IEEE Systems, Man and Cybernetics Magazine - July 2022 - 7

itself due to restrictions of visual angles
and resources. For instance, the camera
mounted on the front of a vehicle captures
only the data from the road ahead;
it cannot collect information from the
side. The local cloud (LC) is a new
communication paradigm, where cloud
members collaboratively generate data
locally by sharing
resources. Thus, the
integration of the IoT
and LC (ITLC) is an
effective way to overcome
the resource
restrictions of an individual
device. Named
data networking
(NDN) is a novel and
efficient communication
mechanism, and
its features can assist
in realizing the ITLC and enhancing the
efficiency of ITLC-based data delivery.
However, the authors point out that
NDN has different architectures and
features from the ITLC. Therefore, it is
challenging to exploit NDN to make the
ITLC a reality. They propose an edgeassisted
NDN-based ITLC framework
and use evaluation results to verify the
advances of the framework.
" The Need for Quantification of Uncertainty
in Artificial Intelligence for
Clinical Data Analysis: Increasing the
Level of Trust in the Decision-Making
Process " is authored by Moloud Abdar,
Abbas Khosravi, Sheikh Mohammed
Shariful Islam, U. Rajendra Acharya,
and Athanasios V. Vasilakos, another
international
research team representing
Deakin University, Australia;
Singapore University of Social Sciences;
and Fuzhou University, China. In this article,
trust and certainty are considered
important elements in the research of AI
applications. The authors assume that
the level of trust in AI can be measured
and achieved by distinguishing uncertainties
in predicting AI methods used
in medical studies. They believe that it
is necessary to propose effective uncertainty
quantification (UQ) and measurement
methods to have trustworthy AI
clinical decision support systems.
The authors present practical guidelines
for developing and using UQ methThe
other important
feature of
this issue is that
half of the articles
were produced by
international collaborations.
ods
while applying various AI procedures.
At first, they briefly discuss a few
important applications of AI methods in
different areas. Thereafter, they present
the definition of uncertainty in AI methods
and techniques to deal with it. They
then describe the main UQ and calibration
for AI methods in clinical decision
making. They then describe
key directions
and functions of UQ
methods, major challenges,
and how these
are related to the previous
work on UQs.
Then, they discuss the
foremost research areas
to apply UQ methods
in medical data
analysis. Finally, they
offer conclusions by
providing clinical implications and clarifying
the requirement for UQ in AI methods
to facilitate medical data analysis.
Next comes " Impulsive Consensus
of Fractional-Order Takagi-Sugeno
Fuzzy Multiagent Systems With Average
Dwell Time Approach and Its
Applications: Achieving Finite-Time
Consensus, " by M. Syed Ali, G. Narayanan,
Jianan Wang, Syeda Asma Kauser,
H.I. Abdul Ghaffar, and Ahmed A. Zaki
Diab, a collaborative team of researchers
from India, China, Saudi Arabia, and
Egypt. They study the Takagi-Sugeno
(T-S) fuzzy-based impulsive consensus
problem of a fractional-order multiagent
system (FOMAS) with an average dwell
time (ADT). FOMASs are nonlinear systems
and can be modeled as linear subsystems
by using a T-S fuzzy model. The
authors investigate a T-S fuzzy FOMAS
that is subject to a class of impulse time
sequences with the ADT approach. They
propose an impulsive control scheme to
make the tracking error converge in a
finite-time consensus into a small neighborhood
of origin. Based on impulsive
fractional differential equations theory,
they design a Lyapunov functional approach
with the ADT technique, resulting
in an impulsive controller to achieve
finite-time consensus of a T-S fuzzy FOMAS.
They use practical examples to
ensure the effectiveness and superiority
of the proposed approach.
Article five is " Accurate Prediction
Using Triangular Type-2 Fuzzy Linear
Regression, " by Assef Zare, Afshin Shoeibi,
Narges Shafaei Bajestani, Parisa
Moridian, Roohallah Alizadehsani, Majid
Hallaji, and Abbas Khosravi, a research
team from Australia. The authors
present a new type 2 fuzzy (T2F) regression
model to avoid high complexity by
considering the more general form of
T2F membership functions. The performance
of the developed model is evaluated
using the Taiwan Stock Exchange
Capitalization Weighted Stock Index and
COVID-19 forecasting data sets. The developed
model reaches the highest performance
when compared with the other
state-of-the-art techniques. The developed
method is ready to be tested with
more uncertain data and has the potential
to predict weather and stock values.
Finally, " Applying a MSVR Method to
Forecast a 3D Soft Actuator for a Nonlinear
Control System: Simulation and
Experiments " is by Toru Usami and
Mingcong Deng, Tokyo University of
Agriculture and Technology. The authors
propose a new method to provide
tip position coordinate control of a
three-degree-of-freedom soft actuator.
Such a soft actuator consists of three
artificial muscles and can facilitate
more complex motions than conventional
soft actuators. The proposed
model and control system can handle
multiple-input patterns, and therefore
various motions are possible. In addition,
the authors use a machine learning
technique called multioutput support
vector regression to compensate
for the complexity of multiple-input,
multiple-output systems. They verify the
proposed system through experiments.
The magazine welcomes more diverse
and collaborative research from all the involved
disciplines throughout the Society.
About the Author
Haibin Zhu (haibinz@nipissingu.ca)
is a full professor and the coordinator
of the Computer Science Program, the
founding director of the Collaborative
Systems Laboratory, and a member of
the Arts and Science Executive Committee,
Nipissing University, North
Bay, Ontario, P1B 8L7, Canada.
July 2022 IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE
7
IEEE Systems, Man and Cybernetics Magazine - July 2022

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