scholarly journals Multiple Ensemble Neural Network Models with Fuzzy Response Aggregation for Predicting COVID-19 Time Series: The Case of Mexico

Healthcare ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 181 ◽  
Author(s):  
Patricia Melin ◽  
Julio Cesar Monica ◽  
Daniela Sanchez ◽  
Oscar Castillo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Fuzzy Logic ◽  
Network Models ◽  
Neural Network Models ◽  
Validation Data ◽  
Data Set ◽  
Ensemble Neural Networks ◽  
Ensemble Neural Network

In this paper, a multiple ensemble neural network model with fuzzy response aggregation for the COVID-19 time series is presented. Ensemble neural networks are composed of a set of modules, which are used to produce several predictions under different conditions. The modules are simple neural networks. Fuzzy logic is then used to aggregate the responses of several predictor modules, in this way, improving the final prediction by combining the outputs of the modules in an intelligent way. Fuzzy logic handles the uncertainty in the process of making a final decision about the prediction. The complete model was tested for the case of predicting the COVID-19 time series in Mexico, at the level of the states and the whole country. The simulation results of the multiple ensemble neural network models with fuzzy response integration show very good predicted values in the validation data set. In fact, the prediction errors of the multiple ensemble neural networks are significantly lower than using traditional monolithic neural networks, in this way showing the advantages of the proposed approach.

scholarly journals Evolutionary product unit based neural networks for hydrological time series analysis

2010 ◽  
Vol 13 (4) ◽  
pp. 825-841 ◽  
Author(s):  
Dulakshi S. K. Karunasingha ◽  
A. W. Jayawardena ◽  
W. K. Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Meteorological Data ◽  
Time Series Prediction ◽  
Network Models ◽  
Engineering Analysis ◽  
Neural Network Models ◽  
Mlp Neural Network ◽  
Evolutionary Product

Artificial Neural Networks (ANNs) are now widely used in many areas of science, medicine, finance and engineering. Analysis and prediction of time series of hydrological/and meteorological data is one such application. Problems that still exist in the application of ANN's are the lack of transparency and the expertise needed for training. An evolutionary algorithm-based method to train a type of neural networks called Product Units Based Neural Networks (PUNN) has been proposed in a 2006 study. This study investigates the applicability of this type of neural networks to hydrological time series prediction. The technique, with a few small changes to improve the performance, is applied to some benchmark time series as well as to a real hydrological time series for prediction. The results show that evolutionary PUNN produce more transparent models compared to widely used multilayer perceptron (MLP) neural network models. It is also seen that training of PUNN models requires less expertise compared to MLPs.

scholarly journals Development of models for maximum and time variation of storm surges at the Tanshui estuary

2014 ◽  
Vol 14 (9) ◽  
pp. 2313-2320 ◽  
Author(s):  
C.-P. Tsai ◽  
C.-Y. You
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Variation ◽  
Network Models ◽  
Storm Surges ◽  
Physical Parameters ◽  
Neural Network Models ◽  
Modeling And Forecasting ◽  
Time Variations

Abstract. In this study, artificial neural networks, including both multilayer perception and the radial basis function neural networks, are applied for modeling and forecasting the maximum and time variation of storm surges at the Tanshui estuary in Taiwan. The physical parameters, including both the local atmospheric pressure and the wind field factors, for finding the maximum storm surges, are first investigated based on the training of neural networks. Then neural network models for forecasting the time series of storm surges are accordingly developed using the major meteorological parameters with time variations. The time series of storm surges for six typhoons were used for training and testing the models, and data for three typhoons were used for model forecasting. The results show that both neural network models perform very well for the forecasting of the time variation of storm surges.

scholarly journals On the prediction of solar activity using different neural network models

1996 ◽  
Vol 14 (1) ◽  
pp. 20-26 ◽  
Author(s):  
F. Fessant ◽  
S. Bengio ◽  
D. Collobert
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Solar Activity ◽  
Time Series Prediction ◽  
Network Models ◽  
Accurate Prediction ◽  
Experimental Results ◽  
Neural Network Models ◽  
Telecommunication Companies

Abstract. Accurate prediction of ionospheric parameters is crucial for telecommunication companies. These parameters rely strongly on solar activity. In this paper, we analyze the use of neural networks for sunspot time series prediction. Three types of models are tested and experimental results are reported for a particular sunspot time series: the IR5 index.

scholarly journals Spectra Recognition Model for O-type Stars Based on Data Augmentation

2021 ◽  
Vol 8 ◽  
Author(s):  
Wen-Yu Yang ◽  
Ke-Fei Wu ◽  
A-Li Luo ◽  
Zhi-Qiang Zou
Keyword(s):  
Neural Network ◽  
Data Augmentation ◽  
Network Models ◽  
Neural Network Models ◽  
Generative Adversarial Network ◽  
Validation Data ◽  
Data Set ◽  
Recognition Model ◽  
Adversarial Network ◽  
The Neural Network

It is an ongoing issue in astronomy to recognize and classify O-type spectra comprehensively. The neural network is a popular recognition model based on data. The number of O-stars collected in LAMOST is <1% of AFGK stars, and there are only 127 O-type stars in the data release seven version. Therefore, there are not enough O-type samples available for recognition models. As a result, the existing neural network models are not effective in identifying such rare star spectra. This paper proposed a novel spectra recognition model (called LCGAN model) to solve this problem with data augmentation, which is based on Locally Connected Generative Adversarial Network (LCGAN). The LCGAN introduced the locally connected convolution and two timescale update rule to generate O-type stars' spectra. In addition, the LCGAN model adopted residual and attention mechanisms to recognize O-type spectra. To evaluate the performance of proposed models, we conducted a comparative experiment using a stellar spectral data set, which consists of more than 40,000 spectra, collected by the large sky area multi-object fiber spectroscopic telescope (LAMOST). The experimental results showed that the LCGAN model could generate meaningful O-type spectra. In our validation data set, the recognition accuracy of the data enhanced recognition model can reach 93.67%, 8.66% higher than that of the non-data enhanced identification model, which lays a good foundation for further analysis of astronomical spectra.

Modeling and Prediction of the CNY Exchange Rates Using RBF Neural Networks versus GARCH Models

2010 ◽  
Vol 39 ◽  
pp. 375-382 ◽  
Author(s):  
Zhao Cheng Liu ◽  
Xi Yu Liu ◽  
Zi Ran Zheng
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Exchange Rates ◽  
Transfer Functions ◽  
Rbf Neural Network ◽  
Network Models ◽  
Garch Models ◽  
Rbf Neural Networks ◽  
Neural Network Models

The CNY exchange rates can be viewed as financial time series which are characterized by high uncertainty, nonlinearity and time-varying behavior. Predictions for CNY exchange rates of GBP-CNY and USD-CNY were carried out respectively by means of RBF neural network forecasters and GARCH models. GARCH is a mechanism that includes past variances in the explanation of future variances and a time-series technique that we use to model the serial dependence of volatility. The detailed design of architectures of RBF neural network models, transfer functions of the hidden layer nodes, input vectors and output vectors were made with many tests. While experimental results show that the performance of RBF neural networks for forecasting spot CNY exchange rates is better than that of GARCH, both of them are acceptable and effective especially in short term predictions.

scholarly journals On the Determination of Groundwater Level using Temporal and Spatial Parameters: Advanced Machine Learning Methods

2021 ◽  
Author(s):  
Amirhossein Najafabadipour ◽  
Gholamreza Kamali ◽  
Hossein Nezamabadi-pour
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Groundwater Level ◽  
Network Models ◽  
Machine Intelligence ◽  
Neural Network Models ◽  
Validation Data ◽  
Data Set ◽  
Spatial Parameters

Abstract Prediction of groundwater level is a useful tool for managing groundwater resources in the mining area. Water resources management requires identifying potential periods for groundwater drainage to prevent groundwater from entering the mine pit and reduce high costs. For this purpose, four multilayer perceptron (MLP) neural network models and four cascade forward (CF) neural network models optimized with Bayesian Regularization (BR), Levenberg-Marquardt (LM), Resilient Backpropagation (RB), and Scaled Conjugate Gradient (SCG), as well as a radial basis function (RBF) neural network model and a generalized regression (GR) neural network model were developed to predict groundwater level using 1377 data point. This data set includes 12 spatial parameters divided into two categories of sediments and bedrock, and besides, 6 time series parameters have been used. Also, to determine the best models and combine them, 165 extra validation data points have been used. After identifying the best models from the three candidate models with lower average absolute relative error (AARE) value, the committee machine intelligence system (CMIS) model has been developed. The proposed CMIS model predicts groundwater level data with high accuracy with an AARE value of less than 0.11%. Also, the proposed model was compared with ten other models through graphical and statistical error analysis. The results show that the developed CMIS model performs better than other existing models in terms of precision and validity range. The relevancy factor indicates that the electrical resistivity of sediments had the highest effect on the groundwater level. Eventually, the quality of the data used was investigated both statistically and graphically, and the results show satisfactory reliability of the data used.

scholarly journals Dataset for Eye-Tracking Tasks

2020 ◽  
Author(s):  
Ildar Rakhmatulin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Eye Tracking ◽  
Deep Neural Networks ◽  
Network Models ◽  
Neural Network Models ◽  
Data Set ◽  
Tracking Tasks ◽  
Deep Networks ◽  
Using Data

In recent years many different deep neural networks were developed, but due to a large number of layers in deep networks, their training requires a long time and a large number of datasets. Today is popular to use trained deep neural networks for various tasks, even for simple ones in which such deep networks are not required. The well-known deep networks such as YoloV3, SSD, etc. are intended for tracking and monitoring various objects, therefore their weights are heavy and the overall accuracy for a specific task is low. Eye-tracking tasks need to detect only one object - an iris in a given area. Therefore, it is logical to use a neural network only for this task. But the problem is the lack of suitable datasets for training the model. In the manuscript, we presented a dataset that is suitable for training custom models of convolutional neural networks for eye-tracking tasks. Using data set data, each user can independently pre-train the convolutional neural network models for eye-tracking tasks. This dataset contains annotated 10,000 eye images in an extension of 416 by 416 pixels. The table with annotation information shows the coordinates and radius of the eye for each image. This manuscript can be considered as a guide for the preparation of datasets for eye-tracking devices.

Neural Network Techniques for Time Series Prediction: A Review

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Muhammad Faheem Mushtaq ◽  
Urooj Akram ◽  
Muhammad Aamir ◽  
Haseeb Ali ◽  
Muhammad Zulqarnain
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Weather Prediction ◽  
Time Series Prediction ◽  
Series Data ◽  
Prediction Problem ◽  
Neural Network Models ◽  
Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

scholarly journals Opening the black box of neural networks: methods for interpreting neural network models in clinical applications

2018 ◽  
Vol 6 (11) ◽  
pp. 216-216 ◽  
Author(s):  
Zhongheng Zhang ◽  
◽  
Marcus W. Beck ◽  
David A. Winkler ◽  
Bin Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Models ◽  
Black Box ◽  
Clinical Applications ◽  
Neural Network Models

scholarly journals Robotic grasp detection using a novel two-stage approach

2021 ◽  
Vol 1 (1) ◽  
pp. 19-29
Author(s):  
Zhe Chu ◽  
Mengkai Hu ◽  
Xiangyu Chen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Models ◽  
Particle Swarm Optimizer ◽  
Neural Network Models ◽  
Two Stage ◽  
The Neural Network ◽  
End To End ◽  
Small Change ◽  
Robotic Grasp

Recently, deep learning has been successfully applied to robotic grasp detection. Based on convolutional neural networks (CNNs), there have been lots of end-to-end detection approaches. But end-to-end approaches have strict requirements for the dataset used for training the neural network models and it’s hard to achieve in practical use. Therefore, we proposed a two-stage approach using particle swarm optimizer (PSO) candidate estimator and CNN to detect the most likely grasp. Our approach achieved an accuracy of 92.8% on the Cornell Grasp Dataset, which leaped into the front ranks of the existing approaches and is able to run at real-time speeds. After a small change of the approach, we can predict multiple grasps per object in the meantime so that an object can be grasped in a variety of ways.

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