Hybrid Approach based on SARIMA and Artificial Neural Networks for Knowledge Discovery Applied to Crime Rates Prediction

Soares, Felipe A. L.; Silveira, Tiago B.; Freitas, Henrique C.

doi:10.5220/0009412704070415

Cited by 6 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…7.85 billion and the number of people completed vaccination is 3,289,529,390 [51] . To attain the most accurate forecast, the parameters of the SARIMA model are investigated using a grid search approach [46] , [27] . Finally, the best SARIMA model’s parameters are estimated and used to forecast the total number of vaccination for the next 30 days.…”

Section: Experimental Results and Evaluationsmentioning

confidence: 99%

See 1 more Smart Citation

SARIMA model-based forecasting required number of COVID-19 vaccines globally and empirical analysis of peoples’ view towards the vaccines

Malki

Atlam²,

Hassanien³

et al. 2022

Alexandria Engineering Journal

View full text Add to dashboard Cite

Section: Experimental Results and Evaluationsmentioning

confidence: 99%

“…The SARIMA method has seven parameters. The first three trend parameters represented as

describes the non-seasonal part and four of the seasonal parameters represented as

deals with the seasonal part of the model [45] , [46] . The mathematical formula of the SARIMA model is shown in Eq.…”

Section: The Framework Of the Proposed Methodologymentioning

confidence: 99%

SARIMA model-based forecasting required number of COVID-19 vaccines globally and empirical analysis of peoples’ view towards the vaccines

Malki

Atlam²,

Hassanien³

et al. 2022

Alexandria Engineering Journal

View full text Add to dashboard Cite

“…According to Soares et al (2020), this process has five steps: 1—data selection and organization; 2—preprocessing, the data is analyzed and goes through adequacy; 3—data storage to facilitate the use of data mining techniques; 4—data mining application; and 5—the interpretation and evaluation of the results, verifying if the generated information has validity for the proposed problem.…”

Section: Methodsmentioning

confidence: 99%

Automated and intelligent system for World Health Organization data forecasting

Soares,

de Freitas

2023

Expert Systems

View full text Add to dashboard Cite

Technological advances and social transformations have enabled the circulation of a large amount of data in the health area. Analyzing this data becomes more critical and more challenging as the volume of data increases. An alternative to performing this analysis is to use data analysis techniques to process input data sets and build consistent databases for input to machine learning algorithms. Thus, it can forecast future scenarios and collaborate with knowledge discovery. In this context, this work aims to develop a parameterizable system with automated decisions capable of collecting and analyzing many indicators provided by the World Health Organization (WHO). After these analysis steps, the system applies machine learning algorithms for predictions of different indicators to process information automatically, finding different knowledge discovery scenarios. Thus, the contribution of this article is an automated and intelligent system for WHO data forecasting. The efficiency of the system's choices and forecasts was proven with experiments in five different areas of health, obtaining assertiveness by up to 99.92%, root‐mean‐square error (RMSE) by up to 0.0286, and Kling‐Gupta Efficiency (KGE) by up to 0.9988, hitting even the most complex cases, as shown in the confusion matrices. Finally, three case studies were carried out to expand the studies and present the potential of the system in different contexts: anemia in children, age‐standardized suicide rates in men, and number of road traffic deaths.

show abstract