IEEE Systems, Man and Cybernetics Magazine - April 2021 - 3

Editorial

by Saeid Nahavandi

T

he ability of a machine to automatically process data, identify patterns, and achieve
acceptable performance in different tasks is revolutionizing and
reforming our daily lives. Recent
advances in the field of machine
learning techniques have proved
extremely efficient and effective in
mimicking our skills, such as hearing, seeing, and even reasoning and
comprehending. The adoption of
artificial intelligence and machine
learning has become pervasive in
many domains, including science,
engineering, business, health care,
and cybersecurity.
In this issue of IEEE Systems, Man,
and Cybernetics Magazine, we present
four articles related to the fascinating
topic of machine learning and its application for real-world systems. The
article " Ordered Weighted Averaging
Operators " provides a short, yet comprehensive, review of ordered weighted averaging (OWA) operators. Due
to its promising features, this aggregation method has been widely extended by many researchers for data
fusion and the synthesis of information. The article offers an overview of
the trends in the literature and some
of the most significant results, based
on OWA operators.
In " The Universal Functions Originator and Its Extensions, " the author
investigates the usefulness of the universal functions originator (UFO) for
machine learning applications. It is
argued that novel machine learning
techniques have excellent accuracy
but poor explainability and interpretability. These drawbacks limit their application in cybersecurity systems and
Digital Object Identifier 10.1109/MSMC.2021.3058718
Date of current version: 20 April 2021

Applications for
Machine Learning
demand, which is then used in the
scheduling process.
Finally, this issue features the article " Observer-Based Attack Detection and Mitigation for Cyberphysical
Systems. " In it, the authors provide
an in-depth introduction to cyberphysical systems and their most
significant characteristics. This is
followed by a full summary of the
different attacks that have been perpetrated on these systems. It then
reviews the attack-detection methods that have been developed using
secure-control algorithms, which
include the machine learning and
intelligent methods employed for intrusion detection.

Saeid Nahavandi

expose them to many legal issues. The
UFO addresses the inherent shortcomings of traditional and advanced machine learning techniques by explaining everything as pure mathematical
equations. It has a promising built-in
selection process for both features and
functions (kernels), enabling it to drop
redundant or complex variables and
terms from the final model.
" The Energy-Saving Scheduling of
Campus Classrooms " proposes computational, intelligence-based scheduling
strategies that significantly reduce the
number of classrooms with power on
as well as their total power-on time.
The proposed framework applies an
improved cuckoo search optimization
algorithm to find the threshold conditions of powering ON/OFF classrooms.
Long short-term memory neural networks are also used to predict student

	

Ap ri l 2021

About the Author
Saeid Nahavandi (saeid.nahavandi@
deakin.edu.au) earned his Ph.D. degree
from Durham University, United Kingdom, in 1991. He is an Alfred Deakin
professor, pro-vice-chancellor, chair
of engineering, and the founding
director for the Institute for Intelligent Systems Research and Innovation at Deakin University, Victoria,
3216, Australia. His research interests
include the modeling of complex systems, robotics, and haptics. He has
published more than 950 scientific
papers in various international journals and conferences. He is a Fellow
of IEEE, the Australian Academy of
Technological Sciences and Engineering, Engineers Australia, and the Institution of Engineering and Technology.


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IEEE Systems, Man and Cybernetics Magazine - April 2021

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