Author

Ibrahim Ouahbi

h-index 11 View on Semantic Scholar

We have 6 of 59 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access Oct 2026

Uncertainty-aware few-shot epileptic seizure detection for IoMT edge devices using TinyML and Kolmogorov–Arnold networks

Epileptic seizure detection from electroencephalography (EEG) remains challenging in real-world clinical settings due to severe class imbalance, limited labeled ictal data, inter-patient variability, and the computational constraints of edge deployment. To address these challenges, this paper proposes an uncertainty-aw...

Chaymae Yahyati, Ismail Lamaakal, Khalid El Makkaoui et al. · Discover Internet of Things · 0 citations

Open access Oct 2026

Robustness Evaluation of Lightweight YOLO Detectors for Traffic Monitoring Under Visual Corruptions and Adversarial Attacks

Reliable traffic-object detection remains challenging when visual conditions degrade or when the detector is exposed to adversarial perturbations. This study investigates these robustness challenges through a systematic evaluation of lightweight YOLO detectors for intelligent traffic monitoring. Several nano-scale vari...

Chaymae Rami, Siham Essahraui, Ibrahim Ouahbi et al. · Journal of Cybersecurity and... · 0 citations

#small language model Open access Oct 2026

AEGIS: Real-Time Latent-Space Backdoor Detection for Dependable and Secure Small Language Model Inference

Backdoor attacks pose a serious threat to small language models (SLMs) because compromised models can behave normally on benign inputs while producing attacker-specified outputs when a hidden trigger is activated. Existing defenses commonly require model retraining, operate only before deployment, rely on input-level s...

Jamal Boussouf, Ismail Lamaakal, Ibrahim Ouahbi et al. · Technologies · 0 citations

Review Open access Sep 2026

A Review of TinyML for Human Activity Recognition on Edge Devices

This paper presents a comprehensive review of TinyML for HAR, covering foundational concepts, methodologies, and applications, and critically examined the state of the art of existing works and approaches that combine TinyML and HAR.

Ismail Lamaakal, Chaymae Yahyati, Yassine Maleh et al. · Machine Learning and Knowled... · 0 citations

Preprint Aug 2026

Continual Visual Learning under Evolving Semantic Concept Shift

Experiments show that SemReWrite achieves a stronger balance between learning revised semantics and retaining unaffected knowledge than prompt replacement, conventional fine-tuning, parameter-efficient adaptation, and continual-learning strategies.

Ismail Lamaakal, Chaymae Yahyati, Yassine Maleh et al. · 0 citations

Open access Jul 2026

A lightweight deep learning model for real-time in-vehicle driver distraction detection with low-latency inference.

Experiments on the State Farm Distracted Driver Detection dataset show that MobileNetV3-Large provides the best trade-off among the evaluated lightweight models, demonstrating a practical balance between unseen-driver generalization, interpretability, and real-time embedded inference.

Siham Essahraui, Chaymae Rami, Khalid El Makkaoui et al. · Scientific Reports · 0 citations