Moloud Abdar
Moloud Abdar
Alfred Deakin Postdoctoral Research Fellow, Deakin University, Australia
Verified email at - Homepage
Cited by
Cited by
A review of uncertainty quantification in deep learning: Techniques, applications and challenges
M Abdar, F Pourpanah, S Hussain, D Rezazadegan, L Liu, ...
Information fusion 76, 243-297, 2021
ABCDM: An attention-based bidirectional CNN-RNN deep model for sentiment analysis
ME Basiri, S Nemati, M Abdar, E Cambria, UR Acharya
Future Generation Computer Systems 115, 279-294, 2021
A new machine learning technique for an accurate diagnosis of coronary artery disease
M Abdar, W Książek, UR Acharya, RS Tan, V Makarenkov, P Pławiak
Computer methods and programs in biomedicine 179, 104992, 2019
A review of generalized zero-shot learning methods
F Pourpanah, M Abdar, Y Luo, X Zhou, R Wang, CP Lim, XZ Wang
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
Machine learning-based coronary artery disease diagnosis: A comprehensive review
R Alizadehsani, M Abdar, M Roshanzamir, A Khosravi, PM Kebria, ...
Computers in biology and medicine 111, 103346, 2019
A new nested ensemble technique for automated diagnosis of breast cancer
M Abdar, M Zomorodi-Moghadam, X Zhou, R Gururajan, X Tao, PD Barua, ...
Pattern Recognition Letters 132, 123-131, 2020
Performance analysis of classification algorithms on early detection of liver disease
M Abdar, M Zomorodi-Moghadam, R Das, IH Ting
Expert Systems with Applications 67, 239-251, 2017
Automated detection of autism spectrum disorder using a convolutional neural network
Z Sherkatghanad, M Akhondzadeh, S Salari, M Zomorodi-Moghadam, ...
Frontiers in neuroscience 13, 482737, 2020
A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets
ME Basiri, S Nemati, M Abdar, S Asadi, UR Acharrya
Knowledge-Based Systems 228, 107242, 2021
Comparing performance of data mining algorithms in prediction heart diseases
M Abdar, SRN Kalhori, T Sutikno, IMI Subroto, G Arji
Deakin University, 2015
Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning
M Abdar, M Samami, SD Mahmoodabad, T Doan, B Mazoure, ...
Computers in biology and medicine 135, 104418, 2021
Application of new deep genetic cascade ensemble of SVM classifiers to predict the Australian credit scoring
P Pławiak, M Abdar, UR Acharya
Applied Soft Computing 84, 105740, 2019
Spinalnet: Deep neural network with gradual input
HMD Kabir, M Abdar, A Khosravi, SMJ Jalali, AF Atiya, S Nahavandi, ...
IEEE Transactions on Artificial Intelligence, 2022
A novel method for sentiment classification of drug reviews using fusion of deep and machine learning techniques
ME Basiri, M Abdar, MA Cifci, S Nemati, UR Acharya
Knowledge-Based Systems 198, 105949, 2020
DGHNL: A new deep genetic hierarchical network of learners for prediction of credit scoring
P Pławiak, M Abdar, J Pławiak, V Makarenkov, UR Acharya
Information sciences 516, 401-418, 2020
CWV-BANN-SVM ensemble learning classifier for an accurate diagnosis of breast cancer
M Abdar, V Makarenkov
Measurement 146, 557-570, 2019
A novel machine learning approach for early detection of hepatocellular carcinoma patients
W Książek, M Abdar, UR Acharya, P Pławiak
Cognitive Systems Research 54, 116-127, 2019
Using PSO algorithm for producing best rules in diagnosis of heart disease
AH Alkeshuosh, MZ Moghadam, I Al Mansoori, M Abdar
2017 international conference on computer and applications (ICCA), 306-311, 2017
Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991–2020)
R Alizadehsani, M Roshanzamir, S Hussain, A Khosravi, A Koohestani, ...
Annals of Operations Research, 1-42, 2021
Improving the diagnosis of liver disease using multilayer perceptron neural network and boosted decision trees
M Abdar, NY Yen, JCS Hung
Journal of Medical and Biological Engineering 38 (6), 953-965, 2018
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