Zahid Islam's Home Page

Zahid Islam (Full name: Md Zahidul Islam) is a Professor of Computer Science, in the Faculty of Business, Justice and Behavioural Sciences (FoBJBS) in Charles Sturt University, Australia.

He has been serving as the Associate Dean (Research) of the Faculty of Business, Justice and Behavioural Sciences (FoBJBS) since September 2023.

He served as the Director of the Data Science and Engineering Research Unit (DSERU) from January 2019 until September 2023. Besides, he served as the Theme Lead and Charles Sturt University (CSU) Academic Lead for the Cyber Security CRC from January 2021 until September 2023. He also served as the Chair of the School Research Committee of the School of Computing, Mathematics and Engineering until September 2023.

His main research interests are in Data Mining, Classification and Clustering algorithms, Missing value analysis, Outliers detection, Data Cleaning and Preprocessing, Incremental Learning, Transfer Learning, Federated Learning, Privacy Preserving Data Mining, Cyber Security, and Applications of Data Mining in real life.

• Zahid has been ranked #2 and #17 in Australia in the research areas of Decision Tree and Data Mining, respectively, by the recent ScholarGPS rankings. For the Decision Forest research topic, he is in the Top 1.08% out of 275,321 researchers worldwide in this category, and for Genetic Algorithms, he is in the Top 0.49%, out of 90,888 researchers worldwide. Overall, he is ranked in the Top 1.18% among 29,174,400 researchers worldwide. For the last five-year evaluation period, he is ranked in the Top 0.99% globally for all fields of research.
• He has also been included in the 2024 Stanford University - Elsevier World Top 2% Scientists list, which is another esteemed ranking that identifies the most cited scholars in their fields.

For a brief overview on his research leadership and research interest please visit the Research link.

He has published 150+ peer reviewed journal articles and conference papers in top quality journals [such as IEEE Transactions on Services Computing (CORE A*), Pattern Recognition (CORE A*), ACM Computing Surveys (CORE A*), Information Sciences (CORE A), Information Systems (CORE A*), Future Generation Computer Systems (CORE A), Expert Systems with Applications (h-index 271, Impact Factor 7.5) and Knowledge and Information Systems (h-index 160, Impact Factor 7.2), Climatic Change (h-index 217, Impact Factor 5.4) and others] and conferences such as PAKDD (CORE A) and IJCNN (CORE A). A complete list of his publications can be accessed at the Publications link where pre-prints of many of them are available.

He is a co-recipient of more than 25 external grants with a combined cash funding (excluding in kind contributions) of more than $11.0 M to the projects. A list of his external grants is available at the Grants link. He received a number of awards. Some awards are recognition of top quality achievements of some projects. For eaxample, a project funded by the NSW Health received an Innovation Award in 2017 from the NSW Agency for Clinical Innovation. Another project team received the Cyber Security Researcher of the Year Award 2021 from the Australian Information Security Association (AISA) for a project funded by the Cyber Security CRC and Quintessence Lab. A list of his awards is available at the Latest News link.

Zahid has supervised/co-supervised 11 PhD students to successful completion at Charles Sturt University. He is also currently supervising/co-supervising 12 PhD students at Charles Sturt University. A complete list of his PhD students can be seen at the Research link. Any potential PhD students are encouraged to contact him via email at zislam at csu.edu.au

Code for many algorithms co-authored by Zahid is publicly available. A list of the algorithms with publicly available code is available at the Code link. You may also watch short videos on some of his data mining techniques on the Youtube channel, Zahid's Data Mining Channel. He served as a Conference Co-Chair, Program Committee Co-Chair, Session Chair, Section Editor and Reviewer of journals and PhD theses, as presented on the Research page. He served (from Jan. 2012 to Dec. 2018) as the Course Coordinator of the Honours course at the School of Computing, Mathematics and Engineering.

My Teaching Philosphy

I am exploring various teaching pedagogy to find the one that suits the best for me. I change my teaching approach from subject to subject. Sometimes, I concentrate to give students as much information as possinble. However, in some other situations I prefer to allow my students to explore, share and learn by themselves.

For example, in a subject like data mining, graph theory and networking students need to know the theory in the background. In this type of subjects I take a research type approach for my students where my role as a lecturer is to provide them the initial knowledge about a topic. I then give them an opportunity to figure out the advantages and disadvantages of the topic, and a possible solution of the problem. The possible solution leads them towards the next topic.

After the initial introduction my role is to guide them towards the right direction. In a lecture room I allow them to work in groups for sometime so that every group can come up with a possible solution.

For example, I provide the initial information and a general introduction if I am teaching encryption techniques in Cryptography or Network Security subject. I then encourage my students to figure out the advantages and disadvantages of encryption techniques such as Playfair Cipher and Transposition Cipher. Once they figure out the drawbacks of these techniques I engage them to work out a possible solution. Once they reach a level then I provide the next level of information so that they can proceed further. We continue this in a very friendly class room environment until they reach the state of the art. I found this teaching technique as very effective to make students interested and engaged in a topic. I think this way they also learn something that they remember for long time. They get a feeling that as if they invented the state of the art technique.

However, in a practical type subject (such as web development and programing language) I encourage them to learn and code by themselves to enjoy their work. I prefer to show them example of what can be done and ask them to do similar things.

Top of page

Teaching Responsibilities

Session 1, 2012

• ITC106 Programming Principles Bathurst Internal
• ITC106 Programming Principles Bathurst Distance
• ITC303 Software Engineering Project 1 Bathurst Internal
• ITC303 Software Engineering Project 1 Bathurst Distance

Semester 2, 2011

• ITC242 Introduction to Data Communications
• ITC431 Computer Networks

Semester 1, 2011

• ITC161 Introduction to Information Technology
• ITC331 Ethics and Professional Practice
• ITC514 Network and Security Administration using Linux
• ITC555 Linux Networking and Security

Semester 2, 2010

• ITC114 Database Management Systems
• ITC242 Introduction to Data Communications
• ITC431 Computer Networks

Semester 1, 2010

• ITC161 Introduction to Information Technology
• ITC331 Ethics and Professional Practice
• ITC514 Network and Security Administration using Linux
• ITC555 Linux Networking and Security

Semester 1, 2009

• ITC570 Special Subject in IT - Data Mining
• ITC105 Communication and Information Management
• ITC532 IT Specialisation Project
• ITC233 Network Engineering 1
• ITC493 IT Project Management

Trimester 2, 2009

• ITC514 Network and Security Administration using Linux

Semester 2, 2009

• ITC 242 Introduction to Data Communications
• ITC 431 Computer Networks
• ITC 499 IT Project Proposal

Other Teaching Experiences

• ITC 518 Principles of Programming using C#
• SENG 4420 Software Architecture
• COMP 1050/6050 Internet Communications
• SENG 3100 Advanced Software Process
• Administrative Responsibilities
• Associate Course coordinator BIT
• Associate Course coordinator BIT/BBus

External Grants:

1. External Funding from the Ministry of Science in Spain (Knowledge Generation Projects, State Program to Promote Scientific-Technical Research and its Transfer, from the State Plan for Scientific, Technical, and Innovation Research 2023) with the colleagues from the University of Pompeu Fabra (UPF), Spain. Period: 2024-2026. Project Title: GUIDE: Governing Urban Interoperable Data Empowering those the data is about (GUIDE: Gobernar los Datos Urbanos Interoperables: Apoyar Aquellos a Quienes se refieren los Datos) Project Code: PID2023-148115OB-I00 Partner Institutions: DATALOG (Spain), Aigues de Barcelona (Spain), Charles Sturt Unviersity Australia CIs: Professor Vladimir Estivill-Castro (UPF, Spain), Miquel Oliver Riera (UPF, Spain), Carlos Castillo Ocaranza (UPF, Spain), Manuel Portela Charnejovsky (UPF, Spain) and Md Zahidul Islam (CSU, Australia). Funding amount: $221,572 (Euro 133,125). CSU Research Office Project Reference (Research Master) number: 0000104532.
   
2. External Funding from the Soil CRC Call 23-1 Major Investment Round 6 with the CRC HPS Participants: Agricultural Innovation & Research Eyre Peninsula, West Midlands Group, North Central Catchment Management Authority and the Thurd Party Participant: Eyre Peninsula Landscape Board. Period 2024 - 2026. Project Title: Agricultural Social Benchmarking Surveys - Part 3. CIs: Catherine Allan, Md Rafiqul Islam, and Zahid Islam, along with colleagues from Southern Cross University. Funding amount: $421,000 ($80,000 to CSU). CSU Research Office Project Reference (Research Master) number: 0000104048.
   
3. External Funding from Connectivity Innovation Network (CIN). Period 2023 - 2027. Project Title: A Lightweight Adaptive Adversarial Attack-Resistant IDS. PhD scholarship for a PhD student under the supervision of the CIs: Md Rafiqul Islam, Quazi Mamun and Zahid Islam. Funding amount: $105,000. CSU Research Office Project Reference (Research Master) number: 0000104144.
   
4. External Funding from Grains Research and Development Corporation (GRDC). Period 2023 - 2024. Project Title: Measuring the effects on wheat grain protein and dough characteristics following legumes. CIs: Chris Blanchard, Abishek Santhakumar, Randy Adjonu, Felicity Harris and Zahid Islam. Funding amount: $248,748. CSU Research Office Project Reference (Research Master) number: 0000104119.
   
5. External Funding from the Future Drought Fund of the Department of Agriculture, Fisheries and Forestry. Period 2022-2023. Project Title: Supporting rapid intervention through disaster cycles: Helping government, NGOs and community organisations identify where resilience support is needed through rapid identification of risk before, during and after extreme climatic events. CIs: Azizur Rahman, Ryan Ip, Michael Bewong, Cindy Anne Cassidy, and Zahid Islam and other external colleagues from Universtiy of Canberra, University of Wollongong and Australian National University. Funding amount: $609,283. CSU Research Office Project Reference (Research Master) number: 0000103803.
   
6. External Funding from the Grain Research and Development Corporation (GRDC). Period 2023-2024. Project Title: Synthesis of Data to Optimise Yield Potential of Barley in Australian Farming Systems. CIs: Felicity Harris, Azizur Rahman, Chris Blanchard, Zahid Islam and Sabih Rehman in collaboration with colleagues from several other institutions. Funding Amount: $690,245. CSU Research Office Project Reference (Research Master) number: 0000103936.
   
7. External Funding from the Cyber Security CRC (CSCRC), Quintessence Labs, TCS, Government of WA and CISCO. Period 2022-2024. Project Title: SCATES: Securing Critical Agriculture Technology and Emerging Solutions. CIs: Zahid Islam (Project Lead), Arash Mahboubi, Michael Bewong, Mark Morrison, Rafiqul Islam, Abhishek Dwivedi, Anwaar Ul Haq, Cliff Lewis, Arif Khan, Ryan Ip, Tahmid Nayeem, Felicity Small, Jon Medway, Nick Pawsey, Chris Blanchard, Sabih Rehman and Yeslam Al-Saggaf in collaboration with colleagues from QUT, Data61, ECU and UoA. Funding Amount: $1,400,000 ($375,000 to CSU). CSU Research Office Project Reference (Research Master) number: 0000103928.
   
8. External Funding from the Food Agility CRC (FACRC). Period 2022-2025. Project Title: Understanding the relationship between individual and mob-based animal performance metrics derived from autonomous livestock monitoring sources. PhD Scholarship for a PhD student under the supervision of the CIs. CIs: Rafiqul Islam, Zahid Islam, Shawn McGrath and Dave Lamb. Funding amount: $135,000. CSU Research Office Project Reference (Research Master) number: 0000103887.
   
9. External Funding from the Cyber Security CRC (CSCRC) and Paraflare. Period 2022-2024. Project Title: THRD: Threat Detection and Response with Heterogeneous Data Sources. CIs: Arash Mahboubi, Zahid Islam, Rafiqul Islam, and Michael Bewong with colleagues from Data 61. Funding amount: $850,000 ($425,000 to CSU). CSU Research Office Project Reference (Research Master) number: 0000103749.
   
10. External Funding from the Cyber Security CRC (CSCRC), CISCO, Quintessence Lab, TCS, Government of Western Australia, and NSW Department of Customer Service. Period 2022-2024. Project Title: SOCRATES: Software Security with a Focus on Critical Technologies. CIs: Michael Bewong, Zahid Islam, Arash Mahboubi, Rafiqul Islam, Ryan Ip and Muhammad Arif Khan with colleagues from University of Adelaide, Data 61, QUT, Deakin University, and UNSW. Funding amount: $2,105,000 ($161,303 to CSU). CSU Research Office Project Reference (Research Master) number: 0000103844.
    
11. External Funding from Food Agility CRC (FACRC) and Meat & Livestock Australia Limited (MLA). Period 2022-2023. Project Title: Potential implications and benefits for the agrifood technology sector from the introduction of the Australian AgriFood Data Exchange. CIs: Michael Bewong, Branka Krivokapic-Skoko, Zahid Islam, Ryan Ip, Jon Medway, Yeslam Al-Saggaf, and Cliff Lewis. Funding amount: $597,729. CSU Research Office Project Reference (Research Master) number: 0000103896.
    
12. External Funding from the Cyber Security CRC (CSCRC). Period 2022-2024. Project Title: Sharing Cybersecurity Data for Australian Research (SCReeD). CIs: Rafiqul Islam, Ryan Ip, Arash Mahboubi, Michael Bewong, and Zahid Islam with colleagues from Data 61, Deaking University, ECU, QUT, and University of Adelaide. Funding amount: $500,000 ($50,000 to CSU). CSU Research Office Project Reference (Research Master) number: 0000103846.
    
13. External Funding from Cyber Security CRC (CSCRC) and Government of Western Australia. Period 2022-2024. Project Title: Ransomware Resilient File Safe Havens for cloud data. CIs: Arash Mahboubi and Zahid Islam with colleagues from Data61. Funding amount: $640,000 ($320,000 to CSU). CSU Research Office Project Reference (Research Master) number: 0000103757.
    
14. External Funding from GSK Global Pty Ltd. Period 2022. Project Title: Shipping Container ID Automation - Phase II. CIs: Rafiqul Islam, Quazi Mamun, Mano Paul and Zahid Islam. Funding amount: $25,000. CSU Research Office Project Reference (Research Master) number: 0000103770.
    
15. Discovery Translation Fund (DTF) from Campus Plus, Project ID: DTF413. Period 2022-2024. Project Title: HEALTHDM - System Refinement and Early Adoption, CIs: Mark Morrison, Zahid Islam, Damien Limberger, Julian Grant, Lesley Forster, and Yann Guisard. Funding amount: $75,000. CSU Research Office Project Reference (Research Master) number: 0000103771.
    
16. External Funding from Cyber Security CRC (CSCRC). Period 2019 - 2024. Project Title: 2.3 Privacy Preserving Data Sharing in a Hyperconnected World: RF2 - Research Fellow. CIs: Zahid Islam and Tanveer Zia. Funding amount: $843,750. CSU Research Office Project Reference (Research Master) number: 0000102810. (Further extended from 2022 until 2024)
    
17. External Funding from Cyber Security CRC (CSCRC). Period 2019 - 2024. Project Title: Research Fellow In Privacy Preserving Data in a Hyper-connected World. CIs: Zahid Islam and Tanveer Zia. Funding amount: $843,750. CSU Research Office Project Reference (Research Master) number: 0000102809. (Further extended from 2022 until 2024)
    
18. External Funding from VetCompass Australia. Period 2021. Project Title: VetCompass Data Investigation, CIs: Zahid Islam, Martin Combs, Michael Bewong and Ryan Ip. Funding from VetCompass Australia and the University of Sydney. Funding amount: $31,424 (through three work orders). CSU Research Office Project Reference (Research Master) number: 0000103197.
    
19. External Funding from the Cyber Security CRC (CSCRC). Period 2021 - 2024. Project Title: Privacy-Preserving Data Collection for Statistical Aggregation, PhD Scholarship for a PhD student under the supervision of the CIs. CIs: Tanveer Zia, Zahid Islam and Ba Dung Le, Funding amount $180,000. CSU Research Office Project Reference number: 0000103352.
    
20. External Funding from the Cyber Security CRC (CSCRC). Period 2021 - 2024. Project Title: A Novel Technique to Detect and Protect Sensitive Data against Malware Attacks in Hyper-Connected Network, PhD Scholarship for a PhD student under the supervision of the CIs. CIs: Md Rafiqul Islam, Zahid Islam, and Arif Khan, Funding amount $180,000. CSU Research Office Project Reference (Research Master) number: 0000102972.
    
21. External Funding from the Cyber Security CRC (CSCRC) and Quintessence Lab. Period 2020 - 2021. Project Title: Development of Australian Cyber Criteria Assessment. CIs: Chang-Tsun Li, Mengmeng Ge, Leo Zhang, Zahid Islam and Md Rafiqul Islam - $186,000. CSU Research Office Project Reference number: 0000103168.
    
    For this project, the project team received the Cyber Security Researcher of the Year 2021 award from the Australian Information Security Association (AISA).
    
22. External Funding from the Department of Agriculture, Water and Environment and Data61, CSIRO. Period 2020 - 2020. Project Title: Automated Database Schema Matching. CIs: Zahid Islam - $30,030 (including GST). CSU Research Office Project Reference number: 0000103179.
    
23. External Funding from NSW Police. Period 2020 - 2020. Project Title: Crime and Disorder Audit, Wagga Wagga. CIs: Philip Birch and Zahid Islam - $15,000. CSU Research Office Project Reference number: 0000103092.
    
24. External Funding from Food Agility CRC (FA CRC), and idustry partners: SunRice, Rice Research Australia Pty Ltd and Agrifutures Australia. Period 2019 - 2021. Project Title: Enhancing Provenance and Prediction for Whole Grain Rice Quality, CIs: Daniel Waters, Zahid Islam, Chris Blanchard, Sabih Rehman and Leigh Schmidtke - $371,000. CSU Research Office Project Reference number: 0000102660.
    
25. External Funding from Food Agility CRC (FA CRC), Australia. Period 2019 - 2022. Project Title: Image Analysis for Whole Grain Rice Quality, PhD Scholarship for a PhD Student under the supervision of the CIs, CIs: Daniel Waters, Chris Blanchard, and Zahid Islam - $135,000. CSU Research Office Project Reference number: 0000102858.
    
26. External Funding from Murrumbidgee Local Health District (MLHD), NSW Health, Australia. Period 2016 - 2016. Project Title: The Provision of a Predictive Risk Stratification Tool for the Chronic/Complex Healthcare: Engaging Stakeholders and Services (CHESS) Initiative, CIs: Zahid Islam and Mark Morrison - $55,803. CSU Research Office Project Reference number: 0000101620.
    This project received an Innovation Award from the Agency for Clinical Innovation (ACI).
27. External Funding from the Department of Social Services, Australia. Period 2015 - 2017. Project Title: Social and community links - a driver of healthy and active ageing, CIs: Oliver Burmeister (Lead CI), Mark Morrison, Zahid Islam, Maree Bernoth, Rylee Dionigi, and Rahena Akhter - $655,000 ($60,000 to CSU). Partner organisation: Carewest Pty Ltd, Orange, NSW. CSU Research Office Project Reference number: 0000101130.
    
    This project received an "outstanding feedback" from the assessment team when it was evaluated by Deloitte..
    
28. External Funding from Young and Well Cooperative Research Centre, Australia 2014. Project Title: Identifying the medico-legal and ethical challenges in Synergy ecosystem data storage, CIs: Dr Oliver Burmeister, Dr Zahid Islam, Dr Maree Bernoth and Ms Carli Kulmar - $16,500. CSU Research Office Project Reference number: 0000101141.
29. External Funding from Hobart Nursing District, Australia 2013. Project Title: Review of Support Worker Integration with Functional Decline, CIs: Prof Mark Morrison, Dr Maree Bernoth, Dr Oliver Burmeister, and Dr Zahid Islam. - $39,400. CSU Research Office Project Reference number: 0000100780.
30. External Funding from Carewest, Australia 2013. Project Title: Age Care Workforce Reform - Building Communities of Practice Around the Prevention of Functional Decline in the Community. CIs: Prof Mark Morrison, Dr Oliver Burmeister, Dr Zahid Islam, Dr Ramudu Bhanugopan and Dr Maree Bernoth - $25,000. CSU Research Office Project Reference number: 0000100558

Some Internal Grants:

1. COVID-19 Grant at Charles Sturt University in 2020, Project Title: Designing Privacy Preserving and Secure Contact Tracing Mobile Apps to Combat COVID-19, CIs: Ashad Kabir, Anwaar Ul-Haq, Oliver Burmeister, Lihong Zheng, Faizur Rahman (Google) and Zahid Islam, - $12,000.
2. COMPACT Funding in 2018, Project Title: Unstructured Address Data Analysis for Data61 of CSIRO, CIs: A/Prof Zahid Islam, Dr Yanchang Zhao, Dr Yang Wang, Prof Junbin Gao, Prof Vladimir Estivill-Castro, A/Prof Richi Nayak - $17,955.
3. COMPACT Funding in 2018, Project Title: Understanding the effects of expressing emotions in social media using data mining, CIs: A/Prof Yeslam Al-Saggaf and A/Prof Zahid Islam - $13,924.
4. COMPACT Funding 2014, Project Title: Development and Applicaiton of Domain Specific Data Mining Techniques to Predict and Explore the Brand Switching Tendency of Mobile Phone Users, CIs: Dr Zahid Islam and Prof Steven D'Alessandro - $11,236.26.
5. COMPACT Funding 2014, Project Title: Industry Funding, CIs: Dr Zahid Islam and Prof Junbin Gao - $13,108.80.
6. Faculty of Business Research Fellowship 2014 - $40,000
7. Faculty of Business Research Supervision Excellence Award 2014, Charles Sturt University.
8. Research Excellence Award 2013, School of Computing and Mathematics, Charles Sturt University. Jointly awarded to A/Prof Yeslam Al-Saggaf, Dr Xiaodi Huang, and Dr Zahid Islam - $1,500
9. CSU Research Infrastructure Block Grants 2013, Title: Image depth estimation, visualization, and quality assessment using intelligent computing with Dr Manoranjan Paul; Professor Junbin Gao; Dr Michael Antolovich; Dr Zahid Islam and Dr Jim Tulip - $50,000
10. Faculty of Business Research Fellowship 2013 - $40,000
11. COMPACT Funding for a novel clustering technique, with Prof Bossomaier, Prof Estivill-Castro, and A/Prof Brankovic 2012 - $9124.
12. Research Center Fellowship 2012 from Center for Research in Complex Systems (CRiCS) - $40,000
13. Charles Sturt University Research Infrastructure Block Grants (RIBG), 2012 for "Abnormal event detection using eye tracker technology," with Dr Manoranjan Paul, Prof Junbin Gao, Dr Michael Antolovich, and Prof Terry Bossomaier - $43,000.
14. COMPACT Fund 2011 for a research on Data Cleansing and Data Pre-processing, with Prof Bossomaier and Prof Gao - $18,249.
15. COMPACT Fund 2011 for research on Data Mining threats on Privacy of Social Network Site (SNS) users, with Dr Al-Saggaf - $7,000.
16. AusAID Funding of $750,000 on "Improving Water Use Efficiency in Large Irrigation System in Yellow River Basin, China" with A/Prof Mohsin Hafeez, Dr Yann Chemin, and A/Prof John Louis.
17. CRiCS Special Grant for an Intelligent Decision Support System research - $10,000.
18. CRiCS Grant for ARC Linkage Grant Preparation - $4,000.
19. Research Center Fellowship at IC Water 2009 - $40,000.
20. Faculty Seed Grant from the Faculty of Business, CSU for conducting a research on a novel clustering technique, 2009 - $3,000.
21. CSU Small Grant for conducting a research on a novel decision tree classification algorithm, 2009 - $6,000.
22. Faculty of Engineering and Built Environment Postgraduate Research Prize in the Discipline of Computer Science and Software Engineering, University of Newcastle, Australia,2005.

Selected Publications

Please feel free to contact me if you are interested to have a look at any of these published papers.

PhD Thesis:

1. Islam, M. Z. (2008): Privacy Preservation in Data Mining through Noise Addition, PhD thesis in Computer Science, School of Electrical Engineering and Computer Science, The University of Newcastle, Australia.
   
   Thesis Available
   
   

Refereed Journal Articles:

2. Foster, K., Costadopoulos, N., Mahboubi, A., Rehman, S., and Islam, M. Z. (2025): Towards Privacy Preserving Data Sharing - An Australian Healthcare Perspective, IEEE Access, Accepted on 8 February 2025, DOI: .
   
   (SJR Rank Q1, SJR H-Index 204, 2024 Impact Factor 3.9, 2024 Scopus CiteScore 9.0, CiteScore Rank 22/302 in General Engineering)
   
   
3. Ferdous, J., Islam, R., Mahboubi, A., and Islam, M. Z. (2025): A Survey on ML Techniques for Multi-Platform Malware Detection: Securing PC, Mobile Devices, IoT, and Cloud Environments, Sensors, 25(4), 1153, DOI: doi.org/10.3390/s25041153,
   
   (SJR Rank Q1/Q2, SJR H-Index 245, 2023 Impact Factor 3.4, 2023 Scopus CiteScore 7.3, CiteScore Rank 24/141 and 83% percentile in Instrumentation)
   
   Paper Available
   
   
4. Hossain, M. R. H., Islam, R., McGrath, S. R., Islam, M. Z., and Lamb, D. (2025): Learning-Based Estimation of Cattle Weight Gain and its Influencing Factors, Computers and Electronics in Agriculture, Vol. 231, April 2025, 110033, DOI: https://doi.org/10.1016/j.compag.2025.110033.
   
   (SJR Rank Q1, SJR H-Index 168, 2024 Impact Factor 7.7, 2025 Scopus CiteScore 15.3, CiteScore Rank 1/115 and 99% percentile in Horticulture)
   
   Full Paper Available
   
   
5. Thedchanamoorthy, G., Bewong, M., Mohammady, M., Zia, T., and Islam, M. Z. (2025): UD-LDP: A Technique for Optimally Catalyzing User Driven Local Differential Privacy, Future Generation Computer Systems (FGCS), Vol. 166, May 2025, 107712, DOI: https://doi.org/10.1016/j.future.2025.107712.
   
   (CORE 2020 Rank A, SJR Rank Q1, SJR H-Index 164, 2024 Impact Factor 6.2, 2025 Scopus CiteScore 19.9, CiteScore Rank 8/395 and 98% percentile in Computer Networks and Communications)
   
   Pre-print Available.
   
   Full Paper Available
   
   
6. John, G., Favaloro, E., Austin, S., Islam, M. Z., and Santhakumar, A. (2025): From Errors to Excellence: The Pre-Analytical Journey to Improved Quality in Diagnostics. A Scoping Review, Clinical Chemistry and Laboratory Medicine (CCLM), DOI: 10.1515/cclm-2024-1277 .
   
   (SJR Rank Q1, SJR H-Index 121, 2024 Impact Factor 3.8, 2025 Scopus CiteScore 11.3, CiteScore Rank 13/117 and 89% percentile in Clinical Biochemistry)
   
   Full Paper Freely Available
   
   
   
   
7. Gao, Y., Camtepe, S. A., Sultan, N. H., Bui, H. T., Mahboubi, A., Aboutorab, H., Bewong, M., Islam, R., Islam, M. Z., Chauhan, A., Gauravaram, P., and Singh, D. (2024): Security threats to agricultural artificial intelligence: Position and perspective, Computers and Electronics in Agriculture, Vol. 227, Part 1, December 2024, 109557, DOI: https://doi.org/10.1016/j.compag.2024.109557,
   
   (SJR Rank Q1, SJR H-Index 149, 2024 Impact Factor 8.3, 2024 Scopus CiteScore 13.6, CiteScore Rank 1/97 in Horticulture)
   
   Paper Available.
   
   Code Available.
   
   
8. Newell, J., Rehman, S., Mamun, Q., and Islam, M. Z. (2024): EASL: Enhanced Append-only Skip List Index For Agile Block Data Retrieval On Blockchain, Future Generation Computer Systems (FGCS), Vol. 164, March 2025, 107554, DOI: doi.org/10.1016/j.future.2024.107554,
   
   (SJR Rank Q1, SJR H-Index 164, 2024 Impact Factor 6.2, 2024 Scopus CiteScore 19.9)
   
   Paper Available.
   
   Code Available.
   
   
9. Ferdous, J., Islam, R., Mahboubi, A., and Islam, M. Z. (2024): AI-based Ransomware Detection: A Comprehensive Review, IEEE Access, Vol. 12, pp. 136666 - 136695, 2024, DOI: 10.1109/ACCESS.2024.3461965.
   
   (SJR Rank Q1, SJR H-Index 204, 2024 Impact Factor 3.9, 2024 Scopus CiteScore 9.0, CiteScore Rank: 22/302 General Engineering )
   
   Paper Available.
   
   
10. Heiyanthuduwage, S. R., Altas, I., Bewong, M., Islam, M. Z., and Deho, O. B. (2024): Decision Trees in Federated Learning: Current State and Future Opportunities, IEEE Access, DOI: 10.1109/ACCESS.2024.3440998, Vol. 12, pp. 127943 - 127965.
    
    (SJR Rank Q1, SJR H-Index 204, 2024 Impact Factor 3.9, 2024 Scopus CiteScore 9.0, CiteScore Rank: 22/302 General Engineering )
    
    Paper Available.
    
    
11. Raisa, R. A., Rodela, A. S., Yousuf, M. A., Azad, A.K.M., Alyami, S. A., Lio, P., Islam, M. Z., Pogrebna, G., and Moni, M. A. (2024): Deep and Shallow Learning Model-based Sleep Apnea Diagnosis Systems: A Comprehensive Study, IEEE Access, DOI: 10.1109/ACCESS.2024.3426928 , Date of Publication 11 July 2024, Electronic ISSN: 2169-3536.
    
    (SJR Rank Q1, SJR H-Index 204, 2024 Impact Factor 3.9, 2024 Scopus CiteScore 9.0, CiteScore Rank: 22/302 General Engineering )
    
    Paper Available.
    
    
12. Yates, D., Blanchard, C., Clarke, A., Rehman, S., Islam, M. Z. Ford. R, and Walsh, R. (2024): Combined Location Online Weather Data: Easy-to-use Targeted Weather Analysis for Agriculture, Climatic Change, DOI: , Accepted on 26 May 2024.
    
    (SJR Rank Q1, SJR H-Index 217, 2022 Impact Factor 4.8, 2024 Scopus CiteScore 8.5, CiteScore Rank: 18/137 Atmospheric Science, Highest Percentile 87% )
    
    Paper Available.
    
    Pre-print Available.
    
    
13. Clarke, A., Yates, D., Blanchard, C., Islam, M. Z. Ford. R, Rehman, S., and Walsh, R. (2024): Integrating Climate and Satellite Data for Multi-Temporal Pre-Harvest Prediction of Head Rice Yield in Australia, Remote Sensing, 2024; 16 (10): 1815. DOI: https://doi.org/10.3390/rs16101815, .
    
    (SJR Rank Q1, SJR H-Index 193, 2022 Impact Factor 5.0, 2024 Scopus CiteScore 8.3, CiteScore Rank: 16/195 General Earth and Planetary Sciences, Highest Percentile 92% )
    
    Paper Available.
    
    
14. Ispahany, J., Islam, M. R., Islam, M. Z. and Khan, M. A. (2024): Ransomware detection using machine learning: A review, research limitations and future directions, IEEE Access, DOI: 10.1109/ACCESS.2024.3397921.
    
    (SJR Rank Q1, SJR H-Index 204, 2024 Impact Factor 3.9, 2024 Scopus CiteScore 9.0, CiteScore Rank: 22/302 General Engineering )
    
    Paper Available.
    
    
15. Saleem, J., Islam, M. R., and Islam, M. Z. (2024): Darknet Traffic Analysis: A Systematic Literature Review, IEEE Access, Vol. 12, pg. 42423- 42452, Print ISSN 2169-3536, Online ISSN 2169-3536. DOI: 10.1109/ACCESS.2024.3373769.
    
    (SJR Rank Q1, SJR H-Index 204, 2024 Impact Factor 3.9, 2024 Scopus CiteScore 9.0, CiteScore Rank: 22/302 General Engineering)
    
    Paper Available.
    
    
16. Bui, H. T., Aboutorab, H., Mahboubi, A., Gao, G., Sultan, N., Chauhan, A., Parvez, M., Bewong, M., Islam, M. R., Islam, M. Z., Camtepe, S., Gauravaram, P., Dineshkumar, S., Babar, A., and Yan, S. (2024): Agriculture 4.0 and Beyond: Evaluating Cyber Threat Intelligence Sources and Techniques in Smart Farming Ecosystems, Computers and Security, Vol. 140, 2024, 103754, ISSN: 0167-4048, DOI: https://doi.org/10.1016/j.cose.2024.103754.
    
    (SJR Rank Q1, SJR H-Index 112, 2024 Impact Factor 5.6, 2024 Scopus CiteScore 11.1, CiteScore Rank: 8/885 in Law)
    
    Pre-print Available.
    
    Paper Available.
    
    
17. Clarke, A., Yates, D., Blanchard, C., Islam, M. Z., Ford, R., Rehman, S., and Walsh. R. (2024): The Effect of Dataset Construction and Data Pre-processing on the eXtreme Gradient Boosting Algorithm Applied to Head Rice Yield Prediction in Australia, Computers and Electronics in Agriculture, Vol. 219, 2024, 108716, ISSN: 0168-1699.
    
    (SJR Rank Q1, SJR H-Index 149, 2024 Impact Factor 8.3, 2024 Scopus CiteScore 13.6, CiteScore Rank: 1/97 in Horticulture)
    
    Paper Available.
    
    
18. Ip, R. H. L., Bewong, M., Adnan, M. N., and Islam, M. Z. (2024): Estimating the Structural Diversity Introduced by Decision Forest Algorithms : A Theoretical Approach, Knowledge-Based Systems, DOI: https://doi.org/10.1016/j.knosys.2024.111435, ISSN: 0950-7051.
    
    (SJR Rank Q1, SJR H-Index 151, 2024 Impact Factor 8.8, 2024 Scopus CiteScore 12.3, CiteScore Rank: 9/127 in Management Information Systems)
    
    Paper Available.
    
    
19. Ferdous, J., Islam, R., Mahboubi, A., and Islam, M. Z. (2023): A Review of State-of-the-Art Malware Attack Trends and Defense Mechanisms, IEEE Access, DOI: 10.1109/ACCESS.2023.3328351, Electronic ISSN: 2169-3536.
    
    (SJR Rank Q1, SJR H-Index 158, 2021 Impact Factor 3.367, 2020 Scopus CiteScore 4.8, CiteScore Rank: 39/297 in General Engineering)
    
    Paper Available.
    
    
20. Rahman, M. A., Hossain, B. A., Bewong, M., Islam, M. Z., Zhao, Y., Groves, J., and Judith, R. (2023): A Semi-Automated Hybrid Schema Matching Framework for Vegetation Data Integration, Expert Systems With Applications, Vol. 229, Part A, pg. 1- 10, 2023. DOI: https://doi.org/10.1016/j.eswa.2023.120405.
    
    (SJR Rank Q1, SJR H-Index 249, 2021 Impact Factor 8.665, 2021 Scopus CiteScore 12.2, CiteScore Rank: 9/300 in General Engineering)
    
    Paper Available
    
    Preprint Available
    
    
21. Bewong, M., Wondoh, J., Kwashie, S., Liu, L., Li, J., Islam, M. Z., and Kernot, D. (2023): DATM: A Novel Data Agnostic Topic Modelling Technique with Improved Effectiveness for both Short and Long Text, IEEE Access, Vol. 11, pg. 32826-32841, Print ISSN 2169-3536, Online ISSN 2169-3536 DOI: 10.1109/ACCESS.2023.3262653.
    
    (SJR Rank Q1, SJR H-Index 158, 2021 Impact Factor 3.367, 2020 Scopus CiteScore 4.8, CiteScore Rank: 39/297 in General Engineering)
    
    Paper Available.
    
    Code Available.
    
    
22. Sun, N., Li, C-T., Chan, H., Islam, M. Z., Islam, M. R., and Armstrong, W. (2023): On the Development of a Protection Profile Module for Encryption Key Management Components, IEEE Access, Vol. 11, pg. 9113-9121, Print ISSN 2169-3536, Online ISSN 2169-3536 DOI: 10.1109/ACCESS.2023.3239043.
    
    (SJR Rank Q1, SJR H-Index 158, 2021 Impact Factor 3.367, 2020 Scopus CiteScore 4.8, CiteScore Rank: 39/297 in General Engineering)
    
    Paper Available.
    
    
23. Rahman, M. G., and Islam, M. Z. (2022): A Framework for Supervised Heterogeneous Transfer Learning using Dynamic Distribution Adaptation and Manifold Regularization, IEEE Transactions on Services Computing, Vol. 16 (3), pg. 1555-1571, DOI: 10.1109/TSC.2022.3213238.
    
    (CORE 2020 Rank A*, SJR Rank Q1, SJR H-Index 77, 2022 Impact Factor 11.019, 2020 Scopus CiteScore 13.6, CiteScore Rank: 23/747 in Computer Science Applications, Scopus Highest Percentile 96%)
    
    Paper Available.
    
    Code Available.
    
    Pre-print Available.
    
    
24. Condran, S., Bewong, M., Islam, M. Z., Maphosa, L., and Zheng, L. (2022): Machine Learning in Precision Aggriculture: A Survey on Trends, Applications and Evaluations over Two Decades, IEEE Access, Vol. 10, pp. 73786-73803, DOI: 10.1109/ACCESS.2022.3188649.
    
    (SJR Rank Q1, SJR H-Index 158, 2021 Impact Factor 3.367, 2020 Scopus CiteScore 4.8, CiteScore Rank: 39/297 in General Engineering)
    
    Paper Available.
    
    
25. Sun, N., Li, C-T., Chan, H., Islam, M. Z., Islam, M. R., and Armstrong, W. (2022): How Do Organizations Seek Cyber Assurance? Investigations on the Adoption of the Common Criteria and Beyond, IEEE Access, Vol. 10, pg. 71749-71763, ISSN: 2169-3536, DOI: 10.1109/ACCESS.2022.3187211.
    
    (SJR Rank Q1, SJR H-Index 158, 2021 Impact Factor 3.367, 2020 Scopus CiteScore 4.8, CiteScore Rank: 39/297 in General Engineering)
    
    Paper Available.
    
    
26. Sun, N., Li, C-T., Chan, H., Le, B. D., Islam, M. Z., Zhang, L. Y., Islam, M. R., and Armstrong, W. (2022): Defining Security Requirements with the Common Criteria: Applications, Adoptions, and Challenges, IEEE Access, Vol. 10, Pg. 44756-44777, Print ISSN: 2169-3536, Online ISSN: 2169-3536, DOI: 10.1109/ACCESS.2022.3168716.
    
    (SJR Rank Q1, SJR H-Index 158, 2021 Impact Factor 3.367, 2020 Scopus CiteScore 4.8, CiteScore Rank: 39/297 in General Engineering)
    
    Paper Available.
    
    Pre-print Available
    
    
27. Yates, D., and Islam, M. Z. (2022): Data Mining on Smartphones: An Introduction and Survey, ACM Computing Surveys, Vol. 55, Issue 5, Article No. 101, pp. 1 - 38 DOI: https://doi.org/10.1145/3529753.
    
    (CORE 2020 Rank A*, SJR Rank Q1, SJR H-Index 163, 2020 Impact Factor 10.282, 2022 Scopus CiteScore 22.3, CiteScore Rank: 1/226 in General Computer Science)
    
    Paper Available.
    
    
28. Rahman, M. G., and Islam, M. Z. (2022): Adaptive Decision Forest: An Incremental Machine Learning Framework, Pattern Recognition, pg. 108345, vol. 122, ISSN 0031-3203. DOI: https://doi.org/10.1016/j.patcog.2021.108345.
    
    (CORE 2020 Rank A*, SJR Rank Q1, SJR H-Index 210, 2021 Impact Factor 7.740, 2020 Scopus CiteScore 15.7, CiteScore Rank: 9/227 in Artificial Intelligence)
    
    Paper Available
    
    Code Available
    
    Pre-print Available
    
    
29. Islam, M. Z., Ali, R., Haider, A., Islam, M. Z. and Kim, H. S. (2021): PAKES: A Reinforcement Learning-Based Personalized Adaptability Knowledge Extraction Strategy for Adaptive Learning Systems, IEEE Access, Vol. 9, pp. 155123-155137, 2021, DOI: 10.1109/ACCESS.2021.3128578.
    
    (SJR Rank Q1, SJR H-Index 158, 2021 Impact Factor 3.367, 2020 Scopus CiteScore 4.8, CiteScore Rank: 39/297 in General Engineering)
    
    Paper Available.
    
    
30. Furner, M., Islam, M. Z., and Li, C-T. (2021): Knowledge Discovery and Visualisation Framework using Machine Learning for Music Information Retrieval from Broadcast Radio Data, Expert Systems With Applications, Vol. 182, 15 November, 2021, pg. 1-11. DOI: https://doi.org/10.1016/j.eswa.2021.115236.
    
    (SJR Rank Q1, SJR H-Index 207, 2021 Impact Factor 6.954, 2020 Scopus CiteScore 12.7, Scopus Highest CiteScore Percentile: 98%, CiteScore Rank: 5/297 in General Engineering)
    
    Paper Available.
    
    
31. Adnan, M. N., Ip, R.H.L., Bewong, M., and Islam, M. Z. (2021): BDF: A New Decision Forest Algorithm, Information Sciences, Vol. 569, August 2021, pg. 687-705. DOI: https://doi.org/10.1016/j.ins.2021.05.017.
    
    (CORE 2020 Rank A, SJR Rank Q1, SJR H-Index 169, 2021 Impact Factor 5.91, 2018 Scopus Highest CiteScore Percentile: 97%, CiteScore Rank: 4/118 in Theoretical Computer Sciences)
    
    Paper Available.
    
    Pre-print Available
    
    
32. Yates, D. and Islam, M. Z. (2021): FastForest: Increasing Random Forest Processing Speed While Maintaining Accuracy, Vol. 557, January 2021, pg. 130-152, Information Sciences, DOI: https://doi.org/10.1016/j.ins.2020.12.067.
    
    (CORE 2020 Rank A, SJR Rank Q1, SJR H-Index 169, 2021 Impact Factor 5.91, 2018 Scopus Highest CiteScore Percentile: 97%, CiteScore Rank: 4/118 in Theoretical Computer Sciences)
    
    Paper Available.
    
    Code Available.
    
    Pre-print Available
    
    
33. Reza, K. J., Islam, M. Z., and Estivill-Castro, V. (2021): Privacy Protection of Online Social Network Users, against Attribute Inference Attacks, through the use of a Set of Exhaustive Rules, Neural Computing and Applications. DOI: https://doi.org/10.1007/s00521-021-05860-8
    
    (CORE 2020 Rank B, SJR Rank Q1 in Software, SJR H-Index 68, 2019 Impact Factor 4.774, 2019 Scopus Highest CiteScore Percentile: 81%, CiteScore Rank: 37/202 in Artificial Intelligence)
    
    Paper Available.
    
    Pre-print Available
    
    
34. Hossain, D., Kabir, M. A., Adnan, A. and Islam, M. Z. (2021): Detecting Autism Spectrum Disorder using Machine Learning Techniques: An Experimental Analysis on Toddler, Child, Adolescent and Adult Datasets, Health Information Science and Systems, Vol. 9, Issue 1, Article Number 17. DOI: 10.1007/s13755-021-00145-9.
    
    (SJR Rank Q1 in Health Informatics and Health Information Management)
    
    Paper Available.
    
    Pre-print Available
    
    
35. Siers, M. and Islam, M. Z. (2020): Class Imbalance and Cost-Sensitive Decision Trees: A Unified Survey Based on a Core Similarity, ACM Transactions on Knowledge Discovery from Data, Vol. 15, No. 1, Article 4, December 2020, pg. 4:1 - 4:31. DOI: https://doi.org/10.1145/3415156.
    
    (Scopus Highest Percentile: 90%, Scopus Rank 21/206 in General Computer Science, SJR Rank Q1, H-Index 44, 2018 Impact Factor: 2.53)
    
    Paper Available.
    
    Pre-print Available
    
    
36. Costadopoulos, N., Islam, M. Z. and Tien, D. (2020): A Knowledge Discovery and Visualisation Method for Unearthing Emotional States from Physiological Data, International Journal of Machine Learning and Cybernetics, Vol. 12, pg. 843-858. DOI: https://link.springer.com/article/10.1007/s13042-020-01205-4.
    
    (Scopus Highest Percentile: 80%, Scopus Rank 75/373 in Software, SJR Rank Q1 in Software, H-Index 38, 2019 Impact Factor: 3.753)
    
    Paper Available
    
    Pre-print Available
    
    
37. Naqvi, N., Rehman, S., and Islam, M. Z. (2020): A Hyperconnected Smart City Framework: Digital Resources Using Enhanced Pedagogical Techniques, Australasian Journal of Information Systems (AJIS), Vol. 24, September 2020, pg. 1 - 42, doi: https://doi.org/10.3127/ajis.v24i0.2531.
    
    (CORE 2020 Rank B, ABDC 2019 Rank A)
    
    Paper Available
    
    
38. Fletcher, S. and Islam, M. Z. (2019): Decision Tree Classification with Differential Privacy: A Survey, ACM Computing Surveys, Vol. 52, Issue 4, September 2019, Article No. 83, pg. 83:1 -83:33, Paper Available.
    
    (CORE Rank A*, Scopus Highest Percentile: 99%, Scopus Rank 2/206 in General Computer Science, SJR Rank Q1, H-Index 132, 2018 Impact Factor: 6.13)
    
    Pre-print Available
    
    
39. Rahman, M. A. and Islam, M. Z.(2018): Application of a Density Based Clustering Technique on Biomedical Datasets, Applied Soft Computing, Vol. 73, pg. 623-634, 2018 https://www.sciencedirect.com/science/article/pii/S1568494618305295.
    
    (SJR Rank Q1, SJR H-Index 110, 2018 Impact Factor 4.87, 2018 Scopus Highest CiteScore Percentile: 93%, CiteScore Rank: 24/358 in Software)
    
    Pre-print Available
    
    Final Paper Available
    
    
40. Siers, M. J. and Islam, M. Z.(2018): Novel Algorithms for Cost-Sensitive Classification and Knowledge Discovery in Class Imbalanced Datasets with an Application to NASA Software Defects, Information Sciences, Vol. 459, pg. 53-70, https://doi.org/10.1016/j.ins.2018.05.035.
    
    (CORE 2020 Rank A, SJR Rank Q1, SJR H-Index 154, 2018 Impact Factor 5.52, 2018 Scopus Highest CiteScore Percentile: 97%, CiteScore Rank: 4/118 in Theoretical Computer Sciences)
    
    With my PhD student during his PhD studies.
    
    Pre-print Available
    
    YouTube Video Available on a Knowledge Discovery Technique
    
    
41. Islam, M. Z., Estivill-Castro, V., Rahman, M. A. and Bossomaier, T. (2018): Combining K-Means and a Genetic Algorithm through a Novel Arrangement of Genetic Operators for High Quality Clustering, Expert Systems with Applications (ESWA), Vol. 91, pg. 402-417, https://doi.org/10.1016/j.eswa.2017.09.005.
    
    (SJR Rank Q1, SJR H-Index 162, 2018 Impact Factor: 4.29, 2018 Scopus Highest CiteScore Percentile: 98%, CiteScore Rank: 5/275 in General Engineering)
    
    Pre-print Available
    
    Full Paper Available
    
    YouTube Video on Freely Available Software
    
    
42. Fletcher, S. and Islam, M. Z. (2018): Comparing Sets of Patterns with the Jaccard Index, Australasian Journal of Information Systems (AJIS), Vol. 22, pg. 1-17, DOI: http://dx.doi.org/10.3127/ajis.v22i0.1538
    
    (ABDC 2016 Rank A, CORE Rank B)
    
    Pre-print Available
    
    
43. Siddiqui, M. K., Islam, M. Z. and Kabir, A. (2018): A novel Quick Seizure Detection and Localization through Brain Data Mining on ECoG dataset, Neural Computing and Applications (NCAA), https://doi.org/10.1007/s00521-018-3381-9, Springer London, Print ISSN 0941-0643, Online ISSN 1433-3058, pg. 1-14.
    
    (SJR Rank Q1, SJR H-Index 57, 2018 Impact Factor 4.66, CORE Rank B)
    
    Paper Online
    
    
44. Fletcher, S. and Islam, M. Z. (2017): Measuring Rule Retention in Anonymized Data - When One Measure Is Not Enough, Transactions on Data Privacy (TDP), Volume 10, Issue 3, pg. 175 - 201.
    
    With my PhD student during his PhD studies.
    
    Pre-print Available
    
    
    Available from the Journal
    
    
45. Adnan, M. N. and Islam, M. Z. (2017): Forest PA: Constructing a Decision Forest by Penalizing Attributes used in Previous Trees, Expert Systems with Applications (ESWA), Vol. 89, pg. 389 - 403, DOI: https://doi.org/10.1016/j.eswa.2017.08.002.
    
    (SJR Rank Q1, SJR H-Index 162, 2018 Impact Factor: 4.29, 2018 Scopus Highest CiteScore Percentile: 98%, CiteScore Rank: 5/275 in General Engineering)
    
    With my PhD student during his PhD studies.
    
    Pre-print Available
    
    YouTube Video Available
    
    YouTube Video on Freely Available Software for this Paper
    
    
46. Fletcher, S. and Islam, M. Z. (2017): Differentially Private Random Decision Forests using Smooth Sensitivity, Expert Systems with Applications (ESWA), Vol. 78, pg. 16-31, DOI: http://dx.doi.org/10.1016/j.eswa.2017.01.034.
    
    (SJR Rank Q1, SJR H-Index 162, 2018 Impact Factor: 4.29, 2018 Scopus Highest CiteScore Percentile: 98%, CiteScore Rank: 5/275 in General Engineering)
    
    With my PhD student during his PhD studies.
    
    Pre-print Available
    
    The code was implemented by IBM as a library at https://github.com/IBM/differential-privacy-library/blob/main/diffprivlib/models/forest.py
    
    
47. Adnan, M. N. and Islam, M. Z. (2017): ForEx++: A New Framework for Knowledge Discovery from Decision Forests, Australasian Journal of Information Systems (AJIS), Vol. 21, pg. 1-20, ISSN Online: 1326-2238 Hard copy: 1449-8618, DOI http://dx.doi.org/10.3127/ajis.v21i0.1694.
    
    With my PhD student during his PhD studies.
    
    (ABDC 2016 Rank A, CORE Rank B)
    
    Pre-print Available
    
    Final Paper Available
    
    YouTube Video Available
    
    
48. Islam, M. Z., D'Alessandro, S., Furner, M., Johnson, L., Gray, D. and Carter, L. (2016): Brand Switching Pattern Discovery by Data Mining Techniques for the Telecommunication Industry in Australia, Australasian Journal of Information Systems, pg. 1 - 17, Vol. 20, DOI: http://dx.doi.org/10.3127/ajis.v20i0.1420
    (ABDC 2016 Rank A, CORE Rank B)
    
    Pre-print Available
    
    Final Paper Available
    
    
49. Beg, A. H., Islam, M. Z., and Estivill-Castro. V. (2016): Genetic Algorithm with Healthy Population and Multiple Streams Sharing Information for Clustering, Knowledge-Based Systems, Vol. 114, pp. 61-78, ISSN 0219-1377, Springer London. doi: http://dx.doi.org/10.1016/j.knosys.2016.09.030
    
    (SJR Rank Q1, SJR H-Index 94, 2018 Impact Factor: 5.10, 2018 Scopus Highest CiteScore Percentile: 95%, CiteScore Rank: 18/358 in Software)
    
    With my PhD student during his PhD studies.
    
    Pre-print Available
    
    Full paper available
    
    
50. Adnan, M. N. and Islam, M. Z. (2016): Optimizing the Number of Trees in a Decision Forest to Discover a Subforest with High Ensemble Accuracy using a Genetic Algorithm, Knowledge-Based Systems, Vol. 110, pp. 86-97, ISSN 0219-1377, Springer London. doi: http://dx.doi.org/10.1016/j.knosys.2016.07.016 Available at Here
    
    (SJR Rank Q1, SJR H-Index 94, 2018 Impact Factor: 5.10, 2018 Scopus Highest CiteScore Percentile: 95%, CiteScore Rank: 18/358 in Software)
    
    With my PhD student during his PhD studies.
    
    Pre-print Available
    
    YouTube Video on the Paper
    
    
51. Bernoth, M., Burmeister, O. K., Morrison, M., Islam, M. Z., Onslow, F., and Cleary, M. (2016): The impact of a participatory care model on work satisfaction of care workers and the functionality, connectedness and mental health of community dwelling older people, Issues in Mental Health Nursing, Vol. 37, Issue 6, pp. 429-35, doi: 10.3109/01612840.2016.1149260.
    (SJR 2013 Rank Q1)
    
    Pre-print Available
    
    
52. Thomas, C., Burmeister, O. K., Islam, M. Z., Dayhew, M., and Crichton, M. (2016): Client Welfare & Communication of Mental Health Data, Post Publication Review, Australasian Journal of Information Systems, Vol. 20, pg. 1-5.
    (ABDC 2013 Rank A, CORE Rank B)
    
    Pre-print Available
    
    
53. Rahman, M. G., and Islam, M. Z. (2016): Discretization of Continuous Attributes Through Low Frequency Numerical Values and Attribute Interdependency, Expert Systems with Applications (ESWA), Vol. 45, pp. 410-423, DOI: 10.1016/j.eswa.2015.10.005, 1 March, 2016.
    (SJR Rank Q1, SJR H-Index 162, 2018 Impact Factor: 4.29, 2018 Scopus Highest CiteScore Percentile: 98%, CiteScore Rank: 5/275 in General Engineering)
    
    With my PhD student during his PhD studies.
    
    Pre-print Available
    
    YouTube Video on the Paper is Available
    
    
54. Siers, M., and Islam, M. Z. (2015): Software Defect Prediction Using a Cost Sensitive Decision Forest and Voting, and a Potential Solution to the Class Imbalance Problem, Information Systems, Vol. 51, pg. 62-71.
    (CORE Rank A*, SJR Rank Q1, SJR H-Index 76, 2018 Impact Factor: 2.06, 2018 Scopus Highest CiteScore Percentile: 88%, CiteScore Rank: 18/150 in Hardware and Architecture)
    
    With my Honours student during his Honours studies.
    
    Pre-print Available
    
    YouTube Video on the Paper
    
    YouTube Video on Freely Available Software for this Paper
    
    
55. Rahman, M. G., and Islam, M. Z. (2015): Missing Value Imputation using a Fuzzy Clustering Based EM Approach, Knowledge and Information Systems, Vol. 46, Issue 2, pp. 389-422, ISSN 0219-1377, Springer London. doi: 10.1007/s10115-015-0822-y
    
    (SJR Rank Q1, SJR H-Index 56, 2018 Impact Factor: 2.39, 2018 Scopus Highest CiteScore Percentile: 82%, CiteScore Rank: 27/150 in Hardware and Architecture)
    
    With my PhD student during his PhD studies.
    
    Pre-print Available
    
    YouTube Video on the Paper is Available
    
    
56. Burmeister, O., Islam, M. Z., Dayhew, M., Crichton, M. (2015): Enhancing Client Welfare through Better Communication of Private Mental Health Data Between Rural Service Providers, Australasian Journal of Information Systems (AJIS), Vol. 19, pp. 1 -14, DOI: http://dx.doi.org/10.3127/ajis.v19i0.1206.
    
    ABDC 2013 Rank A, CORE Rank B. http://journal.acs.org.au/index.php/ajis/article/view/1206
    
    Pre-print Available
    
    
57. Fletcher, S., and Islam, M. Z. (2015): An Anonymization Technique using Intersected Decision Trees , Journal of King Saud University - Computer and Information Sciences, Vol. 27, Issue 3, pp. 297 - 304, Elsevier.(Available on line at http://dx.doi.org/10.1016/j.jksuci.2014.06.015 )
    
    With my PhD student during his Honours studies.
    
    Pre-print Available
    
    
58. Rahman, M. A., Islam, M. Z., and Bossomaier, T. (2015): ModEx and Seed-Detective: Two Novel Techniques for High Quality Clustering by using Good Initial Seeds in K-Means, Journal of King Saud University - Computer and Information Sciences, Vol. 27, Issue 2, pp. 113 - 128, doi:10.1016/j.jksuci.2014.04.002, Elsevier.
    
    With my PhD student during his PhD studies.
    
    Pre-print Available
    
    
59. Fletcher, S., and Islam, M. Z. (2015): Measuring Information Quality for Privacy Preserving Data Mining, International Journal of Computer Theory and Engineering, Vol. 7, No. 1, pp. 21-28, February 2015, DOI: 10.7763/IJCTE.2015.V7.924. (Available here)
    
    With my PhD student during his PhD studies.
    
    Pre-print Available
    
    
60. Al-Saggaf, Y., and Islam, M. Z. (2015): Data Mining and Privacy of Social Network Sites' Users: Implications of the data mining problem, Science and Engineering Ethics, Vol. 21, Issue 4, pp. 941-966, DOI 10.1007/s11948-014-9564-6, Springer, (available at Springer Link )
    (ERA Rank A, ISI Web of Science Rank Q1, ranked the 3rd out of 56 journals of its category of history and philosphy of science as on 15 Oct 2014, 2013 Impact Factor: 1.516) (CORE Rank A, SJR Rank Q1, SJR H-Index 43, 2018 Impact Factor: 2.27, 2018 Scopus Highest CiteScore Percentile: 90%, CiteScore Rank: 4/36 in Issues, Ethics and Legal Aspects)
    
    Pre-print Available
    
    
61. Rahman, M. A., and Islam, M. Z. (2014): A Hybrid Clustering Technique Combining a Novel Genetic Algorithm with K-Means, Knowledge-Based Systems, Vol. 71, November 2014, pp. 345-365, DOI: 10.1016/j.knosys.2014.08.011, Available at http://dx.doi.org/10.1016/j.knosys.2014.08.011
    (SJR Rank Q1, SJR H-Index 94, 2018 Impact Factor: 5.10, 2018 Scopus Highest CiteScore Percentile: 95%, CiteScore Rank: 18/358 in Software)
    
    With my PhD student during his PhD studies.
    
    Pre-print Available
    
    
62. Rahman, M. G., and Islam, M. Z. (2014): FIMUS: A Framework for Imputing Missing Values Using Co-appearance, Correlation and Similarity Analysis, Knowledge-Based Systems, Vol. 56, pp. 311-327, January 2014, DOI: 10.1016/j.knosys.2013.12.005 Available at http://dx.doi.org/10.1016/j.knosys.2013.12.005
    (SJR Rank Q1, SJR H-Index 94, 2018 Impact Factor: 5.10, 2018 Scopus Highest CiteScore Percentile: 95%, CiteScore Rank: 18/358 in Software)
    
    With my PhD student during his PhD studies.
    
    Pre-print Available
    
    YouTube Video Available
    
    
63. Rahman, M. G., and Islam, M. Z. (2013): Missing Value Imputation Using Decision Trees and Decision Forests by Splitting and Merging Records: Two Novel Techniques, Knowledge-Based Systems, Vol. 53, pp. 51 - 65, ISSN 0950-7051, DOI information: 10.1016/j.knosys.2013.08.023, Available at http://www.sciencedirect.com/science/article/pii/S0950705113002591
    (SJR Rank Q1, SJR H-Index 94, 2018 Impact Factor: 5.10, 2018 Scopus Highest CiteScore Percentile: 95%, CiteScore Rank: 18/358 in Software)
    
    With my PhD student during his PhD studies.
    
    Pre-print Available
    
    YouTube Video Available
    
    YouTube video on a Freely Available Software
    
    
64. Al-Saggaf, Y., and Islam, M. Z. (2013): A Malicious Use of a Clustering Algorithm to Threaten the Privacy of a Social Networking Site User, World Journal of Computer Application and Technology, Vol. 1, Issue 2, pg. 29-34, DOI:10.13189/wjcat.2013.010202, Available at http://www.hrpub.org/download/201309/wjcat.2013.010202.pdf
    
    Pre-print Available
    
    
65. Al-Saggaf, Y., and Islam, M. Z. (2012): Privacy in Social Network Sites (SNS) - the threats from Data Mining, Ethical Space: The International Journal of Communication Ethics, Vol. 9, Issue 4, pg. 32 - 40, ISSN 1742-0105. (available at http://journals.communicationethics.net/index.php )
    (ERA Rank A)
    
    Pre-print Available
    
    
66. Islam, M. Z., and Brankovic, L.(2011): Privacy Preserving Data Mining: A Noise Addition Framework Using a Novel Clustering Technique, Knowledge-Based Systems Vol. 24, Issue 8, ISBN 0950-7051,(December 2011) pg. 1214-1223, (DOI: 10.1016/j.knosys.2011.05.011)
    (SJR Rank Q1, SJR H-Index 94, 2018 Impact Factor: 5.10, 2018 Scopus Highest CiteScore Percentile: 95%, CiteScore Rank: 18/358 in Software)
    
    With my respected PhD supervisor.
    
    Pre-print Available
    
    
67. Zia, T. A., Al-Saggaf, Y., Islam, M. Z., Zheng, L., and Weckert, J. (2009): The Digital Divide in Asia: Cases from Yemen, Bangladesh, Pakistan and China, Journal of Information Ethics (JIE). Vol. 18, No. 2 (Fall 2009). McFarland & Company, Inc. Publishers, pg. 50 - 76.
    (ERA 2010 Rank B)
    
    Pre-print Available
    
    

Book Chapters:

68. Al-Saggaf, Y., and Islam, M. Z. (2023): Privacy in social network sites (SNS): The threats from data mining, Ethical Space - Journal with a difference: Celebrating 20 Years. Bradshaw, T., Joseph, S., Keeble, R. L. & Matheson, D. (eds.). Suffolk, UK: Abramis Academic, Vol. 1. p. 171-186 15 p.
    
    Pre-print Available
    
    
69. Reza, K. J., Islam, M. Z., and Estivill-Castro, V. (2020): Protection of User-Defined Sensitive Attributes on Online Social Networks against Attribute Inference Attack via Adversarial Data Mining, In: Mori P., Furnell S., Camp O. (eds) Information Systems Security and Privacy. ICISSP 2019. Communications in Computer and Information Science, pp. 230 - 249, vol 1221. Springer, Cham, DOI: https://doi.org/10.1007/978-3-030-49443-8_11, Print ISBN: 978-3-030-49442-1, Online ISBN: 978-3-030-49443-8.
    
    Pre-print Available
    
    
70. Brankovic, L, Islam, M. Z. and Giggins H (2007): Privacy-Preserving Data Mining, Security, Privacy and Trust in Modern Data Management, Springer, Editors Milan Petkovic and Willem Jonker ISBN: 978-3-540-69860-9, Chapter 11, pg. 151-166.
    
    Pre-print Available

Refereed Conference Papers:

71. Hasan, M. M., Islam, R., Mamun, Q., Islam, M. Z., and Gao, J. (2024): Enhancing Network Intrusion Detection Systems: A Real-time Adaptive Machine Learning Approach for Adversarial Packet-Mutation Mitigation, The 22nd International Symposium on Network Computing and Applications (NCA 2024), 24 October - 26 October 2024, Italy. Accepted on 1 October 2024. CORE 2023 Rank B.
    
    Paper Available
    
    
    
    
72. Parvez, M. Z., Islam, R. and Islam, M. Z. (2024): CL3: A Collaborative Learning Framework for the Medical Data Ensuring Data Privacy in the Hyperconnected Environment, The 25th International Conference on Web Information Systems Engineering (WISE 2024), 2 December - 5 December, 2024, Doha, Qatar. Accepted on 3 September 2024. CORE 2023 Rank B.
    
    Pre-print Available
    
    Code Available
    
    
73. Thedchanamoorthy, G. , Bewong, M., Mohammady, M., Zia, T., and Islam, M. Z. (2024): FUD-LDP: Fully user-driven local differential privacy for crowdsourced data collection for aggregate statistics, The 25th International Conference on Web Information Systems Engineering (WISE 2024), 2 December - 5 December, 2024, Doha, Qatar. Accepted on 3 September 2024. CORE 2023 Rank B.
    
    
74. Condran, S., Bewong, M., Kwashie, S., Islam, M. Z., Altas, I., and Condran, J. (2024): MAPX: An explainable model-agnostic framework for the detection of false information on social media networks, The 25th International Conference on Web Information Systems Engineering (WISE 2024), 2 December - 5 December, 2024, Doha, Qatar. Accepted on 3 September 2024. CORE 2023 Rank B.
    
    Pre-print Available
    
    Received the Best Paper Award
    
    
75. Jiang, Y., Bewong, M., Mahboubi, A., Halder, S., Islam, R., Islam, M. Z., Ip, R., Gauravaram, P., and Xue, J. (2024): A Graph-Based Approach for Software Functionality Classification on the Web, The 25th International Conference on Web Information Systems Engineering (WISE 2024), 2 December - 5 December, 2024, Doha, Qatar. Accepted on 3 September 2024. CORE 2023 Rank B.
    
    
76. Ispahany, J., Islam, R., Khan, M. A., and Islam, M. Z. (2024): iCNN-LSTM: An incremental CNN-LSTM based ransomware detection system, The 25th International Conference on Web Information Systems Engineering (WISE 2024), PhD Symposium, 2 December - 5 December, 2024, Doha, Qatar. Accepted on 15 October 2024. WISE is CORE 2023 Rank B.
    
    
77. Haldar, S., Bewong, M., Mahboubi, A., Jiang, Y., Islam, M. R., Islam, M. Z., Ip, R., Ahmed, M. E., Ramachandran, G. S., Babar, A. (2024): Malicious Package Detection using Metadata Information, International World Wide Web Conference (The Web Conference), 13 May - 17 May, 2024, Singapore, Accepted on 23 January 2024. The Web Conference is CORE 2023 Rank A*.
    
    
78. Thedchanamoorthy, G., Bewong, M., Mohammady, M., Zia, T. and Islam, M. Z. (2024): Optimization of UD-LDP with statistical prior knowledge, 7th International Workshop on Security, Privacy and Trust in the Internet of Things (SPT-IoT), In Proc. of the IEEE International Conference on Pervasive Computing and Communication (IEEE PerCom), 11 March - 15 March 2024, Biarritz, France, Accepted on 6 January 2024. IEEE PerCom is CORE 2023 Rank A*.
    
    Pre-print Available
    
    
    
    
79. Naqvi, N., Rehman, S. U. and Islam, M. Z. (2022): WinDrift: Early Detection of Concept Drift using Corresponding and Hierarchical Time Windows, In Proc. of the 20th Australasian Data Mining Conference 2022 (AusDM 2022), Sydney, Australia, 12 December - 16 December, 2022, pg. 73 - 89, ISSN 1865-0929, ISBN 978-981-19-8745-8, DOI: https://doi.org/10.1007/978-981-19-8746-5. CORE 2021 Rank Australasian B.
    
    Code Available
    
    Paper Available
    
    
80. Grant, P., and Islam, M. Z. (2022): Signal Classification using Smooth Coefficients of Multiple Wavelets to Achieve High Accuracy from Compressed Representation of Signal, In Proc. of the 18th International Conference on Advanced Data Mining and Applications (ADMA 2022), Brisbane, Australia, 30 November - 2 December, 2022. Lecture Notes in Computer Science, pp 173 - 186, Vol 13726. Springer, Cham. Chen, W., Yao, L., Cai, T., Pan, S., Shen, T., Li, X. (eds). Print ISBN 978-3-031-22136-1. Online ISBN 978-3-031-22137-8. DOI: https://doi.org/10.1007/978-3-031-22137-8_13. CORE 2021 Rank B.
    
    Paper Available
    
    
81. Heiyanthuduwage, S. R., Rahman, M. A., and Islam, M. Z. (2022): Enhancing Cluster Quality of Numerical Datasets with Domain Ontology, In Proc. of the 9th IEEE Asia-Pacific Conference on Computer Science and Data Engineering 2022 (IEEE CSDE 2022), Gold Coast, Australia, 18 - 20 December, 2022. pg. 1-6, DOI 10.1109/CSDE56538.2022.10089260.
    
    Pre-print Available
    
    Paper Available
    
    
82. Alsinglawi, B., Zheng, L., Kabir, M. A., Islam, M. Z., Swain, D. and Swain, W. (2022): Internet of Things and Microservices in Supply Chain: Cybersecurity Challenges, and Research Opportunities, the 5-th International Workshop on Internet of Everything and Machine Learning Applications (IOEMLA-2022), In Proc. of the 36-th International Conference on Advanced Information Networking and Applications (AINA-2022), Barolli, L., Hussain, F. & Enokido, T. (eds.), Lecture Notes in Networks and Systems, Vol. 451, pg. 556-566. Sydney, Australia, April 13-15. DOI: 10.1007/978-3-030-99619-2_52. January, 2022. CORE 2021 Rank B.
    
    Paper Available
    
    
    
    
83. Newell, J., Mamun, Q., Rehman, S. and Islam, M. Z. (2022): Proof-of-Enough-Work Consensus Algorithm for Enhanced Transaction Processing in Blockchain, In Proc. of the IEEE Wireless Communications and Networking Conference 2022 (IEEE WCNC 2022), Austin, TX, USA, April 10-13, pp. 1188-1193, DOI: 10.1109/WCNC51071.2022.9771549 CORE 2021 Rank B.
    
    Paper Online
    
    
    
    
84. Yates, D., Islam, M. Z., Zhao, Y., Nayak, R., Estivill-Castro, V., and Kanhere, S. (2021): PostMatch: a Framework for Efficient Address Matching, In Proc. of the 19th Australasian Data Mining Conference 2021 (AusDM 2021), Brisbane, Australia, December 13-15, 2021, Accepted on 5 October 2021. CORE 2020 Rank Australasian B.
    
    
    
    
85. Beg, A. H., Islam, M. Z., and Estivill-Castro, V. (2020): HeMI++: A Genetic Algorithm based Clustering Technique for Sensible Clusters, In Proc. of the IEEE Congress on Evolutionary Computation (IEEE CEC 2020), Glasgow, UK, July 19-24, 2020, DOI: 10.1109/CEC48606.2020.9185882. CORE 2018 Rank B.
    
    Paper Online
    
    
    
    
86. Yates, D., and Islam, M. Z. (2019): Readiness of Smartphones for Data Collection and Data Mining with an Example Application in Mental Health, In Proc. of the 17th Australasian Data Mining Conference (AusDM 2019), Adelaide, Australia, December 2-5, 2019, Le T. et al. (eds) Data Mining, AusDM 2019, Communication in Computer and Information Science, Vol. 1127, pg. 235-246, Springer, Singapore. ERA 2010 Rank B.
    
    With my PhD student during his PhD studies.
    
    Pre-print Available
    
    Full Paper Available
    
    A Short YouTube Video
    
    
87. Bartlett, S., and Islam, M. Z. (2019): Improving Clustering via a Fine-Grained Parallel Genetic Algorithm with Information Sharing, In Proc. of the 17th Australasian Data Mining Conference (AusDM 2019), Adelaide, Australia, December 2-5, 2019, Le T. et al. (eds) Data Mining, AusDM 2019, Communication in Computer and Information Science, Vol. 1127, pg. 3-15, Springer, Singapore. ERA 2010 Rank B.
    
    With my Honors student during his Honors study.
    
    Pre-print Available
    
    Full Paper Available
    
    
88. Yates, D., Islam, M. Z., and Gao, J. (2019): DataLearner: A Data Mining and Knowledge Discovery Tool for Android Smartphones and Tablets, In Proc. of the 15th International Conference on Advanced Data Mining and Applications (ADMA 2019), pg. 828-838, J. Li et al. (Eds.): ADMA 2019, LNAI 11888, Dalian, China, 21 - 23 November, 2019. CORE 2018 Rank B.
    
    With my PhD student during his PhD studies.
    
    Pre-print Available
    
    A Short Video Available on the App
    
    App Available at Google Play
    
    Source Code Available at GitHub
    
    DOI: https://doi.org/10.1007/978-3-030-35231-8_61
    
    
    
    
89. Grant, P., and Islam, M. Z. (2019): A Novel Approach for Noisy Signal Classification through the use of Multiple Wavelets and Ensembles of Classifiers, In Proc. of the 15th International Conference on Advanced Data Mining and Applications (ADMA 2019), pg. 195-203, J. Li et al. (Eds.): ADMA 2019, LNAI 11888, Dalian, China, 21 - 23 November, 2019. CORE 2018 Rank B.
    
    With my PhD student during his PhD studies.
    
    Pre-print Available
    
    DOI: https://doi.org/10.1007/978-3-030-35231-8_14
    
    
90. Grant, P., and Islam, M. Z. (2019): Clustering Noisy Temporal Data, In Proc. of the 15th International Conference on Advanced Data Mining and Applications (ADMA 2019), pg. 185-194, J. Li et al. (Eds.): ADMA 2019, LNAI 11888, Dalian, China, 21 - 23 November, 2019. CORE 2018 Rank B.
    
    With my PhD student during his PhD studies.
    
    Pre-print Available
    
    DOI: https://doi.org/10.1007/978-3-030-35231-8_13
    
    
91. Costadopoulos, N., Islam, M. Z., and Tien, D. (2019): Using Z-score to Extract Human Readable Logic Rules from Physiological Data, In Proc. of the 11th IEEE International Conference on Knowledge and Systems Engineering (KSE) 2019, , pg.1-6, ISSN: 2164-2508, Da Nang, Vietnam, October 24-26, 2019, DOI: 10.1109/KSE.2019.8919473 .
    
    With my PhD student during his PhD studies.
    
    Pre-print Available
    
    
92. Costadopoulos, N., Islam, M. Z., and Tien, D. (2019): Discovering Emotional Logic Rules from Physiological Data of Individuals, In Proc. of the 18th International Conference on Machine Learning and Cybernetics (ICMLC) 2019, Kobe, Japan, July 7-10, 2019, pp. 468--474.
    
    With my PhD student during his PhD studies.
    
    Pre-print Available
    
    
93. Costadopoulos, N., Islam, M. Z., and Tien, D. (2019): Data Mining and Knowledge Discovery from Physiological Sensors, Workshop on Robotic Sensing in Human-Robot Interaction (RoboSense), In Proc. of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments (PETRA 2019), ISBN 978-1-4503-6232-0, Rhodes, Greece, June 5-7, 2019, pg. 468 - 474, Vol. 7, doi: 10.1145/3316782.3322771
    
    With my PhD student during his PhD studies.
    
    Pre-print Available
    
    
94. Reza, K. J., Islam, M. Z., and Estivill-Castro, V. (2019): Privacy Preservation of Social Network Users against Attribute Inference Attacks via Malicious Data Mining, In Proc. of the 5th International Conference on Information Systems Security and Privacy (ICISSP 2019), Prague, Czech Republic, February 23-25, 2019, Paper available pg. 412 - 420, DOI: 10.5220/0007390404120420.
    
    With my PhD student during his PhD studies.
    
    Pre-print Available
    
    
95. Yates, D., Islam, M. Z., and Gao, J. (2018): SPAARC: A Fast Decision Tree Algorithm, In Proc. of the 16th Australasian Data Mining Conference (AusDM 2018), Bathurst, Australia, November 28-30, 2018, Paper Available Data Mining, Editors: Islam, R., Koh, Y.S., Zhao, Y., Warwock, G., Stirling, D., Li, C-T., and Islam, Z., pp. 43-55, ISBN 978-981-13-6661-1. ERA 2010 Rank B.
    
    With my PhD student during his PhD studies.
    
    Pre-print Available
    
    
96. Yates, D., Islam, M. Z., and Gao, J. (2018): Implementation and Performance Analysis of Data Mining Classification Algorithms on Smartphones, In Proc. of the 16th Australasian Data Mining Conference (AusDM 2018), Bathurst, Australia, November 28-30, 2018, Paper Available Data Mining, Editors: Islam, R., Koh, Y.S., Zhao, Y., Warwock, G., Stirling, D., Li, C-T., and Islam, Z., pp. 331-343, ISBN 978-981-13-6661-1. ERA 2010 Rank B.
    
    With my PhD student during his PhD studies.
    
    Pre-print Available
    
    
97. Adnan, M. N., Islam, M. Z., and Akbar, M. M. (2018): On Improving the Prediction Accuracy of a Decision Tree using Genetic Algorithm, In Proc. of the 14th International Conference on Advanced Data Mining and Applications (ADMA 2018), Nanjing, China, November 16-18, 2018, Paper Available pp. 80 - 94, Editors: Gan, G., Li, B., Li, X., and Wang, S., LNAI 11323, ISBN 978-3-030-05089-4, CORE 2018 Rank B.
    
    Pre-print Available
    
    
98. Reza, K. J., Islam, M. Z., and Estivill-Castro, V. (2017): Social Media Users' Privacy Against Malicious Data Miners, In Proc. of the 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2017), Nanjing, Jiangsu, China, November 24-26, 2017, DOI: 10.1109/ISKE.2017.8258834, Electronic ISBN: 978-1-5386-1829-5. CORE 2017 Rank B.
    
    With my PhD student during his PhD studies.
    
    Pre-print Available
    
    
99. Adnan, M. N., and Islam, M. Z. (2017): Effects of Dynamic Subspacing in Random Forest, In Proc. of the 13th Advanced Data Mining and Applications (ADMA 2017), Singapore, November 5-6, 2017, Lecture Notes in Artificial Intelligence, Vol. 10604, ISSN 0302-9743, pg. 303-312, https://doi.org/10.1007/978-3-319-69179-4. CORE 2017 Rank B.
    
    Pre-print Available
    
    
100. Siddiqui, M. K., Islam, M. Z. and Kabir, A. (2017): Analyzing Performance of Classification Techniques in Detecting Epileptic Seizure, In Proc. of the 13th Advanced Data Mining and Applications (ADMA 2017), Singapore, November 5-6, 2017, Lecture Notes in Artificial Intelligence, Vol. 10604, ISSN 0302-9743, pg. 386-398, https://doi.org/10.1007/978-3-319-69179-4. CORE 2017 Rank B.
     
     With my PhD student during his PhD studies.
     
     
101. Babar, Z., Islam, M. Z., and Mansha, S. (2017): Rank Forest: Systematic Attribute Sub-spacing in Decision Forest, In Proc. of the 15th Australasian Data Mining Conference (AusDM 2017), Melbourne, Australia, August 19-20, 2017, Communications in Computer and Information Science, Springer, Vol. 845, pg. 24-37. ISBN 978-981-13-0291-6, DOI https://doi.org/10.1007/978-981-13-0292-3_2 ERA 2010 Rank B, CORE 2017 Rank Australasian.
     
     Pre-print Available
     
     
102. Reza, K., Islam, M. Z., and Estivill-Castro, V. (2017): 3LP: Three Layers of Protection for Individual Privacy in Facebook, In Proc. of the 32nd International Conference on ICT Systems Security and Privacy Protection (IFIP SEC 2017), Rome, Italy, May 29-31, 2017, pp. 108-123, DOI: 10.1007/978-3-319-58469-0_8. CORE 2017 Rank B. Conference Link. Acceptance rate 19.39% (38 papers from 196 submissions).
     
     With my PhD student during his PhD studies.
     
     Pre-print Available
     
     
103. Islam, M. Z., Furner, M., and Siers, M. (2016): WaterDM: A Knowledge Discovery and Decision Support Tool for Efficient Dam Management, In Proc. of the 14th Australasian Data Mining Conference (AusDM), Canberra, Australia, December 6 - 8, 2016, Conferences in Research and Practice in Information Technology (CRPIT), pp. 199 - 204, Vol. 170, ISBN 978-1-921770-50-0, ISSN 1445-1336. ERA 2010 Rank B.
     
     Pre-print Available
     
     YouTube Video Available
     
     
104. Fletcher, S. and Islam, M. Z. (2016): Measuring the Similarity between Rule Lists, In Proc. of the 14th Australasian Data Mining Conference (AusDM), Canberra, Australia, December 6 - 8, 2016, Conferences in Research and Practice in Information Technology (CRPIT), pp. 171 - 178, Vol. 170, ISBN 978-1-921770-50-0, ISSN 1445-1336. ERA 2010 Rank B.
     
     With my PhD student during his PhD studies.
     
     Pre-print Available
     
     
105. Adnan, M. N. and Islam, M. Z. (2016): Knowledge Discovery from a Data Set on Dementia through Decision Forest, In Proc. of the 14th Australasian Data Mining Conference (AusDM), Canberra, Australia, December 6 - 8, 2016, Conferences in Research and Practice in Information Technology (CRPIT), pp. 111 - 118, Vol. 170, ISBN 978-1-921770-50-0, ISSN 1445-1336. ERA 2010 Rank B.
     
     With my PhD student during his PhD studies.
     
     Pre-print Available
     
     
106. Siers, M. and Islam, M. Z. (2016): Addressing Class Imbalance and Cost Sensitivity in Software Defect Prediction by Combining Domain Costs and Balancing Costs, In Proc. of the 12th International Conference on Advanced Data Mining and Applications (ADMA), Gold Coast, Australia, December 12 - 15, 2016, Lecture Notes in Artificial Intelligence (LNAI), pp. 156-171, Vol. 10086, ISBN 978-3-319-49586-6, DOI: 10.1007/978-3-319-49586-6. ERA 2010 Rank B, CORE 2014 Rank B.
     
     Published as a Spotlight Research Paper, where only 18 (17%) out of 105 submitted papers were accepted as spotlight research papers.
     
     With my PhD student during his PhD studies.
     
     Pre-print Available
     
     
107. Adnan, M. N. and Islam, M. Z. (2016): On Improving Random Forest for Hard-to-Classify Records, In Proc. of the 12th International Conference on Advanced Data Mining and Applications (ADMA), Gold Coast, Australia, December 12 - 15, 2016, Lecture Notes in Artificial Intelligence (LNAI), pp. 558-566, Vol. 10086, ISBN 978-3-319-49586-6, DOI: 10.1007/978-3-319-49586-6. ERA 2010 Rank B, CORE 2014 Rank B.
     
     With my PhD student during his PhD studies.
     
     Pre-print Available
     
     
108. Siddiqui, M. K. and Islam, M. Z. (2016): Data Mining Approach in Seizure Detection, In Proc. of the IEEE TENCON 2016, Singapore, November 22 - 25, 2016, pg. 3579 - 3583, DOI: 10.1109/TENCON.2016.7848724, Electronic ISBN: 2159-3450. ERA 2010 Rank C.
     
     With my PhD student during his PhD studies.
     
     
109. Beg, A. H. and Islam, M. Z. (2016): A Novel Genetic Algorithm-Based Clustering Technique and its Suitability for Knowledge Discovery from a Brain Dataset, In Proc. of the IEEE Congress on Evolutionary Computation (IEEE CEC 2016), Vancouver, Canada, July 24 - 29, 2016, pp. 948- 956. DOI: 10.1109/CEC.2016.7743892, available here ERA 2010 Rank A, CORE 2014 Rank B.
     
     With my PhD student during his PhD studies.
     
     Pre-print Available
     
     YouTube Video Available
     
     
110. Beg, A. H. and Islam, M. Z. (2016): Novel Crossover and Mutation Operation in Genetic Algorithm for Clustering, In Proc. of the IEEE Congress on Evolutionary Computation (IEEE CEC 2016), Vancouver, Canada, July 24 - 29, 2016, pp. 2114- 2121. DOI: 10.1109/CEC.2016.7744049, Available here ERA 2010 Rank A, CORE 2014 Rank B.
     
     With my PhD student during his PhD studies.
     
     Pre-print Available
     
     YouTube Video Available
     
     
111. Adnan, M. N. and Islam, M. Z. (2016): Forest CERN: A New Decision Forest Building Technique, In Proc. of the 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2016, April 19-22, pp. 304 - 315, Part I, LNAI 9651, DOI: 10.1007/978-3-319-31753-3 25, ISBN 978-3-319-31752-6, Auckland, New Zealand.
     (ERA 2010 Rank A, CORE 2014 Rank A)
     
     With my PhD student during his PhD studies.
     
     Pre-print Available
     
     YouTube Video Available
     
     
112. Beg, A. H. and Islam, M. Z. (2016): Branches of Evolutionary Algorithms and their Effectiveness for Clustering Records, In Proc. of the 11th IEEE Conference on Industrial Electronics and Applications (ICIEA 2016), Hefei, China, June 5 - 7, 2016. Electronic ISSN: 2158-2297, DOI: 10.1109/ICIEA.2016.7604010
     ERA 2010 Rank A.
     
     Pre-print Available
     
     
113. Beg, A. H. and Islam, M. Z. (2016): Advantages and Limitations of Genetic Algorithms for Clustering Records, In Proc. of the 11th IEEE Conference on Industrial Electronics and Applications (ICIEA 2016), Hefei, China, June 5 - 7, 2016. Electronic ISSN: 2158-2297, DOI: 10.1109/ICIEA.2016.7604009
     ERA 2010 Rank A.
     
     Pre-print Available
     
     
114. Siers, M. and Islam, M. Z. (2016): RB Clust: High quality class-specific clustering using rule-based classification, In Proc. of the 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2016), Bruges, Belgium, April 27 - 29, 2016, pg. 593 - 598. ERA 2010 Rank B, CORE 2014 Rank B.
     
     With my PhD student during his PhD studies.
     
     Pre-print Available
     
     
115. Beg, A. H. and Islam, M. Z. (2016): Genetic Algorithm with Novel Crossover, Selection and Health Check for Clustering, In Proc. of the 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2016), Bruges, Belgium, April 27 - 29, 2016, pg. 575 - 580. ERA 2010 Rank B, CORE 2014 Rank B.
     
     With my PhD student during his PhD studies.
     
     Pre-print Available
     
     
116. Fletcher, S. and Islam, M. Z. (2015): A Differentially-Private Random Decision Forest using Reliable Signal-to-Noise Ratios, In Proc. of the 28th Australasian Joint Conference on Artificial Intelligence (AI 2015), Canberra, Australia, 30 November - 4 December, 2015, Lecture Notes in Computer Science (LNCS), pp. 192-203, Vol. 9457, ISBN 978-3-319-26350-2, DOI: 10.1007/978-3-319-26350-2_17, ERA 2010 Rank B.
     
     With my PhD student during his PhD studies.
     
     Pre-print Available
     
     
117. Siers, M. and Islam, M. Z. (2015): Standoff-Balancing: A Novel Class Imbalance Treatment Method Inspired by Military Strategy, In Proc. of the 28th Australasian Joint Conference on Artificial Intelligence (AI 2015), Canberra, Australia, 30 November - 4 December, 2015, Lecture Notes in Computer Science (LNCS), pp. 517 - 525, Vol. 9457, ISBN 978-3-319-26350-2, DOI: 10.1007/978-3-319-26350-2_46, ERA 2010 Rank B.
     
     With my PhD student during his PhD studies.
     
     Pre-print Available
     
     YouTube Video on the Paper
     
     
118. Fletcher, S. and Islam, M. Z. (2015): A Differentially Private Decision Forest, In Proc. of the 13th Australasian Data Mining Conference (AusDM 15), Sydney, Australia, 8- 9 August, 2015. CRPIT Vol. 168, pp. 99- 108, ISBN 978-1-921770-18-0.
     ERA 2010 Rank B
     
     ( The paper is available here.)
     
     With my PhD student during his PhD studies.
     
     Pre-print Available
     
     
119. Adnan, M. N. and Islam, M. Z. (2015): Complement Random Forest, In Proc. of the 13th Australasian Data Mining Conference (AusDM 15), Sydney, Australia, 8- 9 August, 2015. CRPIT Vol. 168, pp. 89- 98, ISBN 978-1-921770-18-0.
     ERA 2010 Rank B.
     
     ( The paper is available here.)
     
     With my PhD student during his PhD studies.
     
     Pre-print Available
     
     
120. Furner, M. and Islam, M. Z. (2015): Multiple Imputation on Partitioned Datasets, In Proc. of the 13th Australasian Data Mining Conference (AusDM 15), Sydney, Australia, 8- 9 August, 2015. CRPIT Vol. 168, pp. 59- 68, ISBN 978-1-921770-18-0.
     ERA 2010 Rank B.
     
     ( The paper is available here.)
     
     With my Honours student based on his Honours studies.
     
     Pre-print Available
     
     
121. Rahman, M. A. and Islam, M. Z. (2015): AWST: A Novel Attribute Weight Selection Technique for Data Clustering, In Proc. of the 13th Australasian Data Mining Conference (AusDM 15), Sydney, Australia, 8- 9 August, 2015. CRPIT Vol. 168, pp. 51- 58, ISBN 978-1-921770-18-0.
     ERA 2010 Rank B.
     
     ( The paper is available here.)
     
     With my PhD student based on his PhD studies.
     
     Pre-print Available
     
     
122. Beg, A. H., and Islam, M. Z. (2015): Clustering by Genetic Algorithm - High Quality Chromosome Selection for Initial Population, In Proc. of the 10th IEEE Conference on Industrial Electronics and Applications (ICIEA 2015), Auckland, New Zealand, 15 -17 June, 2015, pg. 129 - 134. (ERA 2010 Rank A).
     
     With my PhD student during his PhD studies.
     
     Pre-print Available
     
     
123. Adnan, M., and Islam, M. Z. (2015): One Vs All Binarization Technique in the Context of Random Forest, In Proc. of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015), pp. 385 - 390, Bruges, Belgium, April 22 - 24, 2015 (CORE 2014 Rank B).
     
     With my PhD student during his PhD studies.
     
     Pre-print Available
     
     
124. Adnan, M., and Islam, M. Z. (2015): Improving the Random Forest Algorithm by Randomly Varying the Size of the Bootstrap Samples for Low Dimensional Data Sets, In Proc. of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015), pp. 391 - 396, Bruges, Belgium, April 22 - 24, 2015 (CORE 2014 Rank B).
     
     With my PhD student during his PhD studies.
     
     Pre-print Available
     
     
125. Akhter, A., Bernoth, M., Burmeister, O., Ee Von, C., Dionigi, R., Dresser, G., Evans-Barr, G., Islam, M. Z., Morrison, M., & Robergs, R. (2015): Social and community links: Drivers of healthy and active ageing, In Proc. of the Regional Studies Association 2015 (RSA 2015), pp. 1 - 7.
     
     Paper Available
     
     
126. Adnan, M., and Islam, M. Z. (2014): A Comprehensive Method for Attribute Space Extension for Random Forest, In Proc. of the 17th International Conference on Computer and Information Technology (ICCIT 14), pp. 25-29, 22-23 December, Dhaka, Bangladesh. (ERA 2010 Rank C).
     
     With my PhD student during his PhD studies.
     
     Pre-print Available
     
     
127. Burmeister, O., Islam, M. Z., Dayhew, M., Crichton, M. (2014): Interagency Communication of Private Mental Health Data, In Proc. of the 25th Australasian Conference on Information Systems (ACIS 2014), Auckland, New Zealand, 8-10 December, 2014. (ERA 2010 Rank A, CORE 2014 Rank Australasian)
     Conference Link
     Paper available here
     Received Best Paper Award
     
     
     Pre-print Available
     
     
128. Islam, M. Z., Mamun, Q., and Rahman, M. G. (2014): Data Cleansing During Data Collection from Wireless Sensor Networks, In Proc. of the 12th Australasian Data Mining Conference (AusDM 2014), Brisbane, Australia, 27-28 November, 2014. CRPIT, Vol. 158, pp. 195- 203, ISBN 978-1-921770-17-3.
     (ERA 2010 Rank B, CORE 2014 Rank Australasian)
     
     ( The paper is available here.)
     
     Pre-print Available
     
     
129. Adnan, M., and Islam, M. Z. (2014): ComboSplit: Combining Various Splitting Criteria for Building a Single Decision Tree, In Proc. of the International Conference on Artificial Intelligence and Pattern Recognition (AIPR 2014), Kuala Lumpur, Malaysia, 17-19 November, 2014, pp 1-8, ISBN 978-1-941968-02-4.(CORE 2014 Rank C) Conference Link
     
     With my PhD student during his PhD studies.
     
     Pre-print Available
     
     
130. Xiang, Z., and Islam, M. Z. (2014): Hartigan's Method for K-Mode Clustering and its Advantages, In Proc. of the 12th Australasian Data Mining Conference (AusDM 2014), Brisbane, Australia, 27-28 November, 2014. CRPIT, Vol. 158, pp. 25- 30, ISBN 978-1-921770-17-3.
     (ERA 2010 Rank B, CORE 2014 Rank Australasian)
     
     ( The paper is available here.)
     
     Pre-print Available
     
     
131. Siers, M., and Islam, M. Z. (2014): Cost Sensitive Decision Forest and Voting for Software Defect Prediction, In Proc. of the 13th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2014), pp. 929 - 936, LNAI 8862, Gold Coast, Australia, 1-5 December, 2014. (CORE 2014 Rank B) Conference Link
     
     With my Honours student during his Honours studies.
     
     Pre-print Available
     
     
132. Xiang, Z., and Islam, M. Z. (2014): The Performance of Objective Functions for Clustering Categorical Data, In Proc. of the 2014 Pacific Rim Knowledge Acquisition Workshop (PKAW 2014), pp. 16 - 28, LNCS 8863, DOI: 10.1007/978-3-319-13332-4_2, ISBN 978-3-319-13331-7, Gold Coast, Australia, 1- 2 December, 2014.(CORE 2014 Rank B) Conference Link
     
     Pre-print Available
     
     
133. Estivill-Castro, V., Hough, P., and Islam, M. Z. (2014): Empowering Users of Social Networks to Assess Their Privacy Risks, In Proc. of the IEEE International Conference on Big Data (IEEE BigData 2014), Washington DC, USA, 27-30 October 2014, pg. 644-649, ISBN 978-1-4799-5666-1 and ISBN 978-1-4799-5665-4. Conference Link
     
     With my Honours student and a colleague. Author names are in alphabetic order.
     
     Available here.
     
     
134. Fletcher, S., and Islam, M. Z. (2014): Quality Evaluation of an Anonymized Dataset, In Proc. of the 22nd International Conference on Pattern Recognition (ICPR 2014), pg. 3594-3599, DOI 10.1109/ICPR.2014.618, Stockholm, Sweden, 24-28 August 2014. (ERA 2010 Rank B, CORE 2014 Rank B)
     
     With my PhD student during his PhD studies.
     
     Pre-print Available
     
     
135. Adnan, M., Islam, M. Z., and Kwan, P. (2014): Extended Space Decision Tree, In Proc. of the 13th International Conference on Machine Learning and Cybernetics (ICMLC 2014), pp. 219 - 230, DOI: 10.1007/978-3-662-45652-1_23, Lanzhou, China, 13-16 July, 2014. (Best Paper Award Nomination)(ERA 2010 Rank C)
     
     With my PhD student during his PhD studies.
     
     Pre-print Available
     
     
136. Rahman, M. G., and Islam, M. Z. (2014): iDMI: A Novel Technique for Missing Value Imputation using a Decision Tree and Expectation-Maximization Algorithm, In Proc. of the 16th International Conference on Computer and Information Technology (ICCIT 14), Khulna, Bangladesh, pp. 496-501, 08-10 March 2014.(CORE 2014 Rank C)
     
     With my PhD student during his PhD studies.
     
     Pre-print Available
     
     
137. Rahman, M. A., Islam, M. Z. and Bossomaier, T. (2014): DenClust: A Density Based Seed Selection Approach for K-Means, In Proc. of the 13th International Conference on Artificial Intelligence and Soft Computing (ICAISC 2014), pg. 784-795, Part II, Lecture Notes in Computer Science, Vol. 8468, Springer International Publishing Switzerland, L. Rutkowski et al. Eds., Zakopane, Poland, 1-5 June 2014. (Available from http://link.springer.com/chapter/10.1007%2F978-3-319-07176-3_68)
     
     With my PhD student during his PhD studies.
     
     Pre-print Available
     
     
138. Rahman, M. G., and Islam, M. Z. (2013): kDMI: A Novel Method for Missing Values Imputation Using Two Levels of Horizontal Partitioning in a Data set, In Proc. of the 9th International Conference on Advanced Data Mining and Applications(ADMA 13), Hangzhou, China, 14-16 December 2013, pg. 250-263, DOI: 10.1007/978-3-642-53917-6, ISSN: 0302-9743, H. Motoda et al. (Eds.). (ERA 2010 Rank B)
     
     With my PhD student during his PhD studies.
     
     Pre-print Available
     
     
139. Rahman, M. G., and Islam, M. Z. (2013): A Novel Framework Using Two Layers of Missing Value Imputation, In Proc. of the 11th Australasian Data Mining Conference (AusDM 13), Canberra, Australia, 13-15 November 2013. CRPIT, Vol. 146, pp. 149- 160. ISBN 978-1-921770-16-6.
     ERA 2010 Rank B.
     
     ( The paper is available here.)
     
     With my PhD student during his PhD studies.
     
     Received Best Paper Award
     
     Pre-print Available
     
     
140. Rahman, M. G., and Islam, M. Z. (2013): Data Quality Improvement by Imputation of Missing Values, In Proc. of the 2013 International Conference on Computer Science and Information Technology, Yogyakarta, Indonesia, 16 - 18 June, 2013, pg. 82-88, ISBN: 978-979-3812-20-5.
     
     With my PhD student during his PhD studies.
     
     Pre-print Available
     
     
141. Rahman, M. A., and Islam, M. Z. (2012): CRUDAW: A Novel Fuzzy Technique for Clustering Records Following User Defined Attribute Weights, In Proc. of the 10th Australasian Data Mining Conference (AusDM 12), Sydney, Australia. December 4 - 7, 2012, CRPIT, Vol. 134, Zhao, Y., Li, J., Kennedy, P., and Christen, P. Eds., ACS, pg. 27 - 42.
     ERA 2010 Rank B.
     
     ( The paper is available here.)
     
     With my PhD student during his PhD studies.
     
     Pre-print Available
     
     
142. Khan, M. A., Islam, M. Z., and Hafeez, M. (2012): Evaluating the Performance of Several Data Mining Methods for Predicting Irrigation Water Requirement, In Proc. of the 10th Australasian Data Mining Conference (AusDM 12), Sydney, Australia. December 4 - 7, 2012, CRPIT, Vol. 134, Zhao, Y., Li, J., Kennedy, P., and Christen, P. Eds., ACS, pg. 199 - 208.
     ERA 2010 Rank B.
     
     ( The paper is available here.)
     
     With a PhD student, who I co-supervised, during his PhD studies.
     
     Pre-print Available
     
     
143. Bossomaier, T., Islam, M. Z., Duncan, R., and Gao, J. (2012): Simulation of House Prices for Improved Land Valuation, In Proc. of the 11th International Conference on Modeling and Applied Simulation, MAS 2012, Held at the International Multidisciplinary Modeling and Simulation Multiconference, I3M 2012, Vienna, Austria, September 19 - 21, 2012, pp. 86-93, DIME University of Genoa, ISBN/ISSN 978-88-97999-01-0.
     
     Pre-print Available
     
     
144. Rahman, M. G., Islam, M. Z., Bossomaier, T., and Gao, J. (2012): CAIRAD: A Co-appearance based Analysis for Incorrect Records and Attribute-values Detection, In Proc. of IEEE International Joint Conference on Neural Networks (IJCNN 12), Brisbane, Australia. June 10 - June 15, 2012, pg. 2190-2199.(ERA 2010 Rank A)
     
     With my PhD student during his PhD studies.
     
     Pre-print Available
     
     YouTube Video on the Paper is Available
     
     YouTube Video on the Freely Available Software Tool for CAIRAD
     
     
145. Islam, M. Z. and Giggins, H. (2011): Knowledge Discovery through SysFor: A Systematically Developed Forest of Multiple Decision Trees, In Proc. of the Ninth Australasian Data Mining Conference (AusDM 11), Ballarat, Australia. December 01 - December 02, 2011. CRPIT, 121. Vamplew, P., Stranieri, A., Ong, K.-L., Christen, P. and Kennedy, P. J. Eds., ACS. 195-204.
     ERA 2010 Rank B.
     
     ( The paper is available here.)
     
     Pre-print Available
     
     YouTube Video Available
     
     YouTube Video on Freely Available Software for this Paper
     
     
146. Rahman, M. G. and Islam, M. Z. (2011): A Decision Tree-based Missing Value Imputation Technique for Data Pre-processing, In Proc. of the Ninth Australasian Data Mining Conference (AusDM 11), Ballarat, Australia. December 01 - December 02, 2011, CRPIT, 121. Vamplew, P., Stranieri, A., Ong, K.-L., Christen, P. and Kennedy, P. J. Eds., ACS. 41-50.
     ERA 2010 Rank B.
     
     ( The paper is available here.)
     
     With my PhD student during his PhD studies.
     
     Pre-print Available
     
     YouTube video on Freely Available Software
     
     
147. Rahman, M. A. and Islam, M. Z. (2011): Seed-Detective: A Novel Clustering Technique Using High Quality Seed for K-Means on Categorical and Numerical Attributes, In Proc. of the Ninth Australasian Data Mining Conference (AusDM 11), Ballarat, Australia. December 01 - December 02, 2011, CRPIT, 121. Vamplew, P., Stranieri, A., Ong, K.-L., Christen, P. and Kennedy, P. J. Eds., ACS. 211-220.
     ERA 2010 Rank B.
     
     ( The paper is available here.)
     
     With my PhD student during his PhD studies.
     
     Pre-print Available
     
     
148. Khan, M. A., Islam, M. Z., and Hafeez, M. (2011): Irrigation Water Demand Forecasting - A Data Pre-processing and Data Mining Approach Based on Spatiotemporal Data, In Proc. of the Ninth Australasian Data Mining Conference (AusDM 11), Ballarat, Australia. December 01 - December 02, 2011, CRPIT, 121. Vamplew, P., Stranieri, A., Ong, K.-L., Christen, P. and Kennedy, P. J. Eds., ACS. 183-194.
     ERA 2010 Rank B.
     
     ( The paper is available here.)
     
     ( the Best Student Paper Award )
     
     With a PhD student, who I co-supervised, during his PhD studies.
     
     Pre-print Available
     
     
149. Islam, M. Z. (2010): EXPLORE: A Novel Decision Tree Classification Algorithm, In Proc. of the 27th British National Conference on Databases (BNCOD 2010), LNCS Vol. 6121, Data Security and Security Data, Springer, Berlin/Heidelberg (2012), L.M. MacKinnon (Ed.) ISBN 978-3-642-25703-2, June 29- July 01, 2010, Dundee, Scotland. L.M. MacKinnon (Ed.) 55-71. (ERA 2010 Rank B)
     
     Pre-print Available
     
     YouTube Video Available
     
     
150. Zia, T.A., and Islam, M. Z. (2010): Communal Reputation and Individual Trust (CRIT) in Wireless Sensor Networks, In Proc. of the 5th International Conference on Availability, Reliability and Security (ARES 2010), Published by IEEE Computer Society, February 15-18, 2010, Krakow, Poland, 347-352. (ERA 2010 Rank B)
     
     Pre-print Available
     
     
151. Islam, M. Z. and Brankovic, L (2005): DETECTIVE: A Decision Tree Based Categorical Value Clustering and Perturbation Technique in Privacy Preserving Data Mining, In Proc. of the 3rd IEEE International Conference on Industrial Informatics (INDIN 2005), pg. 701 - 708, 10-12 August, Perth, Australia.
     With my respected PhD supervisor.
     
     Pre-print Available
     
     
152. Alfalayleh, M., Brankovic, L., Giggins, H and Islam, M. Z. (2004): Towards the Graceful Tree Conjecture: A Survey, In Proc. of AWOCA 2004, 7-9 July, Ballina, Australia. (ERA 2010 Rank B)
     
     Author names are in alphabetic order.
     
     Pre-print Available
     
     
153. Islam, M. Z. and Brankovic, L. (2004): A Framework for Privacy Preserving Classification in Data Mining,In Proc. of Australasian Workshop on Data Mining and Web Intelligence (DMWI 2004), Dunedin, New Zealand, CRPIT, 32, Purvis, M., Ed. ACS, 163-168.
     With my respected PhD supervisor.
     
     Pre-print Available
     
     
154. Islam, M. Z., Barnaghi, P. M. and Brankovic, L.(2003): Measuring Data Quality: Predictive Accuracy vs.Similarity of Decision Trees, In Proceedings of the 6 th International Conferenceon Computer & Information Technology (ICCIT 2003), Dhaka, Bangladesh, Vol. 2, 457-462. (ERA 2010 Rank C)
     
     The Paper is Available
     
     With my respected PhD supervisor.
     
     
155. Islam, M. Z. and Brankovic, L. (2003): Noise Addition for Protecting Privacy in Data Mining, In Proceedings of the 6 th Engineering Mathematics and Applications Conference (EMAC 2003), Sydney, Australia, 85-90 .
     With my respected PhD supervisor.
     
     Pre-print Available
     
     

Some Edited Books:

156. Islam, M. R., Koh, Y. S., Zhao, Y., Warwick, G., Stirling, D., Li, C-T., and Islam, M. Z. (2018): Data Mining, Proceedings of the Sixteenth Australasian Data Mining Conference (AusDM 2018), Bathurst, NSW, Australia, 28-30, November 2018, Springer, ISSN 1865-0929, DOI: https://doi.org/10.1007/978-981-13-6661-1.
     
     
157. Zhao, Y., Islam, M. Z., Stone, G., Ong, K-L., Sharma, D., and Williams, G. (2016): Data Mining and Analytics 2016, Proceedings of the Fourteenth Australasian Data Mining Conference (AusDM 2016), Canberra, Australia, 6-8, December 2016, Conferences in Research and Practice in Information Technology (CRPIT), Volume 170, Australian Computer Society Inc. ACM Digital Library, ISSN 1445-1336, ISBN 978-1-921770-50-0. Full Proceedings .
     
     
158. Ong, K-L., Zhao, Y., Stone, G., and Islam, M. Z. (2015): Data Mining and Analytics 2015, Proceedings of the Thirteenth Australasian Data Mining Conference (AusDM 2015), Sydney, Australia, 8-9 August 2015, Conferences in Research and Practice in Information Technology (CRPIT), Vol. 168, Australian Computer Society Inc. Australia, ACM Digital Library, ISSN 1445-1336, ISBN 978-1-921770-18-0.
     
     

Some Editorial Articles/Reports:

159. Yates, D. (author), Clarks, A. (author), Blanchard, C. (contributor), Islam, Z. (contributor), Rehman, S. (contributor) (2024): Enhancing Provenance and Prediction for Whole Grain Rice Quality. Food Agility CRC, Sydney, NSW Australia.
     
     ISBN: 978-0-6486338-5-3
     
     Full Report .
     
     
160. Bewong, M., Ho Leung Ip, R., Lewis, C., Krivokapic-Skoko, B., Islam, Z., Al-Saggaf, Y., Medway, J., Ali, B., & Dixon, E. (2023): Potential implications and benefits for the agricultural technology sector from the introduction of the Australian Agricultural Data Exchange. Food Agility CRC, Sydney, NSW Australia.
     
     eBook ISBN: 978-0-6454652-7-3
     
     Full Report .
     
     
161. Brown, K., Schirmer, J., Amorsen, G., & Kerkhoff, L. V. et al. (2023). Sharing early insights for more resilient communities: Stage 1 report. Southern NSW Drought Resilience Adoption and Innovation Hub/CSU.
     
     Full Report .
     
     
162. Birch, P., Islam, M.Z., Rahman, M. G., and Sicard, L. (2021). Crime and Disorder Audit 2009-2019 � Wagga Wagga: Preliminary Report. 29 January 2021, Charles Sturt University. 35 p. Commisioning/Funding Body: NSW Police Force.
     
     
163. Birch, P., Islam, M.Z., Rahman, M. G., and Sicard, L. (2021). Crime and Disorder Audit 2009-2019, Wagga Wagga: Final Report. 31 March 2021, Charles Sturt University. 58 p. Commisioning/Funding Body: NSW Police Force.
     
     
164. Birch, P., Islam, M.Z., Rahman, M. G., and Sicard, L. (2021). Crime and Disorder Audit 2009-2019 � Wagga Wagga: Preliminary Report. 29 January 2021, Charles Sturt University. 35 p. Commisioning/Funding Body: NSW Police Force.
     
     
165. Iner, D., Asquith, N., Ip, R. H. L., Islam, M. Z., Mason, G., Vergani, M., & Zayied, I. (2019). Islamophobia in Australia - II (2016-2017). Sydney: Charles Sturt University, Commissioned Report. Available here
     
     Estimated potential reach: 725,911,569 excl. broadcasting; 728,705,996 incl. broadcasting
     *provided by the Meltwater Media monitoring service, derived from their algorithm calculation
     The Report and news release generated media attention that had an estimated potential audience of more than 750 million people worldwide in the first week of the release.
     
     Some of the more notable international outlets that covered the story and their estimated potential readership/viewers include Japan Today (1.2m), The Straits Times (2m), The New Indian Express (3.2m), Yahoo India News (1.3m), The Star Online (4.36m), and Times Now (13m).
     
     The report and/or the news release were covered by many agencies including
     • Online (405 results): news.com.au, ABC News, Sydney Morning Herald, SBS, Daily Telegraph, The Age, The Daily Mail, Yahoo7 and ACM
     • Broadcast (308 results): ABC State Radio, ABC National Radio, ABC Local Radio, ABC TV and
     • Print: The Sydney Morning Herald and Ballarat Courier.
     
     We used, discussed and cited our Forest PA algorithm in the report for data analysis.
     
     
166. Islam, M. Z. (2018): Preface to the Data Mining, Proceedings of the Sixteenth Australasian Data Mining Conference (AusDM 2018), Bathurst, Australia, 28-30, November 2018, Springer, ISSN 1865-0929, DOI: https://doi.org/10.1007/978-981-13-6661-1.
     
     
167. Ong K-L, Islam M.Z., and Zhao Y. (2017): Preface to the Special Section of Selected Papers from the Australasian Conference on Data Mining (AusDM) 2016. Australasian Journal of Information Systems (AJIS). Vol. 21, DOI: https://doi.org/10.3127/ajis.v21i0.1694
     
     

Recent Research Projects

External Funding from Food Agility CRC (FA CRC), Australia. Period 2019 - 2022. Project Title: Image Analysis for Whole Grain Rice Quality, PhD Scholarship for a PhD Student under the supervision of the CIs, CIs: Daniel Waters, Chris Blanchard, and Zahid Islam - $135,000. CSU Research Office Project Reference number: 0000102858.

• We apply data mining techniques, knowledge discovery and image analysis for whole grain yield prediction.

External Funding from Murrumbidgee Local Health District (MLHD), NSW Health, Australia. Period 2016 - 2016. Project Title: Patient Risk Stratification Project, CIs: Zahid Islam and Mark Morrison - $55,803. CSU Research Office Project Reference number: 0000101620.

• We apply data mining techniques to develop tools for effective health management.

External Funding from the Department of Social Services, Australia. Period 2015 - 2017. Project Title: Social and community links - a driver of healthy and active ageing, CIs: Oliver Burmeister (Lead CI), Mark Morrison, Zahid Islam, Maree Bernoth, Rylee Dionigi, and Rahena Akhter - $655,000 ($60,000 to CSU). Partner organisation: Carewest Pty Ltd, Orange, NSW. CSU Research Office Project Reference number: 0000101130.

• A component of the research project is the use of data mining and data analytics to discover hidden knowledge related to various research issues on healthy ageing. Besides, data mining techniques will also be used to evaluate the effectiveness of proposed intervention techniques.

External Funding from Young and Well Cooperative Research Centre 2014. Project Title: Synergy ecosystem data storage: medico-legal and ethical challenges. CIs: Dr Oliver Burmeister, Dr Zahid Islam, Dr Maree Burnoth and Ms Carli Kulmar - $16,500.

• The Young and Well (Y&W) Collaborative Research Centre (CRC) has received $5 million for an innovative new product called the Synergy Ecosystem. The Synergy Ecosystem is being rolled out progressively, beginning with hAppiness Central, a university-based online wellbeing resource run through the student services portal. This project addresses the medical, legal, ethical and privacy challenges associated with data collection, storage, sharing and analysis for the ES, focusing on the first product, but also addressing the longer term needs of the Synergy Ecosystem . The project addresses the data security, ownership, privacy, integrity, and management including reporting and access.

External Fundign from Hobart District Nursing 2013. Project Title: Review of Support Worker Integration with Functional Decline, CIs: Prof Mark Morrisson, Dr Maree Bernoth, Dr Oliver Burmeister, and Dr. Zahid Islam - $39,400.

• The main aim of this project is to suggest reasons and possible remedies of the functional decline for the employess and care receivers of Hobart District Nursing. The project also suggests and assesses some intervention plans. We apply data mining and other data analyses on the collected data in order to evaluate the effectiveness of the proposed interventions.

External Funding from Carewest, Australia 2013. Project Title: Age Care Workforce Reform - Building Communities of Practice Around the Prevention of Functional Decline in the Community. CIs: Prof Mark Morrison, Dr Oliver Burmeister, Dr Zahid Islam, Dr Ramudu Bhanugopan and Dr Maree Bernoth - $25,000.

• This research seeks to investigate whether improved training and use of technology by clinicians (support workers) and training of volunteers improves human resource management outcomes among employees and volunteer carers who are involved in reducing the rate of functional decline among seniors. This research involves the use of experiments and pre-post surveys of subjects. Various data mining techniques are applied on the survey data for extracting the patterns and information in order to evaluate the impact of various interventions.

COMPACT Funding 2014, Project Tile: Development and Applicaiton of Domain Specific Data Mining Techniques to Predict and Explore the Brand Switching Tendency of Mobile Phone Users, CIs: Dr Zahid Islam and Prof Steven D'Alessandro - $11,236.26.

• In this project we propose and use novel data mining algorithms to analyse Brand Switching Tendency of Mobile Phone Users.

COMPACT Funding 2014, Project Tile: Industry Funding, CIs: Dr Zahid Islam and Prof Junbin Gao - $13,108.80.

• In this project we accomplish the tasks for our industry partners.

Automatic and Natural Clustering of Records ($9124 Faculty COMPACT Fund 2012): Dr Islam, Prof Bossomaier, Prof Estivill-Castro, A/Prof Brankovic

• In this study we aim to further improve our clustering techniques in order to group records in more meaningful clusters with automatic cluster number selection, attribute weights for clustering and so on. Clustering results will also be �validated by various existing evaluation techniques and novel evaluation techniques.

Data Cleansing and Data Pre-processing Techniques($18,249 Faculty COMPACT Fund 2011): Dr Islam, Prof Bossomaier, Prof Gao

• In this study we develop novel data cleansing techniques for improving data quality. The improved data will then be used in making better decision for an organisation. We have also developed an Agent Based Modeling (ABM) which is powered by data mining techniques in order to simulate the property market and thereby predict and assess property prices. We are also developing various techniques for more acceptable property valuation.
  
  

Data Mining threats on Privacy of Social Network Site (SNS) users ($7,000 Faculty COMPACT Fund 2011): Dr Al-Saggaf, Dr Islam

• The aim of the study is to explore the potential of data mining as a technique that could be used by malicious data miners to threaten the privacy of Social Network Sites (SNS) users.
  
  

Novel Data Mining Techniques for an Intelligent Business Decision Support System ($10,000 CRiCS Seed Grant 2010): Dr Islam

• Due to the recent development of information processing technology and storage capacity businesses typically collect huge amount of data nowadays. Data are not useful unless necessary information is extracted from them. Various data mining tasks including data cleaning, classification, clustering, and prediction are usually applied on collected data for knowledge discovery.
  
  In this project we are developing an intelligent Decision Support System (DSS) that will integrate available data of an organisation by automatically extracting information from textual data through text data mining, detecting and correcting any corrupt (incorrect) data, and imputing all missing values. Our intelligent decision support system will be capable of grouping similar records, extracting multiple sets of patterns (instead of a single set of patterns or logic rules) through classification, and predicting future with very high accuracy. While grouping similar records our DSS will give a user huge flexibility to assign different weights on different attributes and thereby experience different groups of similar records in order to explore various patterns. The DSS will also provide a classification algorithm for extracting interesting patterns that are generally ignored by existing algorithms. For faster analysis of huge amount of real time data, our DSS will use GPGPU for parallel processing of our algorithms. GPU (instead of CPU) can process independent calculations in parallel using a single kernel on many records requiring similar calculations. We will use CUDA architecture for parallel computation.
  
  As a result the DSS can be used in various purposes such as diagnosis and prevention of diseases, behaviour analysis of equipments and predict any future breakdown, making decision on bank loan applications, and identification of suspicious tax returns - just to name a few.
  
  

A Novel Decision Tree Classification Algorithm ($6,000 CSU Small Grant 2009):Dr Islam

• The aim of this project is to develop a novel decision tree algorithm that will extract useful patterns (that are currently ignored by existing algorithms) from a data set. We study various existing classification algorithms such as Decision tree algorithms, Neural networks, Bayesian algorithms and Genetic algorithms. We propose some modifications to existing algorithms and test the algorithm by applying it on a number of data sets. We compare the efficiencies of the proposed algorithm with various existing algorithms such as See 5, J48, and REPTree. The efficiencies are evaluated based on quality of extracted pattern, simplicity of the trees, Performance and Significance of logic rules. Our initial experimental results are very encouraging.
  
  These patterns can then be used in our original noise addition techniques and similarity evaluation of categorical values. My current Seed Grant project (2009) is for developing clustering technique where as the aim of this Small Grant project is to develop a classification technique. Although these two studies are significantly different to each other, they will both be used in our noise addition framework.
  
  

A Novel Clustering Technique for Categorical and Numerical Values ($3,000 Faculty of Business Seed Grant 2009): Dr Islam

• Advances in information processing technology and storage capacity have enabled collection of huge amount of data for various data analyses. Data mining techniques such as classification are often applied on these data to extract hidden information. During the whole process of data mining these data get exposed to several parties which can potentially lead to breaches of individual privacy.
  
  During our previous research, we have presented a framework that uses a few novel noise addition techniques for protecting individual privacy while maintaining a high data quality. We added noise to all attributes, both numerical and categorical. Noise addition techniques developed for numerical attributes are not suitable for noise addition to categorical attributes, due to the absence of natural ordering in categorical attributes. Therefore, we presented a novel technique for clustering categorical values, and used it for noise addition to those values. We also proposed an extension of the technique for clustering records (not just attribute values) having categorical and/or numerical attributes. Our initial experiments indicate suitability of these techniques in clustering categorical values and in noise addition to these values. However, the clustering techniques (specially the one for clustering records) need to be sufficiently experimented and improved. They need to be compared with existing techniques in order to demonstrate their advantages.
  
  The aim of this project is to carry out extensive experiments to evaluate the performance of the proposed clustering techniques by comparing them with existing techniques such as CACTUS, ROCK and QROCK. The study also aims to improve the proposed clustering techniques. Their usefulness for being used in the noise addition framework will also be evaluated through the data quality and level of privacy in a perturbed data set.
  
  

A Privacy Preserving Data Mining Technique (Faculty of Business Research Assistants Support for Honours Students):Dr Islam

• Due to the development of data collection and storage facilities, organisations these days collect a huge amount of data in almost every sector of life. Collected data (such as a patient dataset of a hospital) typically contain sensitive individual information. While the data can be useful in research, business analysis, decision making and prediction an organisation (such as a hospital) often is restricted to share and disclose the collected data due to potential breach of individual privacy. This restriction limits the usefulness of sophisticated data mining techniques in exploring patterns and other hidden information (such as causes of a disease and possible prevention techniques) from the collected data. We aim to develop techniques to ensure the privacy of data subjects while releasing the data for sharing among interested parties for knowledge discovery.
  
  We previously presented a complete framework for noise addition to all attributes of a data set in order to protect individual privacy in a data set while maintaining its original data quality. That is, we added noise to both numerical and categorical attributes in such a way so that the original patterns are preserved in a perturbed data set. We also presented an extended framework, which is also capable of incorporating previously proposed noise addition techniques that maintain the statistical parameters including correlations among attributes of a data set. Thus the perturbed data set can be used not only for classification but also for other statistical analysis.
  
  We tested the framework and extended framework on different data sets, and compared our frameworks with some other noise addition techniques, through data quality of perturbed data sets. Data quality of a perturbed data set was evaluated through a few parameters such as the similarities of decision trees obtained from the original data set and the perturbed data set, prediction accuracy of a decision tree obtained from the perturbed data set, and the correlation matrices produced from the perturbed data set and the original data set. We also presented a technique for security analysis of a data set. An initial experiment indicates the existence of higher level of security in a data set perturbed by our framework than the security level in the original data set.
  
  In this project we are working on further improvement of our noise addition techniques and security analysis.
  
  

Privacy Preserving Multi-party Data Mining

• Often multiple parties (such as the Commonwealth Bank and the Westpac Bank) intend to perform data mining on their combined dataset in order to extract more useful knowledge than the knowledge that can be extracted through data mining on their individual datasets separately. However, they typically hesitate to release their datasets to other parties due to the obligations to their customers for preserving individual privacy. Moreover, a party may not also want to disclose sensitive rules (such as they never approve loans to a particular group of people like the residents of a suburb simply due to the fact that they live there) to other parties. Such a disclosure can easily damage their reputation and image publicly. In this project we aim to develop techniques that will help the parties to effectively mine their combined data while preserving individual privacy and sensitive rules.

SysFor: A Systematically Developed Forest of Multiple Trees: Dr Islam

• Decision tree based classification algorithms like C4.5 and Explore build a single tree from a data set. The two main purposes of building a decision tree are to extract various patterns/logic-rules existing in a data set, and to predict the class attribute value of an unlabeled record. Often a set of decision trees, rather than just a single tree, are also generated from a data set. The set of multiple trees, when used wisely, typically have better prediction accuracy on unlabeled records. In this project we present a novel technique for building a set of multiple trees called SysFor. Our initial experimental results demonstrate that SysFor is suitable for multiple pattern extraction and knowledge discovery. Moreover, it also has higher prediction accuracy than a couple of existing champion techniques.

I am interested in group research. Please feel free to contact me if you are interested. Potential PhD students are also encouraged to contact me at my email address (zislam@csu.edu.au).

Latest News

News, Awards and Achievements.

We have received the Best Paper Award in the 25th International Web Information System Engineering (WISE) Conference held in Qatar from 2 - 5 December 2024. Congratulations to all authors of the paper!

We are delighted to share with you that the code for our following paper was implemented by IBM as a library which is available at https://github.com/IBM/differential-privacy-library/blob/main/diffprivlib/models/forest.py

• Fletcher, S. and Islam, M. Z. (2017): Differentially Private Random Decision Forests using Smooth Sensitivity, Expert Systems with Applications (ESWA), Vol. 78, pg. 16-31, DOI: http://dx.doi.org/10.1016/j.eswa.2017.01.034.
  
  (SJR Rank Q1, SJR H-Index 162, 2018 Impact Factor: 4.29, 2018 Scopus Highest CiteScore Percentile: 98%, CiteScore Rank: 5/275 in General Engineering)
  
  

I am delighted to see that an excellent CSU Postdoctoral Researcher, Dr Darren Yates developed the mobile phone app, RiversNearMe that provides information about the river water levels for rivers in NSW within the 50-Kilometer radius from an app user. The app was developed from our Food Agility CRC funded project RM Ref 102660.

Here are some radio interviews by Dr Darren Yates on the app:

1. Radio 2GB - River level data technology helping keep farmers hAPPy - Here
2. Radio 3AW - River level data technology helping keep farmers hAPPy - Here
3. Radio 4BC - River level data technology helping keep farmers hAPPy - Here
4. ABC Goulburn Murray 29-May-2023; Sandra Moon/Breakfast (time-start:2hrs12mins) - Here
5. ABC Riverina 29-May-2023; Sally Bryant/Breakfast (time-start: 48mins) - Here
6. ABC Mid North Coast 30-May-2023; Cameron Marshall/Breakfast (time-start: 2hrs17mins) - Here

I am delighted to see that an excellent CSU Postdoctoral Researcher, Dr Darren Yates developed the CLOWD App making the historical and recent climate data and analysis available to farmers, from our Food Agility CRC funded project RM Ref 102660.

Here are some radio interviews by Dr Darren Yates on the app:

1. Radio 2GB - Simplifying weather data for farmers - Here
2. Radio 3AW - Simplifying weather data for farmers - Here
3. Radio 4BC - Simplifying weather data for farmers - Here

My sincere thanks to the CSU Defence Trade Controls Committee for their kind Certificate of Appreciation Certificate of Appreciation 2023.

The project team for the Cyber Security CRC (CSCRC) funded project (titled "Development of Australian Cyber Criteria Assessment") received the prestigious Cyber Security Researcher of the Year Award 2021 from the Australian Information Security Association (AISA).

On 31 January 2019 we received the Research Excellence Award 2018 from the School of Computing and Mathematics, Charles Sturt University.

On 1 February 2018 we received a Teaching Excellence Award in 2017 for achieving the 2nd place in teaching excellence in the School of Computing and Mathematics. This award was gived due to achieving high student satisfaction on my teaching. Many thanks to my beloved students for their wonderful feedback again and again.

Our Predictive Risk Stratification Tool for reducing avoidable hospital readmission for Murrumbidgee Local Health District of the NSW Health has received an Innovation Award in 2017 from the Agency for Clinical Innovation (ACI). See an Award Photo and CSU News.

We have again received the Faculty of Business Research Supervision Excellence Award 2016 in recognition of the achievements in supervising high quality PhD students at Charles Sturt University.

We have received the Research Excellence Award 2015 in the School of Computing and Mathematics, Charles Sturt University.

We have won the Subject Team Award 2015 in the Faculty of Business for wonderful team teaching in ITC106 and ITC558. The team teaching was led by Dr Zahid Islam as the convenor of the subjects. Other team members are Dr Anisur Rahman, Dr Ken Eustace, Dr Xiaodi Huang, Dr Recep Ulusoy and Dr Sudath Heiyanthuduwage.

We have received the Faculty of Business Research Supervision Excellence Award 2014 in recognition of the achievements in supervising high quality PhD students at Charles Sturt University.

We have received the Research Excellence Award 2013 in the School of Computing and Mathematics, Charles Sturt University.

We have received the Best Paper Award in the 25th Australasian Conference on Information Systems (ACIS 2014)in Auckland, New Zealand.(ERA 2010 Rank A).

We have received the Best Paper Award in the Eleventh Australasian Data Mining & Analytics Conference (AusDM 2013) in Canberra, Australia.

We have received the Best Student Paper Award in the Ninth Australasian Data Mining Conference (AusDM 2011) in Ballarat, Australia for our paper with a PhD student.

Faculty of Business Research Supervision Excellence Award 2014.	Faculty of Business Research Award 2014.
SCM Research Excellence Award 2015.	Faculty Research Supervision Excellence Award 2016.
Agency for Clinical Innovation (ACI) Award 2017.	Cyber Security Researcher of the Year 2021, from the Australian Information Security Association (AISA), team award for the Cyber Security CRC project titled "Development of Australian Cyber Criteria Assessment".

Visits of our research collaborators.

Our research group regularly invites collaborators and researchers from all over the places. Recently Prof Vladimir Estivill-Castro of Griffith University and A/Prof Ljiljana Brankovic of Newcastle University visited us.

Prof Vlad Estivill-Castro discussing community partitioning to our serious PhD students, in 2013.	Research is more like a fun when we do it in a hard working group.
Prof Ljiljana Brankovic visited us in Bathurst, 2012.	A group of three Professors from China and Queensland visited us on 18 October, 2016.

Invited Talks in Various Universities.

We regularly give invited talks in various universities. For example, I have recently given invited talks at seminars in the University of New England, Australia, Independent University of Bangladesh (IUB), Charles Sturt University, Australia and Deakin University, Burwood Campus at Melbourne, Australia.

Invited talk at the Independent University of Bangladesh in 2014.

Invited talk at VICPU, 2015.

Attendeding Various Conferences on Data Mining

Members of our group regularly attend high quality international conferences. For example, in 2013 we attended ADMA 13 in Hangzhou, China, AusDM 13 in Canberra Australia, in 2012 we attended AusDM 2012, IJCNN 2012, and ACSW 2012.

A wonderful moment with my PhD students in AusDM 2012 at Sydney	A Relaxed afternoon with the Data Mining Research Group after my Seminar at CSU in April, 2015.
On behalf of Mahmood Khan I am receiving the best student paper award for our paper with Mahmood at AusDM 2011 at Ballarat	My talk on our novel Decision Forest technique at AusDM 2011 at Ballarat
With my PhD students at AusDM 2011	A happy research team. A very nice moment at AusDM 2011 at Ballarat

We are uploading the source code of some of our papers. The programs are free software. You can redistribute them and/or modify them under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version - if not otherwise stated on the actual code.

ALL SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESSED OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF FITNESS FOR A PARTICULAR PURPOSE, MERCHANTABILITY, AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY DAMAGES, CLAIM, OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE

Algorithm	Paper Title	Authors	Citation	Code
DATM	DATM: A Novel Data Agnostic Topic Modelling Technique with Improved Effectiveness for both Short and Long Text	Michael Bewong, John Wondoh, Selasi Kwashie, Lin Liu, Jixue Liu, Jiuyong Li, Md Zahidul Islam, David Kernot
TLF	A Framework for Supervised Heterogeneous Transfer Learning using Dynamic Distribution Adaptation and Manifold Regularization	Md Geaur Rahman, Md Zahidul Islam
ADF	Adaptive Decision Forest: An Incremental Machine Learning Framework	Md Geaur Rahman, Md Zahidul Islam
FastForest	FastForest: Increasing random forest processing speed while maintaining accuracy	Darren Yates, Md Zahidul Islam
WinDrift	WinDrift: Early Detection of Concept Drift using Corresponding and Hierarchical TimeWindows	Naureen Naqvi, Sabih Ur Rehman, Md Zahidul Islam
SmoothPrivateForest	Differentially Private Random Decision Forests using Smooth Sensitivity This algorithm is also available in WEKA. Watch a short YouTube video to learn how to use it in WEKA. WEKA is a freely available software.	Sam Fletcher, Md Zahidul Islam
CAIRAD	CAIRAD: A Co-appearance based Analysis for Incorrect Records and Attribute-values Detection This algorithm is also available in WEKA. Watch a short YouTube video to learn how to use it in WEKA. WEKA is a freely available software.	Md Geaur Rahman, Md Zahidul Islam, Terry Bossomaier, Junbin Gao
ForEx++	ForEx++: A New Framework for Knowledge Discovery from Decision Forests This algorithm is also available in WEKA. Watch a short YouTube video to learn how to use it in WEKA. WEKA is a freely available software.	Md Nasim Adnan, Md Zahidul Islam
DataLearner	DataLearner: A Data Mining and Knowledge Discovery Tool for Android Smartphones and Tablets The app is freely available at Google Play Watch a short YouTube video to learn how to use the app.	Darren Yates, Md Zahidul Islam, Junbin Gao
SPAARC	SPAARC: A Fast Decision Tree Algorithm This algorithm is also available in WEKA. You can download this from WEKA package manager the same way as other algorithms on this page. WEKA is a freely available software tool.	Darren Yates, Md Zahidul Islam, Junbin Gao
DMI	Missing Value Imputation Using Decision Trees and Decision Forests by Splitting and Merging Records: Two Novel Techniques	Md Geaur Rahman, Md Zahidul Islam
	A Decision Tree-based Missing Value Imputation Technique for Data Pre-processing	Md Geaur Rahman, Md Zahidul Islam
	DMI was presented in the above two papers. This algorithm will also be available in WEKA. Watch a YouTube video to learn how to use it in WEKA. WEKA is a freely available software.	-	-
SysFor	Knowledge discovery through SysFor: a systematically developed forest of multiple decision trees This algorithm is also available in WEKA. Watch a YouTube video to learn how to use it in WEKA. WEKA is a freely available software.	Md Zahidul Islam, Helen Giggins
ForestPA	Forest PA: Constructing a Decision Forest by Penalizing Attributes used in Previous Trees This algorithm is also available in WEKA. Watch a YouTube video to learn how to use it in WEKA. WEKA is a freely available software.	Md Nasim Adnan, and Md Zahidul Islam
GenClust++	Combining K-Means and a genetic algorithm through a novel arrangement of genetic operators for high quality clustering This algorithm is also available in WEKA. Watch a YouTube video to learn how to use it in WEKA. WEKA is a freely available software.	Md Zahidul Islam, Vladimir Estivill-Castro, Md Anisur Rahman, Terry Bossomaier
CSForest	Cost sensitive decision forest and voting for software defect prediction	Michael J. Siers, and Md Zahidul Islam
	Software Defect Prediction using a cost sensitive decision forest and voting, and a potential solution to the class imbalance problem	Michael J. Siers, and Md Zahidul Islam
	CSForest was presented in the above two papers. This algorithm is also available in WEKA. Watch a YouTube video to learn how to use it in WEKA. WEKA is a freely available software.	-	-
Standoff-Balancing	Standoff-Balancing: A novel class imbalance treatment method inspired by military strategy	Michael J. Siers, and Md Zahidul Islam

1. Please cite the relevant papers as recorded in the above table if you use any of the above code either from this web page or any other web pages or WEKA.
2. The CSForest code and Standoff-Balancing code are also available at Michael J. Siers' webpage.