Faculty profile
Rasha Kashef
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Research
Latest papers
MM-NIDS: A Novel Multimodal Ensemble Fusion Network Intrusion Detection System Using Numeric, Text, Graph, and Quantum Representations.
Sensors (Basel, Switzerland) · 2026 · senior author
Automated adversarial red-teaming for evaluating robustness in LLM-based recommender systems.
Discover artificial intelligence · 2026 · senior author
A Survey on Privacy Preservation Techniques in IoT Systems.
Sensors (Basel, Switzerland) · 2025 · senior author
Latest funding
- $35,485
Empowering Canadian SMEs to Compete with e-Commerce Giants: A Novel Generative AI-based Cross-Vendor Collaboration Approach
SSHRC · 2025 · Co-investigator
- $25,000
Fortifying Online Recommendation Systems Against Complex Multilingual Obfuscated Attacks
NSERC · 2024 · Principal investigator
- $12,500
Towards Robust and Trustworthy Recommendation Systems
NSERC · 2022 · Principal investigator
11 publications.
MM-NIDS: A Novel Multimodal Ensemble Fusion Network Intrusion Detection System Using Numeric, Text, Graph, and Quantum Representations.
AboulEla S, Kashef R
Automated adversarial red-teaming for evaluating robustness in LLM-based recommender systems.
Shehmir S, Kashef R
A Survey on Privacy Preservation Techniques in IoT Systems.
Kaur R, Rodrigues T, Kadir N, Kashef R
Penalized GANs with latent perturbation for robust shilling attack generation in recommender systems.
Nawara D, Kashef R
Advanced Monocular Outdoor Pose Estimation in Autonomous Systems: Leveraging Optical Flow, Depth Estimation, and Semantic Segmentation with Dynamic Object Removal.
Ghasemieh A, Kashef R
PSA-FL-CDM: A Novel Federated Learning-Based Consensus Model for Post-Stroke Assessment.
Razfar N, Kashef R, Mohammadi F
Predicting adverse outcomes in pregnant patients positive for SARS-CoV-2: a machine learning approach- a retrospective cohort study
Young D, Houshmand B, Tan CC, Kirubarajan A, Parbhakar A, Dada J, Whittle W, Sobel ML, Gomez LM, Rüdiger M
Automatic Post-Stroke Severity Assessment Using Novel Unsupervised Consensus Learning for Wearable and Camera-Based Sensor Datasets.
Razfar N, Kashef R, Mohammadi F
Deep Learning for LiDAR Point Cloud Classification in Remote Sensing.
Diab A, Kashef R, Shaker A
E2DR: A Deep Learning Ensemble-Based Driver Distraction Detection with Recommendations Model.
Aljasim M, Kashef R
Empowering Canadian SMEs to Compete with e-Commerce Giants: A Novel Generative AI-based Cross-Vendor Collaboration Approach
Principal investigators: Doha, Ahmed
Keywords: e-Commerce; SMEs; Recommendation Systems; Business Model Innovation
Fortifying Online Recommendation Systems Against Complex Multilingual Obfuscated Attacks
Principal investigators: Kashef, Rasha RK
Keywords: large language models; mathematical modeling; multi-lingual; natural language processing; obfuscated attacks; recommendation systems
Towards Robust and Trustworthy Recommendation Systems
Principal investigators: Kashef, Rasha
Towards Robust and Trustworthy Recommendation Systems
Principal investigators: Kashef, Rasha
Keywords: recommendation systems; shillings attacks; graph theory; detection approaches; optimization methods; big data analytics; adversarial machine learning; e-commerce; large-scale architectures; hybrid learning
Detecting Multilingual Shillings Attacks on Recommendation Systems
Principal investigators: Kashef, Rasha R
Automatic COVID-19 pregnant patient prognosis algorithm_x000d_
Principal investigators: Sussman, Dafna
From CIHR, NSERC and SSHRC funding decisions: CIHR since 2008, NSERC since 1991 and SSHRC since 1998, including their latest published competition results.
Frequent collaborators
- Farah Mohammadi and Rasha Kashef: 2 shared papers
- Rohan D'Souza and Dafna Sussman: 1 shared paper
- Rohan D'Souza and Rasha Kashef: 1 shared paper
- Dafna Sussman and Rasha Kashef: 1 shared paper
- Electical, Computer, and Biomedical Engineering
- The Department of Electrical, Computer and Biomedical Engineering
- Electrical, Computer and Biomedical Engineering
- Obstetrics & Gynecology
Co-authors at Toronto Metropolitan University, colored by department. Thicker lines mean more shared papers; select anyone to open their profile and their own map.
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