Mohamed Shahawy
Ph.D. Candidate
Mohamed Shahawy is a dedicated Ph.D. Candidate at Staffordshire University, where he is deeply immersed in the exploration of cutting-edge technologies within the realms of Neural Architecture Search, AutoML, Continual Learning, and Adaptive AI. His thesis entitled "Evolving Intelligence: Designing Adaptive AI Systems through Automated Neural Architecture Search and Continual Multimodal Learning" is supervised by Prof. Elhadj Benkhelifa. His research journey is marked by a strong commitment to advancing the understanding and application of artificial intelligence to address complex challenges. Shahawy's work has been recognized through various scholarly contributions, highlighting his innovative approaches to leveraging AI for real-world impact.
Throughout his academic career, Shahawy has made significant contributions to the scientific community, evidenced by his involvement in various research projects and publications. Notably, his work on "Mining and analysis of air quality data to aid climate change," co-authored with L Babu Saheer and J Zarrin, showcases his commitment to utilizing AI for environmental sustainability. This particular research, presented at the AIAI 2020, underlines the potential of artificial intelligence applications in analyzing air quality data to combat climate change. Additionally, his exploration into the intersection between Neural Architecture Search and Continual Learning, as well as his development of the HiveNAS framework using Artificial Bee Colony Optimization, reflect Shahawy's innovative spirit and dedication to pushing the boundaries of AI research.
Shahawy's versatility and prowess in AI research are further exemplified by his work on a self-supervised approach for urban tree recognition on aerial images, which was presented at the IFIP International Conference on Artificial Intelligence Applications. This project, among others, highlights his ability to apply AI solutions to diverse domains, from environmental conservation to urban planning. Moreover, his contribution to the development of SIoTSim, a simulator for the Social Internet of Things, underscores his foresight in anticipating and shaping the future of IoT systems. As Mohamed Shahawy continues his journey at Staffordshire University, his research not only contributes to the academic community but also paves the way for practical AI applications that address pressing global issues.
Latest Publications by Mohamed Shahawy
HiveNAS: Neural Architecture Search using Artificial Bee Colony Optimization
Cited by 1 | Year 2022The traditional Neural Network-development process requires substantial expert knowledge and relies heavily on intuition and trial-and-error. Neural Architecture Search (NAS) frameworks were introduced to robustly search for network topologies, as well as facilitate the automated development of Neural Networks. While some optimization approaches -- such as Genetic Algorithms -- have been extensively explored in the NAS context, other Metaheuristic Optimization algorithms have not yet been evaluated. In this paper, we propose HiveNAS, the first Artificial Bee Colony-based NAS framework.
Exploring the Intersection between Neural Architecture Search and Continual Learning
Cited by 0 | Year 2022Self-supervised approach for urban tree recognition on aerial images
Cited by 3 | Year 2021In the light of Artificial Intelligence aiding modern society in tackling climate change, this research looks at how to detect vegetation from aerial view images using deep learning models. This task is part of a proposed larger framework to build an eco-system to monitor air quality and the related factors like weather, transport, and vegetation, as the number of trees for any urban city in the world. The challenge involves building or adapting the tree recognition models to a new city with minimum or no labeled data. This paper explores self-supervised approaches to this problem and comes up with a system with 0.89 mean average precision on the Google Earth images for Cambridge city.
Posts by Mohamed Shahawy
Can AI Outgrow Its Creators? Towards Self-Optimising Networks
Discovering the adaptive qualities in AI using Continual Neural Architecture Search
Viewed 216 times· 3 min read