Analog In-Memory Computing with Uncertainty Quantification for Efficient Edge-based Medical Imaging Segmentation

no code implementations • 1 Feb 2024 • Imane Hamzaoui, Hadjer Benmeziane, Zayneb Cherif, Kaoutar El Maghraoui

This work investigates the role of the emerging Analog In-memory computing (AIMC) paradigm in enabling Medical AI analysis and improving the certainty of these models at the edge.

Medical Image Segmentation Uncertainty Quantification

Genetic Algorithm enhanced by Deep Reinforcement Learning in parent selection mechanism and mutation : Minimizing makespan in permutation flow shop scheduling problems

no code implementations • 10 Nov 2023 • Maissa Irmouli, Nourelhouda Benazzoug, Alaa Dania Adimi, Fatma Zohra Rezkellah, Imane Hamzaoui, Thanina Hamitouche, Malika Bessedik, Fatima Si Tayeb

At each generation, the RL agent's action is determining the selection method, the probability of the parent selection and the probability of the offspring mutation.

Q-Learning Reinforcement Learning (RL) +1

Skip Connections in Spiking Neural Networks: An Analysis of Their Effect on Network Training

no code implementations • 23 Mar 2023 • Hadjer Benmeziane, Amine Ziad Ounnoughene, Imane Hamzaoui, Younes Bouhadjar

Spiking neural networks (SNNs) have gained attention as a promising alternative to traditional artificial neural networks (ANNs) due to their potential for energy efficiency and their ability to model spiking behavior in biological systems.

Hyperparameter Optimization Position