ORCID Profile
0000-0002-8916-759X
Current Organisation
RMIT University School of Computing Technologies
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Publisher: Springer Science and Business Media LLC
Date: 15-11-2017
Publisher: Wiley
Date: 28-01-2019
DOI: 10.1002/HTJ.21425
Publisher: Research Square Platform LLC
Date: 24-10-2022
DOI: 10.21203/RS.3.RS-1714971/V1
Abstract: A life-threatening atherosclerosis plaque has been termed as Thin Cap Fibroatheroma (TCFA). Atherosclerotic plaque located between two borders recognized by Virtual Histology-Intravascular Ultrasound (VH-IVUS) images. In order to improve the reliability of plaque classification and TCFA detection, two approaches are suggested which are firstly based on the feature extraction technique and secondly by employing a set of ensemble classification techniques using Support Vector Machine (SVM), Radial Basis Function (RBF), and Extreme Learning Machine (ELM) as base classifiers. Plaque Burden Assessment by Local Search (PBALS) is proposed for extracting the plaque features. The geometric features are extracted from the plaque region and combined with IVUS features. In the classification part, different types of ensemble methods have been proposed and employed to identify the non-TCFA plaques from TCFA plaques with the expected reliability and robustness. 599 in-vivo IVUS along with their matching VH-IVUS images which are gathered from 10 patients are used for the experiment. According to the results, the combination of VH-IVUS with IVUS features performed better than standalone VH-IVUS features in terms of accuracy (22), sensitivity (17), and specificity (21) out of 23 different proposed methods. Furthermore, M2 model which only used 5 features (GFC3) with the combination of SVM, RFB, and ELM for the ensemble classifier performed well using either both VH-IVUS with IVUS features or single alone VH-IVUS features. Finally, prediction models which were built using HDCT-DWT features did not perform as well as the proposed set of features and ensemble prediction model.
Publisher: Springer Nature Singapore
Date: 2022
Publisher: IEEE
Date: 10-2010
Publisher: IEEE
Date: 11-2018
Publisher: Springer International Publishing
Date: 2014
Publisher: IGI Global
Date: 2017
DOI: 10.4018/978-1-5225-3923-0.CH007
Abstract: Quality assurance tasks such as testing, verification and validation, fault tolerance, and fault prediction play a major role in software engineering activities. Fault prediction approaches are used when a software company needs to deliver a finished product while it has limited time and budget for testing it. In such cases, identifying and testing parts of the system that are more defect prone is reasonable. In fact, prediction models are mainly used for improving software quality and exploiting available resources. Software fault prediction is studied in this chapter based on different criteria that matters in this research field. Usually, there are certain issues that need to be taken care of such as different machine-learning techniques, artificial intelligence classifiers, variety of software metrics, distinctive performance evaluation metrics, and some statistical analysis. In this chapter, the authors present a roadmap for those researchers who are interested in working in this area. They illustrate problems along with objectives related to each mentioned criterion, which could assist researchers to build the finest software fault prediction model.
Publisher: ACM
Date: 24-02-2022
Publisher: Springer Science and Business Media LLC
Date: 07-04-2020
Publisher: Springer International Publishing
Date: 2015
Publisher: Elsevier BV
Date: 07-2020
Publisher: Elsevier BV
Date: 09-2020
Publisher: UUM Press, Universiti Utara Malaysia
Date: 03-04-2023
Abstract: Node churn, or the constant joining and leaving of nodes in a network, can impact the performance of a blockchain network. The difficulties of performing research on the actual blockchain network, particularly on a live decentralized global network like Bitcoin, pose challenges that good simulators can overcome. While various tools, such as NS-3 and OMNet++, are useful for simulating network behavior, SimBlock is specifically designed to simulate the complex Bitcoin blockchain network. However, the current implementation of SimBlock has limitations when replicating actual node churn activity. In this study, the SimBlock simulator was improved to simulate node churn more accurately by removing churning nodes and dropping their connections, and increasing additional instrumentation for validation. The methodology used in the study involved modeling the Bitcoin node churn behavior based on previous studies and using the enhanced SimBlock simulator to simulate node churn. Empirical studies were then conducted to determine the suitability and limitations of the node churn simulation. This study found that the improved SimBlock could produce results similar to observed indicators in a 100-node network. However, it still had limitations in replicating node churn behavior accurately. It was discovered that SimBlock limits all nodes to operate as mining nodes and that mining is simulated in a way that does not depict churn accurately at any time but only at specific intervals or under certain conditions. Despite these limitations, the study’simprovements to SimBlock and the identification of its limitations can be useful for future research on node churn in blockchain networks and the development of more effective simulation tools.
Publisher: Inderscience Publishers
Date: 2015
Publisher: IGI Global
Date: 2014
DOI: 10.4018/978-1-4666-6026-7.CH023
Abstract: Quality assurance tasks such as testing, verification and validation, fault tolerance, and fault prediction play a major role in software engineering activities. Fault prediction approaches are used when a software company needs to deliver a finished product while it has limited time and budget for testing it. In such cases, identifying and testing parts of the system that are more defect prone is reasonable. In fact, prediction models are mainly used for improving software quality and exploiting available resources. Software fault prediction is studied in this chapter based on different criteria that matters in this research field. Usually, there are certain issues that need to be taken care of such as different machine-learning techniques, artificial intelligence classifiers, variety of software metrics, distinctive performance evaluation metrics, and some statistical analysis. In this chapter, the authors present a roadmap for those researchers who are interested in working in this area. They illustrate problems along with objectives related to each mentioned criterion, which could assist researchers to build the finest software fault prediction model.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 11-2013
Publisher: Penerbit UTM Press
Date: 03-02-2204
DOI: 10.11113/JT.V75.4994
Abstract: Atherosclerosis is the deadliest type of heart disease caused by soft or “vulnerable” plaque (VP) formation in the coronary arteries. Recently, Virtual Histology (VH) has been proposed based on spectral analysis of Intravascular Ultrasound (IVUS) provides color code of coronary tissue maps. Based on pathophysiological studies, obtaining information about existence and extension of confluent pool’s component inside plaque is important. In addition, plaque components’ localization respect to the luminal border has major role in determining plaque vulnerability and plaque–stent interaction. Computational methods were applied to prognostic the pattern's structure of each component inside the plaque. The first step for post-processing of VH methodology to get further information of geometrical features is segmentation or decomposition. The medical imaging segmentation field has developed to assist cardiologist and radiologists and reduce human error in recent years as well. To perform color image clustering, several strategies can be applied which include traditional hierarchical and nonhierarchical. In this paper, we applied and compared four nonhierarchical clustering methods consists of Fuzzy C-means (FCM), Intuitionistic Fuzzy C-means (IFCM), K-means and SOM artificial neural networks in order to automate segmentation of the VH-IVUS images.
Publisher: Elsevier BV
Date: 2015
Publisher: Informa UK Limited
Date: 13-02-2023
Publisher: IEEE
Date: 10-2010
Publisher: IEEE
Date: 11-2017
No related grants have been discovered for Golnoush Abaei.