ORCID Profile
0000-0002-3059-6357
Current Organisation
Deakin University
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Publisher: Elsevier BV
Date: 11-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2020
Publisher: Elsevier BV
Date: 12-2021
Publisher: Elsevier BV
Date: 10-2019
DOI: 10.1016/J.CMPB.2019.104992
Abstract: Coronary artery disease (CAD) is one of the commonest diseases around the world. An early and accurate diagnosis of CAD allows a timely administration of appropriate treatment and helps to reduce the mortality. Herein, we describe an innovative machine learning methodology that enables an accurate detection of CAD and apply it to data collected from Iranian patients. We first tested ten traditional machine learning algorithms, and then the three-best performing algorithms (three types of SVM) were used in the rest of the study. To improve the performance of these algorithms, a data preprocessing with normalization was carried out. Moreover, a genetic algorithm and particle swarm optimization, coupled with stratified 10-fold cross-validation, were used twice: for optimization of classifier parameters and for parallel selection of features. The presented approach enhanced the performance of all traditional machine learning algorithms used in this study. We also introduced a new optimization technique called N2Genetic optimizer (a new genetic training). Our experiments demonstrated that N2Genetic-nuSVM provided the accuracy of 93.08% and F1-score of 91.51% when predicting CAD outcomes among the patients included in a well-known Z-Alizadeh Sani dataset. These results are competitive and comparable to the best results in the field. We showed that machine-learning techniques optimized by the proposed approach, can lead to highly accurate models intended for both clinical and research use.
Publisher: IEEE
Date: 02-2019
Publisher: Springer Science and Business Media LLC
Date: 07-12-2017
Publisher: Elsevier BV
Date: 04-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Elsevier BV
Date: 04-2020
Publisher: Elsevier BV
Date: 05-2020
Publisher: Springer Science and Business Media LLC
Date: 03-10-2019
Publisher: IEEE
Date: 03-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
Publisher: MDPI AG
Date: 13-06-2019
DOI: 10.3390/SYM11060787
Abstract: In this study, a new face recognition architecture is proposed using fuzzy-based Discrete Wavelet Transform (DWT) and fuzzy with two novel local graph descriptors. These graph descriptors are called Local Cross Pattern (LCP). The proposed fuzzy wavelet-based face recognition architecture consists of DWT, Triangular fuzzy set transformation, and textural feature extraction with local descriptors and classification phases. Firstly, the LL (Low-Low) sub-band is obtained by applying the 2 Dimensions Discrete Wavelet Transform (2D DWT) to face images. After that, the triangular fuzzy transformation is applied to this band in order to obtain A, B, and C images. The proposed LCP is then applied to the B image. LCP consists of two types of descriptors: Vertical Local Cross Pattern (VLCP) and Horizontal Local Cross Pattern (HLCP). Linear discriminant analysis, quadratic discriminant, analysis, quadratic kernel-based support vector machine (QKSVM), and K-nearest neighbors (KNN) were ultimately used to classify the extracted features. Ten widely used descriptors in the literature are applied to the fuzzy wavelet architecture. AT& T, CIE, Face94, and FERET databases are used for performance evaluation of the proposed methods. Experimental results show that the LCP descriptors have high face recognition performance, and the fuzzy wavelet-based model significantly improves the performances of the textural descriptors-based face recognition methods. Moreover, the proposed fuzzy-based domain and LCP method achieved classification accuracy rates of 97.3%, 100.0%, 100.0%, and 96.3% for AT& T, CIE, Face94, and FERET datasets, respectively.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Publisher: IEEE
Date: 12-2017
Publisher: Springer Science and Business Media LLC
Date: 23-10-2019
DOI: 10.1038/S41597-019-0206-3
Abstract: We present the coronary artery disease (CAD) database, a comprehensive resource, comprising 126 papers and 68 datasets relevant to CAD diagnosis, extracted from the scientific literature from 1992 and 2018. These data were collected to help advance research on CAD-related machine learning and data mining algorithms, and hopefully to ultimately advance clinical diagnosis and early treatment. To aid users, we have also built a web application that presents the database through various reports.
Publisher: Springer Science and Business Media LLC
Date: 21-05-2019
DOI: 10.1007/S10916-019-1337-Y
Abstract: In this work, a novel method has been proposed for false alarm detection in Intensive Care Unit (ICU) during arrhythmia. To detect false alarm, various inputs are used such as electrocardiogram (ECG) signals, atrial blood pressure (ABP), photoplethysmogram signals (PLETH) and respiration (RESP). The inputs are given to decision tree predictive learner (DTPL) based classifier for thedetection of false alarm. The proposed method has an accuracy of 97% for prediction of false alarm in ICU. Theresult of the proposed method is promising which suggest that it can be used effectively for false alarm detection in ICUs. To the best of our knowledge, there is no such assumption based classification approach.
Publisher: Springer Science and Business Media LLC
Date: 16-11-2019
Publisher: Springer Science and Business Media LLC
Date: 07-06-2019
DOI: 10.1007/S10916-019-1343-0
Abstract: Wart disease (WD) is a skin illness on the human body which is caused by the human papillomavirus (HPV). This study mainly concentrates on common and plantar warts. There are various treatment methods for this disease, including the popular immunotherapy and cryotherapy methods. Manual evaluation of the WD treatment response is challenging. Furthermore, traditional machine learning methods are not robust enough in WD classification as they cannot deal effectively with small number of attributes. This study proposes a new evolutionary-based computer-aided diagnosis (CAD) system using machine learning to classify the WD treatment response. The main architecture of our CAD system is based on the combination of improved adaptive particle swarm optimization (IAPSO) algorithm and artificial immune recognition system (AIRS). The cross-validation protocol was applied to test our machine learning-based classification system, including five different partition protocols (K2, K3, K4, K5 and K10). Our database consisted of 180 records taken from immunotherapy and cryotherapy databases. The best results were obtained using the K10 protocol that provided the precision, recall, F-measure and accuracy values of 0.8908, 0.8943, 0.8916 and 90%, respectively. Our IAPSO system showed the reliability of 98.68%. It was implemented in Java, while integrated development environment (IDE) was implemented using NetBeans. Our encouraging results suggest that the proposed IAPSO-AIRS system can be employed for the WD management in clinical environment.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: Elsevier BV
Date: 05-2019
Publisher: Elsevier BV
Date: 02-2023
Publisher: Wiley
Date: 02-12-2019
DOI: 10.1111/EXSY.12485
Publisher: Elsevier BV
Date: 2017
Publisher: Elsevier BV
Date: 08-2019
DOI: 10.1016/J.COMPBIOMED.2019.103346
Abstract: Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and often leads to a heart attack. It annually causes millions of deaths and billions of dollars in financial losses worldwide. Angiography, which is invasive and risky, is the standard procedure for diagnosing CAD. Alternatively, machine learning (ML) techniques have been widely used in the literature as fast, affordable, and noninvasive approaches for CAD detection. The results that have been published on ML-based CAD diagnosis differ substantially in terms of the analyzed datasets, s le sizes, features, location of data collection, performance metrics, and applied ML techniques. Due to these fundamental differences, achievements in the literature cannot be generalized. This paper conducts a comprehensive and multifaceted review of all relevant studies that were published between 1992 and 2019 for ML-based CAD diagnosis. The impacts of various factors, such as dataset characteristics (geographical location, s le size, features, and the stenosis of each coronary artery) and applied ML techniques (feature selection, performance metrics, and method) are investigated in detail. Finally, the important challenges and shortcomings of ML-based CAD diagnosis are discussed.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
Publisher: Elsevier BV
Date: 10-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Elsevier BV
Date: 11-2021
Publisher: Elsevier BV
Date: 07-2019
Publisher: Elsevier BV
Date: 02-2021
Publisher: Elsevier BV
Date: 09-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2022
Publisher: IEEE
Date: 11-2018
Publisher: IEEE
Date: 09-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
Publisher: IEEE
Date: 09-2017
Publisher: Emerald
Date: 20-11-2017
Abstract: This research intends to look at the regional characteristics through an analysis of crowd preference and confidence, and investigates how regional characteristics are going to affect human beings at all aspects in a scenario of sharing economy. The purpose of this paper is to introduce an approach to provide an understandable rating score. Furthermore, the paper aims to find the relationships between different features classified in this study by using machine learning methods. Furthermore, due to the importance of performance of methods, the performance of the features is also improved. The Rating Matching Rate (RMRate) approach is proposed to provide score in terms of simplicity and understandability for all features. The relationships between features can be extracted from accommodation data set using decision tree (DT) algorithms (J48, HoeffdingTree, and REPTree). Usability of these methods was evaluated using different metrics. Two techniques, “ClassBalancer” and “SpreadSubs le,” are applied to improve the performance of algorithms. Experimental outcomes using the RMRate approach show that the scores are very easy to understand. Three property types are very popular almost in all of selected countries in this study (“apartment”, “house”, and “bed and breakfast”). The findings also indicate that “Entire home/apt” is the most common room-type and 4.5 and 5 star-rating are the most given star-rating by users. The proposed DT algorithms can find the relationships between features significantly. In addition, applied CB and SS techniques could improve the performance of algorithms efficiently. This study gives precise details about the guests’ preferences and hosts’ preferences. The proposed techniques can effectively improve the performance in predicting the behavior of users in sharing economy. The findings can also help group decision making in P2P platforms efficiently.
Publisher: Elsevier BV
Date: 08-2021
Publisher: Informa UK Limited
Date: 13-10-2019
Publisher: IEEE
Date: 06-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 09-2021
Publisher: Elsevier BV
Date: 11-2019
No related grants have been discovered for Moloud Abdar.