Dr Moloud Abdar
Dr

Moloud Abdar

Email: 

Availability

Dr Moloud Abdar is:
Available for supervision

Search Professor Moloud Abdar’s works on UQ eSpace

74 works between 2015 and 2025
All (74) Journal Article (64) Conference Publication (10)

1 - 20 of 74 works

2025

Journal Article

BayTTA: Uncertainty-aware medical image classification with optimized test-time augmentation using Bayesian model averaging

Sherkatghanad, Zeinab, Abdar, Moloud, Bakhtyari, Mohammadreza, Pławiak, Paweł and Makarenkov, Vladimir (2025). BayTTA: Uncertainty-aware medical image classification with optimized test-time augmentation using Bayesian model averaging. Knowledge-Based Systems, 327 114123, 114123-327. doi: 10.1016/j.knosys.2025.114123

BayTTA: Uncertainty-aware medical image classification with optimized test-time augmentation using Bayesian model averaging

2025

Journal Article

Survey on Leveraging Uncertainty Estimation Towards Trustworthy Deep Neural Networks: The Case of Reject Option and Post-training Processing

Hasan, Md Mehedi, Abdar, Moloud, Khosravi, Abbas, Aickelin, Uwe, Lio, Pietro, Hossain, Ibrahim, Rahman, Ashikur and Nahavandi, Saeid (2025). Survey on Leveraging Uncertainty Estimation Towards Trustworthy Deep Neural Networks: The Case of Reject Option and Post-training Processing. ACM Computing Surveys, 57 (9) 236, 1-35. doi: 10.1145/3727633

Survey on Leveraging Uncertainty Estimation Towards Trustworthy Deep Neural Networks: The Case of Reject Option and Post-training Processing

2025

Journal Article

Attention-guided hierarchical fusion U-Net for uncertainty-driven medical image segmentation

Munia, Afsana Ahmed, Abdar, Moloud, Hasan, Mehedi, Jalali, Mohammad S., Banerjee, Biplab, Khosravi, Abbas, Hossain, Ibrahim, Fu, Huazhu and Frangi, Alejandro F. (2025). Attention-guided hierarchical fusion U-Net for uncertainty-driven medical image segmentation. Information Fusion, 115 102719. doi: 10.1016/j.inffus.2024.102719

Attention-guided hierarchical fusion U-Net for uncertainty-driven medical image segmentation

2025

Conference Publication

Machine learning in electroconvulsive therapy

Lundin, Robert, Abdar, Moloud, Khosravi, Abbas, Loo, Colleen and Berk, Michael (2025). Machine learning in electroconvulsive therapy. 6th International Brain Stimulation Meeting, Kobe, Japan, 23–26 February 2025. Philadelphia, PA United States: Elsevier. doi: 10.1016/j.brs.2024.12.395

Machine learning in electroconvulsive therapy

2025

Journal Article

It is time to reassess reporting of electroconvulsive therapy data in New Zealand: A 17-year retrospective analysis of treatment data from Waikato

Lundin, Robert M, Kannangara, Savani, Jenkins, Matthew, Carroll, Teresa, Wakefield, Kathrine, Patrick, Colin, Abdar, Moloud, Khosravi, Abbas, Loo, Colleen and Berk, Michael (2025). It is time to reassess reporting of electroconvulsive therapy data in New Zealand: A 17-year retrospective analysis of treatment data from Waikato. Australian and New Zealand Journal of Psychiatry, 59 (5), 423-432. doi: 10.1177/00048674251324795

It is time to reassess reporting of electroconvulsive therapy data in New Zealand: A 17-year retrospective analysis of treatment data from Waikato

2024

Conference Publication

Can commonsense knowledge improve CLIP's performance in cross-domain VQA?

N. C., Mohamad Hassan, Jha, Ankit, Abdar, Moloud and Banerjee, Biplab (2024). Can commonsense knowledge improve CLIP's performance in cross-domain VQA?. ICVGIP 2024: Indian Conference on Computer Vision Graphics and Image Processing, Bengaluru, Karnataka, India, 13-15 December 2024. New York, United States: Association for Computing Machinery. doi: 10.1145/3702250.3702265

Can commonsense knowledge improve CLIP's performance in cross-domain VQA?

2024

Journal Article

UniBFS: A novel uniform-solution-driven binary feature selection algorithm for high-dimensional data

Ahadzadeh, Behrouz, Abdar, Moloud, Foroumandi, Mahdieh, Safara, Fatemeh, Khosravi, Abbas, García, Salvador and Suganthan, Ponnuthurai Nagaratnam (2024). UniBFS: A novel uniform-solution-driven binary feature selection algorithm for high-dimensional data. Swarm and Evolutionary Computation, 91 101715, 101715. doi: 10.1016/j.swevo.2024.101715

UniBFS: A novel uniform-solution-driven binary feature selection algorithm for high-dimensional data

2024

Journal Article

Machine learning in electroconvulsive therapy a systematic review

Lundin, Robert M., Falcao, Veronica Podence, Kannangara, Savani, Eakin, Charles W., Abdar, Moloud, O'Neill, John, Khosravi, Abbas, Eyre, Harris, Nahavandi, Saeid, Loo, Colleen and Berk, Michael (2024). Machine learning in electroconvulsive therapy a systematic review. Journal of ECT, 40 (4), 245-253. doi: 10.1097/YCT.0000000000001009

Machine learning in electroconvulsive therapy a systematic review

2024

Journal Article

Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

Alizadehsani, Roohallah, Roshanzamir, Mohamad, Hussain, Sadiq, Khosravi, Abbas, Koohestani, Afsaneh, Zangooei, Mohammad Hossein, Abdar, Moloud, Beykikhoshk, Adham, Shoeibi, Afshin, Zare, Assef, Panahiazar, Maryam, Nahavandi, Saeid, Srinivasan, Dipti, Atiya, Amir F. and Acharya, U. Rajendra (2024). Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020). Annals of Operations Research, 339 (3), 1077-1118. doi: 10.1007/s10479-021-04006-2

Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

2024

Conference Publication

Unknown prompt, the only lacuna: unveiling CLIP's potential for open domain generalization

Singha, Mainak, Jha, Ankit, Bose, Shirsha, Nair, Ashwin, Abdar, Moloud and Banerjee, Biplab (2024). Unknown prompt, the only lacuna: unveiling CLIP's potential for open domain generalization. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, United States, 16-22 June 2024. Washington, DC, United States: IEEE Computer Society. doi: 10.1109/CVPR52733.2024.01264

Unknown prompt, the only lacuna: unveiling CLIP's potential for open domain generalization

2024

Journal Article

A review of deep learning for video captioning

Abdar, Moloud, Kollati, Meenakshi, Kuraparthi, Swaraja, Pourpanah, Farhad, McDuff, Daniel, Ghavamzadeh, Mohammad, Yan, Shuicheng, Mohamed, Abduallah, Khosravi, Abbas, Cambria, Erik and Porikli, Fatih (2024). A review of deep learning for video captioning. IEEE Transactions on Pattern Analysis and Machine Intelligence. doi: 10.1109/tpami.2024.3522295

A review of deep learning for video captioning

2024

Journal Article

Improved binary differential evolution with dimensionality reduction mechanism and binary stochastic search for feature selection

Ahadzadeh, Behrouz, Abdar, Moloud, Safara, Fatemeh, Aghaei, Leyla, Mirjalili, Seyedali, Khosravi, Abbas, Garcia, Salvador, Karray, Fakhri and Acharya, U. Rajendra (2024). Improved binary differential evolution with dimensionality reduction mechanism and binary stochastic search for feature selection. Applied Soft Computing, 151 111141, 1-26. doi: 10.1016/j.asoc.2023.111141

Improved binary differential evolution with dimensionality reduction mechanism and binary stochastic search for feature selection

2023

Journal Article

SpinalNet: deep neural network with gradual input

Kabir, H. M. Dipu, Abdar, Moloud, Khosravi, Abbas, Jalali, Seyed Mohammad Jafar, Atiya, Amir F., Nahavandi, Saeid and Srinivasan, Dipti (2023). SpinalNet: deep neural network with gradual input. IEEE Transactions on Artificial Intelligence, 4 (5), 1165-1177. doi: 10.1109/tai.2022.3185179

SpinalNet: deep neural network with gradual input

2023

Journal Article

Binarized multi-gate mixture of Bayesian experts for cardiac syndrome X diagnosis: a clinician-in-the-loop scenario with a belief-uncertainty fusion paradigm

Abdar, Moloud, Mehrzadi, Arash, Goudarzi, Milad, Masoudkabir, Farzad, Rundo, Leonardo, Mamouei, Mohammad, Sala, Evis, Khosravi, Abbas, Makarenkov, Vladimir, Acharya, U. Rajendra, Saadatagah, Seyedmohammad, Naderian, Mohammadreza, Garcia, Salvador, Sarrafzadegan, Nizal and Nahavandi, Saeid (2023). Binarized multi-gate mixture of Bayesian experts for cardiac syndrome X diagnosis: a clinician-in-the-loop scenario with a belief-uncertainty fusion paradigm. Information Fusion, 97 101813, 1-20. doi: 10.1016/j.inffus.2023.101813

Binarized multi-gate mixture of Bayesian experts for cardiac syndrome X diagnosis: a clinician-in-the-loop scenario with a belief-uncertainty fusion paradigm

2023

Journal Article

An optimal nonlinear model predictive control-based motion cueing algorithm using cascade optimization and human interaction

Qazani, Mohammad Reza Chalak, Asadi, Houshyar, Chen, Yutao, Abdar, Moloud, Karkoub, Mansour, Mohamed, Shady, Lim, Chee Peng and Nahavandi, Saeid (2023). An optimal nonlinear model predictive control-based motion cueing algorithm using cascade optimization and human interaction. IEEE Transactions on Intelligent Transportation Systems, 24 (9), 9191-9202. doi: 10.1109/TITS.2023.3271361

An optimal nonlinear model predictive control-based motion cueing algorithm using cascade optimization and human interaction

2023

Journal Article

Using traditional machine learning and deep learning methods for on- and off-target prediction in CRISPR/Cas9: a review

Sherkatghanad, Zeinab, Abdar, Moloud, Charlier, Jeremy and Makarenkov, Vladimir (2023). Using traditional machine learning and deep learning methods for on- and off-target prediction in CRISPR/Cas9: a review. Briefings in Bioinformatics, 24 (3) bbad131, 1-25. doi: 10.1093/bib/bbad131

Using traditional machine learning and deep learning methods for on- and off-target prediction in CRISPR/Cas9: a review

2023

Journal Article

Toxicity prediction in pelvic radiotherapy using multiple instance learning and cascaded attention layers

Elhaminia, Behnaz, Gilbert, Alexandra, Lilley, John, Abdar, Moloud, Frangi, Alejandro F., Scarsbrook, Andrew, Appelt, Ane and Gooya, Ali (2023). Toxicity prediction in pelvic radiotherapy using multiple instance learning and cascaded attention layers. IEEE Journal of Biomedical and Health Informatics, 27 (4), 1958-1966. doi: 10.1109/jbhi.2023.3238825

Toxicity prediction in pelvic radiotherapy using multiple instance learning and cascaded attention layers

2023

Journal Article

Synthetic Datasets for Numeric Uncertainty Quantification: Proposing Datasets for Future Researchers

Kabir, H M Dipu, Abdar, Moloud, Khosravi, Abbas, Nahavandi, Darius, Mondal, Subrota Kumar, Khanam, Sadia, Mohamed, Shady, Srinivasan, Dipti, Nahavandi, Saeid and Suganthan, Ponnuthurai Nagaratnam (2023). Synthetic Datasets for Numeric Uncertainty Quantification: Proposing Datasets for Future Researchers. IEEE Systems, Man, and Cybernetics Magazine, 9 (2), 39-48. doi: 10.1109/msmc.2022.3218423

Synthetic Datasets for Numeric Uncertainty Quantification: Proposing Datasets for Future Researchers

2023

Journal Article

Quantum face recognition protocol with ghost imaging

Salari, Vahid, Paneru, Dilip, Saglamyurek, Erhan, Ghadimi, Milad, Abdar, Moloud, Rezaee, Mohammadreza, Aslani, Mehdi, Barzanjeh, Shabir and Karimi, Ebrahim (2023). Quantum face recognition protocol with ghost imaging. Scientific Reports, 13 (1) 2401, 1-9. doi: 10.1038/s41598-022-25280-5

Quantum face recognition protocol with ghost imaging

2023

Journal Article

UncertaintyFuseNet: Robust uncertainty-aware hierarchical feature fusion model with Ensemble Monte Carlo Dropout for COVID-19 detection

Abdar, Moloud, Salari, Soorena, Qahremani, Sina, Lam, Hak-Keung, Karray, Fakhri, Hussain, Sadiq, Khosravi, Abbas, Acharya, U. Rajendra, Makarenkov, Vladimir and Nahavandi, Saeid (2023). UncertaintyFuseNet: Robust uncertainty-aware hierarchical feature fusion model with Ensemble Monte Carlo Dropout for COVID-19 detection. Information Fusion, 90, 364-381. doi: 10.1016/j.inffus.2022.09.023

UncertaintyFuseNet: Robust uncertainty-aware hierarchical feature fusion model with Ensemble Monte Carlo Dropout for COVID-19 detection

Availability

Dr Moloud Abdar is:
Available for supervision

Looking for a supervisor? Read our advice on how to choose a supervisor.

Enquiries

For media enquiries about Dr Moloud Abdar's areas of expertise, story ideas and help finding experts, contact our Media team:

communications@uq.edu.au