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1. Blockchain based Smart Healthcare 5.0 model using
explainable Artificial Intelligence
Journal: IEEE Access
Manuscript ID Access-2022-21976
Manuscript Type: Regular Manuscript
Date Submitted by the
Author:
23-Aug-2022
Complete List of Authors: GHAZAL , TAHER ; Skyline University College; Universiti Kebangsaan
Malaysia; American University in the Emirates
Hasan, Mohammad Kamrul; Universiti Kebangsaan Malaysia
Abdullah, Siti Norul Huda Sheikh; Universiti Kebangsaan Malaysia
Abu Bakar, Khairul Azmi; Universiti Kebangsaan Malaysia
Al Hamadi, Hussam M. N.; University of Dubai
Yeun, Chan Yeob; Khalifa University College of Engineering
Issa, Ghassan F.; Skyline University College
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Computational and artificial intelligence, Biomedical Engineering
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Blockchain, Artificial intelligence, Machine learning, Medical Internet of
Things
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This manuscript is related to IoMT and Machine learning.
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In this aerticle authors proposed the Blockchain based Smart
Healthcare 5.0 model using explainable Artificial Intelligence.
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Implementation of fusion and Block chain using machine learning
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3. VOLUME XX, 2017 1
Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.
Digital Object Identifier 10.1109/ACCESS.2017.Doi Number
Blockchain based Smart Healthcare 5.0 model
using explainable Artificial Intelligence
Taher M. Ghazal1,2,3
, Mohammad Kamrul Hasan2
, Siti Norul Huda Sheikh Abdullah4
, Khairul
Azmi Abu Bakar2
, Hussam Al Hamadi5
, Chan Yeob Yeun6
, Ghassan F. Issa1
1
School of Information Technology, Skyline University College, University City Sharjah, 1797, Sharjah, UAE.
2
Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebansaan Malaysia (UKM), 43600 Bangi, Selangor,
Malaysia.
3
College of Computer and Information Technology, American University in the Emirates, Dubai Academic City, Dubai, UAE
4
Center for Cyber Security, Universiti Kebangsaan Malaysia (UKM), Malaysia.
5
College of Engineering and IT University of Dubai.
6
Center for Cyber-Physical Systems Khalifa University.
Corresponding author: Taher M. Ghazal (e-mail: taher.ghazal@skylineuniversity.ac.ae).
Acknowledgement: This work supported by the Universiti Kebangsaan Malaysia (UKM) under the Research Grant Scheme FRGS/1/ 2020/ICT03/UKM/02/6.
ABSTRACT In the current era, the Internet of Medical Things (IoMT) is familiar with clinical tools and
medical care devices to work with superior patient medical services, knowledge perceptive clinical
arrangements, brilliant drugs, and, surprisingly, advanced excellent medical care administrations. IoMT is an
accelerated information field with a consistently increasing rate that should be gotten without being altered
in real-time. The advancements like Artificial Intelligence (AI), blockchain innovation, and fused AI are
imagined as significant advancements to overcome the concerns in smart healthcare services like protection,
precision, security, and execution. Blockchain is a promising domain that has captivated the world inferable
from its private as well as public nature advancements. In this work, an intelligent medical services
framework is proposed with the arising IoMT innovations that are coordinated with blockchain to offer more
reasonable answers to overcome safe information sharing.
INDEX TERMS Blockchain, smart healthcare model, explainable, artificial intelligence
I. INTRODUCTION
The IoMT has modernized the medical care places and the
medical services industry by diminishing the medical care
administration's intake. The ongoing medical care industry
arrangements are working on the nature of medical services
administrations to patients. These medical services
frameworks gather information as well as monitor, brighten
and advise the guardians and update the medical care suppliers
with the refreshed information to distinguish early recognition
for better medical services. IoMT empowered devices are not
planned because of the security concern that makes it
presented to think twice about terms of safety and protection
risks. Medical care IT groups are taking the affluence of
information, data loss and implementation control as a top
concern [1].
The IoMT is an authentic utilization of IoT devices, going
head-to-head with medical facilities devices utilized in
medical services monitoring [2]. This innovation of
interconnected gadgets permits the patients to monitor their
ailment as indicated by the therapy ideas of the specialists
through an application while making it easy for the specialists
to recognize the patients' clinical history before the exam
through the assortment of continuous information utilizing
IoMT empowered sensors [3].
With the expanded network safety hazards nowadays, each
web-based exchange of information intends to be further
secure to defend the protection of individuals. Similarly, the
difficulties in the IoMT innovation are the exchange of
patients' information safely to the medical services framework
without the impedance of unapproved individuals [4].
Blockchain innovation is considered an arising pattern
while combining the Internet of things and Internet of clinical
things advancements. There are two classes of blockchain:
public blockchain has a permission-less nature through private
blockchain has a permission nature and needs to apply in
nature. The private blockchain is utilized to break the limits
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4. VOLUME XX, 2017 9
that were inclined before. The fascination of private
blockchain is its permission or authorized nature rather than
the permission-less nature to address the network safety
concerns [5].
Artificial intelligence (AI) is used for machine learning [26-
32], to permit frameworks to naturally gain from information
and leave with choices that contradict human assistance
requirements. AI is granting frameworks to obtain from a fact
and recover themselves without being explicitly altered. AI
can mechanize and expand the productivity of smart medical
care frameworks while bringing down medical care costs in a
better, more effective, and compact way [6,7, 23].
Data fusion is the most common way of joining data and
information from remote and distinct sources with inadequate
fundamental information to attach careful, complete data
about a substance and bring data together [8,9]. Special-level
information combinations can create a singular choice for a
private source before joining choices from different sources to
deliver a smarter choice about an activity. By seeing the
information designs from different AI calculations, choice-
level information combination with AI [10, 24, 25] may assist
in conveying a superior choice.
IoMT surrounded by a private blockchain might endorse the
assortment and exchange of various source information with
upgraded secure correspondence. However, decision-level
fused AI may help encourage change by combining decisions
in light of individual AI calculation expectations.
II. LITERATURE REVIEW
Many investigations on IoMT and clinic officials have been
led utilizing different strategies, and a couple of them have
been examined as far as clinic the board as well as smart
medical care checking components. In this study, authors [11]
depicted that an RFID have perceived as a very helpful
approach to working on checking patients, medications, and
clinical assets in clinics, where digitalization further develops
productivity and wellbeing. This commitment analyzed the
best in a class of RFID for healthcare applications, portraying
how it can assist with working on clinical benefits while
calling attention to its restrictions. It was found that much
effort was placed into programming improvement, and a
careful assessment of the actual layer was rarely completed.
Although the RFID framework referenced in this commitment
is restricted to a small clinic region, there is no security
component to guarantee secure correspondence.
The dissemination of body pressure is estimated utilizing a
strain-detecting sleeping cushion put underneath the
individual's body, information is shipped off a PC for
handling, and outcomes are conveyed for observation and
conclusion. The authors [12] designed an Internet of Things
(IoT) framework application for far-off clinical observation in
this exploration. The clinical space has different applications
for such a framework, making it reasonable for clinical use in
regions, for example, rest studies, central careful measures,
clinical imaging strategies, and different regions, including
assurance of body act on a sleeping cushion.
In this inspection, the authors [13] adopted an IoT-based
framework that monitors and tracks mental imbalance patients
utilizing sensors that gather signals from the mind. There are
no security limitations in this exploration while gathering
sensor information, which might be more destructive to the
patients assuming that the patients are mistreated in light of the
mocking information.
The Authors [14], in this consideration, have cultivated a
methodology of a healthcare monitoring framework engrossed
in a distributed computing stage to carry out unavoidable
health monitoring, including modules, for example, cloud
storage [15] and numerous inhabitants' access control layer,
medical care information explanation layer, and medical care
information investigation layer.
In another analysis, the authors [16] made an IoT-based back-
end stage to associate and monitor older patients' health while
utilizing a start-to-finish clinical health care procedure. As per
this research, [17] presented a revolutionary thought in the
field of cardio flags and proposed a method for heart patients
called iCarMa, which determined the sincerity of the
cardiovascular patients and its convenient discovery and
determination.
In this review, the author [18] highlighted the significance
of IoMT, which was extremely valuable for the patients if
critical issues were expected at the beginning phase. This was
conceivable with the assistance of IoT, which helped the
patients in the field of far-off medical services frameworks.
The proposed medical care framework gathered information
from IoMT-empowered sensors without the utilization of a
confusing instrument.
This article [19] proposed a medical care framework to
empower the sharing of clinical records of medical clinics and
facilities utilizing unit connection innovation to upgrade trust
among clinical gadgets. The limitations of this research
include the decentralized idea of blockchain, which might
bring about inaccurate communication from its permission-
less nature and slow performance speed while transferring
countless exchanges for performance.
Fusion-based approaches might further develop the
monitoring healthcare services' nature to predict a patient's
disease. In this work [20], a model is projected to resolve the
basic issues of elderly individuals with neurological concerns
to recognize the disease using IoMT-empowered sensors. The
proposed model was supported by intertwined AI.
III. PROPOSED METHODOLOGY
A key factor in all data handling is the protection of
sensitive information, such as in the case of smart medical care
frameworks. This research is introducing improved security
and decision-making of IoMT applications, including
blockchain for information security and Explainable Artificial
Intelligence (EAI) for patient monitoring in a more effective
way. EAI is essential for assisting healthcare providers in
evaluating black-box
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5. VOLUME XX, 2017 9
Figure 1: Proposed smart healthcare system
Figure 1 presents that the arrangement of the proposed
healthcare model is being evaluated utilizing the training and
validation phases. The training phase contains the five layers
specifically; IoMT infrastructure, blockchain layer,
information Data acquisition layer, preprocessing layer,
application layer, fusion approach and performance layer. The
Internet of Medical Things (IoMT) is a foundation of clinical
tools, programming applications, healthcare frameworks, and
healthcare administrations that sense and send information
from human body sensors to the blockchain layer. It is an
important layer used to overcome security risks by using a
blockchain. After the blockchain, the encrypted data is sent to
the data acquisition layer. The data acquisition layer collects
data from the blockchain layer, converts it into electrical
signals, and does the required processing on it. Then the stored
data is passed through the preprocessing layer, which is
important while reducing the amount of noise generated due
to the wireless communication. The preprocessed data is then
promoted to the application layer using the XGBoost
approach. This algorithm is responsible for predicting
patterns. The objective function (loss function and
regularization) of XGBoost at iteration t that is needed to
minimize is the following:
ᶋ𝑡
= ∑ 𝑙(𝑦𝑖, 𝑦
̂𝑖
(𝑡−1)
) + 𝑓𝑡(𝑥𝑖)) + Ω(𝑓𝑡)
𝑛
𝑖=1 (1)
Then the output of XGBoost is fed forward for the
predictions. EAI methods use predictions and health data to
generate explanations. EAI aids medical specialists in
interpreting black-box models and their decision-making
procedure to verify a specific decision taken by a machine
learning model that is extremely important in the medical
field. The primary goal of EAI is to build trust in machine
learning through the importance of local and global variables
using post-hoc explanations.
EAI is a research field that goals to make AI systems results
more understandable to medical specialists. EAI focuses on
the challenge of deciphering the mysteries of the black boxes,
but it also implies Responsible AI because it can aid in creating
transparent models. A human being can explain an
interpretable system, and this is closely related to the idea of
explainability. Enabling explainability in ML aims to make it
easier for end-users and other stakeholders to understand the
reasoning behind algorithmic decisions.
After the EAI predictions, it is checked if the information
regarding disease prediction is found or not. In the case of
“No”, the XGBoost algo. will be retrained, and so on, but in
the case of “Yes”, the disease prediction outcome is stored
on a cloud database. In the Validation stage, the trained
patterns are imported from the cloud data set and it is
anticipated whether the patient is healthy or unhealthy.
IV. Simulation Results
In this research work, the EAI approach is being used for
IoMT to calculate an insightful framework for better and more
effective patient medical service monitoring using primary
sources of data. The proposed fusion approach is applied to a
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6. VOLUME XX, 2017 9
dataset gathered from the UCI Machine Learning information
dataset. The dataset is separated into training sets of 70%
(24675 samples) and 30% for the testing (10575 samples). The
proposed approach is utilized to a total number of samples of
35250 to work on checking the condition of patient medical
care. The following equations define the various initial
conditions that are used in the implementation of various
measurement calculations:
𝑆𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 =
∑ 𝑇𝑟𝑢𝑒 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒
∑ 𝐶𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒
(2)
𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑡𝑦 =
∑ 𝑇𝑟𝑢𝑒 𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒
∑ 𝐶𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛 𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒
(3)
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =
∑ 𝑇𝑟𝑢𝑒 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒+ ∑ 𝑇𝑟𝑢𝑒 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒
∑ 𝑇𝑜𝑡𝑎𝑙 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛
(4)
𝑀𝑖𝑠𝑠 − 𝑅𝑎𝑡𝑒 =
∑ 𝐹𝑎𝑙𝑠𝑒 𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒
∑ 𝐶𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒
(5)
𝐹𝑎𝑙𝑙𝑜𝑢𝑡 =
∑ 𝐹𝑎𝑙𝑠𝑒 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒
∑ 𝐶𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛 𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒
(6)
𝐿𝑖𝑘𝑒𝑙𝑖ℎ𝑜𝑜𝑑 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑅𝑎𝑡𝑖𝑜 =
∑ 𝑇𝑟𝑢𝑒 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑅𝑎𝑡𝑖𝑜
∑ 𝐹𝑎𝑙𝑠𝑒 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑅𝑎𝑡𝑖𝑜
(7)
𝐿𝑖𝑘𝑒𝑙𝑖ℎ𝑜𝑜𝑑 𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒 𝑅𝑎𝑡𝑖𝑜 =
∑ 𝑇𝑟𝑢𝑒 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑅𝑎𝑡𝑖𝑜
∑ 𝐹𝑎𝑙𝑠𝑒 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑅𝑎𝑡𝑖𝑜
(8)
𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑣𝑒 𝑉𝑎𝑙𝑢𝑒 =
∑ 𝑇𝑟𝑢𝑒 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒
∑ 𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 𝐶𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒
(9)
𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒 𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑖𝑣𝑒 𝑉𝑎𝑙𝑢𝑒 =
∑ 𝑇𝑟𝑢𝑒 𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒
∑ 𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 𝐶𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛 𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒
(10)
TABLE 1: PROPOSED MODEL SMART HEALTHCARE SYSTEM TRAINING
(XGBOOST)
Proposed Model Training
Input
Total number of
samples (24675)
Result (output)
Expected output Predicted Positive Predicted
Negative
True Positive
(T.P.)
False Positive
(F.P.)
12707 Positive 12310 397
False Negative
(F.N.)
True Negative
(TN)
11968 Negative 1456 10512
Table 1 shows the proposed smart healthcare
system training while predicting the disease. Throughout the
training, individually, a sum of 24675 samples is utilized,
which are isolated into 12707,11968 positives as well as
negative samples. 12707 true positives are effectively
anticipated, and no disease is recognized; however, 397
records are mistakenly anticipated as negatives,
demonstrating disease. Essentially, 11968 samples are
acquired, with a negative appearance of disease and a
positive appearance of no disease, with 10512 samples
accurately recognized as awkward appearance disease and
1456 samples mistakenly anticipated as sure, demonstrating
no disease regardless of the presence of the disease.
TABLE 2: PROPOSED MODEL SMART HEALTHCARE SYSTEM VALIDATION
(XGBOOST)
Proposed Model Validation
Input
Total number of
samples (10575)
Result (output)
Expected output Predicted Positive Predicted
Negative
True Positive
(T.P.)
False Positive
(F.P.)
5213 Positive 4823 390
False Negative
(F.N.)
True Negative
(TN)
5362 Negative 703 4659
Table 2 shows the proposed smart healthcare
system validation while predicting the disease. During
validation, a sum of 10575 samples is utilized, separated into
5213,5362 positive and negative samples. 4823 true
positives are effectively anticipated, and no disease is
distinguished; however, 390 records are mistakenly
anticipated as negatives, demonstrating disease. Essentially,
5362 samples are gotten, with the negative appearance of
disease in addition to positive appearance of no disease, with
4659 samples accurately distinguished as awkward
appearance disease and 703 samples incorrectly anticipated
as sure, showing no disease despite the presence of the
disease.
TABLE 3: PROPOSED SMART HEALTHCARE SYSTEM PERFORMANCE
EVALUATION IN TRAINING AND VALIDATION USING DIFFERENT
STATISTICAL MEASURES
Accuracy
Sensitivity
TPR
SpecificityTNR
Miss-Rate
(%)
FNR
Fall-out
FPR
LR+
LR-
PPV
(Precision)
NPV
XGBoost
Training
0.92
0.89
0.96
0.08
0.036
24.72
0.083
0.96
0.87
Validation
0.89
0.87
0.92
0.11
0.077
11.30
0.120
0.92
0.86
Table 3 (XGBoost) further shows that throughout
training, the proposed system's accuracy, sensitivity,
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7. VOLUME XX, 2017 9
specificity, miss rate, and precision are 0.92, 0.89, 0.96, 0.08,
and 0.96, consecutively.
Figure 2: Features with Eli5
but during validation, the suggested model is 0.89, 0.87,
0.92, 0.11, and 0.92. Furthermore, the propsoed system
yields 0.036, 24.72, 0.083, and 0.87 throughout training and
0.077, 11.30, 0.120, and 0.86 during validation in terms of
dropout likelihood positive ratio, likelihood negative ratio,
Figure 2 is showing the weight with each feature in the
given set of parameters. Figure 3 shows the LIME
explanations for one instance randomly selected from the
patient dataset. The top left figure depicts the patient's
disease's projected result with probability. Class negative
disease is found in patients with an unhealthy outcome, while
class positive disease is not found in patients with a good
outcome. The orange color symbolizes the target class
positive, while the blue colour indicates the target class
negative. Color denotes the weight for each feature with their
anticipated class. They represent the local positive or
negative weights ascribed to each characteristic. The longer
the colour bar, the larger the weight.
Figure 3: LIME explanation for one instance
FIGURE 4: MODEL
OUTPUT VALUE
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8. VOLUME XX, 2017 9
It is shown in Figure 4 that the value would be predicted if
the features are not known for the current instance. The base
value is the average of the model output over the training
dataset. Red represents features that pushed the model score
higher, and blue represents features that pushed the score
lower. The bigger the arrow, the bigger the impact of the
feature on the output. The amount of decrease or increase in
the impact can be seen on the x-axis.
TABLE 4. COMPARISON OF THE PROPOSED FRAMEWORK WITH PREVIOUS
MACHINE LEARNING ALGORITHMS
Method
Accuracy
(%)
Miss-rate
(%)
MC-CNN [21] 87.14 12.86
RF [22] 85.3 14.7
The proposed smart healthcare system 0.89 11
The accuracy and miss rate of several machine learning
algorithms are compared in Table 6, demonstrating that the
proposed system is significantly more reliable than
alternative algorithms.
V. Conclusion:
Internet of Medical Things (IoMT) has been used in parallel
with other approaches to supervising patient health, taking
reasonable care of front-line staff, and boosting efficiency by
minimizing the risk of complications in people's existence in
many nations. In addition to slowing down IoMT device
communication, the health care industry's current blockchain
security measures also limit the processing power of IoMT-
enabled devices. Patients may be physically harmed or
severely impaired due to unauthorized access to information.
Blockchain technology and Explainable artificial
intelligence will address security concerns while providing
strong decision-making for enhanced patient health care
quality monitoring in this planned research project. Due to
its usefulness in healthcare, a blockchain is an aggressive
technology that can be connected with other domains to
address processing capacity concerns operationally and a
degree of control over the transaction. New IoMT
technologies may be more feasible if they are integrated with
the integration of blockchain technology, and EAI. The
suggested technique gives superior outcomes to the prior
systems, with an 89% accuracy rate and an 11% miss rate,
which is better as compared to the previously published
approaches.
ACKNOWLEDGMENT
Not applicable
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TAHER M. GHAZAL (Member, IEEE) has
received B.Sc. degree in Software
Engineering from Al Ain University (2011),
M.Sc. degree in Information Technology
Management from The British University in
Dubai, associated with The University of
Manchester and The University of Edinburgh.
(2013), PHD in IT/Software Engineering from
Damascus University (2019) and PHD in
Information Science and Technology from
Universiti Kebangsaan Malaysia (2023). He
has more than 10 years of extensive and diverse experience as lecturer,
Instructor, Tutor, Researcher, Teacher, IT Support/Specialist Engineer and
Business/Systems Analyst. He served in Engineering, Computer Science,
ICT, Head of STEM and Innovation Departments, also he involved in
Quality Assurance, Accreditation and Data Analysis, in several
governmental and private educational institutions under KHDA, Ministry of
Education and Ministry of Higher Education and Scientific Research, UAE.
His research interests are centered towards IoT, IoMT, Artificial Intelligent,
Machine Learning, Deep Learning,
Information Systems, Software Engineering, Security, Building Info.
Modeling, IT, Quality of Education, Management, Big Data, IoT,
Innovation, Information Science & Technology, Quality of Software, and
Project Management.
MOHAMMAD KAMRUL HASAN
(M’13–SM’17) is currently working as a
Senior Lecturer, in the Faculty of Information
Science and Technology, at Center for Cyber
Security, Universiti Kebangsaan Malaysia
(UKM). He completed Doctor of Philosophy
(Ph.D.) degree in Electrical and
Communication Engineering from the faculty
of Engineering, International Islamic
University, Malaysia in 2016. He is
specialized with elements pertaining to
cutting-edge information centric networks; Computer networks, Data
communication and security, Mobile Network and Privacy Protection,
Cyber-physical systems, Industrial IoT, Transparent AI, and Electric
Vehicles Networks. He has published more than 150 indexed papers in
ranked journals and conference proceedings. Dr. Kamrul is a Senior
Member of the Institute of Electrical and Electronics Engineers (SMIEEE-
90852712), Member of Institution of Engineering and Technology (MIET-
1100572830), and the member of Internet Society (198312). Dr. Kamrul is
a certified professional technologist, Malaysia. He also served the IEEE
student branch as chair from 2014 to 2016. He has actively participated in
many events/workshops/trainings for the IEEE and IEEE humanity
programs in Malaysia. He works as the editorial member in many
prestigious high-impact journals Such as IEEE, IET, Elsevier, Frontier and
MDPI, and general chair, co-chair, and speaker for conferences and
workshops for the shake of society and academy knowledge building and
sharing and learning. He has been contributing and working as a volunteer
for under privileged children for the welfare of society.
SITI NORUL HUDA SHEIKH
ABDULLAH Received her first Degree
in Computing at the University of
Manchester Institute of Science and
Technology, United Kingdom. She
furthered her master`s study in the area
of Artificial Intelligence in Universiti
Kebangsaan Malaysia. Later, she
continued her Ph.D. Study in the area of
Computer Vision at Faculty of Electrical
Engineering, Universiti Teknologi
Malaysia. Starting her career, she
involved in conducting national and international activities such as Cyber
Security Academia Malaysia (CSAM), FIRA, Asian Foundation, IMWU,
DFRS, MIAMI, MACE, and IDB Alumni. After serving as a Chairperson
of the Center for Cyber Security, she now holds a post as the Deputy Dean
(Research and Innovation), Faculty of Information Science and Technology,
Universiti Kebangsaan Malaysia, and Chairperson of CSAM. Her research
focuses are Digital Forensics, Pattern Recognition, and Computer Vision
Surveillance systems. She has published four books entitled " Pattern
Recognition", Computational Intelligence in Data Science Application”, "
Smart Prediction of Suspect`s Serial Crime Location ", and " Optical Script
Reader: Texture Binary Innovation" and more than 50 journal and 100
conference manuscripts correspondingly. Beliau menerima Ijazah Sarjana
Muda dalam bidang Komputeran di Institut Universiti Sains dan Teknologi
Manchester, United Kingdom. Beliau melanjutan pelajaran di peringkat
Sarjana dalam bidang Kecerdasan Buatan di Universiti Kebangsaan
Malaysia. Kemudian, beliau menyambung pelajarannya di peringkat Doktor
Falsafah pula. Bidang kajian yang dijalankan adalah Visi Komputer di
Fakulti Kejuruteraan Elektrik, Universiti Teknologi Malaysia. Kerjaya
beliau bermula apabila beliau melibatkan diri dalam mengendalikan aktiviti
dalam dan luar negara seperti Pertubuhan Akademia Keselamatan Siber
Malaysia (CSAM), Federation of International Robot Soccer Association
(FIRA), Asian Foundation, IMWU, DFRS, MIAMI, MACE dan IBD
Alumni. Setelah berkhidmat sebagai Pengerusi Pusat Keselamatan Siber,
beliau kini memegang jawatan sebagai Timbalan Dekan (Penyelidikan dan
Inovasi), Fakulti Sains dan Teknologi Maklumat, Universiti Kebangsaan
Malaysia dan Pengerusi CSAM. Fokus kajian beliau adalah Forensik
Digital, Pengecaman Pola, dan Sistem Pengawasan Visi Komputer. Beliau
juga telah menghasilkan empat buah buku berjudul "Pengecaman Pola”,
"Kecerdasan Komputasi bagi Aplikasi Sains Data", "Ramalan Pintar Lokasi
Suspek Jenayah Bersiri" dan "Pembaca Skrip Optikal: tekstur dan
mempunyai lebih kurang 50 dan 100 manuskrip jurnal dan persidangan.
KHAIRUL AZMI ABU BAKAR received a
degree in Computer Engineering from Iowa State
University, USA and a master's degree in
Communication and Computer Engineering from
Universiti Kebangsaan Malaysia. He was awarded
a PhD degree in Electrical Engineering from the
University of Strathclyde, United Kingdom, for
the study on free-riding nodes in an open MANET.
He is currently a senior lecturer at the Center for
Cyber Security under the Faculty of Information
Science and Technology, Universiti Kebangsaan Malaysia. Before that, he
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was a staff researcher at MIMOS Berhad, Malaysia's national applied
research and development center in microelectronic and ICT. He has been
involved in many R&D projects in micro-controller, smartcards, and
security systems under open-source platforms. His primary research
interests include network security, the internet of things and computer
networks. He is also an IEEE member.
Hussam Al-Hammadi studied computer
engineering at Ajman University, where he
graduated in 2005
with honors. He holds several international
certificates in networking, business, and tutoring,
like MCSA, MCSE, CCNA, CBP, and CTP. He
spent the period between 2005 and 2010 working
as a computer consultant and tutor in several
governmental and private institutions until joining
Khalifa University as a teaching assistant in 2010.
In 2017, he received his Ph.D. degree in computer engineering from Khalifa
University, where he is currently a Research Scientist at KU Center for
Cyber-Physical Systems (C2PS). His research interests focus on AI for
security and security for AI, in addition to applied security protocols for
several systems like: software agents, SCADA, e-health systems, and
autonomous vehicles. It also includes developing e-forensics and security
methodologies for smartphones and drones. He is an IEEE senior member
and the secretary of the IEEE UAE section since 2019. Finally, he is a
frequent reviewer in several journals, including IEEE Access and IEEE
Systems Journal, KSII Transaction on Internet and information, and
International Journal of Communication Systems.
CHAN YEOB YEUN (Senior Member,
IEEE) received the M.Sc. and Ph.D. degrees in
information security from the Royal Holloway,
University of London, in 1996 and 2000,
respectively. After his Ph.D. degree, he joined
Toshiba TRL, Bristol, U.K., and later became
the Vice President at the Mobile Handset
Research and Development Center, LG
Electronics, Seoul, South Korea, in 2005. He
was responsible for developing mobile TV
technologies and related security. He left LG
Electronics, in 2007, and joined ICU (merged with KAIST), South Korea,
until August 2008, and then the Khalifa University of Science and
Technology, in September 2008. He is currently a Researcher in
cybersecurity, including the IoT/USN security, cyber-physical system
security, cloud/fog security, and cryptographic techniques, an Associate
Professor with the Department of Electrical Engineering and Computer
Science, and the Cybersecurity Leader of the Center for Cyber-Physical
Systems (C2PS). He also enjoys lecturing for M.Sc. cyber security and
Ph.D. engineering courses at Khalifa University. He has published more
than 140 journal articles and conference papers, nine book chapters, and ten
international patent applications. He also works on the editorial board of
multiple international journals and on the steering committee of
international conferences.
GHASSAN F. ISSA received the M.S. and
Ph.D. degrees in computer science/artificial
intelligence from Old Dominion University,
VA, USA, in 1987 and 1992, respectively. He
was a Faculty Member and the Department
Chair of computer science at the Pennsylvania
College of Technology (Penn State), USA,
from 1992 to 1995. He also served as the Dean
for computer science at Applied Science
University, Amman, Jordan, from 2003 to
2005, and the Dean for information
technology at the University of Petra,
Amman, from 2008 to 2018. He is a Professor of computer science.
Currently, he is a Professor and the Dean of the School of Information
Technology, Skyline University College, Al Sharjah, United Arab Emirates.
His research interests include AI, and machine learning with work on deep
neural networks fine-tuning, learning by analogy, and associative
classification algorithms.
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