ORCID Profile
0000-0002-6636-8807
Current Organisations
Ministry of Energy & Infrastructure
,
University of Western Australia
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Publisher: IEEE
Date: 06-2023
Publisher: IEEE
Date: 11-2021
Publisher: IEEE
Date: 09-2015
Publisher: IEEE
Date: 2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2023
Publisher: IEEE
Date: 09-2018
Publisher: IEEE
Date: 10-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 10-2010
Publisher: Informa UK Limited
Date: 22-08-2023
Publisher: IEEE
Date: 12-2007
Publisher: British Institute of Radiology
Date: 24-10-2023
DOI: 10.1259/BJR.20221178
Publisher: Oxford University Press (OUP)
Date: 2010
Abstract: We present a new method by which the total masses of galaxies including dark matter can be estimated from the kinematics of their globular cluster systems (GCSs). In the proposed method, we apply the convolutional neural networks (CNNs) to the 2D maps of line-of-sight velocities (V) and velocity dispersions (σ) of GCSs predicted from numerical simulations of disc and elliptical galaxies. In this method, we first train the CNN using either only a larger number ($\\sim 200\\, 000$) of the synthesized 2D maps of σ (‘one-channel’) or those of both σ and V (‘two-channel’). Then, we use the CNN to predict the total masses of galaxies (i.e. test the CNN) for the totally unknown data set that is not used in training the CNN. The principal results show that overall accuracy for one-channel and two-channel data is 97.6 per cent and 97.8 per cent, respectively, which suggests that the new method is promising. The mean absolute errors (MAEs) for one-channel and two-channel data are 0.288 and 0.275, respectively, and the value of root mean square errors (RMSEs) are 0.539 and 0.51 for one-channel and two-channel, respectively. These smaller MAEs and RMSEs for two-channel data (i.e. better performance) suggest that the new method can properly consider the global rotation of GCSs in the mass estimation. We also applied our proposed method to real data collected from observations of NGC 3115 to compare the total mass predicted by our proposed method and other popular methods from the literature.
Publisher: Informa UK Limited
Date: 25-07-2015
Publisher: Informa UK Limited
Date: 16-07-2020
Publisher: American Society of Mechanical Engineers
Date: 05-06-2022
Abstract: Achieving and maintaining a suitable level of bolt pre-load is critical to ensure structural reliability under the Fatigue Limit State for bolted ring-flanges in offshore wind turbine structures. Bolt pre-load is likely to vary over lifetime, with re-tensioning applied if relaxation exceeds design guideline allowance. An approach to assess the influence of varying bolt pre-load may be useful in the operational context. Recent work has demonstrated the suitability of a Gaussian Process surrogate model to emulate Finite Element Method structural simulations models of bolted ring-flanges, with computational efficiency gains. In this paper we predict cumulative fatigue damage in bolts over time, given uncertainty in bolt pre-load estimation, using a Gaussian Process surrogate model. We perform Structural Reliability Analysis to deliver approximations of annual Probability of Failure and the Reliability Index, under the Fatigue Limit State. Our approximations are compared to targets defined in relevant design standards. Furthermore, we incorporate observations, and maintenance actions, in updating the Structural Reliability Analysis during operation, and suggest practical applications of this method to inform inspection and maintenance practices.
Publisher: IEEE
Date: 10-2010
Publisher: Elsevier BV
Date: 02-2021
Publisher: Australasian Society for Computers in Learning in Tertiary Education
Date: 19-03-2021
DOI: 10.14742/AJET.5487
Abstract: A barrier to using head mounted display (HMD) virtual reality (VR) in education is access to hardware for large classes. This paper compares students’ learning when engaging with an HMD VR simulation as the operator and as the observer, to evaluate whether benefits of HMD VR can be achieved without requiring all students to operate the equipment. Postgraduate engineering students (N = 117) completed a safety hazard identification exercise in a workshop. The performance of students who operated and observed was compared. Results showed that students performed similarly in the exercise that followed the simulation whether they operated HMD VR (n = 33) or observed (n = 84). The finding suggests that educators may be able to use HMD VR simulations in classes with a large enrolment, by reducing the need for investment and management of a large number of sets of HMD VR equipment. Implications for practice or policy: Engineering educators can use HMD VR simulations to teach students about safety in design. Engineering students are able to identify safety hazards in a HMD VR simulation effectively whether they are operating the equipment or observing another student in their group operating the VR equipment. One HMD VR set per student group is sufficient. HMD VR simulations can be used inclusively, even when some students are unable or unwilling to wear the headset.
Publisher: IEEE
Date: 2007
Publisher: Elsevier BV
Date: 02-2023
Publisher: Elsevier BV
Date: 04-2022
Publisher: Science Publications
Date: 04-2011
Publisher: Informa UK Limited
Date: 26-05-2023
Publisher: IEEE
Date: 03-2020
Publisher: Frontiers Media SA
Date: 11-2021
Abstract: Metastatic Prostate Cancer (mPCa) is associated with a poor patient prognosis. mPCa spreads throughout the body, often to bones, with spatial and temporal variations that make the clinical management of the disease difficult. The evolution of the disease leads to spatial heterogeneity that is extremely difficult to characterise with solid biopsies. Imaging provides the opportunity to quantify disease spread. Advanced image analytics methods, including radiomics, offer the opportunity to characterise heterogeneity beyond what can be achieved with simple assessment. Radiomics analysis has the potential to yield useful quantitative imaging biomarkers that can improve the early detection of mPCa, predict disease progression, assess response, and potentially inform the choice of treatment procedures. Traditional radiomics analysis involves modelling with hand-crafted features designed using significant domain knowledge. On the other hand, artificial intelligence techniques such as deep learning can facilitate end-to-end automated feature extraction and model generation with minimal human intervention. Radiomics models have the potential to become vital pieces in the oncology workflow, however, the current limitations of the field, such as limited reproducibility, are impeding their translation into clinical practice. This review provides an overview of the radiomics methodology, detailing critical aspects affecting the reproducibility of features, and providing ex les of how artificial intelligence techniques can be incorporated into the workflow. The current landscape of publications utilising radiomics methods in the assessment and treatment of mPCa are surveyed and reviewed. Associated studies have incorporated information from multiple imaging modalities, including bone scintigraphy, CT, PET with varying tracers, multiparametric MRI together with clinical covariates, spanning the prediction of progression through to overall survival in varying cohorts. The methodological quality of each study is quantified using the radiomics quality score. Multiple deficits were identified, with the lack of prospective design and external validation highlighted as major impediments to clinical translation. These results inform some recommendations for future directions of the field.
Publisher: IEEE
Date: 10-2010
Publisher: Universitat Politècnica de Catalunya
Date: 09-2022
DOI: 10.5821/CONFERENCE-9788412322262.1253
Abstract: Learning objectives are important as they provide direction to teaching staff towards what content should be taught, what activities should be undertaken and what assessments are to be used to confirm understanding. Two decades ago, the evolution of new learning modes such as recorded, remote, and simulation/virtual started the research process to define and better understand learning objectives in the teaching laboratory. Much is still to be learnt about laboratory learning objectives including which are most important, and if what is deemed important is universal. For ex le, do academics in Europe and Australasia align in which objectives are most important and which are not? To answer this question, European and Australasian engineering academics were asked to rank laboratory objectives across the cognitive, psychomotor, and affective domain using a predefined tool called Laboratory Learning Objectives Measurement. A total of 113 academics from Australasia and 25 from Europe responded to the survey. A statistical analysis was conducted to compare the rankings. The findings from this survey show that substantial alignment occurs across the cognitive and psychomotor domains but differs across the affective domain.
Publisher: Springer Science and Business Media LLC
Date: 11-08-2023
DOI: 10.1007/S00259-023-06371-5
Abstract: The O-(2-[ 18 F]-fluoroethyl)- l -tyrosine (FET) PET in Glioblastoma (FIG) trial is an Australian prospective, multi-centre study evaluating FET PET for glioblastoma patient management. FET PET imaging timepoints are pre-chemoradiotherapy (FET1), 1-month post-chemoradiotherapy (FET2), and at suspected progression (FET3). Before participant recruitment, site nuclear medicine physicians (NMPs) underwent credentialing of FET PET delineation and image interpretation. Sites were required to complete contouring and dynamic analysis by ≥ 2 NMPs on benchmarking cases ( n = 6) assessing biological tumour volume (BTV) delineation (3 × FET1) and image interpretation (3 × FET3). Data was reviewed by experts and violations noted. BTV definition includes tumour-to-background ratio (TBR) threshold of 1.6 with crescent-shaped background contour in the contralateral normal brain. Recurrence seudoprogression interpretation (FET3) required assessment of maximum TBR (TBR max ), dynamic analysis (time activity curve [TAC] type, time to peak), and qualitative assessment. Intraclass correlation coefficient (ICC) assessed volume agreement, coefficient of variation (CoV) compared maximum/mean TBR (TBR max /TBR mean ) across cases, and pairwise analysis assessed spatial (Dice similarity coefficient [DSC]) and boundary agreement (Hausdorff distance [HD], mean absolute surface distance [MASD]). Data was accrued from 21 NMPs (10 centres, n ≥ 2 each) and 20 underwent review. The initial pass rate was 93/119 (78.2%) and 27/30 requested resubmissions were completed. Violations were found in 25/72 (34.7% 13/12 minor/major) of FET1 and 22/74 (29.7% 14/8 minor/major) of FET3 reports. The primary reasons for resubmission were as follows: BTV over-contour (15/30, 50.0%), background placement (8/30, 26.7%), TAC classification (9/30, 30.0%), and image interpretation (7/30, 23.3%). CoV median and range for BTV, TBR max , and TBR mean were 21.53% (12.00–30.10%), 5.89% (5.01–6.68%), and 5.01% (3.37–6.34%), respectively. BTV agreement was moderate to excellent (ICC = 0.82 95% CI, 0.63–0.97) with good spatial (DSC = 0.84 ± 0.09) and boundary (HD = 15.78 ± 8.30 mm MASD = 1.47 ± 1.36 mm) agreement. The FIG study credentialing program has increased expertise across study sites. TBR max and TBR mean were robust, with considerable variability in BTV delineation and image interpretation observed.
Publisher: Springer Science and Business Media LLC
Date: 17-10-2023
Publisher: IEEE
Date: 09-2010
Publisher: IEEE
Date: 10-2010
Publisher: Elsevier BV
Date: 02-2018
Publisher: Informa UK Limited
Date: 03-02-2023
Publisher: Science Publications
Date: 03-2011
Publisher: Springer International Publishing
Date: 2022
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: IEEE
Date: 10-2010
Publisher: IEEE
Date: 04-12-2021
Publisher: Elsevier BV
Date: 04-2019
Publisher: IEEE
Date: 07-2009
Publisher: SCITEPRESS - Science and Technology Publications
Date: 2021
Publisher: IEEE
Date: 11-2021
Publisher: MDPI AG
Date: 03-06-2022
DOI: 10.3390/APP12115702
Abstract: Epilepsy is one of the most common brain diseases that affects more than 1% of the world’s population. It is characterized by recurrent seizures, which come in different types and are treated differently. Electroencephalography (EEG) is commonly used in medical services to diagnose seizures and their types. The accurate identification of seizures helps to provide optimal treatment and accurate information to the patient. However, the manual diagnostic procedures of epileptic seizures are laborious and require professional skills. This paper presents a novel automatic technique that involves the extraction of specific features from epileptic seizures’ EEG signals using dual-tree complex wavelet transform (DTCWT) and classifying them into one of the seven types of seizures, including absence, complex-partial, focal non-specific, generalized non-specific, simple-partial, tonic-clonic, and tonic seizures. We evaluated the proposed technique on the TUH EEG Seizure Corpus (TUSZ) ver.1.5.2 dataset and compared the performance with the existing state-of-the-art techniques using the overall F1-score due to class imbalance of seizure types. Our proposed technique achieved the best results of a weighted F1-score of 99.1% and 74.7% for seizure-wise and patient-wise classification, respectively, thereby setting new benchmark results for this dataset.
Publisher: ACM
Date: 16-12-2009
Publisher: SCITEPRESS - Science and Technology Publications
Date: 2021
Publisher: Springer Science and Business Media LLC
Date: 08-2023
DOI: 10.1007/S10723-023-09679-6
Abstract: Containers have emerged recently as a cloud technology for improving and managing cloud resources. They improve resource sharing by allowing instances to run on top of the host’s operating system. Container-based virtualization runs and manages hosted instances via the host kernel. Resource sharing can cause resource contention. In addition, dependent jobs, which may be deployed across multiple hosts, require frequent communication, resulting in a high volume of network traffic and network contention. The majority of existing research focuses on load balancing, with no consideration for the fact that network contention also plays a significant role in container performance. In this research, we propose a Dependency-aware Scheduling algorithm (DAScheduler) that deploys jobs into containers while accounting for both load balancing and job dependencies. The experimental results show that DAScheduler reduces network traffic by more than half and balances the loads. In comparison to one of the existing state-of-the-art techniques, DAScheduler improves overall cloud performance.
Publisher: MDPI AG
Date: 23-09-2022
DOI: 10.3390/INFORMATICS9040073
Abstract: While the consumption of visual information becomes a daily commodity integrated into our lives, data visualisation is dominated by dashboards and charts. The main contribution of this article is an advanced way to visualise data as a data story. We converged paradigms from digital storytelling, serious games, and data visualisation to turn data into useful insights. The creation, management, and analysis of data have been increasingly given more attention in industry and professional practices. However, the potential of packaging data and analytic results into easily digestible and visually explorable content intended for non-professional audiences has not yet been investigated to its full extent. We contributed towards overcoming the gap between data analytics and data presentation. By integrating a story-like environment and entertainment into data visualisation, we explore the possibilities of efficiently communicating data and insights to general audiences in a casual context. We present this modular approach to customising messages for visual data storytelling from an information and communication perspective, including a test prototype developed to illustrate our data storytelling framework.
Publisher: Springer Science and Business Media LLC
Date: 08-08-2023
DOI: 10.1007/S00259-023-06382-2
Abstract: This study aimed to (i) validate the Response Evaluation Criteria in PSMA (RECIP 1.0) criteria in a cohort of biochemically recurrent (BCR) prostate cancer (PCa) patients and (ii) determine if this classification could be performed fully automatically using a trained artificial intelligence (AI) model. One hundred ninety-nine patients were imaged with [ 68 Ga]Ga-PSMA-11 PET/CT once at the time of biochemical recurrence and then a second time a median of 6.0 months later to assess disease progression. Standard-of-care treatments were administered to patients in the interim. Whole-body tumour volume was quantified semi-automatically (TTV man ) in all patients and using a novel AI method (TTV AI ) in a subset ( n = 74, the remainder were used in the training process of the model). Patients were classified as having progressive disease (RECIP-PD), or non-progressive disease (non RECIP-PD). Association of RECIP classifications with patient overall survival (OS) was assessed using the Kaplan-Meier method with the log rank test and univariate Cox regression analysis with derivation of hazard ratios (HRs). Concordance of manual and AI response classifications was evaluated using the Cohen’s kappa statistic. Twenty-six patients (26/199 = 13.1%) presented with RECIP-PD according to semi-automated delineations, which was associated with a significantly lower survival probability (log rank p 0.005) and higher risk of death (HR = 3.78 (1.96–7.28), p 0.005). Twelve patients (12/74 = 16.2%) presented with RECIP-PD according to AI-based segmentations, which was also associated with a significantly lower survival (log rank p = 0.013) and higher risk of death (HR = 3.75 (1.23–11.47), p = 0.02). Overall, semi-automated and AI-based RECIP classifications were in fair agreement (Cohen’s k = 0.31). RECIP 1.0 was demonstrated to be prognostic in a BCR PCa population and is robust to two different segmentation methods, including a novel AI-based method. RECIP 1.0 can be used to assess disease progression in PCa patients with less advanced disease. This study was registered with the Australian New Zealand Clinical Trials Registry (ACTRN12615000608561) on 11 June 2015.
Publisher: Elsevier BV
Date: 08-2021
Publisher: IEEE
Date: 07-2018
Publisher: MDPI AG
Date: 20-06-2019
DOI: 10.3390/EN12122375
Abstract: Electric vehicles (EVs) could be used to address the issues of environmental pollution and the depletion of non-renewable energy resources. EVs, which are energized by a battery storage system, are becoming attractive because they keep the environment clean. Furthermore, the cost of EVs is becoming cheaper. Thus, EVs will become a significant load on utility distribution system in the future. EV chargers play a significant role in the expansion of EVs. The input current of an EV charger with a high total harmonic distortion (THD) and a high ripple distortion of the output voltage can impact battery life and battery charging time. Furthermore, the high cost and large size of the chargers are considered other issues in EV development. This work presents the complete design process of a universal EV charger with a special focus on its control algorithms. In this regard, a novel control algorithm based on the integration of voltage-oriented control (VOC) and the sinusoidal pulse-width modulation (SPWM) technique is proposed to ensure effective Levels 1, 2, and 3 battery charging. A simulation of the universal EV charger was conducted and assessed in MATLAB–Simulink. Moreover, a laboratory prototype was constructed with a TMS320F28335 digital signal processor (DSP) programmed as the controller to validate its operation and performance. The findings show that the proposed charger is able to provide a controllable and constant charging voltage for a variety of EVs, with an input current of low total harmonic distortion (THD) and an almost unity power factor.
Publisher: Springer Science and Business Media LLC
Date: 17-08-2022
DOI: 10.1007/S00259-022-05927-1
Abstract: This study aimed to develop and assess an automated segmentation framework based on deep learning for metastatic prostate cancer (mPCa) lesions in whole-body [ 68 Ga]Ga-PSMA-11 PET/CT images for the purpose of extracting patient-level prognostic biomarkers. Three hundred thirty-seven [ 68 Ga]Ga-PSMA-11 PET/CT images were retrieved from a cohort of biochemically recurrent PCa patients. A fully 3D convolutional neural network (CNN) is proposed which is based on the self-configuring nnU-Net framework, and was trained on a subset of these scans, with an independent test set reserved for model evaluation. Voxel-level segmentation results were assessed using the dice similarity coefficient (DSC), positive predictive value (PPV), and sensitivity. Sensitivity and PPV were calculated to assess lesion level detection patient-level classification results were assessed by the accuracy, PPV, and sensitivity. Whole-body biomarkers total lesional volume (TLV auto ) and total lesional uptake (TLU auto ) were calculated from the automated segmentations, and Kaplan–Meier analysis was used to assess biomarker relationship with patient overall survival. At the patient level, the accuracy, sensitivity, and PPV were all 90%, with the best metric being the PPV (97.2%). PPV and sensitivity at the lesion level were 88.2% and 73.0%, respectively. DSC and PPV measured at the voxel level performed within measured inter-observer variability (DSC, median = 50.7% vs. second observer = 32%, p = 0.012 PPV, median = 64.9% vs. second observer = 25.7%, p 0.005). Kaplan–Meier analysis of TLV auto and TLU auto showed they were significantly associated with patient overall survival (both p 0.005). The fully automated assessment of whole-body [ 68 Ga]Ga-PSMA-11 PET/CT images using deep learning shows significant promise, yielding accurate scan classification, voxel-level segmentations within inter-observer variability, and potentially clinically useful prognostic biomarkers associated with patient overall survival. This study was registered with the Australian New Zealand Clinical Trials Registry (ACTRN12615000608561) on 11 June 2015.
Publisher: Science Publications
Date: 03-2011
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: EasyChair
Date: 2018
DOI: 10.29007/FZRQ
Abstract: Search and optimization problems are a major arena for the practical application of Artificial Intelligence. However, when supply chain optimization and scheduling is tackled, techniques based on linear or non-linear programming are often used in preference to Evolutionary Computation such as Genetic Algorithms (GAs). It is important to analyse whether GA are suitable for continuous real-world supply chain scheduling tasks which need regular updates. We analysed a practical situation involving iron ore train networks which is indeed one of significant economic importance. In addition, iron ore train networks have some interesting and distinctive characteristics so analysing this situation is an important step toward understanding the performance of GA in real-world supply chain scheduling. We compared the performance of GA with Nonlinear programming heuristics and existing industry scheduling approaches. The main result is that our comparison of techniques here produce an ex le in which GAs perform well and is a cost effective approach.
Publisher: IEEE
Date: 12-2019
Publisher: MDPI AG
Date: 27-06-2023
DOI: 10.3390/S23135960
Abstract: Neurological disorders have an extreme impact on global health, affecting an estimated one billion in iduals worldwide. According to the World Health Organization (WHO), these neurological disorders contribute to approximately six million deaths annually, representing a significant burden. Early and accurate identification of brain pathological features in electroencephalogram (EEG) recordings is crucial for the diagnosis and management of these disorders. However, manual evaluation of EEG recordings is not only time-consuming but also requires specialized skills. This problem is exacerbated by the scarcity of trained neurologists in the healthcare sector, especially in low- and middle-income countries. These factors emphasize the necessity for automated diagnostic processes. With the advancement of machine learning algorithms, there is a great interest in automating the process of early diagnoses using EEGs. Therefore, this paper presents a novel deep learning model consisting of two distinct paths, WaveNet–Long Short-Term Memory (LSTM) and LSTM, for the automatic detection of abnormal raw EEG data. Through multiple ablation experiments, we demonstrated the effectiveness and importance of all parts of our proposed model. The performance of our proposed model was evaluated using TUH abnormal EEG Corpus V.2.0.0. (TUAB) and achieved a high classification accuracy of 88.76%, which is higher than in the existing state-of-the-art research studies. Moreover, we demonstrated the generalization of our proposed model by evaluating it on another independent dataset, TUEP, without any hyperparameter tuning or adjustment. The obtained accuracy was 97.45% for the classification between normal and abnormal EEG recordings, confirming the robustness of our proposed model.
Publisher: Elsevier BV
Date: 09-2021
Publisher: Wiley
Date: 18-07-2022
DOI: 10.1002/MP.15840
Abstract: The use of deep learning (DL) to improve cone‐beam CT (CBCT) image quality has gained popularity as computational resources and algorithmic sophistication have advanced in tandem. CBCT imaging has the potential to facilitate online adaptive radiation therapy (ART) by utilizing up‐to‐date patient anatomy to modify treatment parameters before irradiation. Poor CBCT image quality has been an impediment to realizing ART due to the increased scatter conditions inherent to cone‐beam acquisitions. Given the recent interest in DL applications in radiation oncology, and specifically DL for CBCT correction, we provide a systematic theoretical and literature review for future stakeholders. The review encompasses DL approaches for synthetic CT generation, as well as projection domain methods employed in the CBCT correction literature. We review trends pertaining to publications from January 2018 to April 2022 and condense their major findings—with emphasis on study design and DL techniques. Clinically relevant endpoints relating to image quality and dosimetric accuracy are summarized, highlighting gaps in the literature. Finally, we make recommendations for both clinicians and DL practitioners based on literature trends and the current DL state‐of‐the‐art methods utilized in radiation oncology.
Publisher: Springer International Publishing
Date: 2022
Publisher: IEEE
Date: 2015
Publisher: Elsevier BV
Date: 09-2016
Publisher: IEEE
Date: 2009
Publisher: IEEE
Date: 12-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 07-2023
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Elsevier BV
Date: 05-2019
Publisher: IEEE
Date: 11-2016
Publisher: Elsevier BV
Date: 12-2016
Publisher: Springer Nature Switzerland
Date: 2023
Location: United States of America
No related grants have been discovered for Ghulam Mubashar Hassan.