ORCID Profile
0000-0003-2113-3390
Current Organisations
Khalifa University
,
Macquarie University Faculty of Science and Engineering
,
Data61
,
Macquarie University
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Publisher: ACM
Date: 12-11-2019
Publisher: ACM
Date: 13-05-2019
Publisher: SAGE Publications
Date: 08-08-2023
DOI: 10.1177/14707853231194984
Abstract: Drawing substantially on epistemologies developed in the context of vulnerable populations and responding to calls for greater epistemic justice in marketing research, an intersectional framework involving positionality, partiality, reflexivity, and situated knowledges is proposed to reflect on undertaking market research with Muslim minority populations in non-Muslim majority countries. This population is often highly vulnerable due to stigmatisation, Islamophobia and processes of othering that affect Muslim consumer behaviour, practices and identity. The framework is derived from reflexive experiences of market research undertaken by the authors on and with Islamic consumers both on an in idual and collective basis and from relevant literature. The framework highlights the relational nature of the research experience and the situatedness and positionality of both the researcher and researched. In the case of research with Muslim populations we also draw out the significance of religious identity, ideology and religiosity intersectionalities, including gender and religious and cultural power as framed by cultural and institutional practices and which affect notions of class and attitudes to the other. We propose an approach that helps overcome Muslim/non-Muslim binaries that flatten the lived notions of the Muslim experience and consumption practices and instead provide for a richer and more representative account of Muslim identity. However, this approach also heightens researcher sensitivity to the situatedness of Muslims within social norms and the implications that this has for anonymity.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: ACM
Date: 30-04-2023
Publisher: IEEE
Date: 12-2008
Publisher: Springer International Publishing
Date: 2022
Publisher: IEEE
Date: 10-2019
Publisher: Informa UK Limited
Date: 29-09-2022
Publisher: MDPI AG
Date: 02-12-2021
DOI: 10.3390/S21238050
Abstract: Organophosphates (OPs) are neurotoxic agents also used as pesticides that can permanently block the active site of the acetylcholinesterase (AChE). A robust and sensitive detection system of OPs utilising the enzyme mimic potential of the cysteamine capped gold nanoparticles (C-AuNPs) was developed. The detection assay was performed by stepwise addition of AChE, parathion ethyl (PE)-a candidate OP, acetylcholine chloride (ACh), C-AuNPs, and 3, 3′, 5, 5′-tetramethylbenzidine (TMB) in the buffer solution. The whole sensing protocol completes in 30–40 min, including both incubations. The Transmission Electron Microscopy (TEM) results indicated that the NPs are spherical and have an average size of 13.24 nm. The monomers of C-AuNPs exhibited intense catalytic activity (nanozyme) for the oxidization of TMB, revealed by the production of instant blue colour and confirmed by a sharp peak at 652 nm. The proposed biosensor’s detection limit and linear ranges were 5.8 ng·mL−1 and 11.6–92.8 ng·mL−1, respectively, for PE. The results strongly advocate that the suggested facile colorimetric biosensor may provide an excellent platform for on-site monitoring of OPs.
Publisher: MDPI AG
Date: 30-03-2022
DOI: 10.3390/IJMS23073800
Abstract: Fat mass and obesity-associated protein (FTO) is the first reported RNA N6-methyladenosine (m6A) demethylase in eukaryotic cells. m6A is considered as the most abundant mRNA internal modification, which modulates several cellular processes including alternative splicing, stability, and expression. Genome-wide association studies (GWAS) identified single-nucleotide polymorphisms (SNPs) within FTO to be associated with obesity, as well as cancer including endometrial cancer, breast cancer, pancreatic cancer, and melanoma. Since the initial classification of FTO as an m6A demethylase, various studies started to unravel a connection between FTO’s demethylase activity and the susceptibility to obesity on the molecular level. FTO was found to facilitate adipogenesis, by regulating adipogenic pathways and inducing pre-adipocyte differentiation. FTO has also been investigated in tumorigenesis, where emerging studies suggest m6A and FTO levels are dysregulated in various cancers, including acute myeloid leukemia (AML), glioblastoma, cervical squamous cell carcinoma (CSCC), breast cancer, and melanoma. Here we review the molecular bases of m6A in tumorigenesis and adipogenesis while highlighting the controversial role of FTO in obesity. We provide recent findings confirming FTO’s causative link to obesity and discuss novel approaches using RNA demethylase inhibitors as targeted oncotherapies. Our review aims to confirm m6A demethylation as a risk factor in obesity and provoke new research in FTO and human disorders.
Publisher: IEEE
Date: 25-04-2022
Publisher: ACM
Date: 21-06-2009
Publisher: Elsevier BV
Date: 08-2022
DOI: 10.1016/J.SCR.2022.102853
Abstract: Here we report the generation of the first Emirati iPSC line in the United Arab Emirates and name it KUSTi001-A. CD34+ hematopoietic cells purified from peripheral blood of a 27-year-old healthy female donor were reprogrammed using Sendai vectors. Twenty days post-reprogramming colonies were manually picked, and expanded clones were verified for transgene clearance by RT-PCR. Pluripotency was validated by pluripotency genes and differentiation into all three germ layers. Finally, chromosome stability was confirmed by testing 8 common abnormality loci. KUSTi001-A, alternatively called UAE001, is an Emirati hiPSC line that holds great potential for UAE specific regenerative medicine, disease modelling and drug screening.
Publisher: ACM
Date: 02-07-2019
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: IEEE
Date: 12-2008
Publisher: IEEE
Date: 09-2009
Publisher: SCITEPRESS - Science and Technology Publications
Date: 2021
Publisher: IEEE
Date: 11-2018
Publisher: Association for Computing Machinery (ACM)
Date: 06-12-2021
DOI: 10.1145/3417987
Abstract: Security and privacy of users have become significant concerns due to the involvement of the Internet of Things (IoT) devices in numerous applications. Cyber threats are growing at an explosive pace making the existing security and privacy measures inadequate. Hence, everyone on the Internet is a product for hackers. Consequently, Machine Learning (ML) algorithms are used to produce accurate outputs from large complex databases, where the generated outputs can be used to predict and detect vulnerabilities in IoT-based systems. Furthermore, Blockchain (BC) techniques are becoming popular in modern IoT applications to solve security and privacy issues. Several studies have been conducted on either ML algorithms or BC techniques. However, these studies target either security or privacy issues using ML algorithms or BC techniques, thus posing a need for a combined survey on efforts made in recent years addressing both security and privacy issues using ML algorithms and BC techniques. In this article, we provide a summary of research efforts made in the past few years, from 2008 to 2019, addressing security and privacy issues using ML algorithms and BC techniques in the IoT domain. First, we discuss and categorize various security and privacy threats reported in the past 12 years in the IoT domain. We then classify the literature on security and privacy efforts based on ML algorithms and BC techniques in the IoT domain. Finally, we identify and illuminate several challenges and future research directions using ML algorithms and BC techniques to address security and privacy issues in the IoT domain.
Publisher: ACM
Date: 21-06-2009
Publisher: ACM
Date: 20-03-2009
Publisher: Elsevier BV
Date: 08-2001
Publisher: Springer Nature Switzerland
Date: 2022
Publisher: IEEE
Date: 10-2009
Publisher: Elsevier BV
Date: 2019
Publisher: SCITEPRESS - Science and Technology Publications
Date: 2021
Publisher: Privacy Enhancing Technologies Symposium Advisory Board
Date: 22-12-2017
Abstract: Numerous tools have been developed to aggressively block the execution of popular JavaScript programs in Web browsers. Such blocking also affects functionality of webpages and impairs user experience. As a consequence, many privacy preserving tools that have been developed to limit online tracking, often executed via JavaScript programs, may suffer from poor performance and limited uptake. A mechanism that can isolate JavaScript programs necessary for proper functioning of the website from tracking JavaScript programs would thus be useful. Through the use of a manually labelled dataset composed of 2,612 JavaScript programs, we show how current privacy preserving tools are ineffective in finding the right balance between blocking tracking JavaScript programs and allowing functional JavaScript code. To the best of our knowledge, this is the first study to assess the performance of current web privacy preserving tools in determining tracking vs. functional JavaScript programs. To improve this balance, we examine the two classes of JavaScript programs and hypothesize that tracking JavaScript programs share structural similarities that can be used to differentiate them from functional JavaScript programs. The rationale of our approach is that web developers often “borrow” and customize existing pieces of code in order to embed tracking (resp. functional) JavaScript programs into their webpages. We then propose one-class machine learning classifiers using syntactic and semantic features extracted from JavaScript programs. When trained only on s les of tracking JavaScript programs, our classifiers achieve accuracy of 99%, where the best of the privacy preserving tools achieve accuracy of 78%. The performance of our classifiers is comparable to that of traditional two-class SVM. One-class classification, where a training set of only tracking JavaScript programs is used for learning, has the advantage that it requires fewer labelled ex les that can be obtained via manual inspection of public lists of well-known trackers. We further test our classifiers and several popular privacy preserving tools on a larger corpus of 4,084 websites with 135,656 JavaScript programs. The output of our best classifier on this data is between 20 to 64% different from the tools under study. We manually analyse a s le of the JavaScript programs for which our classifier is in disagreement with all other privacy preserving tools, and show that our approach is not only able to enhance user web experience by correctly classifying more functional JavaScript programs, but also discovers previously unknown tracking services.
Publisher: Walter de Gruyter GmbH
Date: 2012
Publisher: IEEE
Date: 10-2017
Publisher: Association for Computing Machinery (ACM)
Date: 28-04-2020
DOI: 10.1145/3380466
Abstract: The web is a tangled mass of interconnected services, whereby websites import a range of external resources from various third-party domains. The latter can also load further resources hosted on other domains. For each website, this creates a dependency chain underpinned by a form of implicit trust between the first-party and transitively connected third parties. The chain can only be loosely controlled as first-party websites often have little, if any, visibility on where these resources are loaded from. This article performs a large-scale study of dependency chains in the web to find that around 50% of first-party websites render content that they do not directly load. Although the majority (84.91%) of websites have short dependency chains (below three levels), we find websites with dependency chains exceeding 30. Using VirusTotal, we show that 1.2% of these third parties are classified as suspicious—although seemingly small, this limited set of suspicious third parties have remarkable reach into the wider ecosystem. We find that 73% of websites under-study load resources from suspicious third parties, and 24.8% of first-party webpages contain at least three third parties classified as suspicious in their dependency chain. By running sandboxed experiments, we observe a range of activities with the majority of suspicious JavaScript codes downloading malware.
Publisher: SAGE Publications
Date: 04-2014
DOI: 10.1155/2014/148595
Abstract: The availability of inexpensive hardware such as CMOS cameras and microphones has fostered the development of wireless multimedia sensor networks (WMSNs). In WMSNs, wirelessly interconnected devices enable ubiquitously retrieving multimedia contents such as video and audio streams, and still images along with scalar data from surroundings for wide range of applications are constrained by processing, memory, and power resources. Image compression via low-complexity and resource efficient transforms has been addressed by several researchers to prolong network lifetime where energy conservation is achieved through sharing computational load among sensor nodes and by adjusting the transmission ranges of camera nodes. However, those schemes are not adaptive to the presence and changes of energy level of computational sensor nodes and to the amount of computational load. We propose a resource and energy efficient distributed image compression algorithm that dynamically configures according to the energy levels and the forwarding strategy that is based on the entropy of the image. The simulation results show that our adaptive distributed image compression scheme significantly prolongs the network lifetime and improves the network utilization efficiency, while maintaining adequate image quality.
Publisher: BMJ
Date: 16-06-2021
DOI: 10.1136/BMJ.N1248
Abstract: To investigate whether and what user data are collected by health related mobile applications (mHealth apps), to characterise the privacy conduct of all the available mHealth apps on Google Play, and to gauge the associated risks to privacy. Cross sectional study Health related apps developed for the Android mobile platform, available in the Google Play store in Australia and belonging to the medical and health and fitness categories. Users of 20 991 mHealth apps (8074 medical and 12 917 health and fitness found in the Google Play store: in-depth analysis was done on 15 838 apps that did not require a download or subscription fee compared with 8468 baseline non-mHealth apps. Primary outcomes were characterisation of the data collection operations in the apps code and of the data transmissions in the apps traffic analysis of the primary recipients for each type of user data presence of adverts and trackers in the app traffic audit of the app privacy policy and compliance of the privacy conduct with the policy and analysis of complaints in negative app reviews. 88.0% (n=18 472) of mHealth apps included code that could potentially collect user data. 3.9% (n=616) of apps transmitted user information in their traffic. Most data collection operations in apps code and data transmissions in apps traffic involved external service providers (third parties). The top 50 third parties were responsible for most of the data collection operations in app code and data transmissions in app traffic (68.0% (2140), collectively). 23.0% (724) of user data transmissions occurred on insecure communication protocols. 28.1% (5903) of apps provided no privacy policies, whereas 47.0% (1479) of user data transmissions complied with the privacy policy. 1.3% (3609) of user reviews raised concerns about privacy. This analysis found serious problems with privacy and inconsistent privacy practices in mHealth apps. Clinicians should be aware of these and articulate them to patients when determining the benefits and risks of mHealth apps.
Publisher: SCITEPRESS - Science and Technology Publications
Date: 2019
Publisher: ACM Press
Date: 2018
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Oxford University Press (OUP)
Date: 12-08-2021
Abstract: We conduct a first large-scale analysis of mobile health (mHealth) apps available on Google Play with the goal of providing a comprehensive view of mHealth apps’ security features and gauging the associated risks for mHealth users and their data. We designed an app collection platform that discovered and downloaded more than 20 000 mHealth apps from the Medical and Health & Fitness categories on Google Play. We performed a suite of app code and traffic measurements to highlight a range of app security flaws: certificate security, sensitive or unnecessary permission requests, malware presence, communication security, and security-related concerns raised in user reviews. Compared to baseline non-mHealth apps, mHealth apps generally adopt more reliable signing mechanisms and request fewer dangerous permissions. However, significant fractions of mHealth apps expose users to serious security risks. Specifically, 1.8% of mHealth apps package suspicious codes (eg, trojans), 45.0% rely on unencrypted communication, and as much as 23.0% of personal data (eg, location information and passwords) is sent on unsecured traffic. An analysis of the app reviews reveals that mHealth app users are largely unaware of the surfaced security issues. Despite being better aligned with security best practices than non-mHealth apps, mHealth apps are still far from ensuring robust security guarantees. App users, clinicians, technology developers, and policy makers alike should be cognizant of the uncovered security issues and weigh them carefully against the benefits of mHealth apps.
Location: Saudi Arabia
Location: United States of America
Location: United Kingdom of Great Britain and Northern Ireland
Location: Germany
Location: Australia
Location: United Arab Emirates
No related grants have been discovered for Ikram Muhammad.