2019 6th International Conference on - ISCMI · Program 2019 6th International Conference on Soft...
Transcript of 2019 6th International Conference on - ISCMI · Program 2019 6th International Conference on Soft...
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: [email protected]
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
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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
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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
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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
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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
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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
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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.
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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
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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
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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.
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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
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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.
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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.
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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
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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
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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
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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
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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
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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
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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
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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
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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 ( ).
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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.
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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
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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