Adel Samir ElZemity

Office B2.12 Keynes College

University of Kent

Canterbury, Kent, UK CT2 7FS

Previously

Earned a bachelor’s degree in computer engineering from Nile University in Egypt.
Did exchange semesters at Riga Technical University in Latvia and Fayetteville State University in the USA, in computer science.
Worked as Machine Learning Engineer at Spanish National Cancer Research Center (CNIO) in Spain, specializing in Natural Language Processing (NLP).

Currently

Pursuing a Ph.D. in Computer Science at University of Kent, UK.
Focusing on Security of Large Language Models (LLMs) and Internet of Things (IoT).
Developing light-weight ML to counter threats of ransomware attacks in IoT as part of the “Countering HArms caused by Ransomware on the Internet of Things (CHARIOT)” project, in collaboration with my Ph.D. supervisor, Dr Budi Arief, Dr George Oikonomou, Dr James Pope, Dr Calvin Brierley, Dr Yuxiang Huang.

Selected Publications

CyberLLMInstruct: A New Dataset for Analysing Safety of Fine-Tuned LLMs Using Cyber Security Data

Adel ElZemity, Budi Arief , and Shujun Li

2025

Bib PDF Website

@misc{elzemity2025cyberllminstructnewdatasetanalysing,
  title = {CyberLLMInstruct: A New Dataset for Analysing Safety of Fine-Tuned LLMs Using Cyber Security Data},
  author = {ElZemity, Adel and Arief, Budi and Li, Shujun},
  year = {2025},
  eprint = {2503.09334},
  archiveprefix = {arXiv},
  primaryclass = {cs.CR},
}

Privacy Threats and Countermeasures in Federated Learning for Internet of Things: A Systematic Review

Adel ElZemity, and Budi Arief

In 2024 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics , 2024

Bib PDF Website

@inproceedings{10731741,
  author = {ElZemity, Adel and Arief, Budi},
  booktitle = {2024 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics},
  title = {Privacy Threats and Countermeasures in Federated Learning for Internet of Things: A Systematic Review},
  year = {2024},
  pages = {331-338},
  keywords = {Privacy;Differential privacy;Social computing;Computational modeling;Multi-party computation;Robustness;Blockchains;Internet of Things;Time factors;Security;Federated Learning;Internet of Things;Privacy Threats;Defensive Measures;Systematic Literature Review},
  doi = {10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics62450.2024.00072}
}

A Comparative Analysis of Time Series Transformers and Alternative Deep Learning Models for SSVEP Classification

Heba Ali , Adel ElZemity, Amir E Oghostinos , and 1 more author

In International Conference on Model and Data Engineering , 2023

Abs Bib Website

Steady State Visually Evoked Potentials (SSVEPs) are intrinsic responses to specific visual stimulus frequencies. When the retina is activated by a frequency ranging from 3.5 to 75 Hz, the brain produces electrical activity at the same frequency as the visual signal, or its multiples. Identifying the preferred frequencies of neurocortical dynamic processes is a benefit of SSVEPs. However, the time consumed during calibration sessions limits the number of training trials and gives rise to visual fatigue since there is significant human variation across and within individuals over time, which weakens the effectiveness of the individual training data. To address this issue, we propose a novel cross-subject-based classification method to enhance the robustness of SSVEP classification by employing cross-subject similarity and variability. Through an efficient time-series transformer, we compared Time Series Transformers (TST) with different deep learning approaches in the literature. We utilized the TST to speed up calibration processes and improve classification precision for new users. Then we compare this technique to other techniques: EEGNet, FBtCNN, and C-CNN. Our suggested framework’s outcomes are validated using two datasets with two different time window lengths. The experimental results suggest that cross-subject time-series transformers and EEGNet achieve better performance with specific subjects than state-of-the-art techniques when compared to other techniques that have high potential for building high-speed BCIs.
@inproceedings{ali2023comparative, title = {A Comparative Analysis of Time Series Transformers and Alternative Deep Learning Models for SSVEP Classification}, author = {Ali, Heba and ElZemity, Adel and Oghostinos, Amir E and Selim, Sahar}, booktitle = {International Conference on Model and Data Engineering}, pages = {3--16}, year = {2023}, organization = {Springer}, isbn = {978-3-031-55729-3}, publisher = {Springer Nature Switzerland}, doi = {10.1007/978-3-031-55729-3_2} }

A Transformer-Based Deep Learning Architecture for Accurate Intracranial Hemorrhage Detection and Classification

Adel ElZemity, Maryam ElFdaly , Shorouk Abdelfattah , and 6 more authors

In 2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) , 2023

Bib Website

@inproceedings{10391388,
  author = {ElZemity, Adel and ElFdaly, Maryam and Abdelfattah, Shorouk and Abdelwahab, Ahmed and Ramadan, Mohamed and Zakzouk, Salma and Ameen, Ahmed and Elkhishen, Rawan and Darweesh, M. Saeed},
  booktitle = {2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)},
  title = {A Transformer-Based Deep Learning Architecture for Accurate Intracranial Hemorrhage Detection and Classification},
  year = {2023},
  volume = {},
  number = {},
  pages = {215-220},
  keywords = {Technological innovation;Deep architecture;Computer architecture;Streaming media;Transformers;Convolutional neural networks;Hemorrhaging;Intracranial Hemorrhage;Transformer;Swin Transformer},
  doi = {10.1109/3ICT60104.2023.10391388},
}

Wastewater treatment model with smart irrigation utilizing PID control

Adel ElZemity, Ahmed Ali Gaafar , Ahmed Khaled Ahmed , and 4 more authors

In 2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES) , 2020

Bib Website

@inproceedings{el2020wastewater,
  title = {Wastewater treatment model with smart irrigation utilizing PID control},
  author = {ElZemity, Adel and Gaafar, Ahmed Ali and Ahmed, Ahmed Khaled and Abdelwahab, Ahmed Sayed and Saad, Hatim Mohamed and Elboushi, Mostafa Khaled and Ibraheem, Amira Mofreh},
  booktitle = {2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)},
  pages = {374--379},
  year = {2020},
  organization = {IEEE},
  doi = {10.1109/NILES50944.2020.9257882},
}