An Improved Type-1 Fuzzy Regression Function for COVID-19 Cases Prediction DOI

Rana Aziz Yousif Almuttalibi,

Aref Jeribi,

Saad Naji Al-Azzawi

et al.

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 32(06), P. 833 - 887

Published: Sept. 1, 2024

The deficiency in data collection and reporting has led to the emergence of uncertainty pandemic process. These matters cause that traditional statistical mathematical models have been functionless unreliable this issue. In respect, study presents an ensemble prediction model with innovative contemporary properties COVID-19 cases UK USA inclusively throughout whole proposed is composed assembly Type-1 fuzzy regression functions elastic net regularization (E-T1FRF) radial basis function neural networks (RBFNNs). Thus, can successfully modelling perspective T1FRF also thoroughly adapt patterns thanks flexible ability RBFNN based on data. With model, entire process from beginning March 2020 end June 2022 were modelled predicted for 23 different periods one-month steps. produced predictions MAPE values below 3% all but except three periods. Also, average obtained at around 2% only 1.5% US. results, a reasonable level error, demonstrated practicality usage community other countries provided valuable information future action.

Language: Английский

Multi-attribute decision-making based on similarity measure between picture fuzzy sets and the MARCOS method DOI
Pratibha Rani, Shyi‐Ming Chen, Arunodaya Raj Mishra

et al.

Information Sciences, Journal Year: 2023, Volume and Issue: 658, P. 119990 - 119990

Published: Dec. 22, 2023

Language: Английский

Citations

25
Blending traditional and novel techniques: Hybrid type-1 fuzzy functions for forecasting DOI
Ali Zafer Dalar, Erol Eğrioğlu

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 148, P. 110445 - 110445

Published: March 6, 2025

Language: Английский

Citations

0
PIFS, ARC and Markov model based hybrid method for fuzzy time series forecasting DOI
Shivani Pant, Sanjay Kumar

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127510 - 127510

Published: April 1, 2025

Language: Английский

Citations

0
Multiattribute decision making based on q-rung orthopair fuzzy Yager prioritized weighted arithmetic aggregation operator of q-rung orthopair fuzzy numbers DOI
Kamal Kumar, Shyi‐Ming Chen

Information Sciences, Journal Year: 2023, Volume and Issue: 657, P. 119984 - 119984

Published: Dec. 12, 2023

Language: Английский

Citations

9
Multiple attribute decision making based on score function of q-connection numbers, q-CNPWG aggregation operator of q-connection numbers, and set pair analysis theory in the environments of q-rung orthopair fuzzy numbers DOI
Kamal Kumar, Shyi‐Ming Chen

Information Sciences, Journal Year: 2024, Volume and Issue: 678, P. 120985 - 120985

Published: June 12, 2024

Language: Английский

Citations

2
A new ensemble intuitionistic fuzzy-deep forecasting model: Consolidation of the IFRFs-bENR with LSTM DOI
Özge Cağcağ Yolcu, Ufuk Yolcu

Information Sciences, Journal Year: 2024, Volume and Issue: 679, P. 121007 - 121007

Published: June 22, 2024

Language: Английский

Citations

2
A hybrid deep recurrent artificial neural network with a simple exponential smoothing feedback mechanism DOI
Özlem Karahasan, Eren Baş, Erol Eğrioğlu

et al.

Information Sciences, Journal Year: 2024, Volume and Issue: 686, P. 121356 - 121356

Published: Aug. 23, 2024

Language: Английский

Citations

1
A Novel Approach for Optimal Cluster Identification and N-Order Hesitation Based Time Series Forecasting DOI
Ankit Dixit, Shikha Jain

SN Computer Science, Journal Year: 2024, Volume and Issue: 5(7)

Published: Sept. 11, 2024

Language: Английский

Citations

0
An Improved Type-1 Fuzzy Regression Function for COVID-19 Cases Prediction DOI

Rana Aziz Yousif Almuttalibi,

Aref Jeribi,

Saad Naji Al-Azzawi

et al.

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, Journal Year: 2024, Volume and Issue: 32(06), P. 833 - 887

Published: Sept. 1, 2024

The deficiency in data collection and reporting has led to the emergence of uncertainty pandemic process. These matters cause that traditional statistical mathematical models have been functionless unreliable this issue. In respect, study presents an ensemble prediction model with innovative contemporary properties COVID-19 cases UK USA inclusively throughout whole proposed is composed assembly Type-1 fuzzy regression functions elastic net regularization (E-T1FRF) radial basis function neural networks (RBFNNs). Thus, can successfully modelling perspective T1FRF also thoroughly adapt patterns thanks flexible ability RBFNN based on data. With model, entire process from beginning March 2020 end June 2022 were modelled predicted for 23 different periods one-month steps. produced predictions MAPE values below 3% all but except three periods. Also, average obtained at around 2% only 1.5% US. results, a reasonable level error, demonstrated practicality usage community other countries provided valuable information future action.

Language: Английский

Citations

0