Program

2019 6th International Conference

on

Soft Computing & Machine Intelligence

(ISCMI 2019)

Johannesburg, South Africa

November 19-20, 2019

Organized by Sponsored by

WELCOME MESSAGE

It is our great pleasure to welcome you in Johannesburg, the largest city in South Africa

for 2019 6th Intl. Conference on Soft Computing & Machine Intelligence (ISCMI 2019),

the annual flagship event of India International Congress on Computational Intelligence

(IICCI). This event will provide a unique opportunity for researchers, scientists and

technologists who are working in the emerging areas of soft computing & machine

intelligence to assemble and share their latest research efforts and findings.

The conference programme includes oral paper presentations, poster sessions along with

keynote speeches by leading researchers. It also includes the annual IICCI Prof. Lotfi

Zadeh Memorial lecture.

We’re confident that over these two days you’ll get the theoretical grounding, practical

knowledge, and personal contacts that will help you build long-term, profitable and

sustainable communication among researchers and practitioners working in a wide

variety of scientific areas with a common interest in soft computing & machine

intelligence.

It is hoped that this conference will provide each one of you with not only a good platform

for networking opportunities and interactions with other delegates from both the

academics and industry, but also a memorable experience of your stay in Johannesburg,

South Africa.

Prof. Suash Deb

(General Chair, ISCMI19)

October 28, 2019

VENUE INFORMATION

Protea Hotel Johannesburg Parktonian All-Suite

Address: 120 De Korte Street, Braamfontein 2000 South Africa

Tel: +27 11 403 5740 | Fax: +27 11 339 7440

Email: banqueting1@parktonian.co.za

Website: https://www.marriott.com/hotels/travel/jnbpa-protea-hotel-johannesburg-

parktonian-all-suite/

Floor Plan:

How to get to Protea Hotel Johannesburg Parktonian All-Suite:

Getting There by Public Transportation:

The nearest airport is O.R. Tambo International Airport (JNB), 30 km from Protea Hotel

Johannesburg Parktonian All-Suite. The suggested way is to take East - West & OR Tambo,

and get off at Marlboro Station, and take North – South, and get off Park Station, and an

8 minute walk will get you to the Protea Hotel Johannesburg Parktonian All-Suite.

Getting There by Taxi:

The most convenient way of getting to Protea Hotel Johannesburg Parktonian All-Suite

from O.R. Tambo International Airport (JNB) is by taxi. Taxis are available at the taxi

stands at the Arrival Halls. The estimated taxi fare is 450 ZAR (one way).

Safety Instructions:

1. Please wear your conference badge (which you can get from the conference

reception at the conference venue) during the whole time of the conference.

2. Please keep your conference badge safe and don't lend it to anyone else. You'll not

be allowed to enter the conference rooms without wearing your conference badge.

3. Please don't leave your personal belongings unattended in the conference rooms.

You are responsible for your belongings at all times. When leaving your seat, please

take your valuable things with you.

TABLE OF CONTENTS

Conference Committees ................................................................................................................ 1

Local Information ........................................................................................................................... 5

Instructions for Presentations ....................................................................................................... 6

Program at a Glance ...................................................................................................................... 7

Inaugural Session ........................................................................................................................... 8

Keynote Speakers .......................................................................................................................... 9

Contents of Sessions .................................................................................................................. 13

Oral Presentation Abstracts ........................................................................................................ 17

Listener ........................................................................................................................................ 39

Author Index ................................................................................................................................. 40

1

CONFERENCE COMMITTEES

Honorary Chairs

Dr. Eddie Tunstel, President-IEEE Systems, Man and Cybernetics Society, USA

Prof. C. L. Philip Chen, University of Macau SAR, China (former President, 2012-13, lEEE

Systems, Man & Cybernetics Society)

General Chair

Suash Deb, Secretary General - India Intl. Congress on Computational Intelligence, India

International Advisory Board

Punam Bedi, University of Delhi, India

Sung-Bae Cho, Yonsei University, South Korea

G. A. Chukwudebe, Pro-term Chair, IEEE African Council, (Federal University of Technology,

Owerri, Nigeria) Nigeria

Andries P. Engelbrecht, Stellenbosch University, South Africa

Tzung-Pei Hong, National University of Kaohsiung, Taiwan

Sardar M. N. Islam, Victoria University, Melbourne, Australia

Mohamed Habib Kammoun, Secretary, lEEE Africa Council (University of Sfax, Tunisia)

Robert Kozma, University of Memphis, USA

Javier Montero, President, Intl. Fuzzy Systems Association (Complutense University of

Madrid, Spain), Spain

Mammo Muchie, Tshwane University of Technology, South Africa

Witold Pedrycz, University of Alberta, Canada

Marios M. Polycarpou, University of Cyprus, Cyprus

Rajkumar Roy, Cranfield University, UK

2

Patrick Siarry, Université Paris-EstCréteil, France

Hideyuki Takagi, Kyushu University, Japan

Lipo Wang, Nanyang Technological University, Singapore

Organizing Chairs

Wesley Doorsamy, University of Johannesburg, South Africa

Barnabas Gatsheni, University of Johannesburg, South Africa

Oluseye Jegede, Human Sciences Research Council, South Africa

Vukosi Marivate, University of Pretoria, South Africa

Nnamdi Nwulu, University of Johannesburg, South Africa

Swapan Kumar Patra, Tshwane University of Technology, South Africa

Babu Sena Paul, University of Johannesburg, South Africa

Surafel Luleseged Tilahun, University of Zululand, South Africa

Program Chairs

Thomas Hanne, University of Applied Sciences and Arts Northwestern Switzerland,

Switzerland

Mohamed Habib Kammoun, Secretary, lEEE Africa Council (University of Sfax, Tunisia)

G A Vijayalakshmi Pai, PSG College of Technology, Coimbatore, India

Ka-Chun Wong, City University of Hong Kong, Hong Kong

Publications Chairs

Zhihua Cui, Taiyuan University of Science & Technology, China

Xiao-Zhi Gao, University of Eastern Finland, Finland

Xin-She Yang, Middlesex University, UK

3

Publicity Co Chairs

Monica Chis, Planet Group International, Romania

Amir H. Gandomi, Stevens Institute of Technology, USA

Robert Oboko, University of Nairobi , Kenya

Sunday O. Ojo, Tshwane University of Technology, South Africa

Sameerchand Pudaruth, University of Mauritius, Mauritius

Sriparna Saha, Indian Institute of Technology Patna, India

Dipti Srinivasan, National University of Singapore, Singapore

International Program Committee

Debashree Guha Adhya, Indian Institute of Technology Kharagpur, India

Dileep A. D., Indian Institute of Technology Mandi, India

Monowar H. Bhuyan, Umea University, Sweden

Junyi Chen, City University of Hong Kong, Hong Kong

Monica Chis, Planet Group International, Romania

Todsanai Chumwatana, Rangsit University, Thailand

Mohammed ElAbd, American University of Kuwait, Kuwait

Mohamed Elkhouli, Sadat Academy for Management Science, Cairo, Egypt

Iztok FisterJr, University of Maribor, Slovenia

Simon Fong, University of Macau, Macau

Tee Yee Kai, UniversitiTunku Abdul Rahman, Malaysia

Somveer Kishnah, University of Mauritius, Mauritius

Jiecong Lin, City University of Hong Kong, Hong Kong

Maba B. Matadi, University of Zululand, South Africa

Hector D. Menendez, University College London, UK

4

Avinash Mungur, University of Mauritius, Mauritius

Elisha Opiyo, University of Nairobi, Kenya

Sameerchand Pudaruth, University of Mauritius, Mauritius

S. Ravi, Pondicherry University, India

Sriparna Saha, Indian Institute of Technology Patna, India

Antonio Sze-To, University of Waterloo, Canada

Shahrel Azmin Suandi, Universiti Sains Malaysia, Malaysia

Nawdha Thakoor, University of Mauritius, Mauritius

Surafel Luleseged Tilahun, University of Zululand, South Africa

Xin-She Yang, Middlesex University, UK

Wuyi Yue, Konan Unversity, Japan

Shi-Xiong Zhang, City University of Hong Kong, Hong Kong

5

LOCAL INFORMATION

Time: UTC/GMT+2

Weather

Temperature

Average high (C/F): 24°C/75°F | Average low (C/F): 13°C/55°F

During the day, the average temperature is 24℃, it is recommended to wear cotton linen

shirt, thin skirt, thin T-shirt and other cool and breathable clothes.

During the night, the average temperature is 13℃, it is recommended to wear suit, jacket,

windbreaker, casual wear, jacket, suit, thin sweater and other warm clothes.

November is the dry season, less rainfall.

Currency Exchange

South Africa's currency is the ZAR, and one rand equals 100 cents. Banks, foreign

exchange offices and larger hotels can exchange money. ATMS are widely distributed and

major international credit cards are widely accepted. Tourists should be alert when they

withdraw cash from ATMS as scammers are said to be operating nearby. All commercial

Banks can exchange foreign exchange.

Transport

Johannesburg is a young and sprawling city, with its public transportation built in its

infancy, geared towards private motorists, and lacks a convenient public transportation

system. A significant number of the city's residents are dependent on the city's informal

minibus taxis.

Johannesburg has two kinds of taxis, metered taxis and minibus taxis. Unlike many cities,

metered taxis are not allowed to drive around the city looking for passengers and instead

must be called and ordered to a destination.

Useful Phone Number

Emergency police: 10111

Ambulance: 10177

Serious and Violent Crime (Murder and Robbery): 0119869000

6

INSTRUCTIONS FOR PRESENTATIONS

Oral Presentations

Time: a maximum of 15 minutes in total, including 12 minutes’ speaking time and 3

minutes’ for discussion. Please make sure your presentation is well timed. Please

keep in mind that the program is full and that the speaker after you would like their

allocated time available to them.

You can use USB flash drive (memory stick), make sure you scanned viruses in your

own computer. Each speaker is required to meet her / his session chair in the

corresponding session rooms 10 minutes before the session starts and copy the

slide file (PPT or PDF) to the computer.

It is suggested that you email a copy of your presentation to your personal inbox as a

backup. If for some reason the files can’t be accessed from your flash drive, you will

be able to download them to the computer from your email.

Please note that each session room will be equipped with a LCD projector, screen,

point device, microphone, and a laptop with general presentation software such as

Microsoft Power Point and Adobe Reader. Please make sure that your files are

compatible and readable with our operation system by using commonly used fronts

and symbols. If you plan to use your own computer, please try the connection and

make sure it works before your presentation.

Movies: If your Power Point files contain movies please make sure that they are well

formatted and connected to the main files.

Poster Presentations

Maximum poster size is 36 inches wide by 48 inches high (3ft.x4ft.)

Posters are required to be condensed and attractive. The characters should be large

enough so that they are visible from 1 meter apart.

Please note that during your poster session, the author should stay by your poster

paper to explain and discuss your paper with visiting delegates.

Dress Code

Please wear formal clothes or national characteristics of clothing.

7

PROGRAM AT A GLANCE

November

19, 2019

(Tuesday)

10:00am-

05:00pm Arrival and Registration Lobby of Hotel

November

20, 2019

(Wednesday)

09:25am-

10:50am Inauguration of ISCMI 2019 Oak Room

10:50am-

11:15am

Prof. Lotfi Zadeh Memorial Session

Chair: Prof. Suash Deb

Speaker: 2019 IICCI Prof. Lotfi Zadeh

Memorial Speech - Dr. Edward Tunstel

Oak Room

11:15am-

11:50am

Keynote Speech I: Soft Computing for

Autonomous Robot Navigation Systems

Dr. Edward Tunstel

United Technologies Research Center (UTRC)

Oak Room

11:50am-

12:25pm

Keynote Speech II: Ranking: The Reality,

Illusion and Manipulation of Objectivity

Prof. Péter Érdi

Kalamazoo College, Kalamazoo, MI, USA

Hungarian Academy of Sciences, Budapest,

Hungary

Oak Room

12:25pm-

01:00pm

Keynote Speech III: 4IR: Foundations,

Possible Influence and Ongoing Investigations

at University of Johannesburg

Prof. Babu Sena Paul

Institute of Intelligent Systems, University of

Johannesburg, Republic of South Africa

Oak Room

01:00pm-

02:00pm Lunch Buffet

Orchard

Restaurant

02:00pm-

04:30pm

Session 1: Machine Learning Algorithms and

Techniques

Chair: Prof. Péter Érdi

Oak West

02:00pm-

04:30pm

Session 2: Neural Network and Image

Processing

Chair: Prof. Babu Sena Paul

Oak East

04:30pm-

05:00pm Coffee Break Foyer

05:00pm-

07:30pm

Session 3: Artificial Intelligence and Intelligent

Computing

Chair: Prof. Mammo Muchie

Oak West

05:00pm-

07:30pm

Session 4: Algorithm Optimization and High

Performance Computing

Chair: TBA

Oak East

07:30pm-

09:30pm Dinner Buffet

Orchard

Restaurant

8

INAUGURAL SESSION

Inauguration of ISCMI 2019

9:25am-9:35am

Welcome Address by :

Prof. Suash Deb

General Chair-ISCMI 2019

Founding Secretary General, IICCI

9:35am-9:50am

Address by :

Prof. Tshilidzi Marwala

Distinguished Guest

Hon’ble Vice Chancellor, University of Johannesburg, Republic of

South Africa

9:50am--10:00am

Address by :

Dr. Edward Tunstel

Chief Guest

United Technologies Research Center (UTRC)

10:00am-10:10am

Address by :

Prof. Mammo Muchie

Guest-of-Honor

Tshwane University of Technology TUT, South Africa

10:10am-10:20am

Address by :

Dr. Albert Lysko

Guest-of-Honor

IEEE South Africa Section Awards & Recognitions Council for Scientific

and Industrial Research

10:20am-10:25am

Address by :

Prof. Peter Erdi

Guest-of-Honor & Key Note Speaker

Kalamazoo College, Kalamazoo, MI, USA

Hungarian Academy of Sciences, Budapest, Hungary

10:25am-10:35am

Vote-of-Thanks by :

Prof. Babu Sena Paul

Organizing Co Chair & Key Note Speaker

Institute of Intelligent Systems, University of Johannesburg, Republic

of South Africa

10:35am-10:50am Group Photo & Coffee Break

9

KEYNOTE SPEAKERS

Dr. Edward Tunstel

United Technologies Research Center (UTRC)

Dr. Edward Tunstel is an Associate Director of Robotics and Robotics Group Leader in the Autonomous & Intelligent Systems United Technologies Research Center (UTRC). He joined UTRC in 2017 after 10 years at Johns Hopkins Applied Physics Laboratory where he served as a senior roboticist in its research department and Intelligent Systems Center, and as space robotics & autonomous control lead in its space department. Prior to APL he was with the NASA Jet Propulsion Laboratory (JPL) for 18 years, where he was a senior robotics engineer and group leader of its Advanced Robotic Controls Group. He earned his bachelor's and master's degrees in mechanical engineering from Howard University and the Ph.D. in electrical engineering from the University of New Mexico. Dr. Tunstel maintains expertise in robotics and intelligent systems with current research interests in mobile robot navigation, autonomous control, cooperative robotics, robotic systems engineering, and soft computing applications to autonomous systems. He has authored over 150 technical publications and co-edited four books in these areas. At JPL, he worked on the NASA Mars Exploration Rovers mission as both a flight systems engineer responsible for autonomous rover navigation, and as rover engineering team lead for the

mobility and robotic arm subsystems. He was involved in the daily performance assessment, planning, and operations of the Spirit and Opportunity rovers during their first four years on Mars and in early stages of the later Curiosity Mars rover design. At APL he was recently engaged in modular open systems development efforts supporting advanced explosive ordnance disposal robotic systems programs, as well as robotics and autonomy research for future national security and space applications. At UTRC, he is now additionally engaged in human-collaborative robotics enabling applications relevant to businesses spanning the aerospace and building industries, including manufacturing. Dr. Tunstel is a Fellow of the IEEE and President of the IEEE Systems, Man, and Cybernetics Society (2018-2019). He is also a member of the IEEE Robotics and Automation Society, NSBE Professionals, and AIAA. He serves on editorial boards of several international engineering journals and interacts with academia through research collaborations, as graduate student co-advisor, and as a member of several master’s thesis and doctoral dissertation committees. Recent recognition of his accomplishments include the Lifetime Achievement in Aerospace Award from the NSBE Professionals’ Space Special Interest Group and an Honorary Professor Award from Obuda University in Budapest, Hungary in 2018.

10

Speech Title: “Soft Computing for Autonomous Robot Navigation Systems”

Abstract: Autonomy for navigation of robotic systems can be facilitated by distributing control and decision-making among a collection of relatively simple computational units. Such an approach requires that decision mechanisms be chosen to ensure goal-oriented interaction between such units. Using soft computing techniques, the computational units and decision mechanisms can be formulated to embed intelligent robot behavior supporting autonomous navigation and means for adaptive modulated behavior in response a robot's perceived environment. An architecture employing such techniques within hierarchical control structures of subsystems comprised of fuzzy logic controllers

and knowledge-based decision systems has proven effective in a number of autonomous navigation systems. This talk presents the underlying approach with focus on its utility for autonomous navigation of mobile robots employing simple modulated behaviors. Effects of exploiting the flexibility inherent in its structure and in its decision mechanisms are discussed including the exhibition of behavioral interaction dynamics similar to those observed in natural intelligent systems. Applications of the approach to various types of mobile robotic systems are highlighted, including related soft computing applications to safe guidance for robotic landing systems and to robotic teleoperation of mobility systems.

Prof. Péter Érdi

Henry R. Luce Professor, Center for Complex Systems Studies, Department

of Physics and Department of Psychology, Kalamazoo College, Kalamazoo,

MI, USA

Institute for Particle and Nuclear Physics, Wigner Research Centre,

Hungarian Academy of Sciences, Budapest, Hungary

Dr. Péter Érdi serves as the Henry R. Luce Professor of Complex Systems Studies at Kalamazoo College. He is also a research professor in his home town, in Budapest, at the Wigner Research Centre of Physics of the Hungarian Academy of Sciences. In addition, he is the founding co-director of the Budapest Semester in Cognitive Science, a study abroad program. Péter is a Member of the Board of Governors of the International Neural Network Society, the past Vice President of Membership of the International Neural Network Society, member of the IEEE Computational Intelligence Society Curriculum Subcommittee, and among others as the Editor-in-Chief of Cognitive Systems Research. His books on mathematical modeling of chemical, biological, and other complex systems have been published by Princeton University Press, MIT Press, Springer Publishing house. His book RANKING. The Unwritten Rules of the Social Game We All Play is being

11

published by the Oxford University Press, (see aboutranking.com). He has been serving as the Honorary Chair of the IJCNN 2019, https://www.ijcnn.org/.

Speech Title: “Ranking: The reality, illusion and manipulation of objectivity”

Abstract: Like it or not, ranking is with us. We are in a paradoxical relationship with ranking: ranking is good because it is informative and objective; ranking is bad because it is biased and subjective and, occasionally, even manipulated. This lecture is based on a book is intended to help Readers understand the paradoxical nature of ranking procedures, and it offers strategies for coping with this paradox. Ranking begins with comparisons. We like to compare ourselves with others and determine who is stronger,

richer, better, or cleverer. Our love of comparisons has led to our passion for ranking. Ranking is about becoming more organized, and we like the idea of being more organized! The practice of ranking is studied in social psychology and political science, the algorithms of ranking in computer science. Are these algorithms reflect real objectivity or its illusion only? “Reputation management” admittedly attempts to modify the ideally objective image. We all know in this room that the challenging question for the future is how to combine human and machine intelligence.

Prof. Babu Sena Paul

Director, Institute of Intelligent Systems, University of Johannesburg, Republic of South Africa

Prof. Babu Sena Paul received his B.Tech and M.Tech degree in Radio physics and Electronics from the University of Calcutta, India. He worked as supporting engineer at Philips India Ltd from 1999-2000. He received his Ph.D. degree from the Department of Electronics and Communication Engineering, Indian Institute of Technology Guwahati, India. He has attended and published over sixty research papers in international and national conferences, symposiums and peer reviewed journals. His research interests are in the area of Cyber Physical Systems, Wireless communication, channel modeling, MIMO systems, relay based communication, mobile-to-mobile communication, Machine Learning, Data Analysis etc. He has successfully supervised several postgraduate students and post-doctoral research fellows. He joined the University of Johannesburg in 2010. He has served as the Head of the Department at the Department of Electrical and Electronic Engineering Technology, University of Johannesburg from 2015 to March 2018. He is the currently serving as the Director of the Institute for Intelligent Systems, University of Johannesburg.

12

Speech Title: “4IR: Foundations, Possible Influence and Ongoing

Investigations at University of Johannesburg”

Abstract: We are at the initial phase of the fourth industrial revolution. The fourth industrial revolution is not about a single technology but a confluence of multiple technology. This talk begins with a brief introduction to the previous three industrial revolutions and their effects. Then we talk about some of the reasons behind the advent of the fourth industrial revolution. How the current revolution is likely to affect some of the sectors like banking, health, smart cities, transportation etc. This is followed by introducing some of the ongoing work done at the Institute for Intelligent Systems (IIS) in the area of the use of machine learning for waste separation and optimization of the mines.

13

CONTENTS OF SESSIONS

Note: Please find out which session your paper is included in and arrive at the session room at

least 10 minutes before the session starts to copy your PPT or PDF presentation file into the

laptop which has been set up in the room.

Session 1: Machine Learning Algorithms and Techniques

Paper ID Authors Title Page No.

MI031

Kennedy Phala, Wesley

Doorsamy and Babu Sena

Paul

Detection and Clustering of Neutral

Section Faults Using Machine Learning

Techniques for SMART Railways

17

MI032 Andronicus A. Akinyelu

Hybrid Machine Learning-Based

Intelligent Technique for Improved Big

Data Analytics

18

MI035

Antonio Luchetta, Francesco

Grasso, Stefano Manetti,

Maria Cristina Piccirilli and

Marco Bindi

Smart Monitoring and Fault Diagnosis

of Joints in High Voltage Electrical

Transmission Lines

18

MI042

Henry Wandera, Vukosi

Marivate, Moinina David

Sengeh

Predicting National School Performance

for Policy Making in South Africa 19

MI052

Anuprabha Arputharaj, Soma

Datta, and Shajadul

Khondker Hasan

Impact of Distance Measures on

Imbalanced Classes for Rule Extraction 19

MI054 Pallavi Satsangi Automation of Tacit Knowledge Using

Machine Learning 20

MI061 K. Moloi, Y. Hamam and J. A.

Jordaan

Fault Detection in Power System

Integrated Network with Distribution

Generators Using Support Vector

Machines

20

MI017 Tuan-Tang Le and Chyi-Yeu

Lin

Random Bin-Picking for Planar USB

Packs 21

MI015

Christine K. Mulunda, Peter

W. Wagacha, Lawrence

Muchemi

Semi-supervised Topic Model for

Sequential Data: A Genetic Algorithm

Approach

21

MI007 Victoria Oguntosin, Ayoola

Akindele, Enock Oladimeji

Gesture-Based Control of Rotary

Pneumatic Soft Robot Using Leap

Motion Controller 22

14

Session 2: Neural Network and Image Processing

Paper ID Authors Title Page

No.

MI014

Mosa Machesa, Tartibu

Lagouge, Modestus Okwu

and Kunzi Tekweme

Evaluation of the Stirling Heat Engine

Performance Prediction Using ANN-PSO

and ANFIS Models

22

MI050 Simon Abbott and Abejide

Ade-Ibijola Algorithms and a Tool for Automatic

Decryption of Clinical Notes

23

MI075 V. Rameshar and W.

Doorsamy

Exploring the Effects of Compression via

Principal Components Analysis on X-ray

Image Classification

23

MI060 Vusi Sithole, Linda

Marshall

A Novel Approach to Training Artificial

Neural Networks for Automatic Indexing

of Locality Sensitive Text Documents

24

MI011 Tshephisho Sefara Yorùbá Gender Recognition from Speech

Using Neural Networks

24

MI055

Desmond Eseoghene

Ighravwe and Daniel

Mashao

Neural Network Based Estimation of

Electricity Generated During a Waste-to-

energy Process

25

MI069

Patrick Philipp, Rafael

Georgi, Sebastian Robert,

Jürgen Beyerer and

Jürgen Beyere

Analysis of Control Flow Graphs Using

Graph Convolutional Neural Networks

25

MI018 A F Mulaba – Bafubiandi,

LK Tartibu

A Predictive Approach for Vibration

Analysis in Underground Mining

Operation

26

MI028 Patricia E. Nalwoga Lutu

Using Twitter Mentions and a Graph

Database to Analyse Social Network

Centrality

26

MI059 Hongbiao Lu, Xiaobao

Liu, YanChao Yin,

Zhicheng Chen A Patent Text Classification Model Based

on Multivariate Neural Network Fusion

27

15

Session 3: Artificial Intelligence and Intelligent Computing

Paper ID Authors Title Page

No.

MI008 Vusi Sithole

Fine-Tuning Semantic Information for

Optimized Classification of the Internet of

Things Patterns Using Neural Word

Embeddings

28

MI044 Katlego Mabunda and

Abejide Ade-Ibijola PathBot: An Intelligent Chatbot for

Guiding Visitors and Locating Venues

29

MI038 Oluwafemi Oriola and

Eduan KotzÉ

Automatic Detection of Toxic South

African Tweets Using Support Vector

Machines with N-Gram Features

29

MI063 Alireza Vafaei Sadr, Bruce

Bassett and Martin Kunz A Flexible Framework for Anomaly

Detection via Dimensionality Reduction

30

MI013

Desmond Eseoghene

Ighravwe and Daniel

Mashao

Predicting Energy Theft under Uncertainty

Conditions: A Fuzzy Cognitive Maps

Approach

30

MI070-A Zong Woo Geem

Harmony Search Algorithm for Soft

Computing & Machine Intelligence

Applications in Africa

31

MI020 Koena Monyai, Terence

van Zyl, Stoyan Stoyvech Peak Detection, Feature Extraction and

Clustering of Peptides Fragments Ions

31

MI047 Abejide Ade-Ibijola Synthesis of Integration Problems and

Solutions

31

MI034

Muhammad Faisal

Masood, Dr. Aimal Khan,

Dr. Farhan Hussain, Dr.

Arslan Shaukat, Babar

Zeb, Rana Muhammad

Kaleem Ullah

Towards the Selection of Best Machine

Learning Model for Student Performance

Analysis and Prediction

32

MI058 Litong Zhang, Yanchao

Yin, Fuzhao Chen,

Shengbo Zhang

Dynamic Fusion Modeling of

Multidimensional Resource Cloud Based

on Petri Nets

32

16

Session 4: Algorithm Optimization and High Performance Computing

Paper ID Authors Title Page

No.

MI066

Zachary Bowditch,

Matthew Woolway and

Terence van Zyl

Comparative Metaheuristic Performance

for the Scheduling of Multipurpose Batch

Plants

33

MI003

Chikomborero Shambare,

Yanxia Sun, Odunayo

Imoru

A Survey on Recent Development of

Asymmetrical Three Phase Short Circuit

Faults Computation in Power Systems

33

MI065 Krupa Prag, Matthew

Woolway, Byron Jacobs

Optimising the Vehicle Routing Problem

with Time Windows under Standardised

Metrics

34

MI040 Avashlin Moodley, Vukosi

Marivate Topic Modelling of News Articles for Two

Consecutive Elections in South Africa

35

MI046

George Obaido, Abejide

Ade-Ibijola, Hima

Vadapalli Synthesis of SQL Queries from Narrations

35

MI023

Iztok Fister, Suash Deb,

Dusan Fister, Iztok Fister

Jr.

How does Selecting a Benchmark

Function Suite Influence the Estimation

of an Algorithm’s Quality?

36

MI083-A Andrew Paskaramoorthy,

Tim Gebbie A Sequential Estimation Framework for

Automated Portfolio Management

36

MI057

Ogechukwu Iloanusi,

Ugogbola Ejiogu, Ife-

ebube Okoye, Ijeoma

Ezika, Samuel Ezichi,

Charles Osuagwu,

Emenike Ejiogu

Voice Recognition and Gender

Classification in the Context of Native

Languages and Lingua Franca

37

MI082

Zulfiqar Ali, Botond

Virginas, Bryan Scotney,

Darryl Charles , Anousheh

Ramezani

Design and Implementation of Autonomic

Simulator 37

MI078 Thabo Mahlangu and

Chunling Tu

Deep Learning Cyberbullying Detection

Using Stacked Embbedings Aproach 38

17

ORAL PRESENTATION ABSTRACTS

Note:

Session photo will be taken at the end of each session.

Upload your PPT or PDF to the laptop 10 minutes before each session starts.

To show respect to other authors, especially to encourage the student authors, we strongly suggest

you attend the whole session

The certificate for oral presentations will be handed out by session chair at the end of each session.

Important: The scheduled time for presentations might be changed due to unexpected situations,

please come as early as you could.

SESSION 1

Machine Learning Algorithms and Techniques

02:00pm-04:30pm

Venue: Oak West

Chair: Prof. Péter Érd

Kalamazoo College, Kalamazoo, MI, USA

Hungarian Academy of Sciences, Budapest, Hungary

MI031

02:00pm-02:15pm

Detection and Clustering of Neutral Section Faults Using Machine

Learning Techniques for SMART Railways

Kennedy Phala, Wesley Doorsamy and Babu Sena Paul

University of Johannesburg, South Africa

Abstract: Fault detection and diagnosis plays an important role particularly in railways

were abnormal events are detected and a detailed root causes analysis is performed to

prevent similar occurrence. The current method used to detect immediate and long-term

faults is through foot inspections and inspection trolleys fitted with cameras proving to be

inefficient and time consuming when analyzing the data. This paper examines the smart

fault detection system on the overhead wires by applying machine learning techniques for

accurate assessment of the neutral section before and after failure thereby grouping the

events into fault bins. Modern computational intelligence has enabled the fault diagnostic

and fault detection to be accurate from the data generated and sensors. The interaction

between the pantograph and contact wire will be monitored using accelerometers and

18

non-contact infrared thermometer sensors were should there be a deviation from the

normal signal spectrum it will be detected. The measured data from onsite will be

conveyed to ThingSpeak for cloud computation thereby providing notifications in real-time

which allows the end user to visualize, analyze and act on data online. A prototype has

been built and tested which shows that the system works reasonably with data collected

from sensors.

MI032

02:15pm-02:30pm

Hybrid Machine Learning-Based Intelligent Technique for Improved Big

Data Analytics

Andronicus A. Akinyelu

University of the Free State, South Africa

Abstract: The average volume of data produced daily is estimated to be over 2.5

quintillion byte. Moreover, by year 2020, it is estimated that 1.79MB of data will be

created every second by each person in the world. Apparently, big datasets contain

tremendous amount of valuable information that can be used for improved decision

making. However, big data requires incredible amount of storage and computational

resources for effective processing. Machine Learning (ML) algorithms are effective tools

popularly used to analyze and extract concealed insights from datasets. However, some

ML algorithms were not originally designed to handle big datasets, hence their

computational complexity decreases with increase in data size. Consequently, this makes

big data analytics extremely slow or unrealistic. Therefore, there is an obvious need for

fast and effective techniques for big data analytics. This paper introduces an intelligent

hybrid ML-based technique suitable for big data analytics (called EDISA_ML). EDISA_ML

is a boundary detection and instance selection algorithm, inspired by edge detection in

image processing. It was evaluated on four ML algorithms and big datasets, and the

results show that it achieved a storage reduction of over 50% and simultaneously

improved the training speed of the evaluated ML algorithms by over 93% (in some cases),

without meaningfully affecting their prediction accuracy.

MI035

02:30pm-02:45pm

Smart Monitoring and Fault Diagnosis of Joints in High Voltage

Electrical Transmission Lines

Antonio Luchetta, Francesco Grasso, Stefano Manetti, Maria Cristina

Piccirilli and Marco Bindi

University of Florence, Italy

Abstract: In this paper an original approach and a theoretical method, based on

techniques of Frequency Response Analysis (FRA), soft computing and machine learning,

are described for the continuous monitoring, prognosis and fault diagnosis of the various

joint regions of overhead lines for power transmission. The proposed procedure can be

19

considered an intelligent measurement module, where a single measurement can be

used by a neural processor to extract important information for the diagnosis of a

complex electrical system.

MI042

02:45pm-03:00pm

Predicting National School Performance for Policy Making in South

Africa

Henry Wandera, Vukosi Marivate, Moinina David Sengeh

University of Pretoria, South Africa

Abstract: This paper presents an Education Data Mining (EDM) approach and machine

learning techniques that were used to identify important features that can predict

performance of high schools in South Africa. In order to be able to extract these factors

we use interpretable machine learning algorithms to make it easier to translate the

predictive power into actionable information that can be used by policymakers. Logistic

regression and Light Gradient Boosting (LightGBM) and tree-based algorithms were

applied on combined data sources from community surveys, school master lists and

school government reports to perform feature importance and training of prediction

models. Availability of clean water, toilets, hospitals, electricity, household goods,

cellphone internet and safety in the communities were identified as important variables

impacting the performance of schools. The two algorithms; LightGBM and Logistic

regression, underlies the developed prediction models and empowered the models with

high accuracy, stability, and easy interpretation as shown by the odd ratios and SHapley

Additive exPlanations (SHAP) values.

MI052

03:00pm-03:15pm

Impact of Distance Measures on Imbalanced Classes for Rule

Extraction

Anuprabha Arputharaj, Soma Datta and Shajadul Khondker Hasan

University of Houston-Clear Lake, US

Abstract: This paper identifies the supervised and unsupervised learning algorithms for

imbalanced classes to extract rules for them. Most Machine learning algorithms work

best when the number of instances of each class is roughly equal, but there are only

specific algorithms to deal with the imbalanced classes. Imbalanced classification

problems mean that the dependent (response) variable has an imbalanced proportion of

classes. This results in biased predictions, misleading inaccuracies, and fluctuating

performances in the datasets and henceforth, imbalanced classes need attention in

machine learning. In the field of machine learning, the traditional model evaluation

methods do not accurately measure the model performances when faced with

imbalanced classes. However, in an imbalanced dataset, the minor class does not

significantly contribute towards accuracy. Hence, accuracy should not be used to

evaluate the models’ performance for an imbalanced dataset. Thus, this paper discusses

20

several data mining methodologies like Sampling, Clustering, Distance measures and

Performance measures to extract more rules for the imbalanced classes.

MI054

03:15pm-03:30pm

Automation of Tacit Knowledge Using Machine Learning

Pallavi Satsangi

Infosys Limited, Bangalore, India

Abstract: This paper presents an approach to capture tacit knowledge effortlessly and

efficiently. It is a well-known fact that at a work place, converting tacit knowledge into

explicit knowledge is difficult and hence organizations lose critical information and best

practices when a skilled employee leaves. Hence, regular capture of implicit data

becomes critical. This paper focusses not only on a technique to acquire this tacit

knowledge but also how to make it as seamless as possible so that it does not become

cumbersome on the employee. This paper discusses about a bot, which captures data

from the employee on a regular basis and transforms this data into tacit knowledge using

text analytics. The bot also self learns about the employee’s role and the work that the

employee is currently working on. The bot tunes itself to ask the right set of questions to

the employees .This approach is generic and can be customized and extended to fit to

one’s project need.

MI061

03:30pm-03:45pm

Fault Detection in Power System Integrated Network with Distribution

Generators Using Support Vector Machines

K. Moloi, Y. Hamam and J. A. Jordaan

Tshwane University of Technology, South Africa

Abstract: The generation of electricity from renewable energy sources (RES) is becoming

more popular globally. This is because primary sources of electricity such as coal have a

negative environmental impact. The introduction of RES into the existing power

distribution grid has brought technical challenges. Fault detection with high reliability in

power distribution network integrated with RES is one of the major challenges. In this

paper, we propose a technique for fault detection in an integrated network. A reduced

22kV integrated power system is modelled in Digsilent Power Factory. Various fault

current signals are generated from the model. Discrete wavelet transform (DWT) is used

to extract statistical features from the fault current obtained through the study of the

model. Subsequently, the extracted features are fed into the support vector machine

scheme for fault detection and classification. In this paper, we also tested the

performance of neural network (NN) and decision tree (DT) classifiers. A combined

technique comprising of DWT and SVM is proposed. The proposed method is tested using

21

a machine learning platform WEKA. The proposed method showed impressive

classification results.

MI017

03:45pm-04:00pm

Random Bin-Picking for Planar USB Packs

Tuan-Tang Le and Chyi-Yeu Lin

National Taiwan University of Science and Technology, Taiwan

Abstract: Random bin-picking for planar objects in a cluttered environment is one of the

common problems in the industry. In this study, we introduce a solution to classify two-

sided of USB packs before performing the pick-and-place task using a 6D robot arm. The

system is a combination of instance segmentation based on deep learning with the novel

method to build the coordinate system for each target instance. Experimental results

showed that the system reaches 100% accuracy in the image processing part for two-

sided identification with successful pickup rate higher than 98%. The results of this study

will be the foundation for building an effective solution for random bin-picking on planar

objects in industry.

MI015

04:00pm-04:15pm

Semi-supervised Topic Model for Sequential Data: A Genetic Algorithm

Approach

Christine K. Mulunda, Peter W. Wagacha, Lawrence Muchemi

University of Nairobi, Kenya

Abstract: Semi-supervised learning in topic models increase accuracy of topic predictions

by introducing labeled data to guide the learning process. Inference algorithm in topic

models is used for approximation of posteriori. This paper adapts the incremental naïve

bayesian classification algorithm to sequentially analyse a set of test documents and

classify them. To overcome the challenges of biased learning and inaccurate inferences

the paper proposes a genetic algorithm approach to semi-supervised learning by

introducing user’s top hourly searched topic as labeled data. The learning process is

continuous, labeled data is introduced into the population every hour while

simultaneously removing the least fit based on the mean attribute to maintain initial

population size. We successfully tested the functionality of the model with a small set of

domain related papers. The results showed that genetic algorithm optimises the topic

model through continuous learning by reducing the computation time complexity from

( ) to ( ).

22

MI007

04:15pm-04:30pm

Gesture-Based Control of Rotary Pneumatic Soft Robot Using Leap

Motion Controller

Victoria Oguntosin, AyoolaAkindele, EnockOladimeji

Covenant University, Ota, Ogun State, Nigeria

Abstract: The development and testing of a rotary soft actuator for gesture-based control

is described in this paper. The rotary soft actuators are fabricated via a moulding process

and connected to a rotary joint as an antagonist and agonist pair which gives rise to

clockwise and counter-clockwise rotation of the joint. The soft robotic system is controlled

using the leap motion controller which is a gesture-based device. Gesture commands

executed are circle, swipe, screen tap and key tap gestures to produce clockwise,

counter-clockwise, stop and start movements of the rotary actuator.

SESSION 2

Neural Network and Image Processing

02:00pm-04:30pm

Venue: Oak East

Chair: Prof. Babu Sena Paul

Institute of Intelligent Systems, University of Johannesburg, Republic of South Africa

MI014

02:00pm-02:15pm

Evaluation of the Stirling Heat Engine Performance Prediction Using

ANN-PSO and ANFIS Models

Mosa Machesa, Tartibu Lagouge, Modestus Okwu and Kunzi

Tekweme

University of Johanessburg, South Africa

Abstract: The work presents the prediction performance results of three algorithms,

namely Artificial Neural Network (ANN), Artificial Neural Network trained with Particle

Swarm Optimization (PSO) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models.

ANFIS and ANN trained by PSO are applied to predict the power and torque values of a

Stirling heat engine with a level controlled displacer driving mechanism. Data from

experimental work done by Karabulut et al. is used to train and assess the algorithms.

MATLAB is used to develop, implement and train the algorithms. The Root Mean Square

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Error (RMSE, Coefficient of determination (R2) and computational time are used to

assess the performance of the algorithms.

MI050

02:15pm-02:30pm

Algorithms and a Tool for Automatic Decryption of Clinical Notes

Simon Abbott and Abejide Ade-Ibijola

Formal Structures, Algorithms, and Industrial Applications Research Cluster, South Africa

Abstract: The benefits and merits of Natural Language Processing (NLP) will revolutionise

the way that clinical notes are read and understood, as plain text, within medical teams.

NLP is one of many Artificial Intelligence(AI) tools being explored and implemented within

Data Science and modern healthcare, for the extraction and generation of user friendly

plain text. Clinical notes are classically originated and derived from various sources of

clinical notes, and narratives such as reports, clinical notes, referral letters, discharge

notes and clinical summaries. Further, NLP tools are about to become mainstream AI

interventions in predicting such medical conditions from electronic health records and

clinical narratives, by the analysis of varied signs and symptoms found, within the

Medical teams notes. Each member of the medical team will be enabled to read and

understand each others medical notes thus simplifying the medical language through

modern NLP techniques thereby enabling the medical team in determining a specific

medical condition. This paper further presents an applied solution of decryption, or the

deciphering, of complex clinical notes, by an applied algorithm in extracting plain text

from various complex clinical narratives. Thereby aiding and supporting more effective,

relevant, medical diagnoses and interventions, in supporting a more informed diagnosis,

in predicting the onset of a medical condition, such as a Diabetic foot ulcer. From the

early prediction of such a chronic medical condition, it paves the way in applying a unique

effective medical intervention, thereby establishing an accurate assessment of a medical

condition, before reaching a traumatic stage, such as a foot amputation, due to a simple

diabetic foot ulcer that is preventable through early detection from an NLP algorithm.

MI075

02:30pm-02:45pm

Exploring the Effects of Compression via Principal Components

Analysis on X-ray Image Classification

V. Rameshar and W. Doorsamy

University of Johannesburg, South Africa

Abstract: Image compression in medical applications implores careful consideration of

the effects on data veracity. The inexorable challenge of assessing the volume-veracity

trade-off is becoming more prevalent in this critical application area, and particularly

when machine learning is used for the purpose of assisted diagnostics. This paper

investigates the impact of compressing X-ray images on the accuracy of fracture

24

diagnostics. The accuracy of the classification system is assessed for X-ray images of

both healthy and fracture bones when subjected to different levels of compression.

Compression is achieved using principal components analysis. Results indicate that

accuracy is only marginally affected under a level one compression but begins to

deteriorate under level two compression. These results are potentially useful as the level

one compression yields gains upto 94% with less than a 2% drop in classification

accuracy.

MI060

02:45pm-03:00pm

A Novel Approach to Training Artificial Neural Networks for Automatic

Indexing of Locality Sensitive Text Documents

Vusi Sithole, Linda Marshall

University of Pretoria, South Africa

Abstract: Automatic Indexing of documents using paragraph vectors is a popular

unsupervised method for learning distributed representations of texts. This method

learns embedding of words with document vectors for document classification. In

addition, this method can be leveraged for sentiment analysis. However, while the results

presented in the original Doc2Vec study were promising, the overall proof of concept was

rather narrow. In this study, we extend the Doc2Vec method, and enhance it to classify

locality sensitive documents, i.e. domain-based documents which are largely similar with

marginal differences. In particular, we use the enhanced Doc2Vec technique to classify

similar documents describing the Internet of Things (IoT) patterns. We observed that our

enhanced locality sensitive Doc2Vec technique performs significantly well to improve

embedding quality. The model performance is in par with state-of-the-art results and can

be qualified as a benchmark for similar vector space models.

MI011

03:00pm-03:15pm

Yorùbá Gender Recognition from Speech Using Neural Networks

Tshephisho Sefara

Council for Scientific and Industrial Research, RSA

Abstract: The impressive improvement in performance obtained using neural networks for

automatic speech recognition (ASR) have motivated the application of neural networks to

other speech technologies such as speaker, emotion, language, and gender recognition.

Prior work has shown significant improvement in gender recognition from images and

videos. This paper uses speech to build a gender recognition system based on neural

networks. Three types of neural networks are investigated to find the best model for

gender recognition system using Yorùbá, namely, feed-forward artificial neural networks

(Multilayer Perceptrons), Recurrent neural networks (long short-term memory), and

Convolutional neural networks. All the classifier models obtained the state-of-the-art

performance in speech-based gender recognition with 99% in accuracy and F1 score.

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MI055

03:15pm-03:30pm

Neural Network Based Estimation of Electricity Generated During a

Waste-to-energy Process

Desmond Eseoghene Ighravwe and Daniel Mashao

University of Johannesburg, South Africa

Abstract: Many emerging economies are embarking on the production of electricity from

food wastes. And this has rekindled the interest of waste-to-energy engineers in these

economies. They are confident that machining learning algorithms will help them to

reduce the computation cost for this process. Here, an artificial neural network (ANN)

model is used to estimate the amount of electricity generated during a waste-to-energy

process. The selected model is a single hidden layer model with five inputs including

methane gas, compression efficiency, boiler efficiency and more - the model's output is

electricity generated. This study evaluated ten ANN architectures for the prediction

purpose; data from nine cities in Nigeria were used to achieve this purpose. The results

obtained show that a 5-4-1 ANN architecture performs better than the other architectures

during their training and testing phases. This model’s training and testing mean square

error is 6.96 x 10-5 and testing 3.62 x 10-5, respectively. Based on the ANN

performance, it was concluded that it can be used to monitor a waste-to-energy process.

MI069

03:30pm-03:45pm

Analysis of Control Flow Graphs Using Graph Convolutional Neural

Networks

Patrick Philipp, Rafael Georgi, Sebastian Robert, Jürgen Beyerer and

Jürgen Beyere

Karlsruhe Institute of Technologie (KIT), Germany

Abstract: With the digital transformation of companies, ever larger amounts of data are

generated and available for analysis. Process mining techniques can be used to extract

and analyze process models from these data. Related techniques have quickly developed

into an important field with constantly increasing investments in recent years. Thus, the

automated analysis of processes has gained an important role in many companies. In

this context, graphs have been shown to be an intuitive representation of how the

gathered processes are carried out using the aforementioned techniques. For the

analysis of these so-called control flow graphs, we investigate the use of convolution

neural networks, which are specially designed for graphs: graph convolution networks

(GCNs). In our contribution, GCNs are used to perform a regression task based on

individual control flows of a process in which farm- ers apply for specific governmental

payments. The approach achieved promising results on this publicly available data set.

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MI018

03:45pm-04:00pm

A Predictive Approach for Vibration Analysis in Underground Mining

Operation

A F Mulaba – Bafubiandi, LK Tartibu

University of Johannesburg, RSA

Abstract: Mine fatalities, accidents and incidents are often associated with ground, roof,

stope or side instability. Attenuation of rock integrity or the presence of (under)ground

pockets of gases or ground waters lead to the collapse of the tunnel. In this paper, the

blast vibration in an Open-pit Lignite Mine has been predicted by incorporating the

frequency, the charge per delay, the distance and scaled distance using Artificial Neural

Network (ANN). The particle velocities (PPV) namely transverse peak, vertical peak and

longitudinal peak are successively the output parameters considered. Particle Swarm

Optimization (PSO) was used to train the neural network with 54 experimental and

monitored blast records. Results were compared based on correlation between

monitored and predicted values of PPV. This study demonstrates the possibility to predict

and control blasting effect.

MI028

04:00pm-04:15pm

Using Twitter Mentions and a Graph Database to Analyse Social

Network Centrality

Patricia E. Nalwoga Lutu

Department of Computer Science, University of Pretoria, South Africa

Abstract: Social networks are one category of social media that facilitates the formation

of communities, sharing of content, and meeting people. Twitter is a popular

microblogging and social networking service. Social media marketers within business

organisations, are interested in identifying popular social network users, known as

influencers who can be targeted for purposes of word-of-mouth branding. For Twitter,

influencers are those users who have many followers. Influencers are typically identified

through graph mining of social networks data. This type of mining involves the analysis of

links between the graph nodes which store data for social network members. Follows

relationships are commonly used to analyse Twitter social networks. The purpose of this

paper is to demonstrate how mentioned relationships in Twitter data can be used to

create a social networks graph database. Centrality measures are then used to analyse

the social networks. It is demonstrated that the analysis of social networks based on the

mentioned relationships can provide more information about influencers compared to the

analysis of social networks based on the follows relationships.

27

04:30pm-05:00pm

MI059

04:15pm-04:30pm

A Patent Text Classification Model Based on Multivariate Neural

Network Fusion

Hongbiao Lu, Xiaobao Liu, YanChao Yin,Zhicheng Chen

Kunming University of Science and Technology

Abstract: In order to improve the efficiency and accuracy of automatic classification of

patent texts, a patent text classification model (C3-BIGRU-AT) based on multivariate

neural network fusion was proposed. Firstly, patent text is segmented and represented by

text preprocessing. Then, the text features of different levels and different characteristics

are extracted through word embedding layer, convolution layer, BIGRU layer and Attention

layer, and text category recognition is carried out through soft Max layer. Finally, case

studies show that C3-BIGRU-AT model has a high ability of patent text recognition, and

can meet the requirements of accurate and efficient classification of a large number of

patent texts.

28

SESSION 3

Artificial Intelligence and Intelligent Computing

05:00pm-07:30pm

Venue: Oak West

Chair: Prof. Mammo Muchie

Tshwane University of Technology TUT, South Africa

MI008

05:00pm-05:15pm

Fine-Tuning Semantic Information for Optimized Classification of the

Internet of Things Patterns Using Neural Word Embeddings

Vusi Sithole, Linda Marshall

University of Pretoria, South Africa

Abstract: Word embeddings is a natural language processing modelling technique used to

map semantically related words and phrases in proximity vectors. Such embeddings

generally reflect semantic similarities between words taken from natural contexts in large

corpora. Nonetheless, most natural contexts tend to also have numerous words which do

not bear any particular close relationship with regard to their meaning. This results in a

lot of noisy data, which also makes the training of word embedding models much more

expensive. In this paper, we show that fine-tuning semantic information provide

additional benefits for training optimized neural word embeddings. In particular, we use

explicit semantic extractions of the Internet of Things patterns attributes as our input

data into the model. We propose extracting specific sentences from a large number of the

IoTrelated documents. These sentences describe the attributes for different IoT patterns.

To make our corpora semantically rich, we further extract synonymous words from a

thesaurus for some individual words taken from the extracted sentences. This also

makes the context of the data more natural. We then embed several IoT pattern names in

vector spaces and surround each pattern name with core word units taken from its

attributes. In this way, each IoT pattern is classified in close vector spaces with words that

represent its core attributes. Furthermore, the IoT patterns belonging in the same family

are also classified in close vector spaces based on their attributes. The word vectors

obtained from such strict supervised training show improved results on intelligent

classification tasks, suggesting that they can be useful in machine learning efforts for

building applications used in the categorization of items into both distinct and indistinct

classes.

29

MI044

05:15pm-05:30pm

PathBot: An Intelligent Chatbot for Guiding Visitors and Locating

Venues

Katlego Mabunda and Abejide Ade-Ibijola

Formal Structures, Algorithms, and Industrial Applications Research Cluster, South Africa

Abstract: This article reports the development of an intelligent Chatbot called PathBot

used for guiding visitors and locating venues. The users of PathBot is University of

Johannesburg(UJ) students. Chatbots are computer programs designed to interact with

users using natural language through sensors and interactions with devices. They are

coded in such a way that they can have verbal conversations which are logical and textual

conversations. The conversation of PathBot happens through a Mobile Application client

where the user will interact with PathBot when they want to go to a specific location and

the evaluation happens through natural language processing using DialogFlow. A Finite

Automaton is used to feed the correct information into PathBot for it to execute accurate

information requested by the user. PathBot uses DialogFlow API which has a database

that stores all the necessary information to execute on the requirements.

MI038

05:30pm-05:45pm

Automatic Detection of Toxic South African Tweets Using Support

Vector Machines with N-Gram Features

Oluwafemi Oriola and Eduan KotzÉ

University of the Free State, South Africa

Abstract: Toxic South African corpus is not available to detect toxic tweets such as

offensive, hate, bullying and violent tweets. But there are some offensive and hate

speech corpora, mostly in English, which have been used to detect toxic tweets. This

paper focuses on automatic detection of toxic South African tweets using a reliable

English corpus. The review of text classification models has shown that Support Vector

Machines have very often outperformed other classic machine learning algorithms, while

word and character n-gram features have performed well with varying prediction

performances in different contexts. This paper therefore evaluated the performance of

different parameter settings of Support Vector Machines and n-gram features for

detection of toxic South African tweets, with a view to hybridize the best among the

classifiers. Different combinations of word and character n-gram features were used for

the classification. The results show that the Support Vector Machine classifier with set of

unigram and bigram as well as set of character n-gram with length sizes from 3 to 7

perform best. By combining the classifiers, the accuracy and F-measure improve from the

initial highest Accuracy and F-Measure scores of 0.9085 and 0.94, respectively to

30

0.9095 and 0.95. The comparison of our results with the performance of previous work

on the English corpus shows that our model is reliable.

MI063

05:45pm-06:00pm

A Flexible Framework for Anomaly Detection via Dimensionality

Reduction

Alireza Vafaei Sadr, Bruce Bassett and Martin Kunz

AIMS/SARAO/SAAO/UCT, South Africa

Abstract: Anomaly detection is challenging, especially for large datasets in high

dimensions. Here we explore a general anomaly detection framework based on

dimensionality reduction and unsupervised clustering. We release DRAMA, a general

python package that implements the general framework with a wide range of built-in

options. We test DRAMA on a wide variety of simulated and real datasets, in up to 3000

dimensions, and find it robust and highly competitive with commonly-used anomaly

detection algorithms, especially in high dimensions. The flexibility of the DRAMA

framework allows for significant optimization once some examples of anomalies are

available, making it ideal for online anomaly detection, active learning and highly

unbalanced datasets.

MI013

06:00pm-06:15pm

Predicting Energy Theft under Uncertainty Conditions: A Fuzzy

Cognitive Maps Approach

Desmond Eseoghene Ighravwe and Daniel Mashao

University of Johannesburg, South Africa

Abstract: Several studies have called the attentions of utility firms to the possibility of

using mathematical models to measure and monitor energy theft. Unfortunately, these

studies have decoupled the contributions of government policies, such as social,

technical and economic policies, from their evaluation process. To address this

knowledge gaps, this article modelled energy theft using soft computing approach: fuzzy

cognitive map (FCM) and swarm algorithm. Fuzzy logic was used to design cognitive maps

for energy theft parameters; second, and swarm algorithm was used to determine the

weights and concepts values. The practicality of the swarm-based model was tested using

experts’ judgements. This model performance was compared with evolutionary-based

FCM and it was observed that it performed better than the evolutionary-based model. And

when the swarm-based model performance was compared with experts’ judgements, it

performed satisfactorily.

31

MI070-A

06:15pm-06:30pm

Harmony Search Algorithm for Soft Computing & Machine Intelligence

Applications in Africa

Zong Woo Geem

Department of Energy IT, Gachon University, Korea

Abstract: Harmony search is a music-inspired algorithm for Soft Computing & Machine

Intelligence problems. This presentation reviews the basic structure of the harmony

search algorithm, then more theoretical issues such as human-experience-based

derivative and parameter-setting-free technique. This presentation also reviews various

applications of the harmony search algorithm, especially performed in Africa. The

applications include engineering design, lane detection in driverless car, and energy

system scheduling, as well as music composition, fine art appreciation, and nuclear

energy management.

MI020

06:30pm-06:45pm

Peak Detection, Feature Extraction and Clustering of Peptides

Fragments Ions

Koena Monyai, Terence van Zyl, Stoyan Stoyvech

CSIR and Wits University, South Africa

Abstract: This work presents a peak detection technique used to detect Proteomics

fragments peaks and investigates if shape-based features and clustering can group the

peaks such that the clusters are homogeneous, i.e. contain peaks from a single class. We

used Continuous Wavelet Transformation (CWT) and two Gaussian Mixture Model (GMM);

K=2 and K=15; for peak detection and clustering, respectively. GMM(K=15) performed

better than GMM(K=2) with an f1-score of 0.81 and 0.57 for the good class and the bad

class, respectively. Additional features and other clustering techniques need to be

investigated to improve the homogeneity of the clusters.

MI047

06:45pm-07:00pm

Synthesis of Integration Problems and Solutions

Abejide Ade-Ibijola

Formal Structures, Algorithms, and Industrial Applications Research Cluster, South Africa

32

Abstract: Problem synthesis is a formal task in Artificial Intelligence. This task involves the

automatic generation of problems and their respective solutions across different domains

(including Mathematics). Techniques for problem synthesis vary widely. In this paper, we

present a newly designed contextfree grammar (or CFG) that specifies the rules governing

the formulation of specific classes of Integration problems and their solutions. These

grammar rules were implemented in a software tool, and produced many Integration

problems and solutions rendered in LATEX. A hundred thousand instances of these

synthesised problems and solutions can be found at: tinyurl.com/integralproblems2019.

The resulting problems and solution may find applications in Education (as assessment

and/or practice problems), hence, aiding the learning of integration as a topic in

Mathematics.

MI034

07:00pm-07:15pm

Towards the Selection of Best Machine Learning Model for Student

Performance Analysis and Prediction

Muhammad Faisal Masood, Dr. Aimal Khan, Dr. FarhanHussain, Dr.

ArslanShaukat, Babar Zeb, Rana Muhammad KaleemUllah

National University of Sciences and Technology (NUST) Islamabad, Pakistan

Abstract: Educational Data Mining (EDM) has become one of the most important fields

now a day because with the development of technology, student’s problems are also

increasing. In order to tackle these problems and help students, educational data mining

has come into existence. In this research paper, a Systematic Literature Review (SLR) has

been carried out to get 20 studies (2012-2019) in the field of EDM. From these studies,

11 highly advanced machine learning models have been obtained and we have

implemented them on 2 public student databases in order to predict their future

outcomes. Feature extraction techniques have been applied and then models have been

trained based on these databases to get the required results. Results of different

machine learning models have been compared in order to find out the best model among

them based on. With these experiments, weak students can be easily identified and

proper precautions can be taken in order to help them.

MI058

07:15pm-07:30pm

Dynamic Fusion Modeling of Multidimensional Resource CloudBased

on Petri Nets

Litong Zhang, Yanchao Yin, Fuzhao Chen, Shengbo Zhang

Kunming University of Science and Technology, China

Abstract: To solve the problem of precise push for knowledge resources in the process of

complex product development, a dynamic fusion modeling method for multi-dimensional

resource and business processes is proposed based on Petri nets. In order to realize

resource clustering, multi-dimensional resource cloud is defined based on domain

ontology, and a dynamic fusion model is constructed based on the Petri nets combined.

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At last, the validity of the proposed method is proved by the accurate matching of

resources and business processes in engineering application process.

SESSION 4

Algorithm Optimization and High Performance Computing

05:00pm-07:30pm

Venue: Oak East

Chair: TBA

TBA

MI066

05:00pm-05:15pm

Comparative Metaheuristic Performance for the Scheduling of

Multipurpose Batch Plants

Zachary Bowditch, Matthew Woolway and Terence van Zyl

University of the Witwatersrand, South Africa

Abstract: Two recent publications by Woolway et al. (2018, 2019) [1, 2] proposed a novel

metaheuristic framework to optimise the scheduling of Multipurpose Batch Plants. This

initial framework implemented three metaheuristic methods to solve the problem with a

Genetic Algorithm (GA) showing superior performance over the others. Two notable

opportunities for improvement in the current solution are improving the

spread/confidence intervals of the percentiles of the solutions discovered by repeated

executions of the GAs and faster convergence. This work considers two adaptations of

the GA to an attempt to improve overall spread and speed on the application to two well-

known literature examples. We have replicated the work in the original papers in a

completely new Julia framework along with our extensions. Results show that our

modifications to the GAs can, in fact, lead to tighter spread as well as faster convergence.

MI003

05:15pm-05:30pm

A Survey on Recent Development of Asymmetrical Three Phase Short

Circuit Faults Computation in Power Systems

Chikomborero Shambare, Yanxia Sun, Odunayo Imoru

University of Johannesburg, South Africa

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Abstract: Asymmetrical three-phase short circuit faults occur more often than symmetrical

three-phase short circuits faults. The asymmetrical three-phase short circuit faults can be

line-to-line faults, double line-to-earth faults or line-to-earth faults. Symmetric

components technique and computer methods like time-domain fault analysis as well as

quasi steady-state fault analysis are the main traditional methods found in the literature

for computing the faults. Some recent software like ETAP (Electrical Transient Analysis

Program), Easy-Power and Matlab can also assist in predicting, calculating and

generating signals (plotting) of short circuit faults. However, the computation of

asymmetrical three-phase short circuit faults in the real world often involves the presence

of noise, non-linearity, uncertain and dynamic environments. These various conditions

interfere with the evaluation processes of these methods and software tools. This paper

presents a survey of comprehensive investigation and analysis of the various algorithms,

computer applications and software used to compute asymmetrical three-phase short

circuit faults. Various methods and algorithms employ different levels of abstraction.

Their strengths and weaknesses are explored in depth and various suggestions are given

respectively.

MI065

05:30pm-05:45pm

Optimising the Vehicle Routing Problem with Time Windows under

Standardised Metrics

Krupa Prag, Matthew Woolway, Byron Jacobs

University of the Witwatersrand, South Africa

Abstract: The Vehicle Routing Problem with Time Windows (VRPTW) is an established N P

-hard Combinatorial Optimisation Problem (COP). While much research has been

undertaken in developing solution mechanisms to the VRPTW, this work has been

developed without comparative metrics. Previous work on the VRPTW has failed to

provide both a comprehensive computational review comparing the performance of

metaheuristics applied to finding solutions to the VRPTW under standardised

experimental conditions, and the effects of the employed metric schemes. This work aims

to introduce a means of comparison between leading metaheuristic methods found in the

literature. Conducted experiments applied Genetic Algorithm (GA) and Particle Swarm

Optimisation algorithm (PSO) under two standardised metrics on a well-known

benchmark dataset. The results verify and resemble previously reported results, question

the design of the applied metric schemes and record the CPU time taken to obtain

solutions to the VRPTW. This computational comparative review critically analyses,

compares and comments on the replicated applied techniques and employed metric

schemes. Significant results include: obtaining competitive timings relative to those which

have been reported if the GA is terminated when the best known solution is met; the

quality of the solutions produced by the GA and the PSO algorithm; insight into the design

of the metric schemes. The results obtained match the benchmark values, and the time

within which the solutions are computed are competitive with the benchmark times. The

solution technique and metric scheme combination which, in general, efficiently obtained

solutions to the VRPTW are the PSO algorithm and Metric A.

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MI040

05:45pm-06:00pm

Topic Modelling of News Articles for Two Consecutive Elections in

South Africa

Avashlin Moodley, Vukosi Marivate

University of Pretoria, South Africa

Abstract: In election cycles, the political-themed articles published by news providers

present a rich source of information about election discourse. Extracting useful themes

from a large article corpus manually is infeasible, text mining techniques such as topic

modelling provide a mechanism to automatically infer themes from a corpus of text.

Exploring the coverage of a single election period uncovers topical discourse that is

relevant to current affairs in that election period. Analysing two consecutive election

periods allows one to analyse the evolution of discourse from one period to another.

Articles published by News24 were sourced to conduct the analysis and answer the

research questions set forth. The articles were cleaned and topic models were built to

identify 20 latent topics. The articles are classified with their topic before a pairwise

cosine similarity comparison is applied on topic corpora to identify similar topics between

election periods. The results of this study provide important insights relating to the two

election periods, some of these include: coverage of corruption-related content is

consistent between the two election periods and most political-themed articles in this

corpus address problematic themes.

MI046

06:00pm-06:15pm

Synthesis of SQL Queries from Narrations

George Obaido, Abejide Ade-Ibijola, Hima Vadapalli

University of the Witwatersrand, South Africa

Abstract: Structured Query Language (SQL) remains a standard language used in

Relational Database Management Systems (RDBMSs), and has found applications in

healthcare (patient registries), businesses (inventories, trend analysis), military, and

education, etc. Although, SQL statements are English-like, the process of writing SQL

queries is often problematic for nontechnical end-users. To address this problem, a tool

called Narrations-2-SQL is developed to allow an end-user to specify a query in natural

language. Narrations-2-SQL is a desktop application that uses a Jumping Finite

Automaton (JFA) – a type of Finite Machine for translating natural language descriptions

into SQL queries, execute the queries, and provide a feedback to a user. An experimental

evaluation was performed on 204 crowdsourced queries in natural language from the

XNorthwind DB. Our results show an accuracy of 88%. To get the users’ perceptions of

this study, we carried out a survey on 167 end-users. Majority of the participants found

Narrations-2-SQL to be very helpful, and agreed that it could be useful in industry. If

implemented on a large scale, the tool may be helpful to many end-users in different

domains.

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MI023

06:15pm-06:30pm

How does Selecting a Benchmark Function Suite Influence the

Estimation of an Algorithm’s Quality?

Iztok Fister, Suash Deb, Dusan Fister, Iztok Fister Jr.

University of Maribor, Slovenia

Abstract: This paper is focused on answering the question how the selection of a testbed

on which the newly proposed algorithms are evaluated influence the estimation of an

algorithm’s quality. New algorithms are usually tested on well-known benchmark function

suites, where the goal is to achieve the best results of the algorithm in the shortest time.

A lot of questions have arisen when looking for the most suitable testbed, for instance,

which benchmark to take, and which version of it is the most representative for

determining the best algorithms. In this study, the newly proposed algorithms introducing

the coalition game concept for solving global optimization were tested by solving two

different benchmark function suites, i.e., CEC-14 and CEC-18, in order to show that

selecting the different CEC benchmark suites does not have a crucial impact on

estimating the algorithm’s quality.

MI083-A

06:30pm-06:45pm

A Sequential Estimation Framework for Automated Portfolio

Management

Andrew Paskaramoorthy, Tim Gebbie

University of Witwatersrand, South Africa

Abstract: This research presents an indirect adaptive control framework to automate

portfolio management. An investment process consists of a long-term benchmark

strategy and a mean-reversion strategy that is used to exploit short-term pricing

deviations. Our proposed framework executes both strategies by feeding online Bayesian

forecasts from an asset pricing model into a mean-variance optimiser.

The novelty of our approach is the specification of feedbacks mechanisms that adaptively

estimate models and update the size of portfolio bets according to forecast errors and

realised portfolio performance. Simulation results confirm that the framework can be

used to earn risk premia and active returns whilst adjusting bet size to account for model

uncertainty. Empirical tests show that the algorithmic portfolio outperforms the 1/n

portfolio and the market portfolio when trading costs are not accounted for. Our

framework is a step towards developing intelligent portfolio selection algorithms that

integrate financial theory, investor views, and data analysis in an online workflow rather

than staging individual components offline in isolation.

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MI057

06:45pm-07:00pm

Voice Recognition and Gender Classification in the Context of Native

Languages and Lingua Franca

Ogechukwu Iloanusi, Ugogbola Ejiogu, Ife-ebube Okoye, Ijeoma Ezika,

Samuel Ezichi, Charles Osuagwu, Emenike Ejiogu

University of Niger, Nigeria

Abstract: Voice verification and gender classification from voice were carried out in the

context of native (mother tongue) languages and lingua franca languages. A total of 3980

voice utterances recorded in English language and 28 native languages were acquired

from 520 bilingual subjects in this paper. We first determined the cross linguistic

influence of mother tongue by bilingual speakers on the verification performance of voice

recognition using English and native languages’ gallery and probe sets. Secondly, we

employed transfer learning in training four convolutional neural network models for

classifying gender from voice, using training and test samples of English language,

exclusively; one dominant native language; and a mixture of 28 native languages. Our

results do show that mother tongue or first language, intonation variations, language

variety in the training or test sets do influence voice verification and gender classification.

MI082

07:00pm-07:15pm

Design and Implementation of Autonomic Simulator

Zulfiqar Ali, Botond Virginas, Bryan Scotney, Darryl Charles , Anousheh Ramezani

Ulster University, United Kingdom

Abstract: Autonomic systems have broad scope in the telecommunication industry, where

the prime objective is to provide quality services to customers whilst obeying certain

financial constraints. Therefore, an autonomic system is designed and developed in this

study to determine a trade-off between cost and quality. The developed simulator is

capable of responding to unforeseen changes appearing over time as well as in business

policy without human intervention. It learns from data using a machine learning algorithm

and performs classification to assign the instances of data to corresponding groups.

Various experiments are carried out to observe the performance and behaviour of the

simulator.

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MI078

07:15pm-07:30pm

Deep Learning Cyberbullying Detection Using Stacked Embbedings

Aproach

Thabo Mahlangu and Chunling Tu

Tshwane University of Technology, South Africa

Abstract: The Cyberspace is one of the humanity’s great inventions that bring great

benefits but also exposes us to cyber threats. Cyberbullying is commonly happened to

each and every person on social platforms. In this paper we propose a framework to

detect cyberbullying messages in the form of text data using deep neural networks and

word embeddings. We stack together the state-of-the-art Bert and Glove embeddings to

improve the performance of the classifier. As a result, the model outperforms the majority

of the traditional machine learning methods such as SVM and Logistic Regression.

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LISTENER

Note:

Session photo will be taken at the end of each session.

The certificate for listeners can be collected at the registration counter.

To show respect to other authors, especially to encourage the student authors, we strongly

suggest you attend the whole session

Listener 1

Geoff Hurly

HurlyWorks Inc, Canada

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AUTHOR INDEX

Name Paper ID Session Page No.

A-G

Abejide Ade-Ibijola MI047 S3 31

Andrew Paskaramoorthy MI083-A S4 36

Andronicus A. Akinyelu MI032 S1 18

Antonio Luchetta MI035 S1 18

Avashlin Moodley MI040 S4 35

Bruce Bassett MI063 S3 30

Chikomborero Shambare MI003 S4 33

Christine K. Mulunda MI015 S1 21

Chyi-Yeu Lin MI017 S1 21

Desmond Eseoghene Ighravwe MI055, MI013 S2, S3 25, 30

George Obaido MI046 S4 35

H-N

Hongbiao Lu MI059 S2 27

Iztok Fister MI023 S4 36

K. Moloi MI061 S1 20

Katlego Mabunda MI044 S3 29

Kennedy Phala MI031 S1 17

Koena Monyai MI020 S3 31

Krupa Prag MI065 S4 34

Litong Zhang MI058 S3 32

LK Tartibu MI018 S2 26

Mosa Machesa MI014 S2 22

Muhammad Faisal Masood MI034 S3 32

O-T

Ogechukwu Iloanusi MI057 S4 37

Oluwafemi Oriola MI038 S3 29

Pallavi Satsangi MI054 S1 20

Patricia E. Nalwoga Lutu MI028 S2 26

Patrick Philipp MI069 S2 25

Simon Abbott MI050 S2 23

Soma Datta MI052 S1 19

Thabo Mahlangu MI078 S4 38

Tshephisho Sefara MI011 S2 24

U-Z

V. Rameshar MI075 S2 23

Victoria Oguntosin MI007 S1 22

Vukosi Marivate MI042 S1 19

Vusi Sithole MI060, MI008 S2, S3 24, 28

Zachary Bowditch MI066 S4 33

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Zong Woo Geem MI070-A S3 31

Zulfiqar Ali MI082 S4 37

Memo

Memo