Prediction of Zika Virus by Multilayer Perceptron Neural Network (MLPNN) using Cloud

doi:10.35940/ijrte.b1041.0982s1119

Cited by 6 publications

(2 citation statements)

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“…For Zika virus disease prediction, we noticed that the authors investigated different algorithms to predict positive cases of the disease. Jarrin et al (73) evaluated support vector machines (SVM) and logistic regression to build their models, whereas Jarrin et al (74) and Mahalakshmi and Suseendran (75) investigated Random Forest and Multilayer Percetron (MLP) algorithms, respectively. Jarrin et al (73) investigated SVM and RL algorithmsimplemented in Python 3.7-to classify individual samples into "infected" or "uninfected" with the Zika virus.…”

Section: Arboviruses Detectionmentioning

confidence: 99%

Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review

Lima

Silva

Moreno

et al. 2022

Front. Public Health

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Arboviruses are a group of diseases that are transmitted by an arthropod vector. Since they are part of the Neglected Tropical Diseases that pose several public health challenges for countries around the world. The arboviruses' dynamics are governed by a combination of climatic, environmental, and human mobility factors. Arboviruses prediction models can be a support tool for decision-making by public health agents. In this study, we propose a systematic literature review to identify arboviruses prediction models, as well as models for their transmitter vector dynamics. To carry out this review, we searched reputable scientific bases such as IEE Xplore, PubMed, Science Direct, Springer Link, and Scopus. We search for studies published between the years 2015 and 2020, using a search string. A total of 429 articles were returned, however, after filtering by exclusion and inclusion criteria, 139 were included. Through this systematic review, it was possible to identify the challenges present in the construction of arboviruses prediction models, as well as the existing gap in the construction of spatiotemporal models.

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Section: Arboviruses Detectionmentioning

confidence: 99%

Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review

Lima

Silva

Moreno

et al. 2022

Front. Public Health

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“…Rubio-Solis A [28] presented the concept of using online extreme learning machines and neural networks to predict the Aedes mosquito larval incidence in Recife (Brazil). Mahalakshmi B [29] predicted zika virus using a cloud-based multilayer perceptron neural network (MLPNN).…”

Section: Introductionmentioning

confidence: 99%

Recurrent neural network for the dynamics of Zika virus spreading

Nisar,

Anjum,

Raja

et al. 2024

AIMSPH

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<abstract> <p>Recurrent Neural Networks (RNNs), a type of machine learning technique, have recently drawn a lot of interest in numerous fields, including epidemiology. Implementing public health interventions in the field of epidemiology depends on efficient modeling and outbreak prediction. Because RNNs can capture sequential dependencies in data, they have become highly effective tools in this field. In this paper, the use of RNNs in epidemic modeling is examined, with a focus on the extent to which they can handle the inherent temporal dynamics in the spread of diseases. The mathematical representation of epidemics requires taking time-dependent variables into account, such as the rate at which infections spread and the long-term effects of interventions. The goal of this study is to use an intelligent computing solution based on RNNs to provide numerical performances and interpretations for the SEIR nonlinear system based on the propagation of the Zika virus (SEIRS-PZV) model. The four patient dynamics, namely susceptible patients S(y), exposed patients admitted in a hospital E(y), the fraction of infective individuals I(y), and recovered patients R(y), are represented by the epidemic version of the nonlinear system, or the SEIR model. SEIRS-PZV is represented by ordinary differential equations (ODEs), which are then solved by the Adams method using the Mathematica software to generate a dataset. The dataset was used as an output for the RNN to train the model and examine results such as regressions, correlations, error histograms, etc. For RNN, we used 100% to train the model with 15 hidden layers and a delay of 2 seconds. The input for the RNN is a time series sequence from 0 to 5, with a step size of 0.05. In the end, we compared the approximated solution with the exact solution by plotting them on the same graph and generating the absolute error plot for each of the 4 cases of SEIRS-PZV. Predictions made by the model appeared to be become more accurate when the mean squared error (MSE) decreased. An increased fit to the observed data was suggested by this decrease in the MSE, which suggested that the variance between the model's predicted values and the actual values was dropping. A minimal absolute error almost equal to zero was obtained, which further supports the usefulness of the suggested strategy. A small absolute error shows the degree to which the model's predictions matches the ground truth values, thus indicating the level of accuracy and precision for the model's output.</p> </abstract>

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