Programme Book SCDS2016

The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

PROGRAMME & ABSTRACT BOOK

The 2nd International Conference on Soft Computing in Data
Science (SCDS 2016)

Science in Analytics: Harnessing Data and Simplifying Solutions

Copyright © 2016 by Faculty of Computer & Mathematical Sciences, Universiti
Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia.
All rights reserved. Personal use of this material is permitted. However, no part of
this publication may be reproduced, stored in a retrieval system, or transmitted in
any form or any means, electronic, mechanical, photocopying, recording or
otherwise, without prior permission, in writing, from the publisher.

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Table of Contents

Table of Contents ii
University Profile 1
Faculty Background 2
Message from the Vice Chancellor 3
Message from the Dean 5
Message from the Conference Chair 7
Introduction to SCDS 2016 9
Keynote Speakers 10
10
Keynote Speaker 1: Dato‘ Seri Ivan Teh 12
Keynote Speaker 2: Dr Dzaharudin Mansor 14
Keynote Speaker 3: Faisal Hajazi 16
Keynote Speaker 4: Professor Dr Azlinah Hj Mohamed 18
Keynote Speaker 5: Associate Professor Dr Dhiya Al-Jumeily 22
Keynote Speaker 6: Dr David R. Hardoon 24
Special Sessions 24
Special Session 1: Data Wrangling in the Big Data World 25
Special Session 2: Analytics in Action
Special Session 3: Solving Data to Insights Challenge with Microsoft 27
29
- R and Azure Machine Learning 30
Special Session 4: Smart Data Discovery with IBM Watson Analytics 33
Conference Schedule 38
Parallel Sessions Schedule 38
Proceeding Abstracts 44
Track : Artificial Neural Networks 58
Track : Classification/Clustering/Visualization 64
Track : Information and Sentiment Analysis 69
Track : Fuzzy Logic 72
Organising Committee 73
International Scientific Committee 76
List of Reviewers 77
Acknowledgements 79
Conference Map
Autograph

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

University Profile

Universiti Teknologi MARA (UiTM) is Malaysia's largest institution of
higher learning in terms of size and population. It has experienced
phenomenal growth since its inception in 1956 and it is still growing.
Beside the main campus in Shah Alam, the university has expanded
nationwide with 12 state campuses, 6 satellite campuses in Shah Alam, 11
state satellite campuses and 21 affiliated colleges. With this vast network
and a workforce of 17,770, the university offers more than 500 academic
programmes in a conducive and vibrant environment. It is also home to
some 175,200 students.
To accommodate the increasing number of students, six new branch
campuses were completed in 2014, namely Phase 2 of Puncak Alam
Campus, Samarahan 2 Campus in Sarawak, Jasin Campus in Melaka, Pasir
Gudang Campus in Johor, Seremban Campus in Negeri Sembilan and
Tapah 2 Campus in Perak.
UiTM’s plan to strengthen the governance of the university resulted in the
establishment of a system called 1 University 2 System ((1U2S). Based on
this system, which was approved by the Board of Directors in 2007, there
is an anchor university and 12 state universities. With the new system of
governance, the university is able to optimise the use of its resources to
enhance excellence in research, technology and learning as well as
community service.

Faculty of Computer & Mathematical Sciences

Prof. Dr Hajah Azlinah Hj. Mohamed
Dean

YBhg. Prof Emeritus Dato’ Dr Hassan Said Faculty of Computer and Mathematical Sciences (FSKM), UiTM founded in
Vice Chancellor 1966 currently offers BSc, MSc. And PhD degrees through its seven
academic centres; Computer Science, Computer Technology and
Faculties Networking, Information System, Information Technology, Mathematics,
SCIENCE & TECHNOLOGY Statistics and Decision Science and Actuarial Science. The aim of these
Faculty of Electrical Engineering programs is to provide students with a general background in their
Faculty of Mechanical Engineering respective areas of specialization and to equip them with the relevant
Faculty of Chemical Engineering knowledge and skills by using the state-of-the-art technology to meet the
Faculty of Civil Engineering rapidly changing demands of a modern society. In keeping with the
Faculty of Pharmacy university’s mission, this faculty is committed in providing high quality
Faculty of Medicine programs of study with inputs from experts from various industries.
Faculty of Dentistry After more than 40 years of existence, FSKM has grown beyond
Faculty of Health Sciences expectations. Since its inception, we have improved tremendously in
Faculty of Applied Sciences terms of our number of faculty staff who are well qualified, experienced
Faculty of Computer & Mathematical Sciences and caring. Our strength lies in our passion for teaching as the success of
Faculty of Architecture, Planning & Surveying students remains strong in our vision. We are mission focused although
Faculty of Sports Science & Recreation our student body is remarkably diverse. This is one of our unique
Faculty of Plantation & Agrotechnology challenges in providing an education par excellence.
SOCIAL SCIENCE & HUMANITIES
Faculty of Law Programmes Offered
Faculty of Administrative Science & Policy Studies Postgraduate
Faculty of Communication & Media Studies Doctor of Philosophy (by research)
Faculty of Art & Design Master of Science (by research)
Faculty of Film, Theater & Animation Master of Science (Applied Statistics)
Faculty of Music Master of Science (Information Technology)
Faculty of Education Master of Science (Quantitative Sciences)
Academy of Contemporary Islamic Studies (ACIS) Master of Science (Computer Science)
Academy of Language Studies (APB) Master of Science (Computer Networking)
BUSINESS & MANAGEMENT Master of Science in Applied Mathematics
Faculty of Accountancy Undergraduate
Faculty of Business & Management Bachelor of Computer Science (Hons)
Faculty of Hotel & Tourism Management Bachelor of Information Technology (Hons)
Faculty of Information Management Bachelor of Science (Hons) Statistics
ACADEMIC CENTRES Bachelor of Science (Hons) Actuarial Science
Institute of Graduate Studies (IPSis) Bachelor of Science (Hons) Computational Mathematics
Institute of Neo Education (INED) Bachelor of Science (Hons) Management Mathematics
Centre of Foundation Studies (CFS) Bachelor of Science (Hons) Mathematics
UiTM-PDRM Academy of Police Bachelor of Info.Tech.(Hons) Intelligent System Engineering
i-Learn Centre (i-LeC) Bachelor of Info.Tech.(Hons) Business Computing
Arshad Ayub Graduate Business School (AAGBS) Bachelor of Info.Tech.(Hons) Information System Engineering
Bachelor of Comp.Sci.(Hons) Data Comm. & Networking
Bachelor of Comp.Sci.(Hons) Netcentric Computing
Bachelor of Comp.Sci.(Hons) Multimedia Computing
Diploma in Computer Science
Diploma in Statistics
Diploma in Actuarial Science
Diploma in Mathematical Sciences

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Faculty Background

Faculty of Computer and Mathematical Sciences (FSKM), UiTM, founded in 1966,
currently offers BSc, MSc. And Ph.D. degrees through its seven academic centres;
Computer Science, Computer Technology and Networking, Information System,
Information Technology, Mathematics, Statistics and Decision Science and
Actuarial Science. The aim of these programs is to provide students with a general
background in their respective areas of specialization and to equip them with the
relevant knowledge and skills by using the state-of-the-art technology to meet the
rapidly changing demands of a modern society. In keeping with the university‘s
mission, this faculty is committed in providing high quality programs of study with
inputs from experts from various industries.

After more than 40 years of existence, FSKM has grown beyond expectations.
Since its inception, we have improved tremendously in terms of our number of
faculty staffs who are well qualified, experienced and caring. Our strength lies in our
passion for teaching as the success of students remains strong in our vision. We
are mission focused although our student body is remarkably diverse. This is one of
our unique challenges in providing an education par excellence.

Programmes Offered

Postgraduate
Doctor of Philosophy (by research)
Master of Science (by research)
Master of Science (Applied Statistics)
Master of Science (Information Technology)
Master of Science (Quantitative Sciences)
Master of Science (Computer Science)
Master of Science (Computer Networking)
Master of Science in Applied Mathematics

Undergraduate
Bachelor of Computer Science (Hons)
Bachelor of Information Technology (Hons)
Bachelor of Science (Hons) Statistics
Bachelor of Science (Hons) Actuarial Science
Bachelor of Science (Hons) Computational Mathematics
Bachelor of Science (Hons) Management Mathematics
Bachelor of Science (Hons) Mathematics
Bachelor of Info.Tech.(Hons) Intelligent System Engineering
Bachelor of Info.Tech. (Hons) Business Computing
Bachelor of Info.Tech. (Hons) Information System Engineering
Bachelor of Comp.Sci. (Hons) Data Comm. & Networking
Bachelor of Comp.Sci. (Hons) Netcentric Computing
Bachelor of Comp.Sci. (Hons) Multimedia Computing
Diploma in Computer Science
Diploma in Statistics
Diploma in Actuarial Science
Diploma in Mathematical Sciences

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Message from the Vice Chancellor

I would like to applaud our Faculty of Computer and Mathematical Sciences (FSKM)
for its unwavering commitment in promoting a culture of research, besides
broadening access to industry linkages, as can be seen from the repeat hosting of
The International Conference on Soft Computing in Data Science (SCDS),
which involves close collaborations with numerous strategic partners from the
industry.

In fact, it is timely that the faculty gives due recognition to and highlights the
importance of data analytics and its applications in SCDS 2016, which is evident
from the conference theme of Science in Analytics – Data Harnessing and
Simplifying Solutions.

This is indeed pertinent, considering that we are living in an era of data technology,
which is relevant to diverse sectors of the economy and vital to the development of
world communities. Hence, it is hoped that this important international event, which
brings together research fraternity representing different disciplines from within and
outside of the country, will help translate their knowledge, expertise and experience
into simple viable initiatives and solutions, with the shared goal of enhancing
societal wellbeing, on top of driving a robust economy which is evenly spread
among countries of the world.

On that score, it is heartening to know that FSKM is on track in pursuing its mission
of bringing the world into its fold by means of intellectual events such as this,
through which a platform is provided for highlighting the challenges faced by
ministries, corporations and industries, to harness their vast volumes of data, and
putting forward the availability of advanced technology and techniques for big data
analytics. Ultimately, of utmost significance is the opportunity for academia and
industries to share their knowledge and expertise in solving complex problems and
providing solutions for the benefit of communities at home and across the globe.

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

The 21st century is where big data technologies and practices are moving quickly,
and developing countries like Malaysia need to get on board and stay ahead of the
game. There is still, however, a shortage of data professionals and data scientists in
the country. Thus, I am delighted to note that FSKM is taking the lead to train and
engender qualified data professionals and data scientist graduates, thus meeting
the demands of the industry.
Allow me to express my heartfelt appreciation to all our sponsors and friends of
UiTM, for finding it worth your time to support our programmes and efforts in
increasing our academic visibility. My wish is for everyone in this conference to
have two days of rewarding exchanges; and, with new knowledge and
collaborations, it is hoped that you will move forward, bringing forth creative and
innovative ideas for the benefit of the global community. To our guests, esteemed
speakers, participants and strategic partners, I wish all of you a memorable stay in
Malaysia.
In closing, let us mull over Henry Ford‘s words of wisdom: “Coming together is a
beginning, keeping together is progress, working together is SUCCESS.”

Professor Emeritus Dato’ Dr Hassan Said
Vice Chancellor
Universiti Teknologi MARA

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Message from the Dean

Assalamualaikum Warahmatullahi Wabarakatuh and warm greetings to everyone.

First of all, I am absolutely delighted to welcome all keynote and special sessions
speakers, and delegates to the 2nd International Conference on Soft Computing in
Data Science (SCDS 2016) hosted by Faculty of Computer and Mathematical
Sciences, Universiti Teknologi MARA. The First International Conference on Soft
Computing in Data Science (SCDS 2015) was a celebrated success to us and has
brought forward greater awareness of Big Data Analytics among the academia and
industry. This year, having the theme of ?Science in Analytics: Harnessing Data and
Simplifying Solutions?, SCDS 2016 evidently arose to accomplish much more of the
agenda.

I am happy to announce that SCDS 2016 conference proceedings will be published
by Springer in Communications in Computer and Information Science series. We
received paper submissions from various countries which include Macao, Japan,
Korea, India, Thailand, Ireland, Spain, Iran, Iraq, Taiwan, Maldives, Pakistan and
China. All in due appreciation of our partners, contributors and supporters.

Our utmost thanks and sincere gratitude goes out to the Advanced Analytics and
Engineering Centre, Universiti Teknologi MARA, University of Tennessee, Data
Analytics and Collaborative Computing Group, University of Macau, and The
National ICT Association of Malaysia (PIKOM). Thank you for your generous
contribution and strong support.

Also, we are most excitedly proud and happy to have FUSIONEX as our gold
sponsor. I sincerely thank Dato‘ Seri Ivan Teh, Managing Director of FUSIONEX for
his great support and also for being a Keynote Speaker of this event. I am also
acknowledging Microsoft, IBM, Azendian Solutions, Pertubuhan Jaringan Pakar
Berjaya, Advanced Business Analytics (M) Sdn Bhd and SAS Malaysia. I would like
to thank Bank Islam, Malaysia Development Corporation (MDEC), Axiata and
Permodalan Nasional Berhad (PNB) for their sustenance of support for this event.

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Your generous contributions demonstrate your kind attention towards the
investment from which knowledge transfer, skill-building, greater partnerships and
collaborations for the advancement of education and research flourish. And for that,
our gratitude knows no bounds.
I applaud the efforts of the organizing committees of this event. They have shown
great commitment and team efforts to ensure that SCDS 2016 will be a success. I
also congratulate the conference and workshop committee for successfully
organising two relevant and useful workshops: ?Data Mining Using WEKA: Tools
and Techniques? by Dr Shuzlina Abdul Rahman, Ezzatul Kamaru-Zaman and
Sofianita Mutalib, and, ?Data Management using R? by Associate Professor Dr
Sayang Mohd Deni and Dr Norshahida Shaadan. Also to Professor Dr Michael W.
Berry from University of Tennessee. I am deeply touched by your continuous
commitment and support of our initiatives and programs.
Big Data Analytics involves multi-disciplines and I do hope the outcome of this
conference will be collaborations among local and international delegates on
research that can help improve the advancement of knowledge and society. I wish
everyone a wonderful time here in SCDS 2016 and may this conference add value
to your professional career and research endeavours.

Professor Dr Hajah Azlinah Hj Mohamed
Dean
Faculty of Computer and Mathematical Sciences
Universiti Teknologi MARA

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Message from the Conference Chair

On behalf of Universiti Teknologi MARA, I would like to extend a warm welcome or
?Selamat Datang? to all our honorable guests, keynote and special sessions
speakers and participants of the 2nd International Conference on Soft Computing in
Data Science 2016 (SCDS 2016) with the theme ?Science in Analytics: Harnessing
Data and Simplifying Solutions?. The success of the First International Conference
on Soft Computing in Data Science (SCDS 2015), and the positive feedbacks from
attendees motivated the planning and organizing of SCDS 2016.

SCDS 2016 aims to provide a platform for knowledge sharing on leading edge
analytical methods and also addressing challenges, problems and issues in Big
Data Analytics. We highly appreciate the great support from our sponsors
FUSIONEX, Bank Islam, MDEC (Malaysia Digital Economy Corporation), Microsoft,
Permodalan Nasional Berhad (PNB), Pertubuhan Jaringan Pakar Berjaya, IBM,
Azendian, Axiata, ABA and SAS. Your generous support of SCDS 2016 shows your
commitment towards working with Universiti Teknologi MARA to be in the frontiers
of producing skilled workforce to meet the demands of the industry. We hope to
have strategic partnership with our sponsors and relevant parties to push Faculty of
Computer and Mathematical Science‘s BDA initiatives to a higher level.

We have invited six distinguished keynote speakers: Dato‘ Seri Ivan Teh,
FUSIONEX, Professor Dr Azlinah Hj Mohamed, Universiti Teknologi MARA; Dr
Dzahar Mansor, Microsoft, Mr Faisal Hajazi, Microsoft, Dr David R. Hardoon,
Azendian Solutions and Associate Professor Dr Dhiya Al-Jumeily, Liverpool John
Moores University, UK. Additionally, we have four special sessions by Mr Gan
Chun Yee (FUSIONEX), Mr Laurence Liew (REVOLUTION ANALYTICS), Mr
Shawn Tan (IBM WATSON ANALYTICS), and Dr Mark Chia (SAS). We hope that
the keynote and special sessions add value to your knowledge and career.

SCDS 2016 has accepted a total of 27 exceptional papers out of 66 paper
submissions, an acceptance rate of 41%. I would like to thank Professor Dr Michael
W. Berry and Professor Dr Azlinah Hj Mohamed for their contributions as editors of
SCDS 2016 proceeding which will be published by Springer in the Communications

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

in Computer and Information Science series. I gratefully acknowledge the wonderful
support provided by all the technical reviewers who generously sacrifice their time
for reviewing the papers.
I greatly appreciate the dedicated support of our SCDS 2016 committee, workshop
speakers and facilitators who have worked tirelessly to ensure another successful
conference. Our heartfelt appreciation to Professor Dr Michael W. Berry, University
of Tennessee, USA for his continuous support and great commitment to ensure
another successful Springer indexed proceeding and conference. Special thanks
are also extended to our co-organisers: Advanced Analytics Engineering Centre,
FSKM Centre of Excellence, Data Analytics and Collaborative Computing Group,
University of Macau and PIKOM (The National ICT Association of Malaysia). We
highly appreciate all those who have contributed directly or indirectly to the success
of SCDS 2016.
I sincerely hope that SCDS 2016 has provided a venue for knowledge sharing,
publication of good research findings and new industry-university collaborations
Last but not least, I wish everyone an enjoyable and memorable experience at
SCDS 2016 and in Malaysia.
Thank you (Terima Kasih).

Professor Dr Yap Bee Wah
Conference Chair

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Introduction to SCDS 2016

International Conference on Soft Computing in Data Science 2016 (SCDS 2016) is
held in Pullman Kuala Lumpur, Bangsar Malaysia from 21st to 22nd of September
2016. The theme of the conference is =Science in Analytics: Harnessing Data and
Simplifying Solutions‘.

Data science can improve corporate decision-making and performance, personalize
medicine and healthcare services and improve organizations efficiency and
performance. Data Science is about extracting valuable information from data of
various forms. With the advancement in computer technology, huge amount of data
can be stored and harnessed. Data science and analytics plays an important role in
various disciplines including business, medical and health informatics, social
sciences, manufacturing, economics, accounting and finance.

SCDS 2016 aims to provide a platform for discussions on leading edge methods
and also addressing challenges, problems and issues in machine learning in data
science and analytics. The role of machine learning in data science and analytics is
significantly increasing in every field from engineering to life sciences and with
advanced computer algorithms, solutions for complex real problems can be
simplified. For the advancement of society in the 21st century, there is a need to
transfer knowledge and technology to industrial applications to solve real-world
problems. SCDS 2016 have invited renowned international and local keynote
speakers who are academia or practitioners to share their knowledge and
experience in the area of machine learning in data science and analytics.

SCDS 2016 aims to attract researchers who are actively engaged both in theoretical
and practical aspects of Soft Computing in Data Science. The focus is on Machine
Learning for Data Science and Analytics. Research collaborations between the
academia and industry can lead to the advancement of useful analytics and
computing applications for providing real time insights and solutions.

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Keynote Speakers

Keynote Speaker 1: Dato’ Seri Ivan Teh

Managing Director
Fusionex, Malaysia

Dato‘ Seri Ivan is the Founder & Managing Director of Fusionex – a technology
company, specializing in Big Data Analytics, Artificial Intelligence, Deep Learning
and the Internet of Things (IoT). Listed on the London Stock Exchange, Fusionex
has a market cap averaging 1 Billion Ringgit.

Dato‘ Seri Ivan has over 17 years of experience in in the ICT industry. He is
frequently invited to give talks, present his views and share insights in forums and
events globally. Closer to home, Dato‘ Seri Ivan has been invited by BFM,
Bloomberg TV, the Edge, Astro, Ernst & Young, New Straits Times as well as the
Star on a number of occasions to share his views and vision, especially on how
technology can help businesses.

Prior to founding Fusionex, Dato‘ Seri Ivan managed teams at HP, Intel and
Accenture. He has led Fusionex to the creation of global award-winning software
and solutions. Dato‘ Seri Ivan is also the only ASEAN representative at the Global
Business Intelligence Advisory Council where top leaders gather in the United
States. Dato‘ Seri Ivan is a highly respected leader in the information technology
space and has a strong network of world-renowned partners and affiliates
worldwide.

Among the awards and accolades accredited are the Global Business Intelligence &
Analytics Awards, Big Data World Award, MSC Award, APICTA Award, PIKOM ICT
Awards, SITF Awards, ASOCIO Top ICT Company Award, and Asia Pacific
Analytics Award etc. Dato‘ Seri Ivan has also been recently declared as the winner
of the Ernst & Young Technology Entrepreneur award, the Brandlaureate (Best of
Brands) Great Entrepreneur ICON Leadership award, the Asia Corporate
Excellence & Sustainability Young Entrepreneur Award, as well as the award for
being Asia Pacific‘s Most Outstanding Entrepreneur.

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Making the Most of Big Data

Speaker: Dato‘ Seri Ivan Teh
Managing Director
Fusionex, Malaysia

Abstract
The benefits of conducting Big Data Analytics in an organization have been well
documented. Consolidating a variety of structured and unstructured forms of data
and then processing them to unearth useful information would do wonders for
organizations in executing their long-term strategic plans. A sufficiently robust BDA
platform could also provide real-time insights to increase efficiency in an
organization‘s day-to-day operations.
Due to the remarkable potential value that BDA could bring, organizations (both
public and private), need to begin adopting such technologies which will enable
them to make data-driven decisions.
This session on ?Making the most of Big Data? aims to showcase the various ways
in which Big Data can be applied to organizations across a multitude of industries.
For example, the type, quantity, and complexity of data collected by a retail chain
would differ from a bank, which would differ from an airline company. We will also
present the latest in emerging BDA tech that organizations may utilize to increase
accuracy in decision-making, maximize profits, and meet their goals in record time.

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Keynote Speaker 2: Dr Dzaharudin Mansor

National Technology Officer
Microsoft Malaysia

Dr. Dzaharudin Mansor is the National Technology Officer (?NTO?) for Microsoft
Malaysia. As the NTO, Dzahar drives the engagement with key national technology
stakeholders, which include academics, policy makers & advisors, and interest
groups with the intention to builds trust while contribute to national development.

Dr Dzahar joined Microsoft in 2005 and has more than 31 years of professional
experience in ICT and telecommunications. He started his career as a lecturer at
the department of Computer Science, La Trobe University, and moved on to as a
R&D engineer at Telecom Australia Research Laboratories in Melbourne. On
returning to Malaysia, he joined Celcom as a R&D manager, and left the company
as the Vice President for R&D and IT divisions. He subsequently worked at HP in
Singapore, Vsource (M) Sdn. Bhd. and Object Innovations (M) Sdn. Bhd. in R&D,
operations, business, as well as leadership positions.

He also presently holds, and has held, several associate positions including as an
Adjunct Professor at IIUM, a councillor at PIKOM and academic advisor at several
public and private universities. In 2010, he had the honor of leading the Business
Services Economic Transformation Program (ETP) Labs that has been one of the
key initiatives by the Malaysian Government to transform Malaysia into a developed
nation by 2020. He is a senior member of IEEE.

Dr. Dzahar received a First Class Honors Degree in Computer Systems
Engineering from Monash University, Australia in 1985, and subsequently awarded
Australian University Graduate scholarships to complete his PhD in Computer
Science at La Trobe University, Australia in 1988. He is passionate about
technology and aspires to contribute towards the nation‘s socio-economic progress.

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Democratizing Machine Learning

Speaker: Dr Dzahar Mansor
National Technology Officer
Microsoft Malaysia.
Abstract
The recent technology mega-trends powered by the combination of Mobility, Social,
Big Data and Cloud Computing have been given many names; the Third Platform,
Nexus of Forces, Internet Plus and others. Whatever the name may be, we cannot
deny the profound impact that these have had in businesses and our daily lives.
Things are not going to slow down, and in fact technologies such as IoT and
Machine Learning will power the next generation of platforms. The keynote will
address these technology megatrends, and show how they are poised to
democratize Machine Learning to power next generation platforms to accelerate
innovation and research.

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Keynote Speaker 3: Faisal Hajazi

Data Platform Solution Architect
Microsoft

Faisal has 14 years of experience in data platform, specialized in architecture, data
warehousing, business intelligence and analytic across various platforms. He has
very strong experience in defining architecture strategies to deliver value-driven
technology focusing in data driven business. He has deep ability to understand
business requirements, articulate a vision, evaluating risk and craft architecture
roadmaps.
Faisal has worked for major industries like Telco, Banking & Insurance. He has
deep knowledge about these businesses. He helps clients transform their
businesses by recognizing the value of Architecture, and design the future in a way
that is flexible to meet the challenges of a dynamic world.

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Microsoft Advanced Analytics Overview

Speaker: Faisal Hajazi
Data Platform Solution Architect, Microsoft

Abstract
Create and execute advanced analytics wherever your data lives—in the cloud or
on-premises data stores—to deliver deeper insights and predictive results for
smarter, more confident decisions. The real concept behind the Advanced Analytics
in big data context is combining structured and un-structured data which produces
insights that were never possible before.
Machine learning and advanced analytics combined with the power of the cloud &
its unlimited capacity for data storage/computation marks a unique point in history
an opportunity for organizations to automate and innovate with agility and increase
their speed of business, enabling them to shift from:
Looking at historical data to understand what happened and capturing real time
data to understand why it happened. To in the future harnessing predictive analytics
to understand what will happen.
And finally using prescriptive analytics to identify what actions should be taken so
businesses can automate outcomes.

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Keynote Speaker 4: Professor Dr Azlinah Hj Mohamed

Dean
Faculty of Computer and Mathematical Sciences
Universiti Teknologi MARA, Malaysia

Professor Dr Azlinah Hj. Mohamed is a Professor at the Faculty of Computer and
Mathematical Sciences, Universiti Teknologi MARA, Malaysia. She serves as the
Dean of the faculty as well as being the Special Officer at the Academic Affairs and
Development Office in Universiti Teknologi MARA. She received her MSc (Artificial
Intelligence) from University of Bristol, UK and PhD (Decision Support Systems)
from Universiti Kebangsaan Malaysia. Her recent research activities and numerous
professional publications in international conferences and local journals focus on
her interests in the Artificial Intelligence, Decision Support Systems and Soft
Computing. She was also awarded with many competitive grants from
ScienceFund, MOSTI and others on both academic and industrial projects for the
industry, as well as for the government. Her research works includes the
Information Professionals‘ Competency Assessment Model and the Multi-
Parametric Pectin Lyase-Like Protein Function Classifier which had won many
awards. She is also an active member of the Malaysia Information Technology
Society (MITS), Lembaga Akredetasi Negara, Malaysia and Artificial Intelligence
Society.
.

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Big Data Analytics: Getting the Right Match for the Best Fit

Matching Resources, Molding Analytics, Acquiring Significance
Speaker: Professor Dr Azlinah Hj. Mohamed
Dean
Faculty of Computer and Mathematical Sciences
Universiti Teknologi MARA, Shah Alam

Abstract
In this century, with the rapid emergence of digital devices, an unstoppable,
extraordinary knowledge revolution has taken place in opening opportunities and
creating innovative ways to harness and leverage on data. Reflecting this backdrop,
organizations that learn to mine data while utilizing powerful analytics for insights
are of great advantage to succeed.
As immense as data can become, and as abundant as big data opportunities and
technologies are available, challenges still exists for both researchers and industry
as to specifically find the right analytics to tailor their needs. Currently, it seemed
nebulous how big data solutions available today could be used across many
platforms considering that the variety, volume, velocity, and veracity of data in an
organization is often unique to the organization. Hence in the sea of options,
organizations need to match the right algorithms, platforms and tools in order to
procure more meaningful and informed decisions.
There is a great need to investigate the capabilities of various tools and techniques
in handling big data that involves pre-processing and attributes selection for data
analytics. In this session, we present the mapping of data integration tools with data
visualization, analytics and decision support tools for the researchers and industry
to aid in conducting Big Data Analytics.

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Keynote Speaker 5: Associate Professor Dr Dhiya Al-Jumeily

Associate Dean of External Engagement
Faculty of Engineering and Technology
Liverpool John Moores University, UK.

Dr. Dhiya Al-Jumeily is the Associate Dean of External Engagement for the Faculty
of Engineering and Technology. He has extensive research interests covering a
wide variety of interdisciplinary perspectives concerning the theory and practice of
Applied Computing in medicine, human biology, and health care. He has published
well over 150 peer reviewed scientific publications, 4 books and 3 book chapters, in
multidisciplinary research areas including: Technology Enhanced Learning, Applied
Artificial Intelligence, Neural Networks, Signal Prediction, Telecommunication Fraud
Detection, AI-based clinical decision-making, medical knowledge engineering,
Human-Machine Interaction, intelligent medical information systems, wearable and
intelligent devices and instruments. But his current research passion is decision
support systems for self-management of health and disease.

Dhiya has successfully supervised 16 PhD students‘ studies and has been an
external examiner to various UK and overseas Universities for undergraduate,
postgraduate and research degrees. He has been actively involved as a member of
editorial board and review committee for a number peer reviewed international
journals, and is on program committee or as a general chair for a number of
international conferences.

Dhiya is also a successful entrepreneur. He is the head of enterprise for the faculty
of Engineering and Technology. He has been awarded various commercial and
research grants, nationally and internationally, over £3M from Overseas Research
and Educational Partners, UK through British Council and directly from industry with
portfolio of various Knowledge Transfer Programmes between academia and
Industry.

Dhiya has extensive leadership experience including leading the Applied Computing
Research Group, Developing and Managing the Professional Doctorate programme

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

in Engineering and Technology for the University, a founder and Chair of the IEEE
International Conference Series on Developments in eSystems Engineering DeSE
(www.dese.org.uk) since 2007. He has a large number of international contacts and
leads or participates in several international committees in his research fields. Dhiya
has one patent and coordinated over 10 projects at national and international level.
Dr. Al-Jumeily is a member of the IEEE and the British Computer Society (BCS) and
has achieved his Chartered IT Professional status in 2007. He is also a fellow of the
UK Higher Education Academy.

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Data Science and Applied Computing for Multidisciplinary
Applications

Speaker: Associate Professor Dr Dhiya Al-Jumeily
Associate Dean of External Engagement
Faculty of Engineering and Technology
Liverpool John Moores University, UK

Abstract

Data Science is a massive multidisciplinary field that involves using applied
mathematics, pattern recognition techniques and computer programmed algorithms.
These methods are used to gain knowledge by systematically studying datasets of
various sizes. Large and complex datasets are constantly being collected from
numerous sources such as mobile phones, remote sensors, software logs and
social media platforms. These datasets are becoming increasingly heterogeneous
as they are stored in differing formats and unstructured with not yet established,
complex relationships between values. They also tend to be distributed across
different locations and domains.

Applied Computing is the practice of embedding the realisation of Computer
Science‘s latest technological advancements into industrial, business, and scientific
intelligent solutions. Applied Computing stretches to a variety of fields, requiring an
extensive knowledge of the specialised subject area and in many cases large teams
of trained individuals to put into production. Applied Artificial Intelligence is
considered as one of the major fields of Applied Computing. Artificial Intelligence
has been introduced as an important tool in the implementation of Health, Business,
Education, Entitlement, Tourism and more centred solutions as real world
applications.

The theme of Artificial Intelligence transcends computing. However, computing is
perhaps a field at the forefront of exploring intelligence for the purpose of practical
benefits to human society. It is arguable, though informative, to consider the dawn
of computational intelligence as a consequence of the ideas of Alan Turing and the
progression of computation from fixed immutable programs, manifested in hardware
configurations, to entirely software based representations which provide the
necessary potential and flexibility for self-modification and reflexivity as considered
to be necessary conditions of intelligence. It is recognised that Turing was a key
driving force in the paradigm shift from hardware driven designs in computing to the
realisation that computation is in fact universal and can be represented by a
machine capable of spanning the space of all possible computations, without the
need for specialist hardware realisations for each and every class of task
considered. The transcendence of computation from explicit hardware to universal
machines parallels developments such as that of the wheel and the shift in thinking
experienced during the industrial revolution. Such developments however point to
another important consideration, that perhaps the uniquely human processes of

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

thought and intelligence may not be exclusively locked into the biological substrate,
and may instead be contained and cultivated independently using modern
computational platforms as a suitable carrier.
Such a shift in thinking towards the independent representation and elaboration of
intelligent processes offers not only a practical means by which to advance our
economies and commercial enterprises, but forces us to re-examine the nature of
ourselves and the human condition. It is now recognised that a key product of
thought constitutes an advanced information process, the basis of which can be
established to various degrees within a computational paradigm. The shift from
individualisation of thought to recognition of information processes in their own right,
has forced society at all levels to re-examine the definition of productivity and the
relationship between human workers and industry. The externalisation of intelligent
processes enables information processing to be combined at unprecedented scale
and speed, opening up new opportunities in terms of novel applications and
motivating the continued expansion of the scope of data capture. Such a shift in
paradigm forces us to re-evaluate many methodological aspects to ask how we may
convert both physical and cognitive labour processes towards utilising the vast
computational resource we now have available. So far we have been offered a
glimpse of what the future may hold for artificially intelligent systems, especially with
information centric technologies such as the internet and World Wide Web
continuing to generate demand through the connection of data resources with real
world applications. It is expected that the continued advance of information oriented
applications, as initiated by Turing and others, will be sure to produce many
unprecedented and also many unanticipated changes in the near future.

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Keynote Speaker 6: Dr David R. Hardoon

Chief of Analytics (Data Artist)
Azendian Solutions

Dr. David R. Hardoon is an Executive Director at Azendian Solutions Pte. Ltd.
where he heads up the advanced analytics practice and is responsible for the
positioning of business analytics advisory and services to clients across different
business sectors across the region. Previous to his current engagement, he had
established expertise in developing and applying computational analytical models
for business knowledge discovery and analysis through his involvement in a number
of research projects in the domains of taxonomy, neuroscience, aerospace and
finance. He received a B.Sc. in Computer Science and Artificial Intelligence with first
class honors at Royal Holloway, University of London in 2002 and a PhD in
Computer Science in the field of Machine Learning from the University of
Southampton 2006. He is currently an Adjunct Faculty at School of Information
Systems, Singapore Management University and an Honorary Senior Research
Associate at the Centre for Computational Statistics & Machine Learning, University
College London.
David regularly tutors, advises and provided consulting support in Analytics and
Business Analytics. More can be found on www.davidroihardoon.com.

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Operationalizing Data Science

Speaker: Dr. David R. Hardoon
Chief of Analytics (Data Artist)
Azendian Solutions
Abstract
Over the last few years there has been a surge in exploring how to leverage on data
analytics/business analytics/data science to bring value to organization in three
areas: 1. Increasing Revenue 2. Improving Efficiency and 3. Non-compliance
detection/predictions. However, how does Machine Learning become Business
Analytics/Data Science? What are the other aspects that one‘s needs to consider to
achieve successful application? In this session we discuss the considerations of the
shift and the value that can be ultimately derived through the operationalization of
data.

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Special Sessions

Special Session 1: Data Wrangling in the Big Data World

Speaker: Gan Chun Yee
Head of Big Data Analytics

Profile:

Gan Chun Yee has more than a decade's experience in enterprise software project
implementations. He has overseen and been involved in Big Data, Business
Intelligence and Analytics projects in various industries, encompassing large
volumes of data, different data sources to provide in depth analysis of data and
trends. Gan's technical background coupled with his strong accumulated domain
knowledge across manufacturing, market research, financial and asset
management has led him to successfully execute and spearhead enterprise
projects globally spanning across countries such as the U.S, Singapore, Malaysia,
Holland, France, Hong Kong and U.K.

Abstract:

With large amounts of data being generated in various formats at a rate faster than
ever before, it is only natural that the complexity of the process of having these
information being gelled together increases at a similar rate or even higher. While
there are numerous information visualization tools in the market which helps in
human cognition, many of them would require the data to be processed before the
data is consumed for visualization. Raw data that comes in various formats slow
down the steps of consuming the data because they have to go through several
processes. Different formats would require different ways of tackling the processes
and some of these processes can be technically challenging to certain users. Is
there a better way to get this done in a shorter turnaround time? Is there a way to
get this done is a less complex way? To answer these questions, deep insight of
data wrangling in the Big Data realm is required.

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Special Session 2: Analytics in Action

Speaker: Dr Mark Chia
Advanced Analytics Practice Lead
Center of Excellence, SAS

Profile:
Mark is the Advanced Analytics Practice Lead within SAS‘s Center of Excellence in
Malaysia. Mark works with clients across industries to design, develop and
implement analytics solutions within their organizations successfully. He also leads
the Internship and Graduate programs. He has been with SAS since 2011.

Before joining SAS, Mark worked in IT for over ten years with various UK
companies. He has worked for software development companies and an internet
dating company. He has done a variety of roles from software engineering, systems
administration, consulting, systems analysis, project management, product
management and company management.

Mark holds two bachelor degrees from the University of Adelaide, Australia. The
first with honours is in Electrical and Electronic Engineering and the second is in
Applied Mathematics and Computer Science.

He did his PhD in Electrical Engineering at the University of Edinburgh, United
Kingdom. He is also registered as a Chartered Engineer with the Engineering
Council (UK) through the Institution of Engineering and Technology.
Abstract:

For most organizations, big data is the reality of doing business. Big data is a
popular term used to describe the exponential growth and availability of data, both
structured and unstructured. Big data may be as important to business – and
society – as the Internet has become. Why? More data may lead to more accurate
analyses. More accurate analyses may lead to more confident decision making.

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Better decisions can mean greater operational efficiencies, cost reductions and
reduced risk.
As data floods your organization on a daily basis, the question is no longer "What is
big data?". It‘s "What can we do with the big data we have?" The answer, of
course, is integral to the future of your business. Analytics is the process of
examining big data to uncover hidden patterns, unknown correlations and other
useful information that can be used to make better decisions.

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Special Session 3: Solving Data to Insights Challenge with
Microsoft - R and Azure Machine Learning

Speaker: Laurence Liew
General Manager, Asia Pacific
Revolution Analytics

Profile:
Laurence is a veteran of the open source, Linux and High Performance Computing,
Grid and Cloud community and has been promoting the use of
Linux/HPC/Grid/Cloud since 1998. Laurence introduced enterprise R analytics into
Asia when he was the General Manager of Revolution Analytics for Asia Pacific. He
was responsible for Revolution's business in Asia and its Centre of Excellence for
Analytics in Singapore in partnership with the Infocomm Development Authority of
Singapore (IDA). He also ran Revolution's development team responsible for putting
Revolution R in the Cloud. Revolution was acquired by Microsoft in 2015.
Laurence was involved in building the very first commercial Linux cluster for an
A*STAR research institute in 1999 and has since implemented and consulted for
many organizations in APJ, Europe and US, on HPC, Grid, Cloud and now big data
analytics. Laurence is a member of the Singapore IT Masterplan 2025 committee,
and mentor to several startups in Singapore and a long time Editor of Transactions
on Computational Sciences by Springer Journal.
Laurence graduated from National University of Singapore (NUS) with First Class
Honours in Engineering, and holds a Master in Knowledge Engineering from NUS.

27

The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Abstract:
Do you worry how you can effectively use data that you have in your organizations
to predict the following? Example – fraud detection, customer churn, operational
efficiency, predictive maintenance, marketing mix optimization, etc? If these are
questions you want to solve – learn how R and Azure Machine Learning may be
potential solutions. Join us in a 2 hour workshop to get introduced to Microsoft‘s
Advance Analytics offerings such as R on Azure, Azure Machine Learning to get a
headstart.

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Special Session 4: Smart Data Discovery with IBM Watson
Analytics

Speaker: Shawn Tan
Watson Analytics Specialist
Asia Pacific

Profile:
Shawn Tan is Watson Analytics Specialist (Asia Pacific), working with business
users and analysts to find insights from their organizational data.

Abstract:
?Smart Data Discovery with IBM Watson Analytics? is a session designed to
introduce business users and analysts to cognitive smart data discovery. Witness a
presentation and live demonstration of the solution, followed by a guided hands-on
session (where participants are required to bring their own laptop).
What is Watson Analytics?
IBM Watson Analytics is a cognitive analytics solution for smart data discovery that
opens up the benefits of advanced analytics with simplicity to all across the
organization. It guides data exploration, automates predictive analytics and supports
the rapid creation of applicable dashboards. As an added benefit, Watson Analytics
for Social Media delivers powerful social insights to the analysis.

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Conference Schedule

Day 1: 21st September 2016 (Wednesday)

Venue: Ballroom 1, Level 02, Pullman Kuala Lumpur, Bangsar

Time Activities
8:00 - 9:00 am
8.45 am Registration
9:00 - 9:15 am Ballroom 1, Level 02
9:15 - 9:30 am
9:30 - 10:30 am Arrival of YBhg. Prof. Dr Azlinah Hj Mohamed
Dean of Faculty of Computer and Mathematical Sciences,UiTM
10:30 - 11:00 am
11:00 - 11:30 am - Doa Recitation
- Lagu Negaraku
11:30 - 12:00 pm - Lagu Wawasan Setia Warga UiTM

12:00 - 12:30 pm Welcome Speech by Conference Chair:
12:00 - 1:00 pm Professor Dr Yap Bee Wah
1:00 - 2:00 pm Faculty of Computer and Mathematical Sciences, UiTM
2:00 - 6:30 pm
Keynote Speech 1
Making the Most of Big Data
Dato‘ Seri Ivan Teh
Managing Director Fusionex, Malaysia

Tea/Coffee Break
Ballroom 1, Level 02

Keynote Speech 2
Democratizing Machine Learning
Dr Dzaharudin Mansor
National Technology Officer, Microsoft Malaysia

Keynote Speech 3
Microsoft Advanced Analytics Overview
Faisal Hajazi
Data Platform Solution Architect, Microsoft

Q&A

Parallel Session 1
Track : Artificial Intelligence Networks
Venue: Studio 1, Level 03

Lunch Break
Oriental Thai, Level 01

Venue: Ballroom 1, Level 02
Special Session 1: (2:00 – 3.30pm)
Data Wrangling in the Big Data World
Gan Chun Yee
Head of Big Data Analytics
Fusionex, Malaysia

Special Session 2: (3:30 – 4.30pm)
Analytics in Action
Dr Mark Chia
Advanced Analytics Practice Lead, Center of Excellence, SAS

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

2:00 - 5:15 pm Special Session 3: (4:30 – 6.30pm)
5.00 – 5.30 pm Solving Data to Insights Challenge with Microsoft - R and Azure
Machine Learning
Laurence Liew
General Manager, Asia Pacific
Revolution Analytics

Parallel Session 2
Track : Classification/Clustering/Visualization
Venue: Studio 1, Level 03

Tea/Coffee Break
Ballroom 1, Level 02

Day 2: 22nd September 2016 (Thursday)

Venue: Ballroom 1, Level 02, Pullman Kuala Lumpur, Bangsar

Time Activities
8:00-8:30am
8:15 am Registration
8:30 am Ballroom 1, Level 02
8:45 - 9:00 am
9:00 - 9:30 am Arrival of Participants and Guests

9:30 -10:00 am Arrival of YBhg. Prof. Dr Azlinah Hj Mohamed
Dean of Faculty of Computer and Mathematical Sciences, UiTM
10:00 - 10:30 am
- Doa Recitation
10:30 - 11:00 am - Lagu NegaraKu
11:00 - 1:00 pm - Lagu Wawasan Setia Warga UiTM

Keynote Speech 4
Big Data Analytics: Getting the Right Match for the Best Fit
Professor Dr Azlinah Hj Mohamed
Dean of Faculty of Computer and Mathematical Sciences , UiTM

Keynote Speech 5
Data Science and Applied Computing for Multidisciplinary
Applications
Associate Professor Dr Dhiya Al-Jumeily
Liverpool John Moores University, UK

Keynote Speech 6
Operationalizing Data Science
Dr David R Hardoon
Chief of Analytics (Data Artist), Azendian Solutions, Singapore

Tea Break
Ballroom 1, Level 02

Special Session 4
Smart Data Discovery with IBM Watson Analytics
Shawn Tan
Watson Analytics Digital Sales Specialist (Asia Pacific)
Venue: Ballroom 1, Level 02

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

11:00 - 12:30 pm Parallel Session 3
1:00 - 2:00 pm Track : Information and Sentiment Analysis
Venue: Studio 1, Level 03

Lunch Break
Oriental Thai, Level 01

1.45 - 2:45 pm Parallel Session 4
2:30 pm Track : Fuzzy Logic
3:00 - 3:10 pm Venue: Studio 1, Level 03
3:10 - 3:30 pm
3:30 - 4:00 pm Arrival of YBhg. Prof. Emeritus Dato‘ Dr Hassan Said
4:00 - 4:30 pm Vice Chancellor, UiTM
4.30 – 5.00 pm Venue: Ballroom 1, Level 02

- Doa Recitation
- Lagu NegaraKu
- Lagu Wawasan Setia Warga UiTM

Opening and Closing Speech by
YBhg. Prof. Emeritus Dato‘ Dr Hassan Said
Vice Chancellor, UiTM

Presentation of Token of Appreciation to Sponsors by
Vice Chancellor of UiTM

Presentation of Best Paper Awards by
Vice Chancellor of UiTM

Tea/Coffee Break
Ballroom 1, Level 02

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Parallel Sessions Schedule

TRACK 1: Artificial Neural Networks
Venue : Studio 1, Level 03, Pullman Kuala Lumpur, Bangsar
Date : 21 September 2016 (Wednesday)
Parallel Session 1 : 12.00 pm – 1.00 pm
Session Chair : Dr Shuzlina Abd Rahman

NO TIME ID TITLE AUTHORS
1 12.00 pm – 1570286842 Mohd Razif
1570286865 Shallow Network Shamsuddin, Shuzlina
12.15 pm Performance in an Abd Rahman, Azlinah
1570295173 Increasing Image Mohamed
2 12.15 pm – Dimension Jong-Peir Li
12.30 pm 1570300181
Applied Neural Network Azmirul Ashaari, Tahir
3 12.30 pm – Model to Search for Ahmad, Suzelawati
12.45 pm Target Credit Card Zenian,Noorsufia Abdul
Customers Shukor
4 12.45 pm –
1.00 pm Selection Probe of EEG Waddah Waheeb,
using Dynamic Graph of Rozaida Ghazali
Autocatalytic Set (ACS)

Multi-step Time Series
Forecasting Using Ridge
Polynomial Neural
Network with Error-
Output Feedbacks

33

The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

TRACK 2: Classification/Clustering/Visualization
Venue : Studio 1, Level 03, Pullman Kuala Lumpur, Bangsar
Date : 21 September 2016 (Wednesday)
Parallel Session 2 : 2.00 pm – 5.15 pm
Session Chair : Professor Dr Chang-Hwan Lee

NO TIME ID TITLE AUTHORS
1 2.00 pm – 1570300338 Nurizzati Azhari,
Modeling and Maheran Mohd Jaffar
2.15 pm 1570298469 Forecasting Mudharabah
1570300300 Investment With Risk by Tanavich Sithiprom,
2 2.15 pm – 1570286793 Using Geometric Anongnart Srivihok
2.30 pm Brownian Motion
1570295281 Wael M.S.Yafooz, Siti
3 2.30 pm – Comparative Feature Z. Z. Abidin, Nasiroh
2.45 pm 1570295671 Selection of Crime Data Omar, Shadi M S Hilles
in Thailand
4 2.45 pm – 1570290912
3.00 pm 1570299681 Interactive Big Data
Visualization Model
5 3.00 pm – Based on Hot Issues
3.15 pm (Online news articles)

6 3.15 pm – Metabolites selection and Mohammad Nasir
3.30 pm classification of Abdullah, Yap Bee
metabolomics data on Wah, Yuslina Zakaria,
7 3.30 pm – Alzheimer‘s disease Abu Bakar Abdul
3.45 pm using random forest Majeed

8 3.45 pm – A Multi-Objectives Rayner Alfred, Gabriel
4.00 pm Genetic Algorithm Jong Chiye, Yuto Lim,
Clustering Ensembles Chin Kim On, Joe Henry
Based Approach to Obit
Summarize Relational
Data

Automated Generating Sathit Prasomphan,
Thai Stupa Image Panuwut nomrubporn,
Descriptions with Grid Pirat Pathanarat
Pattern and Decision
Tree Hezlin Aryani Abd
Rahman, Yap Bee Wah
Imbalance Effects On
Classification Using
Binary Logistic
Regression

Weak Classifiers Ezzatul Akmal Kamaru-
Performance Measure in Zaman, Andrew Brass,
Handling Noisy Clinical James Weatherall,
Trial Data Shuzlina Abdul Rahman

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

9 4.00 pm – 1570275757 A Review of Feature Kamahazira Zainal,
4.15 pm 1570299722 Extraction Optimization in Mohd Zalisham Jali
1570300282 SMS Spam Messages
10 4.15 pm – 1570286830 Classification
4.30 pm 1570295568
Assigning Different Chang-Hwan Lee
11 4.30 pm – Weights to Feature
4.45 pm Values in Naive bayes Khalifa Chekima,
Rayner Alfred
12 4.45 pm – An Automatic
5.00 pm Construction of Malay
Stop Words Based on
13 5.00 pm – Aggregation Method
5.15 pm
Multi-Script Text Zaidah Ibrahim, Zolidah
Detection and Kasiran , Dino Isa,
Classification from Nurbaity Sabri
Natural Scenes
Jiaqi Pan, Yan Zhuang,
The Impact of Data Simon Fong
Normalization on Stock
Market Prediction: Using
SVM and Technical
Indicators

35

The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

TRACK 3: Information and Sentiment Analysis
Venue : Studio 1, Level 03, Pullman Kuala Lumpur, Bangsar
Date : 22 September 2016 (Thursday)
Parallel Session 3 : 11.00 am – 12.30 pm
Session Chair : Dr Kazuyuki Matsumoto

NO ID TIME TITLE AUTHORS
1570278735
1 11.00 am – Feel-Phy: An Intelligent Kwong Seng Fong,
11.15 am Web-Based Physics QA Chih How Bong, Zahrah
System Ahmad, Norisma Idris

2 11.15 am – 1570275683 Usability Evaluation of Wan Abdul Rahim Wan
11.30 am Secondary School Mohd Isa, Zawaliah
Websites in Malaysia: Ishak, Siti Zulaiha
3 11.30 am – 1570271249 Case of Federal Shabirin
11.45 am Territories of Kuala
Lumpur, Putrajaya And Kazuyuki Matsumoto,
4 11.45 am – 1570298245 Labuan Seiji Tsuchiya, Minoru
12.00pm Yoshida, Kenji Kita
Judgment of Slang based
5 12.00 am – 1570298247 on Character Feature Rayner Alfred, Wong
12.15 pm and Feature Expression Wei Yee, Yuto Lim, Joe
based on Slang's Context Henry Obit
Feature
Rayner Alfred, Leow Jia
Factors Affecting Ren, Joe Henry Obit
Sentiment Prediction of
Malay News Headlines
Using Machine Learning
Approaches

Assessing Factors that
Influence the
Performances of
Automated Topic
Selection for Malay
Articles

6* 12.15 pm – 1570295222 A Comparison of BPNN, Chin Kim On, Teo Kein
12.30 pm RBF, and ENN in Yao, Rayner Alfred1, Ag
Number Plate Asri Ag Ibrahim, Wang
Recognition Cheng, Tan Tse Guan

*Presentation slot upon request

36

The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

TRACK 4: Fuzzy Logic
Venue : Studio 1, Level 03, Pullman Kuala Lumpur, Bangsar
Date : 22 September 2016 (Thursday)
Parallel Session 4 : 1.45 pm – 2.45 pm
Session Chair : Professor Dr Daud Mohamad

NO TIME ID TITLE AUTHORS
1570267098
1 1.45 pm – Algebraic and Graphical Ganeshsree
2.00 pm Interpretation of Complex Selvachandran, Omar
Fuzzy Annulus (an Mashaan, Abdul Ghafur
Extension of Complex Ahmad
Fuzzy Sets)

2 2.00 pm – 1570294352 A Hierarchical Fuzzy Daud Mohamad, Lina
2.15 pm Logic Control System for Mohd Jamal
Malaysian Motor Tariff
with Risk Factors

3 2.15 pm – 1570294851 Modeling Steam Wan Munirah Wan
2.30 pm Generator System of Mohamad, Tahir
Pressurized Water Ahmad, Azmirul Ashaari
4 2.30 pm – 1570295498 Reactor Using Fuzzy
2.45 pm Arithmetic Suzelawati Zenian,
Tahir Ahmad, Amidora
Edge Detection of Flat Idris
Electroencephalography
Image via Classical and
Fuzzy Approach

37

The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Proceeding Abstracts

Track : Artificial Neural Networks

No ID Title Authors

1 1570286842 Shallow Network Performance in an Mohd Razif Shamsuddin,
Increasing Image Dimension Shuzlina Abd Rahman,
2 1570286865 Azlinah Mohamed
Applied Neural Network Model to
3 1570295173 Search for Target Credit Card Jong-Peir Li
Customers
Azmirul Ashaari, Tahir
Selection Probe of EEG using Ahmad, Suzelawati Zenian,
Dynamic Graph of Autocatalytic Set Noorsufia Abdul Shukor
(ACS)

4 1570300181 Multi-step Time Series Forecasting Waddah Waheeb, Rozaida

Using Ridge Polynomial Neural Ghazali

Network with Error-Output

Feedbacks

5 1570295222 A Comparison of BPNN, RBF, and Chin Kim On, Teo Kein Yao,
ENN in Number Plate Recognition Rayner Alfred1, Ag Asri Ag
Ibrahim, Wang Cheng, Tan
Tse Guan

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Shallow Network Performance in an Increasing Image Dimension

Mohd Razif Shamsuddin, Shuzlina Abd Rahman, Azlinah Mohamed
Faculty of Computer and Mathematical Sciences
Universiti Teknologi MARA, Shah Alam, Selangor, MALAYSIA
{razif,shuzlina,azlinah}@fskm.uitm.edu.my

Abstract
This paper describes the performance of a shallow network towards increasing
complexity of dimension in an image input representation. This paper will highlight
the generalization problem in Shallow Neural Network despite its extensive usage.
In this experiment, a backpropagation algorithm is chosen to test the network as it is
widely used in many classification problems. A set of three different size of binary
images are used in this experiment. The idea is to assess how the network
performs as the scale of the input dimension increases. In addition, a benchmark
MNIST handwritten digit sampling is also used to test the performance of the
shallow network. The result of the experiment shows the network performance as
the scale of input increases. The result is then discussed and explained. From the
conducted experiments it is believed that the complexity of the input size and
breadth of the network affects the performance of the Neural Network. Such results
can be as a reference and guidance to people that is interested in doing research
using backpropagation algorithm.
Keywords: Neural Network, shallow network, backpropagation, image recognition.

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Applied Neural Network Model to Search for Target Credit Card
Customers

Jong-Peir Li
Department of Management Information Systems
National Chengchi University, Taipei, Taiwan (R.O.C.)
[email protected]

Abstract
Many credit card businesses are no longer profitable due to antiquated and
increasingly obsolete methods of acquiring customers, and as importantly, they
followed suit when identifying ideal customers. The objective of this study is to
identify the high spending and revolving customers through the development of
proper parameters. We combined the back propagation neural network, decision
tree and logistic methods as a way to overcome each method‘s deficiency. Two sets
of data were used to develop key eigenvalues that more accurately predict ideal
customers. Eventually, after many rounds of testing, we settled on 14 eigenvalues
with the lowest error rates when acquiring credit card customers with a significantly
improved level of accuracy. It is our hope that data mining and big data can
successfully utilize these advantages in data classification and prediction.
Keywords: Credit card, Target customer, Data mining, Neural network.

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Selection Probe of EEG using Dynamic Graph of Autocatalytic
Set (ACS)

Azmirul Ashaari1, Tahir Ahmad2,*, Suzelawati Zenian1,3, Noorsufia Abdul Shukor4
1 Department of Mathematical Science, Faculty of Science
Universiti Teknologi Malaysia, 81310 UTM,Skudai, Johor, Malaysia
2 Centre for Sustainable Nanomaterials
Ibnu Sina Institute for Scientific and Industrial Research
Universiti Teknologi Malaysia, 81310 UTM, Skudai, Johor, Malaysia
3 Department of Mathematics with Computer Graphics
Faculty of Science and Natural Resources, Universiti Malaysia Sabah
Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia
4 Fakulti Sains Komputer dan Matematik
Universiti Teknologi MARA Cawangan Negeri Sembilan
Kampus Seremban 3, 70300 Seremban, Negeri Sembilan, Malaysia
[email protected]

Abstract
Electroencephalography (EEG) machine is a medical equipment which is used to
diagnose seizure. EEG signal records data in the form of graph which consist of
abnormal patterns such as spikes, sharp waves and also spikes and wave
complexes. This pattern also come in multiple line series which then give some
difficulties to analyze. This paper introduce the implementation of dynamic graph of
Autocatalytic Set (ACS) for EEG signal during seizure. The result is then compared
with other publish method namely Principal Component Analysis (PCA) of same
EEG data.
Keywords: Dynamic Graph; Electroencephalography (EEG); Epilepsy; Autocatalytic Set
(ACS)

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The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Multi-step Time Series Forecasting Using Ridge Polynomial
Neural Network with Error-Output Feedbacks

Waddah Waheeb1, 2, Rozaida Ghazali1

1

Faculty of Computer Science and Information Technology
Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia

2

Computer Science Department, Hodeidah University
P.O. Box 3114 Alduraihimi, Hodeidah, Yemen
[email protected], [email protected]

Abstract
Time series forecasting gets much attention due to its impact on many practical
applications. Higher-order neural network with recurrent feedback is a powerful
technique which used successfully for forecasting. It maintains fast learning and the
ability to learn the dynamics of the series over time. For that, in this paper, we
propose a novel model, called Ridge Polynomial Neural Network with Error-Output
Feedbacks (RPNN-EOF), which combines three powerful properties: higher order
terms, output feedback and error feedback. The well-known Mackey–Glass time
series is used to evaluate the forecasting capability of RPNN-EOF. Results show
that the proposed RPNN-EOF provides better understanding for the Mackey–Glass
time series with root mean square error equal to 0.00416. This error is smaller than
other models in the literature. Therefore, we can conclude that the RPNN-EOF can
be applied successfully for time series forecasting. Furthermore, the error-output
feedbacks can be investigated and applied with different neural network models.
Keywords: Time Series forecasting, Ridge polynomial neural network with error-output
feedbacks, Higher order neural networks, Recurrent neural networks, Mackey–Glass
equation.

42

The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

A Comparison of BPNN, RBF, and ENN in Number Plate
Recognition

Chin Kim On1, Teo Kein Yao1, Rayner Alfred1, Ag Asri Ag Ibrahim1, Wang Cheng2,
Tan Tse Guan3
1 Faculty of Computing and Informatics
Universiti Malaysia Sabah, Sabah, Malaysia
2 Faculty of Creative Technology and Heritage
Universiti Malaysia Kelantan, Kelantan, Malaysia
3 School of Economics and Management
Qiqihar University, Qiqihar, Heilongjiang Province, China
{kimonchin, keinya, aaai500, tseguantan}@gmail.com
[email protected], [email protected]

Abstract

In this paper, we discuss a research project that related to autonomous recognition
of Malaysia car plates using neural network approaches. This research aims to
compare the proposed conventional Backpropagation Feed Forward Neural
Network (BPNN), Radial Basis Function Network (RBF), and Ensemble Neural
Network (ENN). There are numerous research articles discussed the performances
of BPNN and RFB in various applications. Interestingly, there is lack of discussion
and application of ENN approach as the idea of ENN is still very young.
Furthermore, this paper also discusses a novel technique used to localize car plate
automatically without labelling them or matching their positions with template. The
proposed method could solve most of the localization challenges. The experimental
results show the proposed technique could automatically localize most of the car
plate. The testing results show that the proposed ENN performed better than the
BPNN and RBF. Furthermore, the proposed RBF performed better than BPNN.
Keywords: Back propagation Feed Forward Neural Network (BPNN), Radial Basis Function
Network (RBF), Ensemble Neural Network (ENN), Car Plate Recognition, Image Processing.

43

The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Track : Classification/Clustering/Visualization

No ID Title Authors

1 1570300338 Modeling and Forecasting Nurizzati Azhari, Maheran
Mudharabah Investment With Risk Mohd Jaffar
2 1570298469 by Using Geometric Brownian
3 1570300300 Motion
4 1570286793
Comparative Feature Selection of Tanavich Sithiprom,
5 1570295281 Crime Data in Thailand Anongnart Srivihok

6 1570295671 Interactive Big Data Visualization Wael M.S.Yafooz, Siti Z. Z.
7 1570290912 Model Based on Hot Issues (Online Abidin, Nasiroh Omar, Shadi
8 1570299681 news articles) M S Hilles

9 1570275757 Metabolites selection and Mohammad Nasir Abdullah,
10 1570299722 classification of metabolomics data Yap Bee Wah, Yuslina
11 1570300282 on Alzheimer‘s disease using Zakaria, Abu Bakar Abdul
12 1570286830 random forest Majeed
13 1570295568
A Multi-Objectives Genetic Rayner Alfred, Gabriel Jong
Algorithm Clustering Ensembles Chiye, Yuto Lim, Chin Kim
Based Approach to Summarize On, Joe Henry Obit
Relational Data
Automated Generating Thai Stupa Sathit Prasomphan,
Image Descriptions with Grid Panuwut nomrubporn, Pirat
Pattern and Decision Tree Pathanarat
Hezlin Aryani Abd Rahman,
Imbalance Effects On Classification Yap Bee Wah
Using Binary Logistic Regression
Ezzatul Akmal Kamaru-
Weak Classifiers Performance Zaman, Andrew Brass,
Measure in Handling Noisy Clinical James Weatherall, Shuzlina
Trial Data Abdul Rahman

A Review of Feature Extraction Kamahazira Zainal, Mohd
Optimization in SMS Spam Zalisham Jali
Messages Classification
Chang-Hwan Lee
Assigning Different Weights to
Feature Values in Naive bayes

An Automatic Construction of Malay Khalifa Chekima, Rayner
Stop Words Based on Aggregation Alfred
Method
Zaidah Ibrahim, Zolidah
Multi-Script Text Detection and Kasiran , Dino Isa, Nurbaity
Classification from Natural Scenes Sabri

The Impact of Data Normalization Jiaqi Pan, Yan Zhuang,
on Stock Market Prediction: Using Simon Fong
SVM and Technical Indicators

44

The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Modeling and Forecasting Mudharabah Investment
With Risk by Using Geometric Brownian Motion

Nurizzati Azhari1, Maheran Mohd Jaffar2
1 ,2 Department of Mathematics, Faculty of Computer and Mathematical Sciences
Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
[email protected], [email protected]

Abstract
This study developed mudharabah investment with risk model by considering the
rate of return as a total of deterministic profit rate and a function of white noise that
is geometric Brownian motion. The result shows that the investment is considered
as accurately forecast when using this developed model. The profit from
mudharabah investment is compared with single party investment. The result
obtained shows that the profit difference between mudharabah investment and
single party investment is very small. It is verified that the developed model can be
used in forecasting the investment and profit for two parties.
Keywords: Mudharabah, geometric Brownian motion, investment

45

The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Comparative Feature Selection of Crime Data in Thailand

Tanavich Sithiprom, Anongnart Srivihok
Department of Computer Science, Kasetsart University
Bangkok, Thailand
{tanavich.s,fsciang}@ku.ac.th

Abstract
The crime is a major problem of community and society which is increasing day by
day. Especially in Thailand, crime is a major problem that affects all aspects of the
country such as tourism, administration of government and problem in daily life.
Therefore, government and private sectors have to understand the several crime
patterns for planning, preventing and solving solution of crime correctly. The
purposes of this study are to generate a crime model for Thailand using data mining
techniques. Data were collected from Dailynews and Thairath online newspapers.
The proposed model can be generated by using more feature selection and more
classification techniques to different model. Experiments show feature selection with
the wrapper of attribute evaluator seems to be an appropriate evaluation algorithm
because data set mostly is the best accuracy rate. This improves efficiency in
identifying offenders more quickly and accurately. The model can be used for the
prevention of crime that will occur in Thailand in the future.
Keywords: Crime, Feature Selection, Classification, Data Mining

46

The 2nd International Conference on Soft Computing in Data Science (SCDS 2016)

Interactive Big Data Visualization Model Based on Hot Issues
(Online news articles)

Wael M.S.Yafooz1, Siti Z. Z. Abidin2, Nasiroh Omar3, Shadi M S Hilles4
1,2 Advanced Analytics Engineering Center (AAEC)
1,2,3 Faculty of Computer and Mathematical Sciences
Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia
4 Faculty of Computer and Information Technology
Al Madinah International University Malaysia
[email protected], {zaleha, nasiroh}@tmsk.uitm.edu.my, [email protected]

Abstract
Big data is a popular term used to describe a massive volume of data, which is a
key component of the current information age. Such data is complex and difficult to
understand, and therefore, may be not useful for users in that state. News
extraction, aggregation, clustering, news topic detection and tracking, and social
network analysis are some of the several attempts that have been made to manage
the massive data in social media. Current visualization tools are difficult to adapt to
the constant growth of big data, specifically in online news articles. Therefore, this
paper proposes Interactive Big Data Visualization Model Based on Hot Issues
(IBDVM). IBDVM can be used to visualize hot issues in daily news articles. It is
based on textual data clusters in textual databases that improve the performance,
accuracy, and quality of big data visualization. This model is useful for online news
reader, news agencies, editors, and researchers who involve in textual documents
domains.
Keywords: Big Data, Visual Analytics, Interactive Visualization, Clustering, Information
Extraction.

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