Dr Mohammad Haque

Dr Mohammad Haque

Research Associate

School of Information and Physical Sciences

Career Summary

Biography

Dr Mohammad Nazmul Haque achieved the Doctor of Philosophy (Computer Science) degree from the University of Newcastle, Australia in February 2017. His PhD research area was on the genetic algorithm-based ensemble of classification methods for biological data classification. Dr Haque possesses interdisciplinary research experiences in data analytics from a diverse source of data (including gene expression, business and consumer behaviour, images etc.) and has worked with super-network and complex Network Analysis for cohesion, community and structural similarity identification using a memetic algorithm. He is currently working on a novel representation based on the Continued Fraction for regression method using a memetic algorithm with the potential application on astronomy, scientific functions and predictions. He has published several papers in peer-reviewed journals, conferences and book chapters.

Dr Haque received the B.Sc and M.Sc in Computer Science & Engineering from Daffodil International University (DIU), Dhaka, Bangladesh in 2006 and 2011, respectively. Before he started the PhD candidature in Aug 2012 at the University of Newcastle, he was involved in academia as Lecturer at Daffodil International University, Bangladesh from 2009 to 2012. He also served as Lecturer of Computing Information Systems at Daffodil Institute of IT, Bangladesh from 2007 to 2009. Just after completing his Bachelor in 2006, he joined as Junior Software Engineer at KMC e-technology, Bangladesh. 

His current research interests include Data Analytics, Evolutionary Computing, Machine Learning, Artificial Intelligence, Image Processing and Health Informatics. 


Qualifications

  • Doctor of Philosophy, University of Newcastle
  • Master of Science, Daffodil International University, Bangladesh

Keywords

  • Artificial Intelligence
  • Bioinformatics
  • Business Analytics
  • Data Analytics
  • Evolutionary Computation
  • Health Informatics
  • Image Processing
  • Machine learning

Languages

  • Bengali (Mother)
  • English (Fluent)

Fields of Research

Code Description Percentage
460299 Artificial intelligence not elsewhere classified 30
460502 Data mining and knowledge discovery 30
461199 Machine learning not elsewhere classified 40

Professional Experience

UON Appointment

Title Organisation / Department
Research Associate University of Newcastle
School of Information and Physical Sciences
Australia
Casual Academic University of Newcastle
School of Electrical Engineering and Computing
Australia

Academic appointment

Dates Title Organisation / Department
2/4/2012 - 14/8/2012 Senior Lecturer Daffodil International University
Department of computer Science & Engineering
Bangladesh
29/1/2009 - 1/4/2012 Lecturer Daffodil International University
Department of computer Science & Engineering
Bangladesh
1/11/2007 - 26/1/2009 Lecturer


Daffodil Institute of IT
School of Computing Information System
Bangladesh

Membership

Dates Title Organisation / Department
20/4/2021 -  Senior Member of IEEE (SMIEEE) Institute of Electrical & Electronic Engineers (IEEE)
United States
17/6/2020 -  Member of ACS (MACS) Australian Computer Society (ACS)
Australia
1/5/2014 -  Member of ACM Association for Computing Machinery (ACM)
United States

Professional appointment

Dates Title Organisation / Department
10/5/2007 - 30/10/2007 Software Engineer KMC e-Technology
Bangladesh
20/6/2006 - 9/5/2007 Junior Software Engineer KMC e-Technology
Bangladesh

Awards

Prize

Year Award
2005 ACM-Solver Coding Championship
Daffodil International University

Scholarship

Year Award
2012 University of Newcastle International Postgraduate Research Scholarship
Faculty of Engineering and Built Environment - The University of Newcastle (Australia)
2012 University of Newcastle Research Scholarship Central
Faculty of Engineering and Built Environment - The University of Newcastle (Australia)

Teaching

Code Course Role Duration
COMP2240 Operating Systems
University of Newcastle - Faculty of Engineering & Built Environment
Tutor 25/7/2016 - 31/12/2016
SENG3150 Software Project 1: Requirements Engineering and Design
University of Newcastle - Faculty of Engineering & Built Environment
Teaching Material Development 12/8/2016 - 31/12/2016
INFO6002 Database Management 2
University of Newcastle - Faculty of Engineering & Built Environment
Lead Web Tutor 22/5/2017 - 31/8/2017
INFO6002 Database Management 2
University of Newcastle - Faculty of Engineering & Built Environment
Lecturer 22/5/2017 - 31/8/2017
COMP3340 Data Mining
University of Newcastle - School of Electrical Engineering and Computing | Australia
Tutor (F2F & Online) 3/8/2020 - 11/11/2020
COMP6340 Data Mining
University of Newcastle - School of Electrical Engineering and Computing | Australia
Tutor (Web) 3/8/2020 - 11/11/2020
COMP3340 Data Mining
University of Newcastle - School of Electrical Engineering and Computing | Australia
Tutor 31/7/2019 - 6/11/2019
COMP4120 Special Topic B : Datamining
Faculty of Engineering and Built Environment - The University of Newcastle (Australia)
Tutor & Guest Lecturer 26/2/2018 - 27/7/2018
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Publications

For publications that are currently unpublished or in-press, details are shown in italics.


Chapter (2 outputs)

Year Citation Altmetrics Link
2019 Haque MN, Moscato P, 'From Ensemble Learning to Meta-Analytics: A Review on Trends in Business Applications', Business and Consumer Analytics: New Ideas, Springer, Switzerland 703-731 (2019) [B1]
DOI 10.1007/978-3-030-06222-4_18
Co-authors Pablo Moscato
2019 Haque MN, de Vries NJ, Moscato P, 'A Multi-objective Meta-Analytic Method for Customer Churn Prediction', Business and Consumer Analytics: New Ideas, Springer, Switzerland 781-813 (2019) [B1]
DOI 10.1007/978-3-030-06222-4_20
Co-authors Pablo Moscato, Natalie Devries

Journal article (7 outputs)

Year Citation Altmetrics Link
2021 Moscato P, Mathieson L, Haque MN, 'Augmented intuition: a bridge between theory and practice', Journal of Heuristics, 27 497-547 (2021) [C1]

Motivated by the celebrated paper of Hooker (J Heuristics 1(1): 33¿42, 1995) published in the first issue of this journal, and by the relative lack of progress of both approximati... [more]

Motivated by the celebrated paper of Hooker (J Heuristics 1(1): 33¿42, 1995) published in the first issue of this journal, and by the relative lack of progress of both approximation algorithms and fixed-parameter algorithms for the classical decision and optimization problems related to covering edges by vertices, we aimed at developing an approach centered in augmenting our intuition about what is indeed needed. We present a case study of a novel design methodology by which algorithm weaknesses will be identified by computer-based and fixed-parameter tractable algorithmic challenges on their performance. Comprehensive benchmarkings on all instances of small size then become an integral part of the design process. Subsequent analyses of cases where human intuition ¿fails¿, supported by computational testing, will then lead to the development of new methods by avoiding the traps of relying only on human perspicacity and ultimately will improve the quality of the results. Consequently, the computer-aided design process is seen as a tool to augment human intuition. It aims at accelerating and foster theory development in areas such as graph theory and combinatorial optimization since some safe reduction rules for pre-processing can be mathematically proved via theorems. This approach can also lead to the generation of new interesting heuristics. We test our ideas with a fundamental problem in graph theory that has attracted the attention of many researchers over decades, but for which seems it seems to be that a certain stagnation has occurred. The lessons learned are certainly beneficial, suggesting that we can bridge the increasing gap between theory and practice by a more concerted approach that would fuel human imagination from a data-driven discovery perspective.

DOI 10.1007/s10732-020-09465-7
Co-authors Pablo Moscato
2021 Moscato P, Sun H, Haque MN, 'Analytic Continued Fractions for Regression: A Memetic Algorithm Approach', Expert Systems with Applications, 179 (2021) [C1]

We present an approach for regression problems that employs analytic continued fractions as a novel representation. Comparative computational results using a memetic algorithm are... [more]

We present an approach for regression problems that employs analytic continued fractions as a novel representation. Comparative computational results using a memetic algorithm are reported in this work. Our experiments included fifteen other different machine learning approaches including five genetic programming methods for symbolic regression and ten machine learning methods. The comparison on training and test generalization was performed using 94 datasets of the Penn State Machine Learning Benchmark. The statistical tests showed that the generalization results using analytic continued fractions provide a powerful and interesting new alternative in the quest for compact and interpretable mathematical models for artificial intelligence.

DOI 10.1016/j.eswa.2021.115018
Co-authors Pablo Moscato
2019 Haque MN, Moscato P, 'The Cohesion-Based Communities of Symptoms of the Largest Component of the DSM-IV Network', JOURNAL OF INTERCONNECTION NETWORKS, 19 (2019) [C1]
DOI 10.1142/S0219265919400024
Co-authors Pablo Moscato
2016 Haque MN, Noman N, Berretta R, Moscato P, 'Heterogeneous ensemble combination search using genetic algorithm for class imbalanced data classification', PLoS ONE, 11 (2016) [C1]

Classification of datasets with imbalanced sample distributions has always been a challenge. In general, a popular approach for enhancing classification performance is the constru... [more]

Classification of datasets with imbalanced sample distributions has always been a challenge. In general, a popular approach for enhancing classification performance is the construction of an ensemble of classifiers. However, the performance of an ensemble is dependent on the choice of constituent base classifiers. Therefore, we propose a genetic algorithm-based search method for finding the optimum combination from a pool of base classifiers to form a heterogeneous ensemble. The algorithm, called GA-EoC, utilises 10 fold-cross validation on training data for evaluating the quality of each candidate ensembles. In order to combine the base classifiers decision into ensemble's output, we used the simple and widely used majority voting approach. The proposed algorithm, along with the random sub-sampling approach to balance the class distribution, has been used for classifying class-imbalanced datasets. Additionally, if a feature set was not available, we used the (a, ß) - k Feature Set method to select a better subset of features for classification. We have tested GA-EoC with three benchmarking datasets from the UCI-Machine Learning repository, one Alzheimer's disease dataset and a subset of the PubFig database of Columbia University. In general, the performance of the proposed method on the chosen datasets is robust and better than that of the constituent base classifiers and many other well-known ensembles. Based on our empirical study we claim that a genetic algorithm is a superior and reliable approach to heterogeneous ensemble construction and we expect that the proposed GA-EoC would perform consistently in other cases.

DOI 10.1371/journal.pone.0146116
Citations Scopus - 35Web of Science - 29
Co-authors Nasimul Noman, Regina Berretta, Pablo Moscato
2014 Whaiduzzaman M, Haque MN, Rejaul Karim Chowdhury M, Gani A, 'A study on strategic provisioning of cloud computing services.', ScientificWorldJournal, 2014 894362 (2014) [C1]
DOI 10.1155/2014/894362
Citations Scopus - 25Web of Science - 1
2014 Whaiduzzaman M, Gani A, Anuar NB, Shiraz M, Haque MN, Haque IT, 'Cloud Service Selection Using Multicriteria Decision Analysis', SCIENTIFIC WORLD JOURNAL, (2014) [C1]
DOI 10.1155/2014/459375
Citations Scopus - 131Web of Science - 62
2011 Haque MN, Uddin MS, 'Accelerating Fast Fourier Transformation for Image Processing using Graphics Processing Unit', Journal of Emerging Trends in Computing and Information Sciences, 2 367-375 (2011) [C1]
Show 4 more journal articles

Conference (5 outputs)

Year Citation Altmetrics Link
2020 Moscato P, Sun H, Haque MN, 'Analytic Continued Fractions for Regression: Results on 352 datasets from the physical sciences', 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings, Glasgow, Scotland (2020) [E1]
DOI 10.1109/CEC48606.2020.9185564
Co-authors Pablo Moscato
2019 Haque MN, Mathieson L, Moscato P, 'A Memetic Algorithm Approach to Network Alignment: Mapping the Classification of Mental Disorders of DSM-IV with ICD-10', GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference, Prague, Czech Republic (2019) [E1]
DOI 10.1145/3321707.3321753
Co-authors Pablo Moscato
2017 Haque MN, Mathieson L, Moscato P, 'A memetic algorithm for community detection by maximising the connected cohesion', 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings, Hawaii, USA (2017) [E1]
DOI 10.1109/SSCI.2017.8285404
Citations Scopus - 3Web of Science - 1
Co-authors Pablo Moscato
2016 Haque MN, Noman N, Berretta R, Moscato P, 'Optimising weights for heterogeneous ensemble of classifiers with differential evolution', 2016 IEEE Congress on Evolutionary Computation (CEC), Vancouver, British Columbia, Canada (2016) [E1]
DOI 10.1109/CEC.2016.7743800
Citations Scopus - 11Web of Science - 9
Co-authors Pablo Moscato, Regina Berretta, Nasimul Noman
2011 Haque MN, Uddin MS, Abdullah-Al-Wadud M, Chung Y, 'Fast reconstruction technique for medical images using graphics processing unit', Communications in Computer and Information Science (2011) [E1]
DOI 10.1007/978-3-642-27183-0_32
Citations Scopus - 1Web of Science - 1
Show 2 more conferences
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Research Supervision

Number of supervisions

Completed4
Current0

Past Supervision

Year Level of Study Research Title Program Supervisor Type
2012 Honours Malignant Cancer Cell Detection by Microscopy Image Analysis Computer Science, Daffodil International University Principal Supervisor
2011 Honours Hardware and software maintenance of an organization Computer Science, Daffodil International University Principal Supervisor
2011 Honours Design and development of exam seat planning system Computer Science, Daffodil International University Principal Supervisor
2010 Honours Design and development of a course registration system using the pre requisite checking constraint Computer Science, Daffodil International University Principal Supervisor
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Research Collaborations

The map is a representation of a researchers co-authorship with collaborators across the globe. The map displays the number of publications against a country, where there is at least one co-author based in that country. Data is sourced from the University of Newcastle research publication management system (NURO) and may not fully represent the authors complete body of work.

Country Count of Publications
Australia 10
United States 3
Canada 2
Malaysia 2
Bangladesh 1
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Dr Mohammad Haque

Positions

Research Associate
School of Information and Physical Sciences
College of Engineering, Science and Environment

Casual Academic
School of Information and Physical Sciences
College of Engineering, Science and Environment

Contact Details

Email mohammad.haque@newcastle.edu.au
Phone (02) 4042 0189

Office

Room EF126
Building EF Building
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