Dr Moloud Abdar
Dr

Moloud Abdar

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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)

21 - 40 of 74 works

2023

Journal Article

SFE: a simple, fast, and efficient feature selection algorithm for high-dimensional data

Ahadzadeh, Behrouz, Abdar, Moloud, Safara, Fatemeh, Khosravi, Abbas, Menhaj, Mohammad Bagher and Suganthan, Ponnuthurai Nagaratnam (2023). SFE: a simple, fast, and efficient feature selection algorithm for high-dimensional data. IEEE Transactions on Evolutionary Computation, 27 (6), 1896-1911. doi: 10.1109/TEVC.2023.3238420

SFE: a simple, fast, and efficient feature selection algorithm for high-dimensional data

2023

Journal Article

Hercules: deep hierarchical attentive multilevel fusion model with uncertainty quantification for medical image classification

Abdar, Moloud, Fahami, Mohammad Amin, Rundo, Leonardo, Radeva, Petia, Frangi, Alejandro F., Acharya, U. Rajendra, Khosravi, Abbas, Lam, Hak-Keung, Jung, Alexander and Nahavandi, Saeid (2023). Hercules: deep hierarchical attentive multilevel fusion model with uncertainty quantification for medical image classification. IEEE Transactions on Industrial Informatics, 19 (1), 274-285. doi: 10.1109/tii.2022.3168887

Hercules: deep hierarchical attentive multilevel fusion model with uncertainty quantification for medical image classification

2022

Journal Article

Intelligent personalized shopping recommendation using clustering and supervised machine learning algorithms

Chabane, Nail, Bouaoune, Achraf, Tighilt, Reda, Abdar, Moloud, Boc, Alix, Lord, Etienne, Tahiri, Nadia, Mazoure, Bogdan, Rajendra Acharya, U. and Makarenkov, Vladimir (2022). Intelligent personalized shopping recommendation using clustering and supervised machine learning algorithms. PLoS ONE, 17 (12 December) e0278364, 1-30. doi: 10.1371/journal.pone.0278364

Intelligent personalized shopping recommendation using clustering and supervised machine learning algorithms

2022

Journal Article

Hybrid genetic-discretized algorithm to handle data uncertainty in diagnosing stenosis of coronary arteries

Alizadehsani, Roohallah, Roshanzamir, Mohamad, Abdar, Moloud, Beykikhoshk, Adham, Khosravi, Abbas, Nahavandi, Saeid, Plawiak, Pawel, Tan, Ru San and Acharya, U Rajendra (2022). Hybrid genetic-discretized algorithm to handle data uncertainty in diagnosing stenosis of coronary arteries. Expert Systems, 39 (7) e12573, 1-17. doi: 10.1111/exsy.12573

Hybrid genetic-discretized algorithm to handle data uncertainty in diagnosing stenosis of coronary arteries

2022

Journal Article

Uncertainty-aware interpretable deep learning for slum mapping and monitoring

Fisher, Thomas, Gibson, Harry, Liu, Yunzhe, Abdar, Moloud, Posa, Marius, Salimi-Khorshidi, Gholamreza, Hassaine, Abdelaali, Cai, Yutong, Rahimi, Kazem and Mamouei, Mohammad (2022). Uncertainty-aware interpretable deep learning for slum mapping and monitoring. Remote Sensing, 14 (13) 3072, 1-17. doi: 10.3390/rs14133072

Uncertainty-aware interpretable deep learning for slum mapping and monitoring

2022

Journal Article

A review of generalized zero-shot learning methods

Pourpanah, Farhad, Abdar, Moloud, Luo, Yuxuan, Zhou, Xinlei, Wang, Ran, Lim, Chee Peng, Wang, Xi-Zhao and Wu, Q. M. Jonathan (2022). A review of generalized zero-shot learning methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45 (4), 4051-4070. doi: 10.1109/tpami.2022.3191696

A review of generalized zero-shot learning methods

2022

Journal Article

MCUa: Multi-Level Context and Uncertainty aware dynamic deep ensemble for breast cancer histology image classification

Senousy, Zakaria, Abdelsamea, Mohammed M., Gaber, Mohamed Medhat, Abdar, Moloud, Acharya, U Rajendra, Khosravi, Abbas and Nahavandi, Saeid (2022). MCUa: Multi-Level Context and Uncertainty aware dynamic deep ensemble for breast cancer histology image classification. IEEE Transactions on Biomedical Engineering, 69 (2), 818-829. doi: 10.1109/tbme.2021.3107446

MCUa: Multi-Level Context and Uncertainty aware dynamic deep ensemble for breast cancer histology image classification

2021

Journal Article

A novel approach based on genetic algorithm to speed up the discovery of classification rules on GPUs

Beheshti Roui, Mohammad, Zomorodi, Mariam, Sarvelayati, Masoomeh, Abdar, Moloud, Noori, Hamid, Pławiak, Paweł, Tadeusiewicz, Ryszard, Zhou, Xujuan, Khosravi, Abbas, Nahavandi, Saeid and Acharya, U. Rajendra (2021). A novel approach based on genetic algorithm to speed up the discovery of classification rules on GPUs. Knowledge-Based Systems, 231 107419, 1-17. doi: 10.1016/j.knosys.2021.107419

A novel approach based on genetic algorithm to speed up the discovery of classification rules on GPUs

2021

Journal Article

BARF: a new direct and cross-based binary residual feature fusion with uncertainty-aware module for medical image classification

Abdar, Moloud, Fahami, Mohammad Amin, Chakrabarti, Satarupa, Khosravi, Abbas, Pławiak, Paweł, Acharya, U. Rajendra, Tadeusiewicz, Ryszard and Nahavandi, Saeid (2021). BARF: a new direct and cross-based binary residual feature fusion with uncertainty-aware module for medical image classification. Information Sciences, 577, 353-378. doi: 10.1016/j.ins.2021.07.024

BARF: a new direct and cross-based binary residual feature fusion with uncertainty-aware module for medical image classification

2021

Journal Article

A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets

Basiri, Mohammad Ehsan, Nemati, Shahla, Abdar, Moloud, Asadi, Somayeh and Acharrya, U. Rajendra (2021). A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets. Knowledge-Based Systems, 228 107242, 1-21. doi: 10.1016/j.knosys.2021.107242

A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets

2021

Journal Article

Automated detection of shockable ECG signals: a review

Hammad, Mohamed, Kandala, Rajesh N. V. P. S., Abdelatey, Amira, Abdar, Moloud, Zomorodi‐Moghadam, Mariam, Tan, Ru San, Acharya, U. Rajendra, Pławiak, Joanna, Tadeusiewicz, Ryszard, Makarenkov, Vladimir, Sarrafzadegan, Nizal, Khosravi, Abbas, Nahavandi, Saeid, EL-Latif, Ahmed A. Abd and Pławiak, Paweł (2021). Automated detection of shockable ECG signals: a review. Information Sciences, 571, 580-604. doi: 10.1016/j.ins.2021.05.035

Automated detection of shockable ECG signals: a review

2021

Journal Article

Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning

Abdar, Moloud, Samami, Maryam, Dehghani Mahmoodabad, Sajjad, Doan, Thang, Mazoure, Bogdan, Hashemifesharaki, Reza, Liu, Li, Khosravi, Abbas, Acharya, U. Rajendra, Makarenkov, Vladimir and Nahavandi, Saeid (2021). Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning. Computers in Biology and Medicine, 135 104418, 1-17. doi: 10.1016/j.compbiomed.2021.104418

Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning

2021

Journal Article

A review of uncertainty quantification in deep learning: techniques, applications and challenges

Abdar, Moloud, Pourpanah, Farhad, Hussain, Sadiq, Rezazadegan, Dana, Liu, Li, Ghavamzadeh, Mohammad, Fieguth, Paul, Cao, Xiaochun, Khosravi, Abbas, Acharya, U. Rajendra, Makarenkov, Vladimir and Nahavandi, Saeid (2021). A review of uncertainty quantification in deep learning: techniques, applications and challenges. Information Fusion, 76, 243-297. doi: 10.1016/j.inffus.2021.05.008

A review of uncertainty quantification in deep learning: techniques, applications and challenges

2021

Journal Article

ABCDM: an Attention-based Bidirectional CNN-RNN Deep Model for sentiment analysis

Basiri, Mohammad Ehsan, Nemati, Shahla, Abdar, Moloud, Cambria, Erik and Acharya, U. Rajendra (2021). ABCDM: an Attention-based Bidirectional CNN-RNN Deep Model for sentiment analysis. Future Generation Computer Systems, 115, 279-294. doi: 10.1016/j.future.2020.08.005

ABCDM: an Attention-based Bidirectional CNN-RNN Deep Model for sentiment analysis

2021

Journal Article

Coronary artery disease detection using artificial intelligence techniques: A survey of trends, geographical differences and diagnostic features 1991–2020

Alizadehsani, Roohallah, Khosravi, Abbas, Roshanzamir, Mohamad, Abdar, Moloud, Sarrafzadegan, Nizal, Shafie, Davood, Khozeimeh, Fahime, Shoeibi, Afshin, Nahavandi, Saeid, Panahiazar, Maryam, Bishara, Andrew, Beygui, Ramin E., Puri, Rishi, Kapadia, Samir, Tan, Ru-San and Acharya, U Rajendra (2021). Coronary artery disease detection using artificial intelligence techniques: A survey of trends, geographical differences and diagnostic features 1991–2020. Computers in Biology and Medicine, 128 104095, 1-16. doi: 10.1016/j.compbiomed.2020.104095

Coronary artery disease detection using artificial intelligence techniques: A survey of trends, geographical differences and diagnostic features 1991–2020

2021

Journal Article

Hybrid particle swarm optimization for rule discovery in the diagnosis of coronary artery disease

Zomorodi-moghadam, Mariam, Abdar, Moloud, Davarzani, Zohreh, Zhou, Xujuan, Pławiak, Pawel and Acharya, U.Rajendra (2021). Hybrid particle swarm optimization for rule discovery in the diagnosis of coronary artery disease. Expert Systems, 38 (1) e12485. doi: 10.1111/exsy.12485

Hybrid particle swarm optimization for rule discovery in the diagnosis of coronary artery disease

2020

Journal Article

Model uncertainty quantification for diagnosis of each main coronary artery stenosis

Alizadehsani, Roohallah, Roshanzamir, Mohamad, Abdar, Moloud, Beykikhoshk, Adham, Zangooei, Mohammad Hossein, Khosravi, Abbas, Nahavandi, Saeid, Tan, Ru San and Acharya, U. Rajendra (2020). Model uncertainty quantification for diagnosis of each main coronary artery stenosis. Soft Computing, 24 (13), 10149-10160. doi: 10.1007/s00500-019-04531-0

Model uncertainty quantification for diagnosis of each main coronary artery stenosis

2020

Journal Article

A novel method for sentiment classification of drug reviews using fusion of deep and machine learning techniques

Basiri, Mohammad Ehsan, Abdar, Moloud, Cifci, Mehmet Akif, Nemati, Shahla and Acharya, U. Rajendra (2020). A novel method for sentiment classification of drug reviews using fusion of deep and machine learning techniques. Knowledge-Based Systems, 198 105949, 1-19. doi: 10.1016/j.knosys.2020.105949

A novel method for sentiment classification of drug reviews using fusion of deep and machine learning techniques

2020

Journal Article

Automated detection of presymptomatic conditions in spinocerebellar ataxia type 2 using monte carlo dropout and deep neural network techniques with electrooculogram signals

Stoean, Catalin, Stoean, Ruxandra, Atencia, Miguel, Abdar, Moloud, Velázquez-Pérez, Luis, Khosravi, Abbas, Nahavandi, Saeid, Rajendra Acharya, U. and Joya, Gonzalo (2020). Automated detection of presymptomatic conditions in spinocerebellar ataxia type 2 using monte carlo dropout and deep neural network techniques with electrooculogram signals. Sensors, 20 (11) 3032, 1-16. doi: 10.3390/s20113032

Automated detection of presymptomatic conditions in spinocerebellar ataxia type 2 using monte carlo dropout and deep neural network techniques with electrooculogram signals

2020

Journal Article

Association between work-related features and coronary artery disease: a heterogeneous hybrid feature selection integrated with balancing approach

Nasarian, Elham, Abdar, Moloud, Fahami, Mohammad Amin, Alizadehsani, Roohallah, Hussain, Sadiq, Basiri, Mohammad Ehsan, Zomorodi-Moghadam, Mariam, Zhou, Xujuan, Pławiak, Paweł, Acharya, U. Rajendra, Tan, Ru-San and Sarrafzadegan, Nizal (2020). Association between work-related features and coronary artery disease: a heterogeneous hybrid feature selection integrated with balancing approach. Pattern Recognition Letters, 133, 33-40. doi: 10.1016/j.patrec.2020.02.010

Association between work-related features and coronary artery disease: a heterogeneous hybrid feature selection integrated with balancing approach

Availability

Dr Moloud Abdar is:
Available for supervision

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