Role
Dr. Gadelhag Mohmed holds concurrent roles as an (HpL) Lecturer within the Department of Computer Science and as a Senior Research Fellow at the School of Animal, Rural, and Environmental Sciences at Nottingham Trent University. Within this institution, he actively contributes as a member of the Sustainable Agriculture Research Group and the Computational Intelligence and Applications (CIA) research group.
Career overview
Dr. Gadelhag Mohmed earned his BSc in Communication and Electronics Engineering from The Higher Institution of Engineering in Tobruk, Libya, graduating with First Honors. He pursued his MSc in Telecommunication and Electronics Engineering, achieving Distinction at Sheffield Hallam University, UK. His doctoral thesis, titled “Fuzzy Finite State Machine for Human Activity Recognition,” extends from his research during his PhD program at Nottingham Trent University.
Previously, Dr. Gadelhag Mohmed served as a Senior Researcher on multiple projects funded by Innovate UK, focusing on sustainable agriculture in collaboration between NTU and various industrial companies. Additionally, he has been actively involved in the development of several AI research initiatives within the Smart Farming sector.
External activity
Dr. Gadelhag's current research focuses on Artificial Intelligence (AI), the Internet of Things (IoT), and Big Data analysis. His work primarily revolves around Human Activity Recognition and Sustainable Agriculture, aiming to enhance human health and well-being through the former and elevate crop productivity in the latter.
Publications
Journal:
- Mohmed, Gadelhag, et al. "Modelling daily plant growth response to environmental conditions in Chinese solar greenhouse using Bayesian neural network." Scientific Reports 13.1 (2023).
- Mohmed, Gadelhag, Ahmad Lotfi, and Amir Pourabdollah. "Human activities recognition based on neuro-fuzzy finite state machine." Technologies 6.4 (2018): 110.
- Mohmed, Gadelhag, Ahmad Lotfi, and Amir Pourabdollah. "Enhanced fuzzy finite state machine for human activity modelling and recognition." Journal of Ambient Intelligence and Humanized Computing 11.12 (2020): 6077-6091.
Conferences:
- Mohmed, Gadelhag, et al. "Using AI Approaches for Predicting Tomato Growth in Hydroponic Systems." UK Workshop on Computational Intelligence. Springer, Cham, 2021.
- Mohmed, Gadelhag, et al. "Modelling Daily Plant Growth Response to Environmental Conditions in Chinese Solar Greenhouse Using Bayesian Neural Network." Available at SSRN 4082794.
- Mohmed, Gadelhag, Ahmad Lotfi, Caroline Langensiepen, and Amir Pourabdollah. "Unsupervised Learning Fuzzy Finite State Machine for Human Activities Recognition." In Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference, pp. 537-544. ACM, 2018.
- Mohmed, Gadelhag, Ahmad Lotfi, Caroline Langensiepen, and Amir Pourabdollah. "Clustering based Fuzzy Finite State Machine for Human Activity Recognition." In Proceedings of the 18th Annual UK Workshop on Computational Intelligence (UKCI2018) Conference.
- Mohmed, Gadelhag, Ahmad Lotfi, and Amir Pourabdollah. ”Convolutional
Neural Network Classifier with Fuzzy Feature Representation for Human
Activity.” 2020 IEEE International Conference on Fuzzy Systems
(FUZZ-IEEE). - Mohmed, Gadelhag, Ahmad Lotfi, and Amir Pourabdollah. ”Employing a Deep Convolutional Neural Network for Human Activity Recognition Based on Binary Ambient Sensor Data.” Proceedings of the 13th PErvasive Technologies Related to Assistive Environments Conference. 2020.
- Mohmed, Gadelhag, et al. ”Unsupervised learning fuzzy finite state machine for human activities recognition.” Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference. 2018. https://doi.org/10.1145/3197768.3201540.
- Mohmed, Gadelhag, et al. ”Clustering-Based Fuzzy Finite State Machine for Human Activity Recognition.” UK Workshop on Computational Intelligence. Springer, Cham, 2018. https://doi.org/10.1007/978-3-319-97982-3_22.
- Mohmed, Gadelhag, Ahmad Lotfi, and Amir Pourabdollah. ”Long short-term
memory fuzzy finite state machine for human activity modelling.” Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments. 2019. https://doi.org/10.1145/3316782.3322781. - Mohmed, Gadelhag, David Ada Adama, and Ahmad Lotfi. ”Fuzzy Feature
Representation with Bidirectional Long Short-Term Memory for Human
Activity Modelling and Recognition.” UK Workshop on Computational
Press expertise
Sustainable Agriculture Research Group
Computational Intelligence and Applications (CIA) research group