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

Rana Aziz Yousif Almuttalibi,

Aref Jeribi,

Saad Naji Al-Azzawi

и другие.

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, Год журнала: 2024, Номер 32(06), С. 833 - 887

Опубликована: Сен. 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.

Язык: 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

и другие.

Information Sciences, Год журнала: 2023, Номер 658, С. 119990 - 119990

Опубликована: Дек. 22, 2023

Язык: Английский

Процитировано

26
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, Год журнала: 2023, Номер 657, С. 119984 - 119984

Опубликована: Дек. 12, 2023

Язык: Английский

Процитировано

10
Blending traditional and novel techniques: Hybrid type-1 fuzzy functions for forecasting DOI
Ali Zafer Dalar, Erol Eğrioğlu

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 148, С. 110445 - 110445

Опубликована: Март 6, 2025

Язык: Английский

Процитировано

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

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 127510 - 127510

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
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, Год журнала: 2024, Номер 678, С. 120985 - 120985

Опубликована: Июнь 12, 2024

Язык: Английский

Процитировано

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

Information Sciences, Год журнала: 2024, Номер 679, С. 121007 - 121007

Опубликована: Июнь 22, 2024

Язык: Английский

Процитировано

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

и другие.

Information Sciences, Год журнала: 2024, Номер 686, С. 121356 - 121356

Опубликована: Авг. 23, 2024

Язык: Английский

Процитировано

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

SN Computer Science, Год журнала: 2024, Номер 5(7)

Опубликована: Сен. 11, 2024

Язык: Английский

Процитировано

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

Rana Aziz Yousif Almuttalibi,

Aref Jeribi,

Saad Naji Al-Azzawi

и другие.

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, Год журнала: 2024, Номер 32(06), С. 833 - 887

Опубликована: Сен. 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.

Язык: Английский

Процитировано

0