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Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches
COVID‐19 is the disease evoked by a new breed of coronavirus called the severe acute
respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Recently, COVID‐19 has become a …
respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Recently, COVID‐19 has become a …
Optimization in the context of COVID-19 prediction and control: A literature review
This paper presents an overview of some key results from a body of optimization studies that
are specifically related to COVID-19, as reported in the literature during 2020-2021. As …
are specifically related to COVID-19, as reported in the literature during 2020-2021. As …
Customer churn prediction model in cloud environment using DFE-WUNB: ANN deep feature extraction with weight updated tuned Naïve bayes classification with …
With progressing competitive market, different organizations were desperate to hold this
churn rate as minimum value, hence to achieve this, building an effective (CCP) customer …
churn rate as minimum value, hence to achieve this, building an effective (CCP) customer …
Epidemic efficacy of Covid-19 vaccination against Omicron: An innovative approach using enhanced residual recurrent neural network
The outbreak of COVID-19 has engulfed the entire world since the end of 2019, causing
tremendous loss of lives. It has also taken a toll on the healthcare sector due to the inability …
tremendous loss of lives. It has also taken a toll on the healthcare sector due to the inability …
A new interval type-2 fuzzy aggregation approach for combining multiple neural networks in clustering and prediction of time series
M Ramírez, P Melin - International Journal of Fuzzy Systems, 2023 - Springer
Inspired by how some cognitive abilities affect the human decision-making process, the
proposed approach combines neural networks with type-2 fuzzy systems. The proposal …
proposed approach combines neural networks with type-2 fuzzy systems. The proposal …
A new perspective for multivariate time series decision making through a nested computational approach using type-2 fuzzy integration
M Ramirez, P Melin - Axioms, 2023 - mdpi.com
The integration of key indicators from the results of the analysis of time series represents a
constant challenge within organizations; this could be mainly due to the need to establish …
constant challenge within organizations; this could be mainly due to the need to establish …
A decision-making approach based on multiple neural networks for clustering and prediction of time series
M Ramirez, P Melin - Hybrid Intelligent Systems Based on Extensions of …, 2023 - Springer
Today, it is extremely important to have computational models to per-form tasks such as the
classification and prediction of historical data, which operate as tools to support decision …
classification and prediction of historical data, which operate as tools to support decision …
Multiple neural networks for clustering and prediction of the particulate matter (PM2. 5): a case study of Bei**g
M Ramírez, P Melin - International Conference on Intelligent and Fuzzy …, 2023 - Springer
Air pollution represents a world health problem for decades. The monitoring of pollutants in
the air is a process that allows using the information recorded in a period to notify what the …
the air is a process that allows using the information recorded in a period to notify what the …
Data mining based techniques for COVID-19 predictions
R Rane, A Dubey, A Rasool, R Wadhvani - Procedia Computer Science, 2023 - Elsevier
COVID-19 is a pandemic that has resulted in numerous fatalities and infections in recent
years, with a rising tendency in both the number of infections and deaths and the pace of …
years, with a rising tendency in both the number of infections and deaths and the pace of …
Clustering and prediction of time series for traffic accidents using a nested layered artificial neural network model
M Ramirez, P Melin - New Perspectives on Hybrid Intelligent System …, 2022 - Springer
Our proposal consists of using a nested layer model in which an unsupervised artificial
neural network method is used as the first layer to perform tasks of clustering time series …
neural network method is used as the first layer to perform tasks of clustering time series …