction and analysis model of controller parameters of grinding robot in complex environments with deep neural networks and then build prediction model with deep learning neural networks for controller parameters of grinding robot. The morphological subfeature graph with multimoment was constructed to describe the block surface morphology using rugosity, standard deviation, skewness, and kurtosis. The results of theoretical analysis and experimental test show that the output traits have an optional effect with joint action. When the input features functioning in prediction increase, higher predicted accuracy can be obtained. And when the output traits involving in prediction increase, more output traits can be predicted. The proposed prediction and analysis model with deep neural networks can be used to find and predict the inherent laws of the data. Compared with the traditional prediction model, the proposed model can predict output features simultaneously and is more stable.

scholarly journals A New Click-Through Rates Prediction Model Based on Deep&Cross Network

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 342
Author(s):  
Guojing Huang ◽  
Qingliang Chen ◽  
Congjian Deng
Keyword(s):  
Neural Networks ◽  
Prediction Model ◽  
Deep Neural Networks ◽  
Prediction Models ◽  
State Of The Art ◽  
Online Advertising ◽  
Optimization Technique ◽  
Proposed Model ◽  
Great Progress ◽  
Online Advertisement

With the development of E-commerce, online advertising began to thrive and has gradually developed into a new mode of business, of which Click-Through Rates (CTR) prediction is the essential driving technology. Given a user, commodities and scenarios, the CTR model can predict the user’s click probability of an online advertisement. Recently, great progress has been made with the introduction of Deep Neural Networks (DNN) into CTR. In order to further advance the DNN-based CTR prediction models, this paper introduces a new model of FO-FTRL-DCN, based on the prestigious model of Deep&Cross Network (DCN) augmented with the latest optimization technique of Follow The Regularized Leader (FTRL) for DNN. The extensive comparative experiments on the iPinYou datasets show that the proposed model has outperformed other state-of-the-art baselines, with better generalization across different datasets in the benchmark.

scholarly journals Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks

2020 ◽  
Vol 14 (3) ◽  
pp. 1083-1104
Author(s):  
Young Jun Kim ◽  
Hyun-Cheol Kim ◽  
Daehyeon Han ◽  
Sanggyun Lee ◽  
Jungho Im
Keyword(s):  
Neural Networks ◽  
Sea Ice ◽  
Prediction Model ◽  
Convolutional Neural Networks ◽  
Route Planning ◽  
Arctic Sea Ice ◽  
Ocean Current ◽  
Sea Ice Concentration ◽  
Model Based ◽  
Arctic Sea

Abstract. Changes in Arctic sea ice affect atmospheric circulation, ocean current, and polar ecosystems. There have been unprecedented decreases in the amount of Arctic sea ice due to global warming. In this study, a novel 1-month sea ice concentration (SIC) prediction model is proposed, with eight predictors using a deep-learning approach, convolutional neural networks (CNNs). This monthly SIC prediction model based on CNNs is shown to perform better predictions (mean absolute error – MAE – of 2.28 %, anomaly correlation coefficient – ACC – of 0.98, root-mean-square error – RMSE – of 5.76 %, normalized RMSE – nRMSE – of 16.15 %, and NSE – Nash–Sutcliffe efficiency – of 0.97) than a random-forest-based (RF-based) model (MAE of 2.45 %, ACC of 0.98, RMSE of 6.61 %, nRMSE of 18.64 %, and NSE of 0.96) and the persistence model based on the monthly trend (MAE of 4.31 %, ACC of 0.95, RMSE of 10.54 %, nRMSE of 29.17 %, and NSE of 0.89) through hindcast validations. The spatio-temporal analysis also confirmed the superiority of the CNN model. The CNN model showed good SIC prediction results in extreme cases that recorded unforeseen sea ice plummets in 2007 and 2012 with RMSEs of less than 5.0 %. This study also examined the importance of the input variables through a sensitivity analysis. In both the CNN and RF models, the variables of past SICs were identified as the most sensitive factor in predicting SICs. For both models, the SIC-related variables generally contributed more to predict SICs over ice-covered areas, while other meteorological and oceanographic variables were more sensitive to the prediction of SICs in marginal ice zones. The proposed 1-month SIC prediction model provides valuable information which can be used in various applications, such as Arctic shipping-route planning, management of the fishing industry, and long-term sea ice forecasting and dynamics.

Sign in / Sign up

Export Citation Format

Share Document