Dr Nasimul Noman
Senior Lecturer
School of Information and Physical Sciences (Computing and Information Technology)
- Email:nasimul.noman@newcastle.edu.au
- Phone:(02) 4042 0488
The nature-inspired design of machines
Dr Nasimul Noman is using nature as a blueprint for improving machine autonomy. It’s a field of study known as evolutionary machine learning and it has the potential to solve many real-world problems.
According to Dr Nasimul Noman, optimisation is part of daily life. Motorists want cars that are fuel efficient, environmentally friendly and stylish; investors seek to maximise the return on their financial portfolio while minimising risk; medical professionals need a range of drugs with optimum dosages for treatment that elicit little to no side effects.
Each problem, Nasimul states, has traditionally been solved using different optimisation algorithms. In this age of machine learning, machine learning algorithms need to be optimised from different perspectives, and to make machines smarter, smarter optimisation techniques must be used.
His research is in evolutionary machine learning, a subfield in which evolutionary algorithms interplay with machine learning. Specifically, Nasimul develops computational algorithms for designing optimal and more proficient machine learning algorithms, and for solving some of our real-world optimisation problems.
“In the field of machine learning, optimisation plays a key role in designing efficient and effective algorithms for extracting valuable information through analysis of data,” explains Nasimul.
Nasimul believes it’s possible to exploit many of nature’s processes in instilling robust and intelligent behaviours in computational algorithms. Using the natural world as his reference point, he hopes computers will one day design and synthesise intelligent systems automatically without human intervention—in other words, learn and improve on their own.
“My long-term goal is to develop computational frameworks for evolving robust and resilient intelligent systems through computational evolution.”
A leaf out of nature’s book
Like others in his field, Nasimul believes that nature is the supreme optimiser. He was introduced to the field of evolutionary algorithms—computational programs that mimic natural evolution—during his masters at the University of Dhaka in Bangladesh.
Subsequent research for his PhD afforded a valuable placement at one of Tokyo University’s laboratories studying computational intelligence that imitates clever, problem-solving techniques found in nature.
“Since my PhD, I have been developing various nature-inspired algorithms to optimise machine learning models and to solve different optimisation problems in life.
“I truly believe we can greatly benefit by understanding and applying the principles used in different spheres of nature for solving various complex problems and machine intelligence is not an exception.”
World-proofing algorithms
When it comes to solving the problems of our world, not only must solutions be optimal, they must withstand the challenges and disruptions that inevitably occur. For example, some minor distortions can never deceive human eyes in identifying an object in an image but can easily trick a machine.
Designing systems that are robust and resilient in automated decision-making is also important from a security perspective. Finding such designs can be very costly, but as part of his research, Nasimul is working towards developing more effective and efficient meta-algorithms that deliver optimal, robust algorithmic solutions. Already his work has had real-life applications.
His developed algorithms have been used in different fields, such as in machine learning for creating optimal deep neural networks, for feature selection and designing classifiers; in systems biology for designing combination therapies for disease, for automatic design and reconstruction of genetic and reaction networks; and in engineering for optimal scheduling of machines and for economic load distribution of power systems.
Shaping the future
Solving problems that benefit others is Nasimul’s greatest motivation for continuing his research.
Several useful applications for machine-learning algorithms already exist—from modelling spam filters to drug designs. New iterations, he stresses, must be more robust and secure—and he’s working to make them so. In the meantime, when Nasimul reflects on his research, he feels inspired.
“Working in a research area that can help shape our future makes me feel excited and proud. I feel committed and motivated because I believe I can draw inspiration from nature in solving problems that are important to us.”
The nature-inspired design of machines
Dr Nasimul Noman is using nature as a blueprint for improving machine autonomy. It’s a field of study known as evolutionary machine learning and it has the potential to solve many real-world problems.
Career Summary
Biography
Nasimul Noman received his
Research Expertise
My current research focus is the application of evolutionary computation for designing robust and effective machine learning models. My research interest also includes the development of optimised and automatic construction of machine learning models in different applications e.g. sentence, image or malware classification.
I am fascinated by the field of Evolutionary Computation. My research has primarily focused on establishing evolutionary algorithms (EAs), in particular memetic algorithms (MAs), as an effective methodology for solving different real-world complex problems. I have been working for the development, analysis and application of EC. I am experienced with many different classes of evolutionary algorithms (e.g. genetic algorithms, memetic algorithms, differential evolution, swarm intelligence). I have studied these EAs, proposed several improvements to them and one particular focus of my research has been to incorporate different hybridization techniques in these algorithms for solving large-scale problems.
To me, the application of evolutionary algorithms for solving different real-world problems is very challenging and appealing. I have also investigated the application of those algorithms for solving different problems, such as gene network reconstruction, production scheduling, power generation problems, deep neural networks’ architectural design etc.
I also worked in the area of modelling, simulation, reconstruction and optimization of biological networks. I have been working with different types of deterministic and stochastic modelling, simulation and analysis of genetic networks for systems and synthetic biology. I have worked in the model-based reconstruction of genetic networks from gene expression data for quite long.
I have active research experience in some bioinformatics research topics. The topics of bioinformatics I am interested/experienced in are protein phosphorylation and glycosylation site detection, promoter site prediction, protein secondary structure prediction, regulatory motif detection, etc.
Teaching Expertise
The courses I have taught in UoN
Administrative Role
Currently, I am working as the convenor for the Bachelor of Computer Science program. Previously I also served as the convenor of the Bachelor of Software Engineering (Hons), Bachelor of Data Science programs and Master of Professional Engineering (Software) Programs. Currently, I am a member of the SIPS Teaching and Learning Committee. I served in the former FEBE Teaching and Learning Committee, FEBE faculty board and in the PALS board.
In my appointment at the
Qualifications
- PhD, University of Tokyo - Japan
- Bachelor of Science (Computer Science), University of Dhaka - Bangladesh
- Master of Science (Computer Science), University of Dhaka - Bangladesh
Keywords
- Artificial Intelligence
- Bioinformatics
- Computational Biology
- Computer Algorithms
- Evolutionary Computation
- Machine Learning
- Robust Design
- Systems Biology
Languages
- English (Fluent)
- Bengali (Mother)
- Hindi (Working)
Fields of Research
Code | Description | Percentage |
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461103 | Deep learning | 30 |
460203 | Evolutionary computation | 50 |
460103 | Applications in life sciences | 20 |
Professional Experience
UON Appointment
Title | Organisation / Department |
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Senior Lecturer | University of Newcastle School of Electrical Engineering and Computing Australia |
Academic appointment
Dates | Title | Organisation / Department |
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11/11/2013 - 31/12/2017 | Lecturer | The University of Newcastle, Faculty of Engineering and built Environment, School of Electrical Engineering and Computing Australia |
1/9/2012 - 1/2/2013 | Visiting Research Fellow | Harvard Medical School Systems Biology United States |
1/3/2012 - 1/11/2013 | Research Fellow | The University of Tokyo School of Information Science and Technology Japan |
1/1/2012 - 1/5/2012 | Associate Professor | University of Dhaka Computer Science and Engineering Bangladesh |
1/11/2009 - 1/11/2011 | Post Doctoral Researcher | The University of Tokyo School of Engineering Japan |
1/3/2007 - 1/1/2012 | Assistant Professor | University of Dhaka Computer Science and Engineering Bangladesh |
1/3/2002 - 1/3/2007 | Lecturer | University of Dhaka Computer Science and Engineering Bangladesh |
Awards
Teaching Award
Year | Award |
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2016 |
FEBE Award for Teaching and Learning Faculty of Engineering and Built Environment - The University of Newcastle (Australia) |
Publications
For publications that are currently unpublished or in-press, details are shown in italics.
Book (4 outputs)
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2020 | Deep Neural Evolution: Deep Learning with Evolutionary Computation, Springer Nature, Singapore (2020) | |||||||
2016 |
Evolutionary Computation in Gene Regulatory Network Research, Wiley (2016)
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2016 |
Evolutionary Computation in Gene Regulatory Network Research, Wiley (2016)
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2011 |
Iba H, Noman N, New frontier in evolutionary algorithms: Theory and applications (2011) This book delivers theoretical and practical knowledge of Genetic Algorithms (GA) for the purpose of practical applications. It provides a methodology for a GA-based search strate... [more] This book delivers theoretical and practical knowledge of Genetic Algorithms (GA) for the purpose of practical applications. It provides a methodology for a GA-based search strategy with the integration of several Artificial Life and Artificial Intelligence techniques, such as memetic concepts, swarm intelligence, and foraging strategies. The development of such tools contributes to better optimizing methodologies when addressing tasks from areas such as robotics, financial forecasting, and data mining in bioinformatics. The emphasis of this book is on applicability to the real world. Tasks from application areas ¿ optimization of the trading rule in foreign exchange (FX) and stock prices, economic load dispatch in power system, exit/door placement for evacuation planning, and gene regulatory network inference in bioinformatics ¿ are studied, and the resultant empirical investigations demonstrate how successful the proposed approaches are when solving real-world tasks of great importance.
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Chapter (7 outputs)
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2024 |
Alshehri D, Noman N, Chiong R, Miah SJ, 'Remote Health Monitoring in the Era of the Internet of Medical Things', Data Modelling and Analytics for the Internet of Medical Things, CRC Press, Abingdon, Oxon 3-18 (2024) [B1]
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2020 |
Noman N, 'A Shallow Introduction to Deep Neural Networks', Deep Neural Evolution. Deep Learning with Evolutionary Computation, Springer Nature, Singapore 35-63 (2020) [B1]
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2020 |
Bakhshi A, Chalup S, Noman N, 'Fast Evolution of CNN Architecture for Image Classification', Deep Neural Evolution. Deep Learning with Evolutionary Computation, Springer, Singapore 209-229 (2020) [B1]
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2016 | Dinh QH, Aubert N, Noman N, Iba H, Rondelez Y, 'Evolving GRN-Inspired In Vitro Oscillatory Systems', Evolutionary Computation in Gene Regulatory Network Research, John Wiley & Sons, Hoboken, New Jersey 269-297 (2016) [B1] | Nova | |||||||||
2016 |
Iba H, Noman N, 'A Brief Introduction To Evolutionary And Other Nature-Inspired Algorithms', Evolutionary Computation in Gene Regulatory Network Research, John Wiley & Sons, Hoboken, New Jersey 3-29 (2016) [B1]
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Journal article (25 outputs)
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2024 |
Abedi M, Chiong R, Noman N, Liao X, Li D, 'A Metaheuristic Framework for Energy-Intensive Industries With Batch Processing Machines', IEEE Transactions on Engineering Management, 71 4502-4516 (2024) Batch processing machines, which operate multiple jobs at a time, are commonly used in energy-intensive industries. A significant amount of energy can be saved in such industries ... [more] Batch processing machines, which operate multiple jobs at a time, are commonly used in energy-intensive industries. A significant amount of energy can be saved in such industries using production scheduling as an approach to enhance efficiency. This study deals with an energy-aware scheduling problem for parallel batch processing machines with incompatible families and job release times. In such an environment, a machine may need to wait until all the jobs in the next batch become ready. During waiting time, a machine can be switched off or kept on standby for more energy-efficient scheduling. We first present a mixed-integer linear programming (MILP) model to solve the problem. However, the presented MILP model can only solve small problem instances. We therefore propose an energy-efficient tabu search (ETS) algorithm for solving larger problem instances. The proposed solution framework incorporates multiple neighborhood methods for efficient exploration of the search space. An energy-related heuristic is also integrated into the ETS for minimizing energy consumption during the waiting time. The performance of our proposed ETS algorithm is validated by comparing it with CPLEX for small problem instances and with two other heuristic algorithms for larger problem instances. The contribution of different components in ETS is also established in our experimental studies. The proposed solution framework is expected to bring many benefits in energy-intensive industries both economically and environmentally.
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2023 |
Rogers B, Noman N, Chalup S, Moscato P, 'A comparative analysis of deep neural network architectures for sentence classification using genetic algorithm', Evolutionary Intelligence, (2023) [C1] Because of the number of different architectures, numerous settings of their hyper-parameters and disparity among their sizes, it is difficult to equitably compare various deep ne... [more] Because of the number of different architectures, numerous settings of their hyper-parameters and disparity among their sizes, it is difficult to equitably compare various deep neural network (DNN) architectures for sentence classification. Evolutionary algorithms are emerging as a popular method for the automatic selection of architectures and hyperparameters for DNNs whose generalisation performance is heavily impacted by such settings. Most of the work in this area is done in the image domain, leaving text analysis, another prominent application domain of deep learning, largely absent. Besides, literature presents conflicting claims regarding the superiority of one DNN architecture over others in the context of sentence classification. To address this issue, we propose a genetic algorithm (GA) for optimising the architectural and hyperparameter settings in different DNN types for sentence classification. To enable the representation of the wide variety of architectures and hyperparameters utilised in DNNs, we employed a generalised and flexible encoding scheme in our GA. Our study involves optimising two convolutional and three recurrent architectures to ensure a fair and unbiased evaluation of their performance. Furthermore, we explore the effects of using F1 score versus accuracy as a performance metric during evolutionary optimisation of those architectures. Our results, using ten datasets, show that, in general, the architectures and hyperparameters evolved using the F1 score tended to outperform those evolved using accuracy and in the case of CNN and BiLSTM the results were significant in statistical measures. Of the five architectures considered, the GA-evolved gated recurrent unit (GRU) performed the strongest overall, achieving good generalisation performance while using relatively few trainable parameters, establishing GRU as the preferred architecture for the sentence classification task. The optimised architectures exhibited comparable performance with the state-of-the-art, given the large difference in trainable parameters.
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2023 | Lewis C, Varadharajan V, Noman N, 'Attacks against Federated Learning Defense Systems and their Mitigation', JOURNAL OF MACHINE LEARNING RESEARCH, 24 (2023) [C1] | ||||||||||
2022 |
Banerjee C, Chen Z, Noman N, 'Improved Soft Actor-Critic: Mixing Prioritized Off-Policy Samples With On-Policy Experiences', IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, [C1]
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2022 |
Banerjee C, Chen Z, Noman N, Zamani M, 'Optimal Actor-Critic Policy With Optimized Training Datasets', IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE, 6 1324-1334 (2022) [C1]
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2020 |
Noman N, Moscato P, 'Designing optimal combination therapy for personalised glioma treatment', Memetic Computing, 12 317-329 (2020) [C1] Background: Like it happens in other tumours, glioma cells co-evolve in a microenvironment consisting of bona fide tumour cells as well as a range of parenchymal cells, which prod... [more] Background: Like it happens in other tumours, glioma cells co-evolve in a microenvironment consisting of bona fide tumour cells as well as a range of parenchymal cells, which produces numerous signalling molecules. Recently, the results of an in silico experiment suggested that a combination therapy that would target multiple key cytokines at the same time may be more effective for suppressing the growth of a tumour. The in silico experiments also showed that the optimal combination therapy is very much dependent on a patient¿s molecular profile. Method: In this work, we employ evolutionary algorithms for designing optimal combination therapy tailored to the patient¿s tumour microenvironment. Experiments were performed using a state-of-the-art glioma microenvironment model, capable of imitating many characteristics of human glioma development, and many virtual patient profiles. Conclusions: Results show that the therapies designed by the presented memetic algorithm were very effective in impeding tumour growth and were tailored to the patient¿s personal tumour microenvironment.
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2020 |
Abedi M, Chiong R, Noman N, Zhang R, 'A multi-population, multi-objective memetic algorithm for energy-efficient job-shop scheduling with deteriorating machines', Expert Systems with Applications, 157 (2020) [C1]
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2019 |
Abu Zaher A, Berretta R, Noman N, Moscato P, 'An adaptive memetic algorithm for feature selection using proximity graphs', Computational Intelligence, 35 156-183 (2019) [C1]
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2019 |
Mirjalili SZ, Mirjalili S, Zhang H, Chalup S, Noman N, 'Improving the reliability of implicit averaging methods using new conditional operators for robust optimization', Swarm and Evolutionary Computation, 51 (2019) [C1]
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2016 |
Noman N, Inniss M, Iba H, Way JC, 'Pulse Detecting Genetic Circuit - A New Design Approach.', PLoS One, 11 (2016) [C1]
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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.
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2015 |
Noman N, Monjo T, Moscato P, Iba H, 'Evolving robust gene regulatory networks.', PLoS One, 10 e0116258 (2015) [C1]
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2015 |
Peng Y, Hasegawa Y, Noman N, Iba H, 'Temperature compensation via cooperative stability in protein degradation', PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 431 109-123 (2015) [C1]
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2015 |
Dinh HQ, Aubert N, Noman N, Fujii T, Rondelez Y, Iba H, 'An effective method for evolving reaction networks in synthetic biochemical systems', IEEE Transactions on Evolutionary Computation, 19 374-386 (2015) [C1] In this paper, we introduce our approach for evolving reaction networks. It is an efficient derivative of the neuroevolution of augmenting topologies algorithm directed at the evo... [more] In this paper, we introduce our approach for evolving reaction networks. It is an efficient derivative of the neuroevolution of augmenting topologies algorithm directed at the evolution of biochemical systems or molecular programs. Our method addresses the problem of meaningful crossovers between two chemical reaction networks of different topologies. It also builds on features such as speciation to speed up the search, to the point where it can deal with complete, realistic mathematical models of the biochemical processes. We demonstrate this framework by evolving credible biochemical answers to challenging autonomous molecular problems: in vitro batch oscillatory networks that match specific oscillation shapes. Our experimental results suggest that the search space is efficiently covered and that, by using crossover and preserving topological innovations, significant improvements in performance can be obtained for the automatic design of molecular programs.
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2014 |
Dinh QH, Noman N, Iba H, 'Oscillatory Synthetic Biological System Construction Using Interactive Evolutionary Computations', Journal of Computer Science, 10 2640-2652 (2014) [C1]
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2007 |
Noman N, Iba H, 'Inferring gene regulatory networks using differential evolution with local search heuristics', IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 4 634-647 (2007)
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Conference (49 outputs)
Year | Citation | Altmetrics | Link | ||||||||
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2023 |
Moscato P, Ciezak A, Noman N, 'Dynamic Depth for Better Generalization in Continued Fraction Regression', GECCO 2023 - Proceedings of the 2023 Genetic and Evolutionary Computation Conference, Lisbon, Portugal (2023) [E1]
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2023 |
Almtiri Z, Miah SJ, Noman N, 'Impact of Business Analytics and Decision Support Systems on E-commerce in SMEs', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Denarau, Fiji (2023) [E1]
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2023 |
Masmali FH, Miah SJ, Noman N, 'An Overview of the Internet of Things (IoT) Applications in the Health Sector in Saudi Arabi', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Denaru, Fiji (2023) [E1]
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2023 |
Banerjee C, Chen Z, Noman N, 'Boosting Exploration in Actor-Critic Algorithms by Incentivizing Plausible Novel States', Proceedings of the IEEE Conference on Decision and Control (2023) Improvement of exploration and exploitation using more efficient samples is a critical issue in reinforcement learning algorithms. A basic strategy of a learning algorithm is to f... [more] Improvement of exploration and exploitation using more efficient samples is a critical issue in reinforcement learning algorithms. A basic strategy of a learning algorithm is to facilitate indiscriminate exploration of the entire environment state space, as well as to encourage exploration of rarely visited states rather than frequently visited ones. Under this strategy, we propose a new method to boost exploration through an intrinsic reward, based on the measurement of a state's novelty and the associated benefit of exploring the state, collectively called plausible novelty. By incentivizing exploration of plausible novel states, an actor-critic (AC) algorithm can improve its sample efficiency and, consequently, its training performance. The new method is verified through extensive simulations of continuous control tasks in MuJoCo environments, using a variety of prominent off-policy AC algorithms.
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2023 |
Hadi Masmali F, Miah SJ, Noman N, 'Different Applications and Technologies of Internet of Things (IoT)', Lecture Notes in Networks and Systems (2023) [E1] Internet of things (IoT) has significantly altered the traditional lifestyle to a highly technologically advanced society. Some of the significant transformations that have been a... [more] Internet of things (IoT) has significantly altered the traditional lifestyle to a highly technologically advanced society. Some of the significant transformations that have been achieved through IoT are smart homes, smart transportation, smart city, and control of pollution. A considerable number of studies have been conducted and continues to be done to increase the use of technology through IoT. Furthermore, the research about IoT has not been done fully in improving the application of technology through IoT. Besides, IoT experiences several problems that need to be considered in order to get the full capability of IoT in changing society. This research paper addresses the key applications of IoT, the architecture of IoT, and the key issues affecting IoT. In addition, the paper highlights how big data analytics is essential in improving the effectiveness of IoT in various applications within society.
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2023 |
Almtiri Z, Miah SJ, Noman N, 'Application of E-commerce Technologies in Accelerating the Success of SME Operation', Proceedings of Seventh International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, London, UK (2023) [E1]
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2022 |
Chiong R, Noman N, 'Playing a Multi-action, Adversarial Game in a Dynamic Environment', Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022, Singapore (2022) [E1]
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2022 |
Rogers B, Noman N, Chalup S, Moscato P, 'Joint Optimization of Topology and Hyperparameters of Hybrid DNNs for Sentence Classification', 2022 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), ITALY, Padua (2022) [E1]
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2022 |
Paardekooper C, Noman N, Chiong R, Varadharajan V, 'Designing Deep Convolutional Neural Networks using a Genetic Algorithm for Image-based Malware Classification', 2022 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), Padua, ITALY (2022) [C1]
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2021 |
Rogers B, Noman N, Chalup S, Moscato P, 'Evolutionary Hyperparameter Optimisation for Sentence Classification', 2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC 2021), ELECTR NETWORK (2021) [E1]
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2020 |
Mirjalili S, Zhang H, Mirjalili S, Chalup S, Noman N, 'A Novel U-Shaped Transfer Function for Binary Particle Swarm Optimisation', Soft Computing for Problem Solving 2019. Proceedings of SocProS 2019, Liverpool, UK (2020) [E1]
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2020 |
Mirjalili SZ, Chalup S, Mirjalili S, Noman N, 'Robust Multi-Objective optimization using Conditional Pareto Optimal Dominance', 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings, Glasgow, UK (2020) [E1]
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2020 |
Mobasher-Kashani M, Noman N, Chalup S, 'Parallel LSTM Architectures for Non-Intrusive Load Monitoring in Smart Homes', 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, Canberra, ACT (2020) [E1]
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2019 |
Truong T, Moscato P, Noman N, 'A Computational Approach for Designing Combination Therapy in Combating Glioblastoma', 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, Wellington, NZ (2019) [E1]
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2019 |
Bakhshi A, Noman N, Chen Z, Zamani M, Chalup S, 'Fast Automatic Optimisation of CNN Architectures for Image Classification Using Genetic Algorithm', 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, Wellington, NZ (2019) [E1]
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2017 |
Abedi M, Chiong R, Noman N, Zhang R, 'A hybrid particle swarm optimisation approach for energy-efficient single machine scheduling with cumulative deterioration and multiple maintenances', 2017 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI), Honolulu, HI (2017) [E1]
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2017 |
Leane M, Noman N, 'An Evolutionary Metaheuristic Algorithm to Optimise Solutions to NES Games', 2017 21st Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES), Hanoi, Vietnam (2017) [E1]
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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]
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2006 |
Noman N, Iba H, 'On the reconstruction of gene regulatory networks from noisy expression profiles', 2006 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-6, Vancouver, CANADA (2006)
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2006 |
Noman N, Iba H, 'Inference of genetic networks using S-system: Information criteria for model selection', GECCO 2006: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOL 1 AND 2, Seattle, WA (2006)
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2006 |
Noman N, Lba H, 'A new generation alternation model for differential evolution', GECCO 2006: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOL 1 AND 2, Seattle, WA (2006)
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2006 |
Noman N, Iba H, 'On the reconstruction of gene regulatory networks from noisy expression profiles', 2006 IEEE Congress on Evolutionary Computation, CEC 2006 (2006) Noise is inevitable in microarray data. The real challenge lies in identifying the biomolecular interactions in spite of the significant noise level present in the expression prof... [more] Noise is inevitable in microarray data. The real challenge lies in identifying the biomolecular interactions in spite of the significant noise level present in the expression proflies that current technology offers. In this paper, we study the usefulness of an evolutionary approach in reverse engineering the biomolecular connections in a gene circuit from observed system dynamics that is contaminated with noise. The method uses an Information Criteria based fitness evaluation for selecting models, represented in decoupled S-system formalism, instead of the conventional Mean Squared Error (MSE) based fitness evaluation. The suitability of the method is tested in experiments of reconstructing an artificial network from gene expression profiles with varying noise levels. The proposed fitness function has been found more suitable for identifying correct network topology and for estimating the accurate parameter values compared to the existing one. © 2006 IEEE.
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2005 |
Noman N, Iba H, 'Inference of gene regulatory networks using S-system and differential evolution', GECCO 2005: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOLS 1 AND 2, Washington, DC (2005)
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2005 |
Noman N, Iba H, 'Enhancing Differential Evolution performance with local search for high dimensional function optimization', GECCO 2005: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOLS 1 AND 2, Washington, DC (2005)
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2005 |
Noman N, Iba H, 'Reverse engineering genetic networks using evolutionary computation.', Genome informatics. International Conference on Genome Informatics (2005) This paper proposes an improved evolutionary method for constructing the underlying network structure and inferring effective kinetic parameters from the time series data of gene ... [more] This paper proposes an improved evolutionary method for constructing the underlying network structure and inferring effective kinetic parameters from the time series data of gene expression using decoupled S-system formalism. We employed Trigonometric Differential Evolution (TDE) as the optimization engine of our algorithm for capturing the dynamics in gene expression data. A more effective fitness function for attaining the sparse structure, which is the hallmark of biological networks, has been applied. Experiments on artificial genetic network show the power of the algorithm in constructing the network structure and predicting the regulatory parameters. The method is used to evaluate interactions between genes in the SOS signaling pathway in Escherichia coli using gene expression data.
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2004 | Noman N, Okada K, Hosoyama N, Iba H, 'Use of clustering to improve the layout of gene network for visualization', CEC2004: PROCEEDINGS OF THE 2004 CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1 AND 2, Portland, OR (2004) | ||||||||||
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Grants and Funding
Summary
Number of grants | 12 |
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Total funding | $195,757 |
Click on a grant title below to expand the full details for that specific grant.
20221 grants / $5,400
Monitoring algorithm development and reporting application for reducing CO2 emissions from mine site-based fleet vehicles$5,400
Funding body: Leica Geosystems Pty Ltd
Funding body | Leica Geosystems Pty Ltd |
---|---|
Project Team | Doctor Nasimul Noman, Professor Stephan Chalup, Mr Mohammad Mobasher Kashani, Sean Perry |
Scheme | University of Newcastle Industry Training and Engagement (UNITE) Internship |
Role | Lead |
Funding Start | 2022 |
Funding Finish | 2022 |
GNo | G2200374 |
Type Of Funding | C3100 – Aust For Profit |
Category | 3100 |
UON | Y |
20211 grants / $20,000
Strategy Formulation in Open Adversarial Environments—An Evolutionary Approach for Strategic Decision Making in an Open Adversarial Environment$20,000
Funding body: Department of Defence
Funding body | Department of Defence |
---|---|
Project Team | Doctor Nasimul Noman, Associate Professor Raymond Chiong |
Scheme | Research Project |
Role | Lead |
Funding Start | 2021 |
Funding Finish | 2022 |
GNo | G2101024 |
Type Of Funding | C2200 - Aust Commonwealth – Other |
Category | 2200 |
UON | Y |
20182 grants / $20,394
Design and program a communication protocol between ResTrackWeb (RTWeb) and ResTrack Controller$12,994
Funding body: Banlaw Pty Ltd
Funding body | Banlaw Pty Ltd |
---|---|
Project Team | Professor Vijay Varadharajan, Doctor Uday Tupakula, Doctor Rukshan Athauda, Doctor Nasimul Noman |
Scheme | Entrepreneurs' Programme: Innovation Connections |
Role | Investigator |
Funding Start | 2018 |
Funding Finish | 2018 |
GNo | G1701620 |
Type Of Funding | C3100 – Aust For Profit |
Category | 3100 |
UON | Y |
Deep Neuroevolution for Controlling a Quadrotor$7,400
Funding body: The University of Newcastle, Faculty of Engineering and built Environment, School of Electrical Engineering and Computing
Funding body | The University of Newcastle, Faculty of Engineering and built Environment, School of Electrical Engineering and Computing |
---|---|
Project Team | Nasimul Noman, Stephan Chalup, Mohsen Zamani and Zhiyong Chen |
Scheme | SEEC Research Incentive Scheme 2018 |
Role | Lead |
Funding Start | 2018 |
Funding Finish | 2018 |
GNo | |
Type Of Funding | Internal |
Category | INTE |
UON | N |
20174 grants / $128,763
Develop and optimise the Business’s Zambezy Intranet Portal Product$49,227
Funding body: Definiti Pty Ltd
Funding body | Definiti Pty Ltd |
---|---|
Project Team | Doctor Rukshan Athauda, Associate Professor Marc Adam, Doctor Nasimul Noman |
Scheme | Entrepreneurs' Programme: Innovation Connections |
Role | Investigator |
Funding Start | 2017 |
Funding Finish | 2018 |
GNo | G1701123 |
Type Of Funding | C3100 – Aust For Profit |
Category | 3100 |
UON | Y |
Develop and optimise the Business’s Zambezy Intranet Portal Product$49,227
Funding body: Department of Industry, Innovation and Science
Funding body | Department of Industry, Innovation and Science |
---|---|
Project Team | Doctor Rukshan Athauda, Associate Professor Marc Adam, Doctor Nasimul Noman |
Scheme | Entrepreneurs' Programme: Innovation Connections |
Role | Investigator |
Funding Start | 2017 |
Funding Finish | 2018 |
GNo | G1701124 |
Type Of Funding | C2100 - Aust Commonwealth – Own Purpose |
Category | 2100 |
UON | Y |
Discovering prognostic features using multi-objective evolutionary algorithm for predicting remaining useful life$20,309
Funding body: Faculty of Engineering and Built Environment - The University of Newcastle (Australia)
Funding body | Faculty of Engineering and Built Environment - The University of Newcastle (Australia) |
---|---|
Project Team | Dr. Nasimul Noman and Prof. Regina Berretta |
Scheme | FEBE Strategic Pilot Grant |
Role | Lead |
Funding Start | 2017 |
Funding Finish | 2017 |
GNo | |
Type Of Funding | Internal |
Category | INTE |
UON | N |
Automatic Improvement of Bioinformatics Programs using Memetic Algorithms$10,000
Funding body: Faculty of Engineering and Built Environment - The University of Newcastle (Australia)
Funding body | Faculty of Engineering and Built Environment - The University of Newcastle (Australia) |
---|---|
Project Team | A/Prof. Hongyu Zhang, Prof. Pablo Moscato and Dr. Nasimul Noman |
Scheme | SEEC Research Incentive Fund |
Role | Investigator |
Funding Start | 2017 |
Funding Finish | 2017 |
GNo | |
Type Of Funding | Internal |
Category | INTE |
UON | N |
20161 grants / $10,000
Multi-objective Memetic Algorithm for Large-Scale Community Detection Considering both Topology and Contents$10,000
Funding body: Faculty of Engineering and Built Environment - The University of Newcastle (Australia)
Funding body | Faculty of Engineering and Built Environment - The University of Newcastle (Australia) |
---|---|
Project Team | A/Prof. Regina Berretta, Dr. Nasimul Noman, Prof. Pablo Moscato and Dr. Luke Mathieson |
Scheme | FEBE Strategic Pilot Grant |
Role | Investigator |
Funding Start | 2016 |
Funding Finish | 2016 |
GNo | |
Type Of Funding | Internal |
Category | INTE |
UON | N |
20151 grants / $5,000
A Multi-Objective Evolutionary Algorithm for Breast Cancer Patient Stratification$5,000
Funding body: Faculty of Engineering and Built Environment - The University of Newcastle (Australia)
Funding body | Faculty of Engineering and Built Environment - The University of Newcastle (Australia) |
---|---|
Scheme | FEBE Strategic Pilot Grant |
Role | Lead |
Funding Start | 2015 |
Funding Finish | 2015 |
GNo | |
Type Of Funding | Internal |
Category | INTE |
UON | N |
20142 grants / $6,200
New Staff Grant$5,000
Funding body: University of Newcastle - Faculty of Engineering & Built Environment
Funding body | University of Newcastle - Faculty of Engineering & Built Environment |
---|---|
Project Team | Doctor Nasimul Noman |
Scheme | New Staff Grant |
Role | Lead |
Funding Start | 2014 |
Funding Finish | 2014 |
GNo | G1400989 |
Type Of Funding | Internal |
Category | INTE |
UON | Y |
15th International Conference on Systems Biology, Melbourne, 14-18 September, 2014$1,200
Funding body: University of Newcastle - Faculty of Engineering & Built Environment
Funding body | University of Newcastle - Faculty of Engineering & Built Environment |
---|---|
Project Team | Doctor Nasimul Noman |
Scheme | Travel Grant |
Role | Lead |
Funding Start | 2014 |
Funding Finish | 2015 |
GNo | G1400795 |
Type Of Funding | Internal |
Category | INTE |
UON | Y |
Research Supervision
Number of supervisions
Current Supervision
Commenced | Level of Study | Research Title | Program | Supervisor Type |
---|---|---|---|---|
2024 | PhD | Reliable IoT Attack Detection Using Artificial Metacognition and Deep Learning | PhD (Computer Science), College of Engineering, Science and Environment, The University of Newcastle | Co-Supervisor |
2023 | PhD | Distributed Anomaly Detection in IoT Devices Using Deep Reinforcement Learning | PhD (Information Technology), College of Engineering, Science and Environment, The University of Newcastle | Co-Supervisor |
2023 | PhD | Security Architecture for Autonomous Systems | PhD (Computer Science), College of Engineering, Science and Environment, The University of Newcastle | Co-Supervisor |
2023 | PhD | Formulation of New Fertilizer Using Golden Apple Snail on Glutinous Rice | PhD (Information Technology), College of Engineering, Science and Environment, The University of Newcastle | Principal Supervisor |
2022 | PhD | Scalability Issue in Blockchain-IoT Applications | PhD (Information Technology), College of Engineering, Science and Environment, The University of Newcastle | Principal Supervisor |
2021 | PhD | Cyber Security Importance, Risks, and Issues | PhD (Information Systems), College of Engineering, Science and Environment, The University of Newcastle | Co-Supervisor |
2020 | PhD | Complex Networks Reconstruction and Control via Memetic Algorithms and Machine Learning | PhD (Computer Science), College of Engineering, Science and Environment, The University of Newcastle | Co-Supervisor |
2020 | PhD | Analytical Continued Fractions for Computational Intelligence in Forecasting with its Application in Precision Agriculture | PhD (Computer Science), College of Engineering, Science and Environment, The University of Newcastle | Co-Supervisor |
2020 | PhD | On the Design of Trustworthy Machine Learning Based Systems for IoT and SDN Infrastructures | PhD (Computer Science), College of Engineering, Science and Environment, The University of Newcastle | Co-Supervisor |
2019 | PhD | Evolutionary Algorithms for Training Deep Neural Networks | PhD (Computer Science), College of Engineering, Science and Environment, The University of Newcastle | Principal Supervisor |
Past Supervision
Year | Level of Study | Research Title | Program | Supervisor Type |
---|---|---|---|---|
2024 | PhD | Impact of Technology Adoption on the Success of E-Commerce Technologies in Small and Medium Enterprises | PhD (Information Systems), College of Engineering, Science and Environment, The University of Newcastle | Co-Supervisor |
2023 | PhD | Acceptance of Internet of Things-based Innovations for Improving Healthcare in Saudi Arabia | PhD (Information Systems), College of Engineering, Science and Environment, The University of Newcastle | Co-Supervisor |
2023 | PhD | A Fuzzy Adaptive Metaheuristic Framework for Optimisation and Prediction Problems | PhD (Information Technology), College of Engineering, Science and Environment, The University of Newcastle | Co-Supervisor |
2023 | PhD | Improving Sample Efficiency in Deep Reinforcement Learning Based Control of Dynamic Systems | PhD (Electrical Engineering), College of Engineering, Science and Environment, The University of Newcastle | Co-Supervisor |
2022 | PhD | Deep Learning for Analysis of Time-Series in Smart Home Environments | PhD (Computer Science), College of Engineering, Science and Environment, The University of Newcastle | Co-Supervisor |
2021 | PhD | Constraint-based Robust Single- and Multi-Objective Optimization | PhD (Computer Science), College of Engineering, Science and Environment, The University of Newcastle | Principal Supervisor |
2021 | PhD | Metaheuristic Optimisation Algorithms for Solving Energy-Efficient Production Scheduling Problems via Machine On/Off and Speed Control Mechanisms | PhD (Information Technology), College of Engineering, Science and Environment, The University of Newcastle | Co-Supervisor |
2020 | PhD | Target Curricula for Multi-Target Classification: The Role of Internal Meta-Features in Machine Teaching | PhD (Computer Science), College of Engineering, Science and Environment, The University of Newcastle | Co-Supervisor |
2018 | Honours | Effect of Experience Replay length On Deep Reinforcement Learning | Computer Science, University of Newcastle - School of Electrical Engineering and Computing | Australia | Principal Supervisor |
2017 | PhD | A New Feature Selection Approach Based on Proximity Graphs and Evolutionary Computation | PhD (Computer Science), College of Engineering, Science and Environment, The University of Newcastle | Co-Supervisor |
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 | |
---|---|---|
Japan | 40 | |
Australia | 37 | |
Bangladesh | 10 | |
Saudi Arabia | 4 | |
China | 3 | |
More... |
Dr Nasimul Noman
Position
Senior Lecturer
School of Information and Physical Sciences
College of Engineering, Science and Environment
Focus area
Computing and Information Technology
Contact Details
nasimul.noman@newcastle.edu.au | |
Phone | (02) 4042 0488 |
Office
Room | ES237 |
---|---|
Building | ES Building |
Location | Callaghan University Drive Callaghan, NSW 2308 Australia |