Ashraf Khalil

Prof. Ashraf Khalil

orcid

Research Focus & Metrics

Academic Career Length

23Years (2004-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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22 March 2024 in Expert Systems with Applications
Verified
Journal Article
Closed access
Mohd Nadhir Ab WahabMohd Nadhir Ab Wahab
Mohd Nadhir Ab Wahab
ANAmril Nazir
Amril Nazir
AKAshraf Khalil
Ashraf Khalil
WHWong Jun Ho
Wong Jun Ho
Muhammad Firdaus Akbar Jalaludin KhanMuhammad Firdaus Akbar Jalaludin Khan
Muhammad Firdaus Akbar Jalaludin Khan
AMAhmad Sufril Azlan Mohamed
Ahmad Sufril Azlan Mohamed

30 June 2020 in IEEE Access
Verified
Journal Article
Open access
AKAshraf Khalil
Ashraf Khalil
SASoha Glal Ahmed
Soha Glal Ahmed
AKAsad Masood Khattak
Asad Masood Khattak
NANabeel Al-Qirim
Nabeel Al-Qirim
Recent studies have demonstrated that most commercial facial analysis systems are biased against certain categories of race, ethnicity, culture, age and gender. The bias can be traced in some cases to the algorithms used and in other cases to insufficient training of algorithms, while in still other cases bias can be traced to insufficient databases. To date, no comprehensive literature review exists which systematically investigates bias and discrimination in the currently available facial analysis software. To address the gap, this study conducts a systematic literature review (SLR) in which the context of facial analysis system bias is investigated in detail. The review, involving 24 studies, additionally aims to identify (a) facial analysis databases that were created to alleviate bias, (b) the full range of bias in facial analysis software and (c) algorithms and techniques implemented to mitigate bias in facial analysis.

4 November 2006
Verified
Conference Paper
Open access
PDF file
AKAshraf Khalil
Ashraf Khalil
KCKay Connelly
Kay Connelly

30 April 2024 in Journal of Retailing and Consumer Services
Verified
Journal Article
Closed access
AMAnkit Mehrotra
Ankit Mehrotra
RAReeti Agarwal
Reeti Agarwal
AKAshraf Khalil
Ashraf Khalil
EAEbtesam Abdullah Alzeiby
Ebtesam Abdullah Alzeiby
VAVaishali Agarwal
Vaishali Agarwal

1 March 2017 in Computers in Human Behavior
Verified
Journal Article
Closed access
ADAmandeep Dhir
Amandeep Dhir
AKAshraf Khalil
Ashraf Khalil
KLKirsti Lonka
Kirsti Lonka
CTChin-Chung Tsai
Chin-Chung Tsai

19 September 2022 in IEEE Transactions on Engineering Management
Verified
Journal Article
Closed access
AKAshraf Khalil
Ashraf Khalil
ASAmit Shankar
Amit Shankar
RBRahul Bodhi
Rahul Bodhi
ABAbhishek Behl
Abhishek Behl
AFAlberto Ferraris
Alberto Ferraris
Recently, the food service delivery industry started using drones for delivering food. The drone food delivery services are not only beneficial for marketers but also provide several benefits to the consumers. Despite several advantages for different stakeholders, the adoption of drone-based delivery is not encouraging. Thus, it is crucial to know why consumers show resistance behavior toward drone-based food delivery services. Toward that end, this study examines the effects of different functional and psychological barriers on consumers’ resistance to drone-based food delivery services through the lens of innovation resistance theory. A moderated-mediation framework has been proposed using anticipated negative emotions as a mediator and consumers’ innovativeness as a moderator. The hypotheses are examined by analyzing the 355 samples collected from online food delivery service users. The results suggest that usage, risk, image, and experience barriers are significantly associated with resistance. The study's findings enrich emerging drone-based delivery services and online food delivery literature. The study's findings furthermore offer practical knowledge to help online food delivery service providers implement strategies to reduce consumers' resistance to the drone food delivery system.