Estimation of parameters based on artificial neural networks and threshold of HIV/AIDS epidemic system in Cuba

Atencia, Miguel; García-Garaluz, Esther; Arazoza, Hector De; Joya, Gonzalo

doi:10.1016/j.mcm.2013.03.007

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…In 2017, Sandhu et al (2018a, b) proposed a method to effectively store healthcare data in the forms of tensors which can be mined easily. In 2013, Atencia et al (2013) proposed parameter estimation based upon NN for the estimation of HIV/AIDS. In 2020, Kang et al (2020) reviewed that how machine learning or artificial NNs can be efficiently used to provide improved healthcare.…”

Section: Related Workmentioning

confidence: 99%

A deep neural network based context-aware smart epidemic surveillance in smart cities

Gill

Sehgal

Verma

2021

LHT

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PurposeEpidemics not only affect the public health but also are a threat to a nation's growth and economy as well. Early prediction of epidemic can be beneficial to take preventive measures and to reduce the impact of epidemic in an area.Design/methodology/approachA deep neural network (DNN) based context aware smart epidemic system has been proposed to prevent and monitor epidemic spread in a geographical area. Various neural networks (NNs) have been used: LSTM, RNN, BPNN to detect the level of disease, direction of spread of disease in a geographical area and marking the high-risk areas. Multiple DNNs collect and process various data points and these DNNs are decided based on type of data points. Output of one DNN is used by another DNN to reach to final prediction.FindingsThe experimental evaluation of the proposed framework achieved the accuracy of 87% for the synthetic dataset generated for Zika epidemic in Brazil in 2016.Originality/valueThe proposed framework is designed in a way that every data point is carefully processed and contributes to the final decision. These multiple DNNs will act as a single DNN for the end user.

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Section: Related Workmentioning

confidence: 99%

A deep neural network based context-aware smart epidemic surveillance in smart cities

Gill

Sehgal

Verma

2021

LHT

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“…In the last couple of decades, medicine has become the playground of traditional machine learning techniques, such as random forests, support vector machines and neural networks [1,3,5,8,10] and more recently of deep learning methods [4,7,11]. The current problem has been tackled by shallow learning methods, however only to grasp the complexity of the classification task: healthy and presymptomatic registers are easily mistaken for each other [2].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Learning as a Complement to Convolutional Neural Network Classification in the Analysis of Saccadic Eye Movement in Spino-Cerebellar Ataxia Type 2

Stoean

Becerra-García

et al. 2019

Advances in Computational Intelligence

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This paper aims at assessing spino-cerebellar type 2 ataxia by classifying electrooculography records into registers corresponding to healthy, presymptomatic and ill individuals. The primary used technique is the convolutional neural network applied to the time series of eye movements, called saccades. The problem is exceptionally hard, though, because the recorded saccadic movements for presymptomatic cases often do not substantially differ from those of healthy individuals. Precisely this distinction is of the utmost clinical importance, since early intervention on presymptomatic patients can ameliorate symptoms or at least slow their progression. Yet, each register contains a number of saccades that, although not consistent with the current label, have not been considered indicative of another class by the examining physicians. As a consequence, an unsupervised learning mechanism may be more suitable to handle this form of misclassification. Thus, our proposal introduces the k-means approach and the SOM method, as complementary techniques to analyse the time series. The three techniques operating in tandem lead to a well performing solution to this diagnosis problem.

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A stochastic age-structured HIV/AIDS model based on parameters estimation and its numerical calculation

Ren

Zhang

et al. 2021

Mathematics and Computers in Simulation

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Estimation of parameters based on artificial neural networks and threshold of HIV/AIDS epidemic system in Cuba

Cited by 5 publications

References 19 publications

A deep neural network based context-aware smart epidemic surveillance in smart cities

A deep neural network based context-aware smart epidemic surveillance in smart cities

Unsupervised Learning as a Complement to Convolutional Neural Network Classification in the Analysis of Saccadic Eye Movement in Spino-Cerebellar Ataxia Type 2

A stochastic age-structured HIV/AIDS model based on parameters estimation and its numerical calculation

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