IEEE Systems, Man and Cybernetics Magazine - July 2022 - 58

of membership functions. This model helps to model more
uncertainty without adding additional complexity. Such
models encourage researchers to use T2F models without
the overheads of their 3D forms. For the evaluation of our
model, TAIEX and COVID-19 forecasting was employed,
which is a commonly employed benchmark to show model
efficiency. From the empirical results, it is inferable that
this model can adequately forecast the Taiwan stock
index and COVID-19 in addition to its intelligible steps;
therefore, it can be employed in tasks with considerable
uncertainty. For future studies, one of the best paths to
investigate is changing the inputs, such as developing a
TT2FLR model with T2F input instead of crisp input. This
change in input may help in modeling uncertainty and can
improve results.
Additionally, in future studies, it is possible to apply the
proposed method for the detection of various diseases,
such as epileptic seizure [44]-[46]; autism spectrum disorder
[47]; schizophrenia [48], [49]; multiple sclerosis [50];
and other diseases [51]-[55]. Also, future studies can use
the new method to test neural networks and so forth,
which have an uncertainty similar to the COVID-19 prediction
[56]-[68].
About the Authors
Assef Zare (assefzare@iau-gonabad.ac.ir) earned his Ph.D.
degree in control and automation systems from Islamic
Azad University Science and Research Branch, Tehran, Iran,
in 2001, his M.Sc. degree in control and automation systems
from the University of Tehran in 1993, and his B.S. degree in
electronic engineering from the Sharif University of Technology,
Tehran, Iran, in 1990. Currently, he is an associate
professor in the Department of Electrical Engineering,
Islamic Azad University, Gonabad Branch, Gonabad, Khorasan
Razavi, 9691664791, Iran. His research interests
include type-2 fuzzy logic, fuzzy control, robust control, and
adaptive control.
Afshin Shoeibi (afshin.shoeibi@gmail.com) earned his
M.Sc. degrees in telecommunication and digital signal processing
engineering from the Electrical Engineering Faculty
of the Islamic Azad University, Gonabad Branch in
2016. Currently, he is a researcher with the Faculty of
Electrical Engineering, FGPA Lab, K. N. Toosi University
of Technology, Tehran, 9691638413, Iran. His research
interests include advanced chaotic biomedical signal processing,
biomedical embedded systems design, deep
learning, fuzzy systems, functional brain imaging systems
(functional magnetic resonance imaging, functional nearinfrared
spectroscopy, and magnetoencephalography),
computational neuroscience, and very large-scale integration
for machine learning.
Narges Shafaei Bajestani (narges.shafaei@gmail.
com) earned her Ph.D. degree from the Islamic Azad
University, Science and Research Branch, Tehran, Iran in
2017, her M.S. degree from the Islamic Azad University,
Gonabad Branch, Gonabad, Iran, in 2009, and her B.S.
58 IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE July 2022
degree from the Ferdowsi University of Mashhad, Iran, in
2006. Currently, she is an assistant professor at the Department
of Electrical Engineering, Islamic Azad University,
Gonabad Branch, Khorasan Razavi, 9691911111, Iran. Her
research interests include type-1 and -2 fuzzy sets and systems,
type-1 and -2 fuzzy regression, intelligent systems, and
their applications.
Parisa Moridian (parisamoridian@yahoo.com)
received her M.Sc. degree in control engineering from the
Islamic Azad University, Science and Research, Tehran
Branch, Iran, in 2017. Currently, she is a researcher with the
Faculty of Engineering, Science and Research Branch,
Islamic Azad University, Tehran, 1651153311, Iran. Her current
research interests include adaptive control, biomedical
signal processing, neural networks, and soft computing.
Roohallah Alizadehsani (r.alizadehsani@deakin.edu.
au) earned his B.S. degree in computer engineeringsoftware
from the Sharif University of Technology, Tehran,
Iran, and his M.S. degree in computer engineering-software
from the Sharif University of Technology, Tehran,
Iran, in 2012. He has worked in the Software Engineering
Lab under the supervision of Dr. Jafar Habibi since 2011.
Currently, he is a Ph.D. student under the supervision of
Dr. Abbas Khosravi at the Institute for Intelligent Systems
Research and Innovation, Deakin University, Victoria
3217, Australia. His research interests include the areas of
data mining, machine learning, bioinformatics, heart disease,
skin disease, diabetes, hepatitis, and cancer. He is a
Member of IEEE.
Majid Hallaji (majid.halaji@gmail.com) earned his
Ph.D. degree in control and automation systems from Semnan
University, Iran, in 2019, his M.S. degree in control and
automation systems from the Islamic Azad University,
Gonabad Branch, Gonabad, Iran, in 2008, and his B.S.
degree in electrical engineering from the Islamic Azad
University, Gonabad Branch in 2005. Currently, he is an
assistant professor in the Electrical Engineering Faculty
of the Islamic Azad University, Neyshabure Branch,
Neyshabure, Khorasan Razavi, Iran. His research interests
include fuzzy control, robust control, optimal control, and
fuzzy calculus.
Abbas Khosravi (abbas.khosravi@deakin.edu.au)
earned his M.Sc. degree (with honors) in electrical engineering
from the Amirkabir University of Technology, Tehran,
Iran, in 2005, and his B.Sc. degree in electrical engineering
from the Sharif University of Technology, Tehran,
Iran, in 2002. He joined the eXiT Group as a research academic
with the University of Girona, Girona, Spain, in 2006,
researching in the area of artificial intelligence (AI) applications.
Currently, he is a senior research fellow with the
Institute for Intelligent Systems Research and Innovation,
Deakin University, Victoria 3217, Australia. His research
interests include the development and application of AI
techniques for (meta) modeling, analysis, control, and optimization
of operations within complex systems. He is a
Senior Member of IEEE.
IEEE Systems, Man and Cybernetics Magazine - July 2022

Table of Contents for the Digital Edition of IEEE Systems, Man and Cybernetics Magazine - July 2022
