CSPC 2022 Program Book

1

Table of Content Page
3
Foreword from the Dean 4
Foreword from the Chair 5
Foreword from the Advisor 6
About CSPC 7
Objectives 8
Schedule
Workshop 10
Invited Talk 14
Forum 16
Keynote Speech 20
Poster Competition 22
3MT Competition 27
Q1 and Q2 Journal Award 38
Recipients
The Advisors 40
The Organizing Committees 41 2

FOREWORD
Dean, School of Computer Sciences

Computer Science Postgraduate Colloquium
(CSPC) has been an annual event since 2004
for our postgraduates to showcase their
research findings and share research ideas as
well as socialize with fellow students and
lecturers.

With Covid-19 pandemic shifting towards an
endemic phase, CSPC 2022 is held through
hybrid platform which involves face-to-face
and live streaming. The theme for this year’s
CSPC, “Computing reSearch Post-Covid," is in
line with the current paradigm which focuses
on post-Covid transformation in terms of
teaching and learning as well as computing-
based research.

With Covid-19 pandemic shifting towards endemic phase, CSPC 2022 is held
through hybrid platform which involves face-to-face and live streaming. The
theme for this year’s CSPC, “Computing reSearch Post-Covid," is in line with
the current paradigm which focuses on post-Covid transformation in terms
of teaching and learning as well as computing-based research.

Remarkably, either by directly or indirectly, all of us have learned both skills
of adaptability and resilience due to Covid-19. Adaptability only allows us to
adjust to any situation at any given time. But resilience teaches us to
recover from a situation quickly and without much hassle. By shifting
towards post-Covid, postgraduates would have the opportunities to
enhance their research quality through hybrid-based supervision, improving
presentation skills and quality plus having a closer communication among
peers and staff.

Being Computer Science students, I believe that all of you could pursue the
revolution of post-Covid with confidence and positivity. Keep up the good
work, everyone. Finally, I would like to extend my heartiest gratitude to CSPC
2022 organizing committee as well as all speakers, event participants,
judges, and attendees for your support and contribution in making this
event successful.

Professor Dato’ Dr. Bahari Belaton 3

Dean,
School of Computer Sciences,
Universiti Sains Malaysia

FOREWORD
Chair, CSPC 2022

Hello everyone, heartiest welcome to all of you for joining
our Computer Science Postgraduate Colloquium (CSPC
2022). The theme for this year is "Computing reSearch Post
Covid".

CSPC is organized by the School of Computer Sciences
annually as a platform allowing all postgraduate students
to share their research ideas, progress, and outcomes with
the CS community. With the ease of movement control
order and the transition phase to coronavirus endemicity
this year, we are back with a hybrid colloquium. A hybrid
colloquium tries to combine a "live" in-person colloquium
with a "virtual" online component which aligns with the
theme "CSPC 2022: Computing reSearch Post Covid".

The hybrid colloquium will be an essential part of the new normal in the events
industry. The committee tries to bring back the physical activities and ensure CPSC still
serves as the platform to connect postgraduates with the industry, other universities,
and alumni. This colloquium, particularly, encourages the interaction between students
and academicians to discuss new and current research.

Hence, we are glad to organize a two-day colloquium with various programs such as a
workshop, poster competition, 3-minutes thesis (3MT) competition, talks, and forum for
postgraduate participation. This year, we are grateful to bring in professors from our
school for a writing workshop on Day 1 of CSPC. This lineup of academics, with their
vast experience, will share the main components of writing articles for publication in
"Improving Research Article Writing Skills" session. We also bring our fellow
coursemates and alumni as forum panels to share their thoughts in "Opportunities
Post Ph.D.: The Way Forward" session. We are also

delighted to include two keynote sessions titled "Graduate on Time: GOT" and "The
Inquisitive Mind." This session is essential to guide us to have a clear target and goal in
hopes that it will benefit all of us in moving forward with our research.

On behalf of the organizing committee, we welcome you to CSPC 2022. We hope you will

receive the utmost benefit from attending this colloquium. We also express our sincere

gratitude to the organizing committee, keynote speakers, session chairs, all

participants, and the volunteers who had dedicated their time and effort in planning,

promoting, organizing, and have worked hard to make this event a success.

Congratulations to our fellow coursemates on their perseverance and determination to

create CSPC 2022 this far. The future is bright and beautiful. Love it, strive for it, and

work for it.

Haziqah Shamsudin 4

Chair, CSPC 2022

FOREWORD

Advisor, CSPC 2022
First, with humility I would like to welcome each of
you to this year’s CSPC event. This year, we are
fortunate to conduct the event face-to-face, albeit
partially. Even though Covid-19 phase has changed
to endemic globally; the fear of infection is still
hovering us daily. We still need to be cautious and
maintain our safety at all times.

Even with the negativity immersed from the
pandemic, the journey we have faced is amazing. In
the hardship of doing research online, all of us have
become more tech-savvy in adopting digital

skills to assist our research’s milestones and deliverables. The commitment
among students towards acquiring knowledge and skills each day

and aligns with life-long learning. Along with exciting careers across
computing fields, the need to be creative with innovations and ideas is
crucial. I believe our postgraduate students are trying their best in creating
solutions that are innovative and ground-breaking.

Thus, I am delighted to present CSPC 2022 which is crafted uniquely and
organized towards sharing the knowledge and skills useful for our beloved
postgraduates facing post-Covid era. This year, we will be having a
workshop series, forum and keynote talks which will emphasise how one can
move forward in conducting research fruitfully. We hope these activities will
mould and support both postgraduates and staff to gear up the momentum
and embrace post-Covid situation.

Finally, I am delighted that this year’s colloquium is led by our research

postgraduate, Haziqah Samsudin as Chair and Thulfiqar Al-Jabar as Co-

chair, together with their team members. The team has done a remarkable

job in organizing the work. I am also pleased to see excellent involvement of

lecturers from our school serving as advisors and competition judges for

this event. I hope all postgraduates can take this opportunity to get

valuable feedback from our academics, especially for those who have

participated in poster and 3MT competitions. To all attendees, do enjoy all

the sessions. Happy learning all.

Associate Professor Dr. Manmeet Kaur Mahinderjit Singh 5

Advisor, CSPC 2022

ABOUT CSPC 2022

Computer Science Postgraduate Colloquium (CSPC) was introduced in the
year 2004 where the first colloquium started as a mini conference where
it was organised exclusively for the research students of the School of
Computer Sciences, USM. Realising that this platform is a very good
channel for the postgraduate students to meet and share their
knowledge, it then becomes an annual event organised by the school.
CSPC then become a catalyst for Computer Science Postgraduate
Students at the School of Computer Sciences, Universiti Sains Malaysia to
gather with academics and industry to communicate ideas and to have
constructive discussions for the advancement of knowledge and to move
the nation forward as a whole. It also becoming the venue to
communicate and to expand research possibilities for our research
students.

6

OBJECTIVES

01 To provide a platform for postgraduate
students to share knowledge and experience

02 To open up channel for industry – community
– university to communicate for a more
sustainable relationship in research and
innovation

03 To foster relationship and communication
among postgraduate students and
Computer Science staff

04 To explore new knowledge in the current
state of the art for respective studies in
Computer Sciences

7

Day – 1 (Online)

Time (GMT +8) Agenda

08.45 am Participants Arrival

9.00 am - 09.05 am Opening Ceremony
Welcome Remarks by Moderator (Ms Asma Sajid)
Opening Remarks by Deputy Dean School of Computer
Sciences (Assoc. Prof. Dr. Cheah Yu-N)

09.05 am - 10.00 am Improving Research Article Writing Skills Workshop

Mentors:
● Prof. Dr. Rosni Abdullah
● Dr. Nuraini Abdul Rashid
● Assoc. Prof. Dr. Umi Kalsom Yusof

10.00 am - 10.15 am Break

10.15 am - 12.15 pm Practical Session for Article Writing

12.15 pm - 01.00 pm Wrap-up Session by Mentors

01.00 pm - 02.00 pm Lunch

02.00 pm - 04.00 pm Virtual Poster Presentation

04.00 pm - 04.15 pm Break

04.15 pm - 04.55 pm Invited Talk Session
Title: The Nooks and Crannies of Scientific Publishing:
From A Students Perspectives
Speaker: Dr. Mohd Nor Akmal Khalid

04.55 pm - 05.00 pm Closing of Day 1. Reminder for Day 2

8

Day - 2 (Physical With Live Streaming @
Olive Tree Hotel Penang)

Time (GMT +8) Agenda

08.30 am – 09.00 am Registrations

09.00 am – 09.20 am OpeningCeremony
• Welcome Remarks by CSPC Chair (Ms Haziqah

Shamsudin)
• Opening and Officiating by Dean, School of

Computer Sciences (Professor Dato' Dr. Bahari
Belaton)

09.30 am - 10.45 am Keynote 1
Title: Graduate on Time (GOT)
Speaker: Prof. Emeritus Dr. Zaharin Yusoff

10.45 am – 11.30 am Poster Evaluation + Coffee Break

11.30 am – 12.45 pm Forum
Title: Opportunities Post PhD:
The Way Forward
Speakers:
Dr. Fakhitah Ridzuan, Dr. Maged Nasser,
Nur Aqilah Paskhal Rostam

12.45 pm - 02.00 pm Lunch

02.00 pm - 03.15 pm 3MT Competition + Coffee Break

03.15 pm - 04.20 pm Keynote 2
Title: The Curious and Inquisitive Mind
Speaker : Prof. Dr. Abd Karim Alias

4.20 pm – 5.00 pm Closing Ceremony
• Closing Remarks & Gifts/Certificates Ceremony
• Officiate CS Society
• Certs Ceremony for CS Society + Photo Session

9

WORKSHOP

Mentors

Professor Dr. Rosni Abdullah
Honorary Professor, School of Computer Sciences,
Universiti Sains Malaysia

Dr. Nuraini Abdul Rashid
Former Lecturer, School of Computer Sciences,
Universiti Sains Malaysia

Associate Professor Dr. Umi Kalsom Yusof
Senior Lecturer, School of Computer Sciences,
Universiti Sains Malaysia

10

WORKSHOP
Mentor

Professor Dr. Rosni Abdullah

Honorary Professor, School of Computer Sciences,
Universiti Sains Malaysia

Rosni Abdullah (Professor) started her career as a lecturer
at USM in Jan 1987, and has retired on 3 June 2020, after 33
years of service.

Upon her retirement, she continued to work at USM on a contract basis at the
Division of Academic and International Affairs, USM under the Flexible Scheme for
Retired Scholars (FSRS) until Dec 2021.

Rosni Abdullah obtained her PhD in April 1997 from Loughborough University,
United Kingdom specializing in the area of Parallel Algorithms. Both her Bachelors
and Masters degree in Computer Science were obtained from Western Michigan
University, Kalamazoo, Michigan, U.S.A. in 1984 and 1986 respectively. Her research
areas included Parallel & Distributed Computing and Computational Biology.

She was Dean for the School of Computer Sciences, USM from 2004-2012, and Jan
2019 until Jun 2020. She was Director for the National Advanced IPv6 Center at USM
from Jan 2015 until Jun 2020. Her major industry linkage was with Intel when she
secured two Intel Research Grants, followed by a joint initiative to train multicore
programming to Malaysian researchers and embedding IOT programming based on
Intel Galileo and Edison boards in the CS curriculum at USM. In 2021, she brought in
the AI FOR UNIVERSITY (AI4U) PROGRAM which is a collaboration program between
MPC, Malaysian Investment Development Authority (MIDA) and Intel Malaysia to
drive AI adoption in university to help non-computer science students embrace AI in
various pockets of their daily lives.

She is currently the CEO for Kolej Teknologi Darul Naim Cawangan Kuala Lumpur

(KTDKL) located in Bangi, Selangor. She is also an Honorary Professor at the School

of Computer Sciences, USM. 11

WORKSHOP
Mentor

Dr. Nuraini Abdul Rashid

Former Lecturer, School of Computer Sciences,
Universiti Sains Malaysia

Nuraini Binti Abdul Rashid (Dr.) received the B.Sc. degree
from Mississippi State University, USA, and the M.Sc. and
Ph.D. degrees from University Sains Malaysia, Malaysia, all
in computer science.
Her Ph.D. thesis was on analyzing and managing protein sequence data. She was
with the School of Computer Sciences, Universiti Sains Malaysia, for over 25 years
and was an Associate Professor with the Department of Computer Sciences,
Princess Nourah bint Abdulrahman University, Saudi Arabia. She has published over
70 refereed papers in bioinformatics, string matching, and information retrieval.
Her research interests include parallel algorithms, information retrieval methods,
and biological big data mining.

12

WORKSHOP
Mentor

Assoc. Prof. Dr Umi Kalsom Yusof

Senior Lecturer, School of Computer Sciences,
Universiti Sains Malaysia

Umi Kalsom Yusof (Associate Prof. Dr.) received the B.Sc.
degree from Western Illinois, Macomb, IL, USA, in 1986, the
M.Sc. degree from Universiti Sains
Malaysia (USM), Penang, Malaysia, in 2004, and the Ph.D. degree in computer
science from Universiti Teknologi Malaysia (UTM), Johor, Malaysia, in 2013. She is
currently an Associate Professor and a Senior Lecturer with the School of Computer
Sciences, USM. She has previously worked at Petronas, Toyota, ASE Electronics, and
Motorola before joining the academia, in 2008. She has published research articles
in national and international journals, conference proceedings, as well as book
chapters. Her research interests include artificial intelligence, machine learning,
computational intelligence, multi-objective optimization, evolutionary computing,
web engineering, manufacturing optimization, crowd behavior in an emergency
evacuation, and health-related and global warming effect studies.

13

INVITED TALK

The Nooks and Crannies of Scientific
Publishing: From A Students Perspectives

Dr. Mohd. Akmal Nor Khalid
Assistant Professor,
Japan Advanced Institute of Science and Technology
(JAIST)

14

INVITED TALK
Speaker

Title: The Nooks and Crannies of Scientific
Publishing: From A Students Perspectives

Dr. Mohd Nor Akmal Khalid

Assistant Professor, Japan Advanced Institute of
Science and Technology (JAIST)

Mohd Nor Akmal Khalid (Dr.) is an assistant
professor at the School of Information Science at
Japan Advanced Institute of Science and Technology,
a member of the
Research Center for Entertainment Science (2019--2020), and a member
of the International Research Center for Artificial Intelligence and
Entertainment Science (2022--current). He obtained his BSc., MSc., and
Ph.D. degrees from the University of Science Malaysia in 2013, 2015, and
2018, respectively. His work focuses specifically on the methods for
optimization and game informatics in the fields of operation research
and artificial intelligence technology. His specializations are artificial
intelligence techniques, game informatics, evolutionary computing and
algorithms, and decision support system. In addition, his topics of
interest include but are not limited to manufacturing systems,
advancement in scheduling and planning, AI systems and techniques,
search algorithms, optimization techniques, game analytics, and game
informatics.

15

FORUM

Opportunities Post PhD: The Way Forward

Duaa Akhtom

PhD Student, School of Computer Sciences
Universiti Sains Malaysia (USM)

Dr. Fakhitah Ridzuan

Lecturer
INTI International College Penang

Dr. Maged Nasser

Postdoctoral Fellow, School of Computer Sciences
Universiti Sains Malaysia (USM)

Nur Aqilah Paskhal Rostam 01

PhD student at School of Computer 16
Science USM and as a Data Scientist
at Intel 16

FORUM
Panel

Dr. Maged Nasser

Postdoctoral Fellow, Universiti Sains Malaysia

Maged Nasser (Dr.) received the bachelor’s degree in
Mathematics and computer science from IBB
University, Yemen in June 2012, the M.Sc. degree from
Banaras Hindu university (BHU), India in May 2016, and the Ph.D. degree
in computer science from Universiti Teknologi Malaysia (UTM), in June
2022. During his PhD he worked hard to get the exceptional academic
and research skills, which enabled him to make significant progress
throughout his PhD and passed his VIVA with only minor corrections.
He demonstrated a remarkable ability to conduct high quality research
independently and creatively, resulted in substantial achievements,
several of which were published in top-tier journals. Throughout his
PhD journey, He has managed to publish several scientific papers
relevant, some of which are published in Q1 and Q2 ISI journals with
high Impact Factors. He has applied for two patents, which are
currently pending approval of Malaysia Intellectual Property Office.
From 2019 – 2021 He was experienced as a research assistant under two
research grants and has consistently demonstrated excellent academic
performance in research. His expertise in computer science disciplines
includes Machine Learning, Deep Learning, Artificial Intelligence, Data
Mining, Knowledge Discovery, Cheminformatics, and programming.
currently he is a Postdoctoral Fellow with the School of Computer
Sciences under Assoc. Prof. Dr. Umi Kalsom Binti Yusof supervision,
USM.

17

FORUM
Panel

Dr. Fakhitah Ridzuan

Lecturer, INTI International College Penang

Fakhitah Ridzuan (Dr.) is a Computer Science
Lecturer at INTI International College Penang. She
graduated with a Bachelor of Computer Science
(Software Engineering) from Universiti Teknologi Malaysia in 2016.
Before she pursued her PhD, she had 10 months of experience in the
industry as a system analyst. Fakhitah received a Royal Scholarship
Award to continue her PhD studies in 2017. Her PhD field is related
to data quality and big data. Throughout her 4 years of study, she
once failed during the proposal presentation. Nevertheless, with
continuous support from her supervisor, she completed her PhD in
November 2021. Throughout her studies, she was blessed with a son,
born 5 months before the Movement Control Order. Even though
initially it was tough, thanks to the earnestness and support from
her supervisor, family, and friends, she was able to maximise her
time during MCO to complete her thesis and ensure that she
finished studying at the appointed time. While waiting for the viva,
she was given the opportunity to work at the Advanced Medical and
Dental Institute, USM as a research assistant before being offered
as a lecturer at INTI. Throughout her studies, she’s not only
published academic articles but also published articles in magazines
and she has two e-books. Fakhitah is also a person who loves
gardening. Thanks to MCO, she finally found her new talent in
planting trees.

18

FORUM
Panel

Nur Aqilah Paskhal Rostam

Data Scientist and Research Enthusiast

Nur Aqilah is a PhD student and currently work as a
Data Scientist at Intel. In 2016, she received her B.Sc.
degree (Hons.) in computer science. After graduated,
she worked as a System Analyst and further study for M.Sc. degree
in software engineering. Juggling between a research student and
software developer for the company she worked, she realised her
passion in research and deciding to pursue her PhD. In 2019, she
enrolled as a PhD student and working as a Graduate Research
Assistant (GRA) until present. Specializing in artificial intelligence,
her current research interests include the development of IoT
application and ecological informatics modelling using deep
learning and time series forecasting algorithm.

19

KEYNOTE
Speaker I

Title: Graduate on Time (GOT)

Professor Emeritus Dr. Zaharin Yusoff
Fellow of the Academy of Science Malaysia

Zaharin Yusoff (Prof. Emeritus Dr.) is a Fellow of the Academy of Sciences Malaysia.
He is currently a professor in computational linguistics at International Medical
University (IMU). He began his career in 1980 at Universiti Sains Malaysia (USM),
where he served for 25 years, including being Coordinator of the Computer-Aided
Translation Unit, the founding Dean of the School of Computer Sciences, and later the
founding Dean of Research (ICT Platform). In August 2005, he went on secondment to
MIMOS, where he served as Senior Director of the Productisation Unit, and then of the
Artificial Intelligence Centre as well as of the Knowledge Technology Centre until May
2007. In the rest of 2007, he was the Dean of the College of Graduate Studies at
UNITEN, before moving on to be the President of Multimedia University (MMU) from
January 2008 to December 2010. He then moved across to Universiti Malaysia
Sarawak (UNIMAS), as well as retained a position as Senior Consultant at Gagasan
Wibawa Sdn. Bhd. under Andaman Berhad.

This was followed by Universiti Pertahanan Nasional Malaysia (UPNM) in October
2013, first as Head of the Artificial Intelligence Section of the Cyber Security Centre,
then as the Deputy Dean of the Faculty, followed by the Director of the Publications
Centre. He then joined Sunway University in October 2016, and tried retirement on 31
December 2020. However, he was called back to service by International Medical
University (IMU) since December 2021 to date. In total, this makes 3 government
universities, 4 private universities, 1 government-linked company (GLC), and 1 private
enterprise. In his academic career, Zaharin has held every single position created for
an academic (with the exception of the position of Deputy Vice Chancellor).

He has published numerous papers, won many research and commercialisation

grants, graduated many postgraduate students, and led various initiatives,

nationally and internationally – including being the Task Force Leader for the MSC

Telemedicine Flagship Application, Founder Member of the Penang ICT Council, Co-

Chair of the National ICT Human Resource Task Force, etc. One of his specialities is

R&D Roadmapping, and he had led such exercises at every single institution he had

worked at, as well as helped other institutions to do the same. He remains very active

in academia and is currently advising many postgraduates nationally and

internationally. 20

KEYNOTE
Speaker II

Title: The Curious and Inquisitive Mind

Professor Dr. Abd Karim Alias
Director of the Centre for Development of
Academic Excellence

Dr. Abd Karim Alias is a Professor of Food Technology at the School of Industrial
Technology, Universiti Sains Malaysia (USM) and the current Director of the Centre for
Development of Academic Excellence. He has taught at USM for over 28 years (since
1994). In addition, he has held several administrative positions, including Deputy
Dean and Director of CDAE, Senate member for many years, and Senate
representative on the university's Board of Governors.

He received the prestigious National Academic Award in 2008 for teaching from the
Ministry of Higher Education (Malaysia). He has taught at USM for more than 28
years (since 1994). In 2008, he received the Ministry of Higher Education of
Malaysia's prestigious National Academic Award for teaching. He has received the
Malaysia's Rising Star 2015 Award (Highest research citation in Agricultural
Sciences/Food Science & Technology), the Malaysia Research Star Award for four
consecutive years (2016, 2017, 2018, 2019), and the World's Most Influential Scientific
Minds by Clarivet Analytics. On a national scale, he is a Master Trainer for AKEPT and
a contributor to the Malaysia Education Blueprint 2015-2025. (Higher Education). As
co-chairman of the National Technical Committee, he led the development of
Malaysia's MOOC.

Dr. Karim is an ardent supporter of utilising the Internet as an alternative learning
and teaching medium. He has created and maintained a variety of teaching-related
portals, websites, online courses, and blogs. As of 20 August 2021, his 402
instructional videos on YouTube have received over 1 million views from over 190
countries, with an estimated 69 thousand hours viewed and over 6500 subscribers.
At IUCEL2022, he was awarded two prestigious national awards, i.e., the Higher
Education e-Learning Maestro Award and the Best Trendsetter Award.

21

POSTER COMPETITION

ONLINE POSTER JUDGES

Pn. Maziani Sabudin
Dr. Mohd Najwadi Yusoff
Dr. Azleena Mohd Kassim
Dr. Nur Hana Samsudin
Dr. Sukumar Lecthmunan
Dr. Tan Tien Ping
Dr. Teh Je Sen
Dr. Zarul Fitri Zaaba

22

POSTER COMPETITION

FACE TO FACE POSTER JUDGES

Assoc. Prof. Mohd Azam Osman
Mr. G. C. Sodhy
Pn. Maziani Sabudin
Dr. Lim Chia Yean
Dr. Nibras Abdullah Ahmed Faqera
Dr. Noor Farizah Ibrahim
Dr. Nur Hana Samsudin
Dr. Nur Intan Raihana Ruhaiyem

23

Poster Schedule

(Virtual)

HALL TIME POSTER STUDENT NAME TITLE

(MALAYSIA) ID FILTER WRAPPER METHOD FOR
TEXT FEATURE SELECTION IN
2.00 – 2.15 VP101 OSAMAH MULTI-CLASS TEXT DOCUMENT
PM MOHAMMED CLASSIFICATION
FADHIL ALYASIRI

2.20 – 2.35 VP102 MOHAMMED THE DIRECTIONS OF
PM NEAMAH CROWDSOURCING USAGE IN
VP103 HAMMOOD ONLINE COLLABORATIVE
HALL 1 VP104 LEARNING
DR GAN YANG JING
FACE AGEING ON GAN
2.40 – 2.55 IDREES FAZILI
https://bi PM AUTOMATED DECISION MODEL FOR
t.ly/cspc2 TRACTOGRAPHY PIPELINES
022hall1 3.00 – 3.15

PM

3.20 – 3.35 VP105 ALI FAWZI ADAPTIVE CLIP LIMIT TILE SIZE
PM MOHAMMED ALI HISTOGRAM EQUALIZATION FOR
NON-HOMOGENIZED INTENSITY
IMAGES

3.40 – 3.55 VP106 ZHANG DAOHUA RESEARCH ON INTERPLANT WEED
PM CONTROL TECHNOLOGY IN PADDY
FIELD BASED ON FASTER-RCNN

24

Poster Schedule

(Virtual)

HALL TIME POSTER STUDENT NAME TITLE

(MALAYSIA) ID ADVANCED PERSISTENT THREATS
(APT) MOBILE SENSORS
2.00 – 2.15 VP201 AMJED AHMED BEHAVIOUR
PM MAJIED AL-
KADHIMI

2.20 – 2.35 VP202 DURAID THAMER EFFECTIVE CYBER SITUATION
PM SALIM AWARENESS MODEL FOR
ADVANCED PERSISTENT THREATS
DETECTION

HALL 2 2.40 – 2.55 VP203 UMAIR MUNEER LEVERAGING TRANSFER LEARNING
DR PM BUTT WITH DEEP LEARNING FOR CRIME
PREDICTION
ANUSHA

https://bi 3.00 – 3.15 VP204 ZHANG HAO ASPECT SENTIMENT QUAD
t.ly/cspc2 PM VP205 PREDICTION WITH PLMS
022hall2 ZHAO
CHUNSHENG RESEARCH PROPOSAL: A DRL-
3.20 – 3.35 BASED END-TO-END APPROACH
PM FOR MULTI-OBJECTIVE
COMBINATORIAL OPTIMIZATION

3.40 – 3.55 VP206 RESEARCH ON CUSTOMER VALUE
PM JIN XINXIN MINING OF CROSS-BORDER E-
COMMERCE BASED ON USER
CHARACTERISTICS

25

Poster Schedule

Face-to-Face

POSTER ID STUDENT NAME TITLE

FP01 HARUNA ABDU DOMESTIC TRASH DETECTION AND
FP02 LIU SUXING CLASSIFICATION WITH DEEP LEARNING
SANI SALISU
FP03 IMAGE CLASSIFICATION METHOD OF BREAST
FP04 HASAN FALAH HASAN CANCER BASED ON DEEP MIGRATION
FP05 SHAMS MHMOOD ABDALI LEARNING
FP06
FP07 A SURVEY ON DEEP LEARNING-BASED 2D
FP08 HUMAN POSE ESTIMATION
FP09
FP10 ENHANCED HTLC SCHEME TO PREVENT
WORMHOLE ATTACK IN PAYMENT CHANNEL
FP11
FP12 NETWORK
FP13
REVOCABLE POLICY-BASED CHAMELEON
HASH FOR REDACTABLE BLOCKCHAIN

PARTIAL VERIFICATION BIAS CORRECTION IN
WAN NOR ARIFIN BIN WAN MANSOR DIAGNOSTIC ACCURACY STUDIES USING

PROPENSITY SCORE-BASED METHODS

HAIFA SALEH ALFURAYJ THE IMPACT OF BYSTANDERS'
INTERVENTION FEATURE IN CYBERBULLYING
KELVIN LIM CHING WEI
NUR AZMINA BINTI MOHAMAD SIMULATED ANNEALING-BASED HYPER-
HEURISTIC FOR FLEXIBLE JOB SHOP
ZAMANI SCHEDULING
AHMED GHAZI HAMEED AL-
SENTIMENT-BASED CRYPTOCURRENCY PRICE
RIKABI PREDICTION USING XLNET AND GRU DEEP
LEARNING MODELS
HUSSEIN ABDULKAREEM YOUNUS
THE MODERATING ROLE OF CIVIL CONFLICTS
NOR SHAMIRA BINTI SABRI ON THE CONTINUED USAGE INTENTION OF
M-LEARNING IN HIGHER EDUCATION IN IRAQ
TAHIRAH BINTI MT TAHIR
MULTIMODAL AGE AND GENDER ESTIMATION
FOR ADAPTIVE HUMAN-ROBOT

INTERACTION: A SYSTEMATIC LITERATURE
REVIEW

MODELLING THE TRANSMISSION OF
TUBERCULOSIS IN CLOSED SPACE USING
MICROSCOPIC PEDESTRIAN SIMULATION

DIGITAL ETIQUETTE AWARENESS ON SOCIAL
MEDIA PLATFORMS AMONG CHILDREN

26

3MT Competition

JUDGES
Ts. Dr. Chew XinYing

Dr. Nasuha Lee Abdullah

Assoc. Prof. Mohd Azam
Osman

Dr. Mohd Halim Mohd Nor

Dr. Zarul Fitri Zaaba

27

3MT Schedule

TIME SPEAKER NO. STUDENT’S NAME TITLE

2:00 PM - 2:04 PM - DR.IZA INTRODUCTION & REMARK
(MODERATOR)

2:05 PM - 2:08 PM SPEAKER 1 HAIFA SALEH THE IMPACT OF BYSTANDERS’ INTERVENTION FEATURE IN

ALFURAYJ CYBERBULLYING

2:10 PM – 2:13 PM SPEAKER 2 IDRIS NA'UMMA IMPACT OF FACEBOOK ADOPTION AS A MARKETING
ABDULLAHI STRATEGY ON BOTH FINANCIAL AND NON-FINANCIAL

PERFORMANCE OF SERVICE-BASED SMES IN
NORTHWESTERN NIGERIA

2:15 PM - 2:18 PM SPEAKER 3 AHMED GHAZI THE MODERATING ROLE OF CIVIL CONFLICTS ON THE
HAMEED AL- CONTINUED USAGE INTENTION OF M-LEARNING IN

RIKABI HIGHER EDUCATION IN IRAQ

2:20 PM - 2:23 PM SPEAKER 4 ZHANG XIAOBO KNOWLEDGE GRAPH BASED QUESTION ANSWERING
SYSTEM

2:25 PM - 2:28 PM SPEAKER 5 HARUNA ABDU CLEAN AND SUSTAINABLE ENVIRONMENT WITH DEEP
LEARNING

2:30 PM - 2:33 PM SPEAKER 6 ABUBAKAR ABBA ANALYSABLE CHAOS-BASED DESIGN PARADIGMS FOR
CRYPTOGRAPHIC APPLICATIONS

2:35 PM - 2:38 PM SPEAKER 7 THEVENDRAN A/L EXPLORING WORKFORCE PLANNING WITH QUANTUM-
MARIMUTHU AIDED DEEP NEURAL NETWORK

2:40 PM - 2:43 PM SPEAKER 8 NUR IZZATI BINTI DETERMINING THE EFFECTS OF CLIMATE CHANGE ON
AB KADER DEPRESSIVE PATIENTS USING LONG SHORT-TERM
MEMORY APPROACH

2:45 PM - 2:48 PM SPEAKER 9 NORISMIZA PREDICTING STUDENTS' SELECTION IN STEM STREAM
ISMAIL USING MACHINE LEARNING

2:50 PM - 2:53 PM SPEAKER 10 LIU SUXING IMAGE CLASSIFICATION METHOD OF BREAST CANCER
BASED ON DEEP MIGRATION LEARNING

2:55 PM - 2:58 PM SPEAKER 11 THULFIQAR JABAR MOBILE ADVANCED PERSISTENT THREAT MITIGATION
ABD BASED HARDWARE RESOURCE USAGE BEHAVI2O8R

The Impact of Bystanders’ Intervention Feature in

Cyberbullying

Haifa Saleh Alfurayj
Supervisor: Dr. Syaheerah Lebai Lutfi

Cyberbullying is a widespread problem that is common in social networking
sites (SNS). Twitter is one of the SNSs that provides the opportunity to
engage not only bullies and victims but also bystanders in cyberbullying
situations, which can lead to negative attitudes and harmful effects on
both the victim and the bystanders. The role of bystanders in cyberbullying
can lead them to intervene in the aggression in different ways: a)
bystander support of the victim (defender), which increases the power of
the victim and decreases the victim's social and psychological suffering
from the cyberbullying experience, b) bystander support of the bully
(instigator), which decreases the power of the victim and increases the
power of the bully. In recent years, many studies have attempted to
improve the detection of cyberbullying by considering various factors, such
as extracting multiple types of features, comparing the performance of
different classifiers, and preparing datasets in myriad ways. However, most
of these studies have not been able to accurately identify cyberbullying for
the following reasons: a) they rely on individual tweets and ignore the
intervention of bystanders, b) they disregard the criterion of power
imbalance provided in the context of cyberbullying. Although several
studies have confirmed the existence of the power imbalance criterion in
cyberbullying, it is rarely considered by researchers to detect cyberbullying.
In other words, bystander intervention has been ignored. In this study, we
suggest to address this gap to improve the detection of cyberbullying. The
two main research questions are "Does a useful relation exist between
bystander-based features and power imbalance criterion in a cyberbully
tweets session, that can be used to improve cyberbully recognition rate?"
and "Does the bystanders related feature improve cyberbullying toxicity
level detection?". The purpose of this study is to answer these questions
and to clarify whether bystander intervention is an effective feature to
measure the power imbalance criterion in session-based detection of
cyberbullying on Twitter.

29

Impact of Facebook Adoption as A Marketing

Strategy on Both Financial and Non-Financial

Performance of Service-Based SMEs in

Northwestern Nigeria

Idris Na'umma Abdullahi
Supervisor: Ts. Dr. Mohd Heikal Husin

Facebook has become an essential business tool for Small and Medium
Enterprises (SMEs) to achieve their competitive advantage in the global
marketplace. However, SMEs in the Nigerian services sector are still
reluctant to adopt the platform. Consequently, their performance was
found to be low. This study aims to investigate the determinants of
Facebook adoption and its impact on the financial and non-financial
performances of service-based SMEs in northwestern Nigeria. The
conceptual framework for this study integrates the Technology-
Organization-Environment (TOE) framework with the Resource-Based-View
(RBV) theory. The TOE framework was employed to investigate the factors
that influence the adoption of Facebook, whereas the RBV theory was
utilized to explain the relationships between Facebook adoption and its
impact on financial and non-financial performances. Moreover, the study
explores the moderating effects of trust in Facebook and adhocracy culture
on the TOE factors and Facebook adoption as well as the mediating effect
that Facebook adoption has on the TOE factors and both financial and non-
financial performance. The study is based on the quantitative approach
with 178 responses from SMEs decision-makers. The PLS-SEM analysis
results reveal that relative advantage, compatibility, perceived risk, top
management support, organizational readiness, customer pressure, and
government support are the important determinants of Facebook
adoption. Facebook adoption was found to have a significant positive
impact on both financial and non-financial performances. Both Trust in
Facebook and adhocracy culture were found to moderate some
relationships between the TOE factors and Facebook adoption. It is
expected that the findings of this study could motivate Nigerian service-
based SMEs to adopt Facebook to improve their business performance and
provide the Nigerian government with useful insight to design an effective
strategy to enhance the adoption of Facebook among the Nigerian services
sectors.

30

The Moderating Role of Civil Conflicts on The
Continued Usage Intention of M-Learning In

Higher Education In Iraq

Ahmed Ghazi Hameed Al-Rikabi
Supervisor: Prof. Dr. Putra Bin Sumari

M-Learning (ML) is the latest technique to deliver services and information
accessibility at the universal level for students, instructors, and other
educational institutions through wireless and mobile technologies. The
usage of ML in Iraq is still low and students’ and instructors’ use of M-
learning has been slowly progressing. This can be attributed to many
factors (such as civil conflicts, lack of infrastructure, lack of awareness, the
effect of social influence, and facilitating conditions) that may hinder M-
learning adoption among learners. However, a thorough review of the
literature reveals that there is a paucity of research that has investigated
the factors influencing the usage of ML in unstable or violent
environments. Therefore, the objective of this study is to investigate the
factors influencing the continued usage intention of M-learning amongst
students of Iraqi universities in an unstable environment. To achieve this
objective, this study uses UTAUT2 as a theoretical basis model, and it
extended the UTAUT2 model by Civil Conflicts (CC), M-Learning System
Awareness (M-LSA), and continued usage intention of M-Learning. The
proposed model consisted of thirteen factors that contribute to ML
success. A quantitative approach and simple random sampling technique
will use for collecting data in this study.

31

Clean and Sustainable Environment with Deep
Learning

Haruna Abdu
Supervisor: Dr. Mohd Halim Mohd Noor

Environmental contamination is a major issue affecting all inhabitants
living in any environment. The domestic environment is engulfed with
many trash items such as solid and toxic trashes, leading to severe
environmental contamination and causing life-threatening diseases if not
appropriately managed. Trash Detection and classification is at the heart
of these issues because the inability to classify the trash leads to difficulty
in recycling. Humans categorize trash based on what they understand
about the trash object rather than on the recyclability status of an object,
which frequently leads to incorrect classification in manual classification.
Additionally, coming into contact with toxic waste directly could be
physically dangerous for those involved. Few machine learning and Deep
Learning (DL) techniques were proposed using benchmarked trash
classification datasets. However, most benchmarked datasets used to train
DL models have a transparent or white background, which leads to a lack of
model generalization, particularly in the real world. In this research, we
propose a Deep Learning model based on the detection algorithm
architecture that can accurately detect and classify various types of trash
objects.

32

Analysable Chaos-Based Design Paradigms
for Cryptographic Applications

Abubakar Abba
Supervisor: Dr. Teh Je Sen

Chaos-based cryptography is an area that has seen huge research in recent

years. Majority of the designs focuses on obscuring security through complex

designs that make them difficult to analyse, improper design structures (ad-

hoc designs) and over reliance on statistical testing as a means to evaluate

security. These compromise the standards of well designed, simple and

secure design principles in cryptographical design protocol and does not

facilitate future cryptanalytic efforts. Moreover, to date, there have not been

any chaos-based cryptosystems being implemented to secure real-world

communications. In this study, a new design paradigms that are analysable

based on conventional ones such as substitution permutation network (SPN),

modular addition, bitwise rotation and bitwise XOR (ARX) and Feistel are

proposed to address these issues. These schemes are based on well-

established cryptographic principles. The proposed paradigms will provide a

new direction for future researchers in chaos-based cryptography. First, an

in-depth review is performed on the current state-of-the-art in the field of

chaos-based cryptographic algorithms to identify the challenges of various

design and evaluation methods that have been developed over the years. The

research investigates the impact of basic operations (substitution and

permutation) on statistical results for encryption process. Experiments are

carried out to compare and select optimal configurations of operations based

on the notion of confusion and diffusion. The study also further investigates

the influence and impact of using different chaotic (1-dimensional and 2-

dimensional maps) and shows that changing the map has minimal impact on

statistical results as long as the map is operating within its chaotic region.

Then, the best configuration is used to design simple yet robust encryption

and hash function algorithms to investigate the security and practicality of

the proposed method. The designed cryptosystems are simple, analysable,

and secure while utilizing chaos as an underlying source of randomness.

Statistical experiments are performed to show the performance and flexibility

of the proposed paradigm keeping in mind analysability as the goal of the

study. The proposed cryptographic primitives will be further analysed using a

classical cryptanalysis approach. Thus, if the objectives of this work are

achieved, chaos-based cryptographic algorithms are one step closer to seeing

real-world applications. 33

Determining the Effects of Climate Change on
Depressive Patients using Long Short-Term

Memory Approach

Nur Izzati Binti Ab Kader

Supervisor: Assoc. Prof. Dr. Umi Kalsom Yusof

Climate change is one of the significant issues nowadays as it is causing

global temperatures to increase. According to the Intergovernmental Panel

on Climate Change (IPCC), the global surface temperature has risen by

around 0.8 °C since the mid-nineteenth century. 2020 was one of the

warmest on record, and temperature is likely to increase more in the

upcoming years. The increasing temperature can affect humans not only

physically but also can affect human mentally. Among the specific mental

illness that received attention and getting severe in prevalence is a

depressive disorder. Several researchers recently report that temperature

rising has potentially increased the hospital admission of depressive

patients. However, the relationship is still unclear and needs further

investigation. Factors such as age and residency also might play a role in

determining the relationship considering the vulnerability and susceptibility

of depressive patients towards the rising temperature. The previous

researchers try to solve this problem using time series analysis. However,

the current approaches focus only on examining the relationship between

variables and do not involve forecasting hospital admission, which is

essential to help experts plan countermeasures and cooling procedures.

Therefore, this research is proposed to formulate the relationship of

temperature rising, age, and residency to the increasing rate of hospital

admission of depressive patients and forecast the depressive patient’s

admission based on the aforementioned factors. Long short-term memory

(LSTM), one of the deep learning approaches with local interpretable model-

agnostic explanations (LIME), is adopted to formulate the relationship

between temperature rising, age, and residency to the increasing rate of

depressive patients. Next, LSTM is optimized through a genetic algorithm

(GA) for depressive patients’ admission forecasting to ensure the feasibility

of the results. The hybridization technique will then be applied to further

improve the performance of the algorithm. The proposed methods will be

tested using benchmark and real case study datasets. They are evaluated

based on evaluation measures such as root mean square error, mean

absolute error, and mean absolute percentage error. The expected

implication of the study is a state-of-the-art deep learning approach that

can formulate and alert the impact of climate change on depressive
34
patients.

Predicting Students' Selection in STEM

Stream Using Machine Learning

Norismiza Ismail

Supervisor: Assoc. Prof. Dr. Umi Kalsom Yusof

The need for talent in science, technology, engineering, and mathematics (STEM)

is becoming more pronounced to fulfil the demand of STEM-based skilled human

capital. However, in recent years, the number of Malaysian students taking up

STEM has been on a declining trend and affected the country’s growth to drive

forward. Due to the aforementioned concerns, STEM interest in schools and

higher education appears to be waning, as evidenced by low secondary school

STEM enrolment. Apart from that, there are students who have chosen STEM

streams who have dropped out and are unable to continue their studies,

switching to non -STEM streams. This resulted in a shortage of qualified

candidates for STEM-based higher education programmes, as well as an

insufficient number of STEM graduates. Age, ethnicity, residence, gender,

parents’ occupation, type of school, examination results, family background, IT

facilities at school, post-covid 19, and many other factors have been mentioned

by previous researchers as influencing students’ selection in STEM. However,

there is still an unclear relationship between these factors towards low

students’ interest in STEM-based streams that need to be further investigated.

In addition, lack of previous research has been conducted to identify clusters of

STEM or non-STEM stream students’ profiling based on these contributing

variables. Therefore, this study is proposed to formulate the correlations

between different variables and to determine the predictors towards students’

intention in STEM stream. Clustering and hybrid Machine Learning are proposed

as a two-phase machine learning approach for predicting results of students’

interest in STEM streams by utilizing both unsupervised and supervised learning

techniques. A real Malaysian students’ dataset is applied in the proposed

approach. The students are clustered based on the closeness of various

educational characteristics and metrics. Clustering investigations using the Self

Organizing Map (SOM) algorithm indicate the presence of multiple coherent

student clusters which to identify students profiling based on variables in

supporting their intention in stream selection (STEM or non-STEM). In order to

predict student enrolment in the STEM stream programmes provided, this

research is proposed to design and evaluate the predictive model based on

Hybrid Machine Learning of optimized Decision Tree and Ant Colony Neural

Network (DT(ACNN)). The models generated are seen to be able to make high

degree accuracy of predictions based on the proposed evaluation measures.

Finally, this research discusses the potential usefulness of the clustering-aided

and hybrid machine learning approach for students’ intention in STEM streams
35
selection at schools and universities.

Knowledge Graph Based Question Answering
System

Zhang XiaoBo
Supervisor: Dr. Tan Tien Ping

In recent years, with the successful building of large-scale knowledge
graph, KGQA has attracted a lot of attentions of researchers and
applied to various search engines and intelligent virtual assistant.

Exploring Workforce Planning with Quantum-
aided Deep Neural Network

Thevendran A/L Marimuthu
Supervisor: Assoc. Prof. Dr. Wong Li Pei

This research focuses on exploring workforce planning (an instance of
combinatorial optimization problem) with deep neural network and
quantum computing. The idea behind this research is about getting deep
neural network trained with quantum computing, and eventually used to
optimize workforce planning.

36

Image Classification Method of Breast Cancer
Based on Deep Migration Learning

Liu SuXing
Supervisor: Dr. Anusha A/P Achutan

Aiming at the problems of small number of breast cancer
pathological image samples, time-consuming design features
and low accuracy of detection and classification, a breast cancer
image classification model algorithm based on the combination
of transfer learning and reinforcement learning is proposed. This
algorithm is based on the densenet structure of the deep neural
network, constructs a network model by introducing the
attention mechanism, and trains the enhanced data set using
multi-level transfer learning and reinforcement learning

Mobile Advanced Persistent Threat Mitigation
Based Hardware Resource Usage Behavior

Thulfiqar Jabar Abd
Supervisor: Assoc. Prof. Dr. Manmeet Mahinderjit Singh

Mitigating Mobile Advanced Persistent Threats (APT) based on hardware
resource usage (CPU, memory, and battery) using a cyber cognitive
situational awareness called Observe–Orient–Decide–Act (OODA) model.
First, the APT fingerprint will be generated from the collected hardware
resource usage data. Then, the risk and trust assessment model is
proposed to identify mobile APT and also protect confidential data. Finally,
this model will be evaluated in terms of effectiveness, security mechanism,
and usability.

37

Q1 and Q2 Journal Award Recipients

Name Title

Abdalla Wasef Predicting Input Validation Vulnerabilities Based on Minimal SSA

Marashdih Features and Machine Learning

AbdulRahman Weakly-supervised temporal action localization: a survey

Baraka

AbdulRahman M. A. Weakly-supervised temporal action localization: a survey

Baraka

Abeer Abdulhakeem Turbo Similarity Searching: Effect of Partial Ranking and Fusion

Mansour Alhasbary Rules on ChEMBL Database

Ali Olow Jimale Fully Connected Generative Adversarial Network For Human

Activity Recognition

Ayokunle A survey on unsupervised learning for wearable sensor-based

OlalekanIge activity recognition

Bhavani Devi Classification of Covid-19 misinformation on social media based

Ravichandran on neuro-fuzzy and neural network: A systematic review

Iza Sazanita Isa Optimizing The Hyperparameter Tuning of YOLOv5 For Underwater

Detection

Kelvin Lim Ching Wei Simulated-annealing- based hyper-heuristic for flexible job-shop

scheduling

Manal Al-rawashdeh IoT Adoption and Application for Smart Healthcare: A Systematic

Review

Moatsum Alawida A chaos-based block cipher based on an enhanced logistic map

and simultaneous confusion-diffusion operations

38

Q1 and Q2 Journal Award Recipients

Name Title

Mullah Nanlir Sallau Improving detection accuracy of politically motivated cyber-hate

using heterogeneous stacked ensemble (HSE) approach

Noratikah binti Suicidal behaviour prediction models using machine learning

Nordin techniques: A systematic review

Osamah Mohammed Wrapper and Hybrid Feature Selection Methods Using

Alyasiri Metaheuristic Algorithms for English Text Classification: A
Systematic Review

Raihanus Saadat Enhancing manufacturing process by predicting component
failures using machine learning

Sa'adatu Abubakar A Representation of 3GPP 5G-V2X Sidelink Enhancements in
Releases 14, 15, 16, and 17

Song-Quan Ong Community-based mosquito surveillance: an automatic mosquito-
on-human-skin recognition system with a deep learning algorithm

Song-Quan Ong Text Mining in Mosquito-Borne Disease: A Systematic Review

Song-Quan Ong Text Mining and Determinants of Sentiments towards the COVID-

19 Vaccine Booster of Twitter Users in Malaysia

Thulfiqar Jabar Exploration of Mobile Device Behavior for Mitigating Advanced

Persistent Threats (APT): A Systematic Literature Review and
Conceptual Framework

Umair Muneer Butt Hybrid of deep learning and exponential smoothing for enhancing
crime forecasting accuracy

Wan Nor Arifin Wan Correcting for partial verification bias in diagnostic accuracy

Mansor studies: A tutorial using R

39

The Advisors

Assoc. Prof. Dr. Manmeet
Kaur Mahinderjit Singh

Advisor (CSPC)

Ts. Dr. Anusha Achuthan

Advisor (Poster)

Dr. Pantea Keikhosrokiani

Advisor (3MT)

Dr. Gan Keng Hoon

Advisor (Special Session)

Dr. Suzi Iryanti Fadilah

Advisor (Form, Certs & Prizes)

Dr. Mohd Nadhir Ab Wahab

Advisor (Website & Program Book)

40

The Committees

Haziqah Shamsudin Thulfiqar Jabar Abd

Chair Co-chair

Tye Yi Wei Fatini Nadhirah
Mohd Nain
Committee
Committee
Aminu Maigari
Abdullahi
Committee Aminu Kazaure

Noratikah Nordin Committee

Committee Zhou Kun

Javed Ahmad Committee

Committee Norismiza Ismail

Committee

Nur Izzati Ab Kader Duraid Thamer Salim

Committee Committee

Noor Manan Taj Asma Sajid

Committee Committee

Shamsuddeen Duaa Akhtom

Committee Committee

41

The Helpers

Fung Chey

Nor Shamira Binti Sabri

Najihah Ibrahim

Kelvin Lim Ching Wei

Dr Iza Sazanita Isa

Saadatu Abubakar

42

43