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Qurat-Ul-Ain Mastoi
Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia

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Journal article
Published: 17 August 2021 in Electronics
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The Internet of Medical Things (IoMT) workflow applications have been rapidly growing in practice. These internet-based applications can run on the distributed healthcare sensing system, which combines mobile computing, edge computing and cloud computing. Offloading and scheduling are the required methods in the distributed network. However, a security issue exists and it is hard to run different types of tasks (e.g., security, delay-sensitive, and delay-tolerant tasks) of IoMT applications on heterogeneous computing nodes. This work proposes a new healthcare architecture for workflow applications based on heterogeneous computing nodes layers: an application layer, management layer, and resource layer. The goal is to minimize the makespan of all applications. Based on these layers, the work proposes a secure offloading-efficient task scheduling (SEOS) algorithm framework, which includes the deadline division method, task sequencing rules, homomorphic security scheme, initial scheduling, and the variable neighbourhood searching method. The performance evaluation results show that the proposed plans outperform all existing baseline approaches for healthcare applications in terms of makespan.

ACS Style

Abdullah Lakhan; Qurat-Ul-Ain Mastoi; Mazhar Ali Dootio; Fehaid Alqahtani; Ibrahim R. Alzahrani; Fatmah Baothman; Syed Yaseen Shah; Syed Aziz Shah; Nadeem Anjum; Qammer Hussain Abbasi; Muhammad Saddam Khokhar. Hybrid Workload Enabled and Secure Healthcare Monitoring Sensing Framework in Distributed Fog-Cloud Network. Electronics 2021, 10, 1974 .

AMA Style

Abdullah Lakhan, Qurat-Ul-Ain Mastoi, Mazhar Ali Dootio, Fehaid Alqahtani, Ibrahim R. Alzahrani, Fatmah Baothman, Syed Yaseen Shah, Syed Aziz Shah, Nadeem Anjum, Qammer Hussain Abbasi, Muhammad Saddam Khokhar. Hybrid Workload Enabled and Secure Healthcare Monitoring Sensing Framework in Distributed Fog-Cloud Network. Electronics. 2021; 10 (16):1974.

Chicago/Turabian Style

Abdullah Lakhan; Qurat-Ul-Ain Mastoi; Mazhar Ali Dootio; Fehaid Alqahtani; Ibrahim R. Alzahrani; Fatmah Baothman; Syed Yaseen Shah; Syed Aziz Shah; Nadeem Anjum; Qammer Hussain Abbasi; Muhammad Saddam Khokhar. 2021. "Hybrid Workload Enabled and Secure Healthcare Monitoring Sensing Framework in Distributed Fog-Cloud Network." Electronics 10, no. 16: 1974.

Article
Published: 20 June 2021 in Cluster Computing
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These days, the usage of the internet of Vehicle Things (IVoT) applications such as E-Business, E-Train, E-Ambulance has been growing progressively. These applications require mobility-aware delay-sensitive services to execute their tasks. With this motivation, the study has the following contribution. Initially, the study devises a novel cooperative vehicular fog cloud network (VFCN) based on container microservices which offers cost-efficient and mobility-aware services with rich resources for processing. This study devises the cost-efficient task offloading and scheduling (CEMOTS) algorithm framework, which consists of the mobility aware task offloading phase (MTOP) method, which determines the optimal offloading time to minimize the communication cost of applications. Furthermore, CEMOTS offers Cooperative Task Offloading Scheduling (CTOS), including task sequencing and scheduling. The goal is to reduce the application costs of communication cost and computational costs under a given deadline constraint. Performance evaluation shows the CTOS and MTOP outperform existing task offloading and scheduling methods in the VCFN in terms of costs and the deadline for IoT applications.

ACS Style

Abdullah Lakhan; Muhammad Suleman Memon; Qurat-Ul-Ain Mastoi; Mohamed Elhoseny; Mazin Abed Mohammed; Mumtaz Qabulio; Mohamed Abdel-Basset. Cost-efficient mobility offloading and task scheduling for microservices IoVT applications in container-based fog cloud network. Cluster Computing 2021, 1 -23.

AMA Style

Abdullah Lakhan, Muhammad Suleman Memon, Qurat-Ul-Ain Mastoi, Mohamed Elhoseny, Mazin Abed Mohammed, Mumtaz Qabulio, Mohamed Abdel-Basset. Cost-efficient mobility offloading and task scheduling for microservices IoVT applications in container-based fog cloud network. Cluster Computing. 2021; ():1-23.

Chicago/Turabian Style

Abdullah Lakhan; Muhammad Suleman Memon; Qurat-Ul-Ain Mastoi; Mohamed Elhoseny; Mazin Abed Mohammed; Mumtaz Qabulio; Mohamed Abdel-Basset. 2021. "Cost-efficient mobility offloading and task scheduling for microservices IoVT applications in container-based fog cloud network." Cluster Computing , no. : 1-23.

Original article
Published: 14 March 2021 in Neural Computing and Applications
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Cardiac arrhythmias impose a significant burden on the healthcare environment due to the increasing ratio of mortality worldwide. Arrhythmia and abnormal ECG heartbeat are the possible symptoms of severe heart diseases that can lead to death. Premature ventricular contraction (PVC) is a common form of cardiac arrhythmia which begins from the lower chamber of the heart, and frequent occurrence of PVC beat might lead to mortality. ECG signals are the noninvasive and primary tool used to identify the actual life threat related to the heart. Nowadays, in society, the computer-assisted technique reduces doctors' burden to evaluate heart disease and heart arrhythmia automatically. Regardless of well-equipped and well-developed health facilities that are available for monitoring the cardiac condition, the success stories are yet unsatisfactorily due to the complexity of the cardiac disorder. The most challenging part in ECG signal analysis is to extract the accurate features relevant to the arrhythmia for classification due to the inter-patient variation. There are many morphological changes present in the ECG signals. Hence, there is a gap in the usage of appropriate methods for the extraction of features and classification models, which reduce the biased diagnosis of PVC arrhythmia. To predict PVC arrhythmia accurately is a quite challenging task owing to (a) QRS negative (b) long compensatory pause (c) p-wave (d) biased diagnosis of PVC detection due to the small feature set. This study presents a new approach for PVC prediction using derived predictor variables from the electrocardiograph (ECG-MLII) signals: R–R wave interval, previous R–R wave interval, QRS duration, and verification of P-wave whether it is present or absent using threshold technique. We propose the machine learning-data mining MACDM integrated approach using five different models of multiple logistic regression and four classifiers, namely, Random Forest (RF), K-Nearest Neighbor (KNN), Support vector machine (SVM), and Naïve Bayes (NB). The experiment was conducted on the public benchmark MIT-BIH-AR to evaluate the performance of our proposed MACDM technique. The multiple logistic regression models constructed as a function of all independent variables achieved an accuracy of 99.96%, sensitivity 98.9%, specificity 99.20%, PPV 99.25%, and Youden's index parameter 98.24%. Thus, it is proved that this computer-aided method helps our medical practitioners improve the efficiency of their services.

ACS Style

Qurat-Ul-Ain Mastoi; Muhammad Suleman Memon; Abdullah Lakhan; Mazin Abed Mohammed; Mumtaz Qabulio; Fadi Al-Turjman; Karrar Hameed Abdulkareem. Machine learning-data mining integrated approach for premature ventricular contraction prediction. Neural Computing and Applications 2021, 33, 11703 -11719.

AMA Style

Qurat-Ul-Ain Mastoi, Muhammad Suleman Memon, Abdullah Lakhan, Mazin Abed Mohammed, Mumtaz Qabulio, Fadi Al-Turjman, Karrar Hameed Abdulkareem. Machine learning-data mining integrated approach for premature ventricular contraction prediction. Neural Computing and Applications. 2021; 33 (18):11703-11719.

Chicago/Turabian Style

Qurat-Ul-Ain Mastoi; Muhammad Suleman Memon; Abdullah Lakhan; Mazin Abed Mohammed; Mumtaz Qabulio; Fadi Al-Turjman; Karrar Hameed Abdulkareem. 2021. "Machine learning-data mining integrated approach for premature ventricular contraction prediction." Neural Computing and Applications 33, no. 18: 11703-11719.

Journal article
Published: 13 January 2020 in Sensors
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Recently, there has been a cloud-based Internet of Medical Things (IoMT) solution offering different healthcare services to wearable sensor devices for patients. These services are global, and can be invoked anywhere at any place. Especially, electrocardiogram (ECG) sensors, such as Lead I and Lead II, demands continuous cloud services for real-time execution. However, these services are paid and need a lower cost-efficient process for the users. In this paper, this study considered critical heartbeat cost-efficient task scheduling problems for healthcare applications in the fog cloud system. The objective was to offer omnipresent cloud services to the generated data with minimum cost. This study proposed a novel health care based fog cloud system (HCBFS) to collect, analyze, and determine the process of critical tasks of the heartbeat medical application for the purpose of minimizing the total cost. This study devised a health care awareness cost-efficient task scheduling (HCCETS) algorithm framework, which not only schedule all tasks with minimum cost, but also executes them on their deadlines. Performance evaluation shows that the proposed task scheduling algorithm framework outperformed the existing algorithm methods in terms of cost.

ACS Style

Qurat-Ul-Ain Mastoi; Teh Ying Wah; Ram Gopal Raj; Abdullah Lakhan. A Novel Cost-Efficient Framework for Critical Heartbeat Task Scheduling Using the Internet of Medical Things in a Fog Cloud System. Sensors 2020, 20, 441 .

AMA Style

Qurat-Ul-Ain Mastoi, Teh Ying Wah, Ram Gopal Raj, Abdullah Lakhan. A Novel Cost-Efficient Framework for Critical Heartbeat Task Scheduling Using the Internet of Medical Things in a Fog Cloud System. Sensors. 2020; 20 (2):441.

Chicago/Turabian Style

Qurat-Ul-Ain Mastoi; Teh Ying Wah; Ram Gopal Raj; Abdullah Lakhan. 2020. "A Novel Cost-Efficient Framework for Critical Heartbeat Task Scheduling Using the Internet of Medical Things in a Fog Cloud System." Sensors 20, no. 2: 441.

Journal article
Published: 18 February 2019 in Applied Sciences
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The abnormal conduction of cardiac activity in the lower chamber of the heart (ventricular) can cause cardiac diseases and sometimes leads to sudden death. In this paper, the author proposed the Reservoir Computing (RC) based Echo State Networks (ESNs) for ventricular heartbeat classification based on a single Electrocardiogram (ECG) lead. The Association for the Advancement of Medical Instrumentation (AAMI) standards were used to preprocesses the standardized diagnostic tool (ECG signals) based on the interpatient scheme. Despite the extensive efforts and notable experiments that have been done on machine learning techniques for heartbeat classification, ESNs are yet to be considered for heartbeat classification as a is fast, scalable, and reliable approach for real-time scenarios. Our proposed method was especially designed for Medical Internet of Things (MIoT) devices, for instance wearable wireless devices for ECG monitoring or ventricular heart beat detection systems and so on. The experiments were conducted on two public datasets, namely AHA and MIT-BIH-SVDM. The performance of the proposed model was evaluated using the MIT-BIH-AR dataset and it achieved remarkable results. The positive predictive value and sensitivity are 98.98% and 98.98%, respectively for the modified lead II (MLII) and 98.96% and 97.95 for the V1 lead, respectively. However, the experimental results of the state-of-the-art approaches, namely the patient-adaptable method, improved generalization, and the multiview learning approach obtained 92.8%, 87.0%, and 98.0% positive predictive values, respectively. These obtained results of the existing studies exemplify that the performance of this method achieved higher accuracy. We believe that the improved classification accuracy opens up the possibility for implementation of this methodology in Medical Internet of Things (MIoT) devices in order to bring improvements in e-health systems.

ACS Style

Qurat-Ul-Ain Mastoi; Teh Ying Wah; Ram Gopal Raj. Reservoir Computing Based Echo State Networks for Ventricular Heart Beat Classification. Applied Sciences 2019, 9, 702 .

AMA Style

Qurat-Ul-Ain Mastoi, Teh Ying Wah, Ram Gopal Raj. Reservoir Computing Based Echo State Networks for Ventricular Heart Beat Classification. Applied Sciences. 2019; 9 (4):702.

Chicago/Turabian Style

Qurat-Ul-Ain Mastoi; Teh Ying Wah; Ram Gopal Raj. 2019. "Reservoir Computing Based Echo State Networks for Ventricular Heart Beat Classification." Applied Sciences 9, no. 4: 702.

Review
Published: 04 February 2018 in Cardiology Research and Practice
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Coronary artery disease (CAD) is the most dangerous heart disease which may lead to sudden cardiac death. However, CAD diagnoses are quite expensive and time-consuming procedures which a patient need to go through. The aim of our paper is to present a unique review of state-of-the-art methods up to 2017 for automatic CAD classification. The protocol of review methods is identifying best methods and classifier for CAD identification. The study proposes two workflows based on two parameter sets for instances A and B. It is necessary to follow the proper procedure, for future evaluation process of automatic diagnosis of CAD. The initial two stages of the parameter set A workflow are preprocessing and feature extraction. Subsequently, stages (feature selection and classification) are same for both workflows. In literature, the SVM classifier represents a promising approach for CAD classification. Moreover, the limitation leads to extract proper features from noninvasive signals.

ACS Style

Qurat-Ul-Ain Mastoi; Teh Ying Wah; Ram Gopal Raj; Uzair Iqbal. Automated Diagnosis of Coronary Artery Disease: A Review and Workflow. Cardiology Research and Practice 2018, 2018, 1 -9.

AMA Style

Qurat-Ul-Ain Mastoi, Teh Ying Wah, Ram Gopal Raj, Uzair Iqbal. Automated Diagnosis of Coronary Artery Disease: A Review and Workflow. Cardiology Research and Practice. 2018; 2018 ():1-9.

Chicago/Turabian Style

Qurat-Ul-Ain Mastoi; Teh Ying Wah; Ram Gopal Raj; Uzair Iqbal. 2018. "Automated Diagnosis of Coronary Artery Disease: A Review and Workflow." Cardiology Research and Practice 2018, no. : 1-9.