Dr. A’aeshah Alhakamy
Computer Science, University of Tabuk, Tabūk, Saudi Arabia
'%3e%3cpath%20d='M22.1642%2019.0924C21.7317%2018.068%2021.1041%2017.1375%2020.3163%2016.3527C19.5309%2015.5656%2018.6006%2014.9381%2017.5766%2014.5048C17.5674%2014.5002%2017.5582%2014.4979%2017.5491%2014.4933C18.9774%2013.4616%2019.9059%2011.7811%2019.9059%209.88504C19.9059%206.74408%2017.3611%204.19922%2014.2201%204.19922C11.0792%204.19922%208.53429%206.74408%208.53429%209.88504C8.53429%2011.7811%209.46282%2013.4616%2010.8912%2014.4956C10.882%2014.5002%2010.8728%2014.5025%2010.8636%2014.5071C9.83652%2014.9404%208.91487%2015.5617%208.1239%2016.355C7.33685%2017.1404%206.70933%2018.0707%206.276%2019.0947C5.85031%2020.0972%205.62072%2021.172%205.59967%2022.2609C5.59905%2022.2853%205.60335%2022.3097%205.61229%2022.3325C5.62123%2022.3553%205.63465%2022.376%205.65174%2022.3935C5.66883%2022.4111%205.68926%2022.425%205.71182%2022.4345C5.73437%2022.444%205.7586%2022.4489%205.78308%2022.4489H7.15868C7.25956%2022.4489%207.3398%2022.3686%207.3421%2022.2701C7.38795%2020.5001%208.09868%2018.8425%209.35506%2017.5861C10.655%2016.2862%2012.3814%2015.5709%2014.2201%2015.5709C16.0588%2015.5709%2017.7852%2016.2862%2019.0852%2017.5861C20.3415%2018.8425%2021.0523%2020.5001%2021.0981%2022.2701C21.1004%2022.3709%2021.1807%2022.4489%2021.2815%2022.4489H22.6571C22.6816%2022.4489%2022.7058%2022.444%2022.7284%2022.4345C22.751%2022.425%2022.7714%2022.4111%2022.7885%2022.3935C22.8056%2022.376%2022.819%2022.3553%2022.8279%2022.3325C22.8369%2022.3097%2022.8412%2022.2853%2022.8406%2022.2609C22.8176%2021.165%2022.5907%2020.0989%2022.1642%2019.0924ZM14.2201%2013.8284C13.1678%2013.8284%2012.1773%2013.418%2011.4322%2012.6729C10.6871%2011.9278%2010.2767%2010.9374%2010.2767%209.88504C10.2767%208.83271%2010.6871%207.84227%2011.4322%207.09715C12.1773%206.35204%2013.1678%205.94165%2014.2201%205.94165C15.2724%205.94165%2016.2629%206.35204%2017.008%207.09715C17.7531%207.84227%2018.1635%208.83271%2018.1635%209.88504C18.1635%2010.9374%2017.7531%2011.9278%2017.008%2012.6729C16.2629%2013.418%2015.2724%2013.8284%2014.2201%2013.8284Z'%20fill='%238590A2'/%3e%3c/g%3e%3c/svg%3e)
Co-authors
Following
Followers
Research Focus & Metrics
Explore full metrics on Scilit
Academic Career Length
8Years (2019-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications25 April 2024 in ACM Computing Surveys
Journal Article
A’aeshah AlhakamyWhen developing XR applications for Industry 4.0, it is important to consider the integration of visual displays, hardware components, and multimodal interaction techniques that are compatible with the entire system. The potential use of multimodal interactions in industrial applications has been recognized as a significant factor in enhancing humans’ ability to perform tasks and make informed decisions. To offer a comprehensive analysis of the current advancements in industrial XR, this review presents a structured tutorial that provides answers to the following research questions: (R.Q.1) What are the similarities and differences between XR technologies, including augmented reality (AR), mixed reality (MR), Augmented Virtuality (AV), and virtual reality (VR) under Industry 4.0 consideration? (R.Q.2) What types of visual displays and hardware devices are needed to present XR for Industry 4.0? (R.Q.3) How did the multimodal interaction in XR perceive and relate to Industry 4.0? (R.Q.4) How have modern adaptations of XR technologies dealt with the theme of Industry 4.0? (R.Q.5) How can XR technologies in Industry 4.0 develop their services and usages to be more solution-inclusive? This review showcases various instances that demonstrate XR’s potential to transform how humans interact with the physical world in Industry 4.0. These advancements can increase productivity, reduce costs, and enhance safety.
1 March 2019
Conference Paper
AAAbeer Alsaiari
Abeer Alsaiari
RRRidhi Rustagi
Ridhi Rustagi
A’aeshah AlhakamyMTManu Mathew Thomas
Manu Mathew Thomas
AFAngus G. Forbes
Angus G. Forbes
Animation studios render 3D scenes using a technique called path tracing which enables them to create high quality photorealistic frames. Path tracing involves shooting 1000's of rays into a pixel randomly (Monte Carlo) which will then hit the objects in the scene and, based on the reflective property of the object, these rays reflect or refract or get absorbed. The colors returned by these rays are averaged to determine the color of the pixel. This process is repeated for all the pixels. Due to the computational complexity it might take 8-16 hours to render a single frame. We implemented a neural network-based solution to reduce the time it takes to render a frame to less than a second using a generative adversarial network (GAN), once the network is trained. The main idea behind this proposed method is to render the image using a much smaller number of samples per pixel than is normal for path tracing (e.g., 1, 4, or 8 samples instead of, say, 32,000 samples) and then pass the noisy, incompletely rendered image to our network, which is capable of generating a high-quality photorealistic image.
21 April 2020
Conference Paper
MTMilka Trajkova
Milka Trajkova
A’aeshah Alhakamy
Francesco CafaroRMRashmi Mallappa
Rashmi Mallappa
SKSreekanth R. Kankara
Sreekanth R. Kankara
Museums have embraced embodied interaction: its novelty generates buzz and excitement among their patrons, and it has enormous educational potential. Human-Data Interaction (HDI) is a class of embodied interactions that enables people to explore large sets of data using interactive visualizations that users control with gestures and body movements. In museums, however, HDI installations have no utility if visitors do not engage with them. In this paper, we present a quasi-experimental study that investigates how different ways of representing the user ("mode type") next-to a data visualization alters the way in which people engage with a HDI system. We consider four mode types: avatar, skeleton, camera overlay, and control. Our findings indicate that the mode type impacts the number of visitors that interact with the installation, the gestures that people do, and the amount of time that visitors spend observing the data on display and interacting with the system.
28 May 2020 in ACM Computing Surveys
Journal Article
A’aeshah AlhakamyMTMihran Tuceryan
Mihran Tuceryan
A realistically inserted virtual object in the real-time physical environment is a desirable feature in augmented reality (AR) applications and mixed reality (MR) in general. This problem is considered a vital research area in computer graphics, a field that is experiencing ongoing discovery. The algorithms and methods used to obtain dynamic and real-time illumination measurement, estimating, and rendering of augmented reality scenes are utilized in many applications to achieve a realistic perception by humans. We cannot deny the powerful impact of the continuous development of computer vision and machine learning techniques accompanied by the original computer graphics and image processing methods to provide a significant range of novel AR/MR techniques. These techniques include methods for light source acquisition through image-based lighting or sampling, registering and estimating the lighting conditions, and composition of global illumination. In this review, we discussed the pipeline stages with the details elaborated about the methods and techniques that contributed to the development of providing a photo-realistic rendering, visual coherence, and interactive real-time illumination results in AR/MR.
1 January 2022 in International Journal of Advanced Computer Science and Applications
Journal Article
SAShoroog Albalawi
Shoroog Albalawi
LALama Alshahrani
Lama Alshahrani
NANouf Albalawi
Nouf Albalawi
RKReem Kilabi
Reem Kilabi
A’aeshah AlhakamyBiometric authentication is becoming more preva-lent as it allows consumers to authenticate themselves without entering a physical address or a personal identification number. Thus, a simple finger gesture or a glance at a camera can still prove one’s identity. In this review, we explain in detail how the concept of authentication and the various types of biometric techniques is used for user identification. Then, we discuss the various ways these techniques can be combined to create a truly multimodal authentication system. For a more organized approach, our overview is classified into two main categories based on human biometric traits. First, the physiolog-ical traits include fingerprint, facial, iris/retina, hand, and finger-vein. Second, the behavioral traits includes voice, signature, and keystroke recognition systems. Finally, we offer a comprehensive comparison of selected methods and techniques and focus on three criteria: algorithms, merits, and drawbacks. Based on this comparison, we provide insight into our future research in iris recognition, by which we combine several artificial intelligence algorithms to develop our system.
Journal Article
A’aeshah AlhakamyAAAreej Alhowaity
Areej AlhowaityAAAnwar Abdullah Alatawi
Anwar Abdullah AlatawiHAHadeel Alsaadi
Hadeel AlsaadiCurrently, owning a car is a necessity, as it plays a significant role in human transportation for different purposes such as going to work and to the hospital. However, with the current economic challenges, buying expensive cars can be a burden. The car market has shifted toward more affordable used cars. Due to the increasing number of used cars being sold, the price of used cars has become a major issue that could affect our sustainable way of living. The objective of this research is to understand the impact of the problem and to find empirical solutions by implementing a variety of machine learning techniques and big data tools on the prices of used cars. Thus, we develop a linear regression model that can estimate used car prices based on various features to answer the following research questions: (R.Q.1) How significantly does an independent feature in the dataset affect the dependent variable (car price)? (R.Q.2) Is a linear regression model effective for prediction of used car prices? (R.Q.3) How does prediction of used car prices support sustainability? Finally, we present our results in the form of answers to these questions, including some limitations and future research.
About Me
A'aeshah Alhakamy is an assistant professor of Computers and Information Technology at the University of Tabuk, Saudi Arabia, where she was awarded a full scholarship to pursue higher education abroad in Computer Graphics, Visualization, and Imaging. She was assigned to several administrative positions such as supervisor of academic affairs, deputy director of the industrial innovation and robotics center (IIRC), a researcher at the artificial intelligence and sensing technologies (AIST) Center, and a member of the permanent supervisory committee for social responsibility.
A'aeshah holds a Ph.D. (2020) in Computer Science from Purdue University in Indianapolis under the supervision of Professor Mihran Tuceryan. She also holds M.Sc. (2016) in Computer and Information Science from the Purdue School of Science, Purdue University Indianapolis, IN, USA. She got her B.Sc. (2005) in Computer Science from Taibah University, Medina, Saudi Arabia.
While studying at IUPUI, A'aeshah worked as a Teaching Assistant at the Department of Computer and Information Science in the fall of 2017. She also worked as Graduate Research Assistant at the School of Informatics and Computing in the Human-Centered Computing Department under the supervision of Professor Francesco Cafaro in Data Lab with NSF funded project named "Aiding Reasoning about Correlation and Causation".
Her primary research interests include Computer Graphics, Computer Vision, Visualization, and Imaging.