ORCID Profile
0000-0003-4955-9684
Current Organisation
Deakin University
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Publisher: Elsevier BV
Date: 07-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Publisher: MDPI AG
Date: 03-10-2020
DOI: 10.3390/A13100252
Abstract: Automated deployment of software components into hardware resources is a highly constrained optimisation problem. Hardware memory limits which components can be deployed into the particular hardware unit. Interacting software components have to be deployed either into the same hardware unit, or connected units. Safety concerns could restrict the deployment of two software components into the same unit. All these constraints hinder the search for high quality solutions that optimise quality attributes, such as reliability and communication overhead. When the optimisation problem is multi-objective, as it is the case when considering reliability and communication overhead, existing methods often fail to produce feasible results. Moreover, this problem can be modelled by bipartite graphs with complicating constraints, but known methods do not scale well under the additional restrictions. In this paper, we develop a novel multi-objective Beam search and ant colony optimisation (Beam-ACO) hybrid method, which uses problem specific bounds derived from communication, co-localisation and memory constraints, to guide the search towards feasibility. We conduct an experimental evaluation on a range of component deployment problem instances with varying levels of difficulty. We find that Beam-ACO guided by the co-localisation constraint is most effective in finding high quality feasible solutions.
Publisher: Elsevier BV
Date: 06-2021
Publisher: MDPI AG
Date: 21-07-2021
DOI: 10.3390/A14080219
Abstract: The increasing demand for work-ready students has heightened the need for universities to provide work integrated learning programs to enhance and reinforce students’ learning experiences. Students benefit most when placements meet their academic requirements and graduate aspirations. Businesses and community partners are more engaged when they are allocated students that meet their industry requirements. In this paper, both an integer programming model and an ant colony optimisation heuristic are proposed, with the aim of automating the allocation of students to industry placements. The emphasis is on maximising student engagement and industry partner satisfaction. As part of the objectives, these methods incorporate ersity in industry sectors for students undertaking multiple placements, gender equity across placement providers, and the provision for partners to rank student selections. The experimental analysis is in two parts: (a) we investigate how the integer programming model performs against manual allocations and (b) the scalability of the IP model is examined. The results show that the IP model easily outperforms the previous manual allocations. Additionally, an artificial dataset is generated which has similar properties to the original data but also includes greater numbers of students and placements to test the scalability of the algorithms. The results show that integer programming is the best option for problem instances consisting of less than 3000 students. When the problem becomes larger, significantly increasing the time required for an IP solution, ant colony optimisation provides a useful alternative as it is always able to find good feasible solutions within short time-frames.
Publisher: Springer International Publishing
Date: 2020
Publisher: American Geophysical Union (AGU)
Date: 03-2023
DOI: 10.1029/2022EF003083
Abstract: We developed a machine learning based surrogate model to identify sustainability pathways through rapid scenario generation and defined the safe operating space for achieving them via scenario discovery. We trained a surrogate model to replicate the Land‐Use Trade‐Offs integrated model of the Australian land system. Latin hypercube s ling was used to create many scenarios exploring the impact of uncertainties in key drivers including future socio‐economic development, climate change mitigation, and agricultural productivity at a granular level. Economic and environmental impacts were evaluated against nationally downscaled SDG targets. Scenario discovery revealed new pathways to achieving five SDG targets for 2050 which required crop yield increases above 1.78 times, a carbon price above 100 AU$ tCO 2 −1 , a % bio ersity levy on carbon plantings, and carefully regulated land‐use policy. Machine learning based surrogate modeling teamed with scenario discovery revealed the policy and scenario settings required for a more sustainable future for the Australian land sector.
Publisher: MDPI AG
Date: 14-11-2022
DOI: 10.3390/A15110428
Abstract: The total capital in cryptocurrency markets is around two trillion dollars in 2022, which is almost the same as Apple’s market capitalisation at the same time. Increasingly, cryptocurrencies have become established in financial markets with an enormous number of transactions and trades happening every day. Similar to other financial systems, price prediction is one of the main challenges in cryptocurrency trading. Therefore, the application of artificial intelligence, as one of the tools of prediction, has emerged as a recently popular subject of investigation in the cryptocurrency domain. Since machine learning models, as opposed to traditional financial models, demonstrate satisfactory performance in quantitative finance, they seem ideal for coping with the price prediction problem in the complex and volatile cryptocurrency market. There have been several studies that have focused on applying machine learning for price and movement prediction and portfolio management in cryptocurrency markets, though these methods and models are in their early stages. This survey paper aims to review the current research trends in applications of supervised and reinforcement learning models in cryptocurrency price prediction. This study also highlights potential research gaps and possible areas for improvement. In addition, it emphasises potential challenges and research directions that will be of interest in the artificial intelligence and machine learning communities focusing on cryptocurrencies.
No related grants have been discovered for Asef Nazari.