A Generalized Feedforward Neural Network Architecture and Its Training Using Two Stochastic Search Methods

Bouzerdoum, Abdesselam; Mueller, Rainer

doi:10.1007/3-540-45105-6_89

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…GFNs are a generalization of the Multi-Layer Perceptrons (MLPs), so that connections can be over one or more layers [63,64]. In theory, MLP models can analyze an algorithm that GFN models can solve, but, in practice, GFNs are much more efficient in exploring such a connection due to the smaller number of training epochs required by the algorithm [65]. There are no specific rules for defining the control parameters, and they are mostly based on the results of previous studies and experts' opinions [66,67].…”

Section: The Model Using Annmentioning

confidence: 99%

The Role of Artificial Intelligence, MLR and Statistical Analysis in Investigations about the Correlation of Swab Tests and Stress on Health Care Systems by COVID-19

2020

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The outbreak of the new Coronavirus (COVID-19) pandemic has prompted investigations on various aspects. This research aims to study the possible correlation between the numbers of swab tests and the trend of confirmed cases of infection, while paying particular attention to the sickness level. The study is carried out in relation to the Italian case, but the result is of more general importance, particularly for countries with limited ICU (intensive care units) availability. The statistical analysis showed that, by increasing the number of tests, the trend of home isolation cases was positive. However, the trend of mild cases admitted to hospitals, intensive case cases, and daily deaths were all negative. The result of the statistical analysis provided the basis for an AI study by ANN. In addition, the results were validated using a multivariate linear regression (MLR) approach. Our main result was to identify a significant statistical effect of a reduction of pressure on the health care system due to an increase in tests. The relevance of this result is not confined to the COVID-19 outbreak, because the high demand of hospitalizations and ICU treatments due to this pandemic has an indirect effect on the possibility of guaranteeing an adequate treatment for other high-fatality diseases, such as, e.g., cardiological and oncological ones. Our results show that swab testing may play a significant role in decreasing stress on the health system. Therefore, this case study is relevant, in particular, for plans to control the pandemic in countries with a limited capacity for admissions to ICU units.

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Section: The Model Using Annmentioning

confidence: 99%

The Role of Artificial Intelligence, MLR and Statistical Analysis in Investigations about the Correlation of Swab Tests and Stress on Health Care Systems by COVID-19

2020

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“…The training and test dataset ration is 3:1. The GFNNs are a generalization of the conventional multilayer perceptron such that connections can jump over one or more layers (Arulampalam and Bouzerdoum, 2003;Bouzerdoum and Mueller, 2003). Theoretical and mathematical details are available in Rojas (1996).…”

Section: Development Of Artificial Neural Network (Ann)mentioning

confidence: 99%

Monthly sunspot number time series analysis and its modeling through autoregressive artificial neural network

Chattopadhyay

Chattopadhyay²

2012

Eur. Phys. J. Plus

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This study reports a statistical analysis of monthly sunspot number time series and observes non homogeneity and asymmetry within it. Using Mann-Kendall test a linear trend is revealed. After identifying stationarity within the time series we generate autoregressive AR(p) and autoregressive moving average (ARMA(p,q)). Based on minimization of AIC we find 3 and 1 as the best values of p and q respectively. In the next phase, autoregressive neural network (AR-NN(3)) is generated by training a generalized feedforward neural network (GFNN). Assessing the model performances by means of Willmott's index of second order and coefficient of determination, the performance of AR-NN(3) is identified to be better than AR(3) and ARMA(3,1).

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“…Generalized feedforward neural networks (GFFNN) are a generalization of the MLP such that connections can jump over one or more layers (Arulampalam and Bouzerdoum, 2003;Bouzerdoum and Mueller, 2003).…”

Section: Brief Review Of the Ann Models Under Studymentioning

confidence: 99%

Predicting daily total ozone over Kolkata, India: skill assessment of different neural network models

Chattopadhyay

Chattopadhyay²

2008

Meteorological Applications

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This paper explores the observation made by the Earth Probe Total Ozone Mapping Spectrometer (EP/TOMS) to analyse the predictability of daily total ozone concentration over Kolkata, India. Latitude, longitude, aerosol index, reflectivity, sulphur dioxide index and total ozone concentration of a given day have been used as independent variables to predict total ozone concentration of the next day. Artificial neural network in the forms of a multilayer perceptron, generalized feed forward neural network, a radial basis function network and a modular neural network have been trained to generate predictive models. Performances of the models in the test cases have been judged with the help of four statistical parameters. Finally the models have been compared with multiple linear regression and the potential of generalized feed forward neural network has been established over the other proposed models.

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A Generalized Feedforward Neural Network Architecture and Its Training Using Two Stochastic Search Methods

Cited by 4 publications

References 2 publications

The Role of Artificial Intelligence, MLR and Statistical Analysis in Investigations about the Correlation of Swab Tests and Stress on Health Care Systems by COVID-19

The Role of Artificial Intelligence, MLR and Statistical Analysis in Investigations about the Correlation of Swab Tests and Stress on Health Care Systems by COVID-19

Monthly sunspot number time series analysis and its modeling through autoregressive artificial neural network

Predicting daily total ozone over Kolkata, India: skill assessment of different neural network models

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