IEEE Systems, Man and Cybernetics Magazine - July 2022 - 57

Table 1. A summary of comparison with other state-of-the-art methods done
using the same database (TAIEX forecasting data).
T2F Models
RMSE
Parameter
Training Data
Test Data
Classic
Regression
212
282
Type-1
Fuzzy [34]
176
189
Type-1 Fuzzy
Regression [9]
163
201
Reference
[35]
139
165
Reference
[25]
113
182
Reference
[27]
107
179
Reference
[30]
66
109
Proposed
Method
49
95
Table 2. A summary of comparison with other state-of-the-art methods done
using the COVID-19 data set [17].
T2F Models
RMSE
Parameter
Training Data
Test Data
Classic
Regression
8,452
9,592
Type-1
Fuzzy [34]
6,923
7,210
Type-1 Fuzzy
Regression [9]
7,245
7,012
uncertainty will have a more favorable final answer. The
TT2FLR model developed the previous models from the
IT2FR model to a triangular T2FR model; in addition, it is
able to perform better in practice. In fact, the proposed
model has shown its efficiency.
Now, we will explain another example about COVID-19
detection in patients. Because of the high pandemic nature
of this disease, better management of this disease across
the world will be extremely useful. This disease is very
infectious, and there are many infected people all over the
world. Consequently, if we are able to predict the number
of potential patients more precisely, it will help us design
better management systems to control the COVID-19 pandemic
[36]-[42].
The data set used in this research was the number of
patients in the world from 22 January 2020 until 14 September
2021 [43]. The results of applying the proposed
method on these data are shown in Table 2.
In Figure 8, the mortality prediction in COVID-19
patients was plotted. According to Figure 8, it is clear that
the proposed method had acceptable performance in the
prediction of mortality in COVID-19 patients.
The advantages of the proposed model are as follows:
1) The proposed model is simple and does not require more
computation.
2) The model is able to overcome the uncertainty efficiently
and predict accurately.
3) The model is robust even in a noisy environment.
4) Such models can also be used in other uncertain prediction
applications, such as stock market prediction and so forth.
The limitations of our work are as follows:
1) The model has been developed using only one database
(TAIEX forecasting data). It needs to be tested with
more data in the future.
Reference
[35]
4356
5583
Reference
[25]
3,934
4,567
Reference
[27]
3,610
4,795
Reference
[30]
3,167
3,845
Proposed
Method
2,960
3,079
2) The value of H is considered by trial and error while it
can be found by an optimization approach.
Conclusion and Future Studies
The current article shows a triangular regression model of
T2F in an efficient and sensible way. The proposed IT2FS
model is less computationally intense to use due to its smaller
dimension. Also, this model can easily extend other types
Total Coronavirus Deaths and Its Prediction
12
10
8
6
4
2
104
Test
Train
010203040
50 60 70 80 90
Date
Actual
Poleshchuk et al. [25] Approach Model
WGP Model
IT2F Regression Model
TT2FR Model
Figure 8. A daily prediction of world mortality in
COVID-19 patients.
July 2022 IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE 57
Total Coronavirus Deaths
IEEE Systems, Man and Cybernetics Magazine - July 2022

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