ORCID Profile
0000-0001-7963-2446
Current Organisation
RMIT University
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Publisher: IEEE
Date: 2006
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11573067_38
Publisher: IEEE
Date: 2005
Publisher: IEEE
Date: 08-2019
Publisher: IEEE
Date: 11-2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2020
Publisher: Elsevier
Date: 2016
Publisher: IEEE
Date: 09-2012
Publisher: IEEE
Date: 09-2012
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Springer Science and Business Media LLC
Date: 07-02-2017
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer International Publishing
Date: 2018
Publisher: IEEE
Date: 08-2019
Publisher: Springer International Publishing
Date: 2018
Publisher: IEEE
Date: 06-2013
Publisher: Figshare
Date: 2011
Publisher: Springer International Publishing
Date: 2018
Publisher: IEEE
Date: 11-2005
Publisher: International Academy Publishing (IAP)
Date: 11-2006
DOI: 10.4304/JCP.1.7.1-8
Publisher: IEEE
Date: 2007
DOI: 10.1109/ICIS.2007.20
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: IEEE
Date: 09-2010
DOI: 10.1109/HPCC.2010.58
Publisher: IEEE
Date: 09-2010
DOI: 10.1109/HPCC.2010.57
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2008
Publisher: IEEE
Date: 09-2010
DOI: 10.1109/HPCC.2010.59
Publisher: Springer Science and Business Media LLC
Date: 04-07-2020
Publisher: IEEE
Date: 12-2010
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: IEEE
Date: 12-2011
Publisher: IEEE
Date: 04-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2013
Publisher: IEEE
Date: 09-2011
Publisher: Elsevier BV
Date: 2014
Publisher: IEEE
Date: 05-2011
Publisher: Elsevier BV
Date: 05-2007
Publisher: IEEE
Date: 09-2012
Publisher: IEEE
Date: 07-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2013
Publisher: IEEE
Date: 08-2011
Publisher: Figshare
Date: 2011
Publisher: Figshare
Date: 2011
Publisher: Springer Singapore
Date: 2019
Publisher: Springer International Publishing
Date: 2016
Publisher: IEEE
Date: 12-2008
Publisher: Figshare
Date: 2011
Publisher: IEEE
Date: 02-2019
Publisher: BMJ
Date: 03-2014
Publisher: IEEE
Date: 12-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2015
Publisher: IEEE
Date: 10-2019
Publisher: IEEE
Date: 12-2016
Publisher: IEEE
Date: 2004
Publisher: ACM
Date: 04-02-2020
Publisher: IEEE
Date: 08-2016
Publisher: ACM
Date: 03-11-2013
Publisher: ACM
Date: 28-10-2009
Publisher: MDPI AG
Date: 17-07-2020
DOI: 10.3390/ELECTRONICS9071163
Abstract: Finding changed and similar functions between a pair of binaries is an important problem in malware attribution and for the identification of new malware capabilities. This paper presents a new technique called Function Similarity using Family Context (FSFC) for this problem. FSFC trains a Support Vector Machine (SVM) model using pairs of similar functions from two program variants. This method improves upon previous research called Cross Version Contextual Function Similarity (CVCFS) e epresenting a function using features extracted not just from the function itself, but also, from other functions with which it has a caller and callee relationship. We present the results of an initial experiment that shows that the use of additional features from the context of a function significantly decreases the false positive rate, obviating the need for a separate pass for cleaning false positives. The more surprising and unexpected finding is that the SVM model produced by FSFC can abstract function similarity features from one pair of program variants to find similar functions in an unrelated pair of program variants. If validated by a larger study, this new property leads to the possibility of creating generic similar function classifiers that can be packaged and distributed in reverse engineering tools such as IDA Pro and Ghidra.
Publisher: IEEE
Date: 07-2015
Publisher: SAGE Publications
Date: 30-01-2017
Abstract: Cultural competence is an important aspect of health service access and delivery in health promotion and community health. Although a number of frameworks and tools are available to assist health service organizations improve their services to erse communities, there are few published studies describing organizational cultural competence assessments and the extent to which these tools facilitate cultural competence. This article addresses this gap by describing the development of a cultural competence assessment, intervention, and evaluation tool called the Cultural Competence Organizational Review (CORe) and its implementation in three community sector organizations. Baseline and follow-up staff surveys and document audits were conducted at each participating organization. Process data and organizational documentation were used to evaluate and monitor the experience of CORe within the organizations. Results at follow-up indicated an overall positive trend in organizational cultural competence at each organization in terms of both policy and practice. Organizations that are able to embed actions to improve organizational cultural competence within broader organizational plans increase the likelihood of sustainable changes to policies, procedures, and practice within the organization. The benefits and lessons learned from the implementation of CORe are discussed.
Publisher: BMJ
Date: 11-06-2015
Publisher: IEEE
Date: 11-2005
Publisher: SAGE Publications
Date: 09-2016
Abstract: Synchronous movement of attached self-lifting scaffolds is traditionally monitored with wired sensors in high-rise building construction, which limits their flexibility of movements. A ZigBee-based wireless sensor system has been suggested in this article to prove the effectiveness of wireless sensor networks in actual implementation. Two optoelectronic sensors are integrated into a ZigBee node for measuring the displacement of attached self-lifting scaffolds. The proposed wireless sensor network combines an end device and a coordinator to allow easy replacement of sensors as compared to a wired network. A wake-up timer algorithm is proposed to reduce the transmitting power during continuous wireless data communication in the wireless sensor network. Furthermore, a variant binary exponential backoff transmission algorithm for data loss avoidance is proposed. The variant binary exponential backoff algorithm reduces packet collisions during simultaneous access by increasing the randomizing moments at nodes attempting to access the wireless channels. The performance of three of the proposed modules—a cable sensor, a 315-MHz sensor, and a ZigBee sensor—is evaluated in terms of packet delivery ratio and the end-to-end delay of a ZigBee-based wireless sensor network. The experimental results show that the proposed variant binary exponential backoff transmission algorithm achieves a higher packet delivery ratio at the cost of higher delays. The average cost of the developed ZigBee-based wireless sensor network decreased by 24% compared with the cable sensor. The power consumption of ZigBee is approximately 53.75% of the 315-MHz sensor. The average current consumption is reduced by approximately 1.5 mA with the wake-up timer algorithm at the same s ling rate.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2020
Publisher: ACM
Date: 29-01-2019
Publisher: Springer International Publishing
Date: 2015
Publisher: Academy Publisher
Date: 06-2006
Publisher: IEEE
Date: 2006
Publisher: IEEE
Date: 2006
Publisher: IEEE
Date: 06-2007
DOI: 10.1109/ICC.2007.572
Publisher: Springer Science and Business Media LLC
Date: 16-11-2019
Publisher: Informa UK Limited
Date: 2012
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11691730_14
Publisher: Springer Science and Business Media LLC
Date: 13-04-2016
Publisher: IEEE
Date: 08-2011
Publisher: Springer International Publishing
Date: 2015
Publisher: IEEE
Date: 06-2011
Publisher: IEEE
Date: 2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2013
Publisher: Springer International Publishing
Date: 2019
Publisher: Elsevier BV
Date: 02-2014
Publisher: Figshare
Date: 2011
Publisher: Zenodo
Date: 2016
DOI: 10.5281/ZENODO.56227
Publisher: IEEE
Date: 12-2015
Publisher: IEEE
Date: 07-2007
DOI: 10.1109/ICIS.2007.52
Publisher: IEEE
Date: 2009
Publisher: IEEE
Date: 10-2011
Publisher: Elsevier BV
Date: 02-2017
Publisher: Elsevier BV
Date: 02-2017
Publisher: IEEE
Date: 2005
Publisher: Elsevier BV
Date: 08-2018
Publisher: MDPI AG
Date: 23-10-2019
DOI: 10.3390/ELECTRONICS8111210
Abstract: The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack to the end nodes. Due to the large number and erse types of IoT devices, it is a challenging task to protect the IoT infrastructure using a traditional intrusion detection system. To protect IoT devices, a novel ensemble Hybrid Intrusion Detection System (HIDS) is proposed by combining a C5 classifier and One Class Support Vector Machine classifier. HIDS combines the advantages of Signature Intrusion Detection System (SIDS) and Anomaly-based Intrusion Detection System (AIDS). The aim of this framework is to detect both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the Bot-IoT dataset, which includes legitimate IoT network traffic and several types of attacks. Experiments show that the proposed hybrid IDS provide higher detection rate and lower false positive rate compared to the SIDS and AIDS techniques.
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11554028_121
Publisher: MDPI AG
Date: 17-01-2020
DOI: 10.3390/ELECTRONICS9010173
Abstract: Cyberttacks are becoming increasingly sophisticated, necessitating the efficient intrusion detection mechanisms to monitor computer resources and generate reports on anomalous or suspicious activities. Many Intrusion Detection Systems (IDSs) use a single classifier for identifying intrusions. Single classifier IDSs are unable to achieve high accuracy and low false alarm rates due to polymorphic, metamorphic, and zero-day behaviors of malware. In this paper, a Hybrid IDS (HIDS) is proposed by combining the C5 decision tree classifier and One Class Support Vector Machine (OC-SVM). HIDS combines the strengths of SIDS) and Anomaly-based Intrusion Detection System (AIDS). The SIDS was developed based on the C5.0 Decision tree classifier and AIDS was developed based on the one-class Support Vector Machine (SVM). This framework aims to identify both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the benchmark datasets, namely, Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) and Australian Defence Force Academy (ADFA) datasets. Studies show that the performance of HIDS is enhanced, compared to SIDS and AIDS in terms of detection rate and low false-alarm rates.
Publisher: IEEE
Date: 2006
Publisher: IEEE
Date: 2011
Publisher: Springer Science and Business Media LLC
Date: 2009
DOI: 10.1155/2009/717136
Publisher: ACM
Date: 21-10-2012
Publisher: IEEE
Date: 08-2006
Publisher: IEEE
Date: 2005
Publisher: IEEE
Date: 12-2014
Publisher: MDPI AG
Date: 16-12-2019
DOI: 10.3390/ELECTRONICS8121552
Abstract: The Internet of Things (IoT) has facilitated services without human intervention for a wide range of applications, including underwater monitoring, where sensors are located at various depths, and data must be transmitted to surface base stations for storage and processing. Ensuring that data transmitted across hierarchical sensor networks are kept secure and private without high computational cost remains a challenge. In this paper, we propose a multilevel sensor monitoring architecture. Our proposal includes a layer-based architecture consisting of Fog and Cloud elements to process and store and process the Internet of Underwater Things (IoUT) data securely with customized Blockchain technology. The secure routing of IoUT data through the hierarchical topology ensures the legitimacy of data sources. A security and performance analysis was performed to show that the architecture can collect data from IoUT devices in the monitoring region efficiently and securely.
Publisher: ACM
Date: 04-02-2020
Publisher: Oxford University Press (OUP)
Date: 24-02-2005
DOI: 10.1093/BIOINFORMATICS/BTI345
Abstract: Microarray data are used in a range of application areas in biology, although often it contains considerable numbers of missing values. These missing values can significantly affect subsequent statistical analysis and machine learning algorithms so there is a strong motivation to estimate these values as accurately as possible before using these algorithms. While many imputation algorithms have been proposed, more robust techniques need to be developed so that further analysis of biological data can be accurately undertaken. In this paper, an innovative missing value imputation algorithm called collateral missing value estimation (CMVE) is presented which uses multiple covariance-based imputation matrices for the final prediction of missing values. The matrices are computed and optimized using least square regression and linear programming methods. The new CMVE algorithm has been compared with existing estimation techniques including Bayesian principal component analysis imputation (BPCA), least square impute (LSImpute) and K-nearest neighbour (KNN). All these methods were rigorously tested to estimate missing values in three separate non-time series (ovarian cancer based) and one time series (yeast sporulation) dataset. Each method was quantitatively analyzed using the normalized root mean square (NRMS) error measure, covering a wide range of randomly introduced missing value probabilities from 0.01 to 0.2. Experiments were also undertaken on the yeast dataset, which comprised 1.7% actual missing values, to test the hypothesis that CMVE performed better not only for randomly occurring but also for a real distribution of missing values. The results confirmed that CMVE consistently demonstrated superior and robust estimation capability of missing values compared with other methods for both series types of data, for the same order of computational complexity. A concise theoretical framework has also been formulated to validate the improved performance of the CMVE algorithm. The CMVE software is available upon request from the authors.
Publisher: IEEE Comput. Soc
Date: 2003
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2012
Publisher: IEEE
Date: 07-2011
Publisher: Figshare
Date: 2011
Publisher: IEEE
Date: 2004
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: IEEE
Date: 02-2019
Publisher: IEEE
Date: 08-2012
Publisher: IEEE
Date: 02-2019
Publisher: IEEE
Date: 2005
Publisher: IEEE
Date: 12-2011
Publisher: IEEE
Date: 10-2013
Publisher: SAGE Publications
Date: 07-03-2012
Abstract: Condition based maintenance (CBM) in the process industry helps in determining the residual life of equipment, avoiding sudden breakdown and facilitating the maintenance staff to schedule repairs by optimizing demand–supply relationships. One of the prevalent issues in CBM is to predict the residual life of the equipment. This paper proposes a novel framework to predict the remnant life of the equipment, called residual life prediction, based on optimally parameterized wavelet transform and multi-step support vector regression (RWMS). In optimally parameterized wavelet transform, a generalized criterion is proposed to select the wavelet decomposition level which works for all the applications decomposition nodes are selected by characterizing their dominancy level based upon relative fault signature–signal energy contents. The prediction model is based on multi-step support vector regression to determine the nonlinear crack propagation in the rotary machine according to Paris’s fatigue model. The results both for the simulated as well as the actual vibration datasets validate the enhanced performance of RWMS in comparison with the existing techniques to predict the residual life of the equipment.
Publisher: IEEE
Date: 06-2013
Publisher: IEEE
Date: 2011
Publisher: IEEE
Date: 06-2013
Publisher: ACM
Date: 30-01-2023
Publisher: Figshare
Date: 2011
Publisher: IEEE
Date: 12-2008
Publisher: Figshare
Date: 2011
Publisher: Figshare
Date: 2011
Publisher: IEEE
Date: 12-2008
Publisher: Elsevier BV
Date: 07-2013
Publisher: IEEE
Date: 04-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2015
Publisher: IEEE
Date: 2005
Publisher: IEEE
Date: 10-2011
Publisher: IEEE
Date: 09-2015
DOI: 10.1109/NCA.2015.16
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Oxford University Press (OUP)
Date: 24-03-2021
DOI: 10.1093/BJS/ZNAB101
Abstract: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18–49, 50–69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351 best case 196, worst case 816) or non-cancer surgery (733 best case 407, worst case 1664). Both exceeded the NNV in the general population (1840 best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population.
Publisher: IEEE
Date: 04-2013
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: SAGE Publications
Date: 24-09-2020
Abstract: Health-related data is stored in a number of repositories that are managed and controlled by different entities. For instance, Electronic Health Records are usually administered by governments. Electronic Medical Records are typically controlled by health care providers, whereas Personal Health Records are managed directly by patients. Recently, Blockchain-based health record systems largely regulated by technology have emerged as another type of repository. Repositories for storing health data differ from one another based on cost, level of security and quality of performance. Not only has the type of repositories increased in recent years, but the quantum of health data to be stored has increased. For instance, the advent of wearable sensors that capture physiological signs has resulted in an exponential growth in digital health data. The increase in the types of repository and amount of data has driven a need for intelligent processes to select appropriate repositories as data is collected. However, the storage allocation decision is complex and nuanced. The challenges are exacerbated when health data are continuously streamed, as is the case with wearable sensors. Although patients are not always solely responsible for determining which repository should be used, they typically have some input into this decision. Patients can be expected to have idiosyncratic preferences regarding storage decisions depending on their unique contexts. In this paper, we propose a predictive model for the storage of health data that can meet patient needs and make storage decisions rapidly, in real-time, even with data streaming from wearable sensors. The model is built with a machine learning classifier that learns the mapping between characteristics of health data and features of storage repositories from a training set generated synthetically from correlations evident from small s les of experts. Results from the evaluation demonstrate the viability of the machine learning technique used.
Publisher: Figshare
Date: 2011
Publisher: IEEE
Date: 09-2008
Publisher: IEEE
Date: 2007
Publisher: IEEE
Date: 08-2019
Publisher: IEEE
Date: 06-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2015
Publisher: Springer International Publishing
Date: 2017
Publisher: IEEE
Date: 11-2019
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11589990_30
Publisher: IEEE
Date: 2006
Publisher: Elsevier BV
Date: 11-2014
Publisher: Zenodo
Date: 2011
Publisher: Zenodo
Date: 2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: IEEE
Date: 2005
Publisher: IEEE
Date: 06-2010
Publisher: IEEE
Date: 2007
DOI: 10.1109/ICIS.2007.42
Publisher: Elsevier BV
Date: 08-2008
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer International Publishing
Date: 2017
Publisher: IEEE
Date: 11-2007
Publisher: IEEE
Date: 2005
Publisher: IEEE
Date: 06-2017
Publisher: IEEE
Date: 02-2019
Publisher: IEEE
Date: 02-2019
No related grants have been discovered for Iqbal Gondal.