Search Results for author: Amr Mohamed

Found 21 papers, 2 papers with code

Haris: an Advanced Autonomous Mobile Robot for Smart Parking Assistance

no code implementations31 Jan 2024 Layth Hamad, Muhammad Asif Khan, Hamid Menouar, Fethi Filali, Amr Mohamed

This paper presents Haris, an advanced autonomous mobile robot system for tracking the location of vehicles in crowded car parks using license plate recognition.

Autonomous Navigation License Plate Recognition +4

Brain Tumor Radiogenomic Classification

no code implementations11 Jan 2024 Amr Mohamed, Mahmoud Rabea, Aya Sameh, Ehab Kamal

The RSNA-MICCAI brain tumor radiogenomic classification challenge aimed to predict MGMT biomarker status in glioblastoma through binary classification on Multi parameter mpMRI scans: T1w, T1wCE, T2w and FLAIR.

Binary Classification Classification +1

Intelligent DRL-Based Adaptive Region of Interest for Delay-sensitive Telemedicine Applications

no code implementations8 Oct 2023 Abdulrahman Soliman, Amr Mohamed, Elias Yaacoub, Nikhil V. Navkar, Aiman Erbad

Telemedicine applications have recently received substantial potential and interest, especially after the COVID-19 pandemic.

Image Compression SSIM

Zero-touch realization of Pervasive Artificial Intelligence-as-a-service in 6G networks

no code implementations21 Jul 2023 Emna Baccour, Mhd Saria Allahham, Aiman Erbad, Amr Mohamed, Ahmed Refaey Hussein, Mounir Hamdi

In this context, we introduce a novel platform architecture to deploy a zero-touch PAI-as-a-Service (PAIaaS) in 6G networks supported by a blockchain-based smart system.

Federated Learning

RL-DistPrivacy: Privacy-Aware Distributed Deep Inference for low latency IoT systems

no code implementations27 Aug 2022 Emna Baccour, Aiman Erbad, Amr Mohamed, Mounir Hamdi, Mohsen Guizani

In this paper, we present an approach that targets the security of collaborative deep inference via re-thinking the distribution strategy, without sacrificing the model performance.

Privacy Preserving Reinforcement Learning (RL)

Motivating Learners in Multi-Orchestrator Mobile Edge Learning: A Stackelberg Game Approach

no code implementations25 Sep 2021 Mhd Saria Allahham, Sameh Sorour, Amr Mohamed, Aiman Erbad, Mohsen Guizani

Therefore, it is crucial to motivate edge devices to become learners and offer their computing resources, and either offer their private data or receive the needed data from the orchestrator and participate in the training process of a learning task.

Energy-Efficient Multi-Orchestrator Mobile Edge Learning

no code implementations2 Sep 2021 Mhd Saria Allahham, Sameh Sorour, Amr Mohamed, Aiman Erbad, Mohsen Guizani

The heterogeneity in edge devices' capabilities will require the joint optimization of the learners-orchestrator association and task allocation.

Total Energy

Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Data

1 code implementation14 Jul 2021 Alaa Awad Abdellatif, Naram Mhaisen, Amr Mohamed, Aiman Erbad, Mohsen Guizani, Zaher Dawy, Wassim Nasreddine

Federated learning (FL) is a distributed learning methodology that allows multiple nodes to cooperatively train a deep learning model, without the need to share their local data.

Federated Learning

Distributed CNN Inference on Resource-Constrained UAVs for Surveillance Systems: Design and Optimization

no code implementations23 May 2021 Mohammed Jouhari, Abdulla Al-Ali, Emna Baccour, Amr Mohamed, Aiman Erbad, Mohsen Guizani, Mounir Hamdi

Unmanned Aerial Vehicles (UAVs) have attracted great interest in the last few years owing to their ability to cover large areas and access difficult and hazardous target zones, which is not the case of traditional systems relying on direct observations obtained from fixed cameras and sensors.

Decision Making

Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence

no code implementations4 May 2021 Emna Baccour, Naram Mhaisen, Alaa Awad Abdellatif, Aiman Erbad, Amr Mohamed, Mounir Hamdi, Mohsen Guizani

The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges.

Recommendation Systems Scheduling

Spatiotemporal Data Mining: A Survey on Challenges and Open Problems

no code implementations31 Mar 2021 Ali Hamdi, Khaled Shaban, Abdelkarim Erradi, Amr Mohamed, Shakila Khan Rumi, Flora Salim

Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics.

Clustering Epidemiology +1

Analysis and Optimal Edge Assignment For Hierarchical Federated Learning on Non-IID Data

no code implementations10 Dec 2020 Naram Mhaisen, Alaa Awad, Amr Mohamed, Aiman Erbad, Mohsen Guizani

Distributed learning algorithms aim to leverage distributed and diverse data stored at users' devices to learn a global phenomena by performing training amongst participating devices and periodically aggregating their local models' parameters into a global model.

Edge-computing Federated Learning

Compress or Interfere?

no code implementations27 Jun 2020 Alaa Awad Abdellatif, Lutfi Samara, Amr Mohamed, Mohsen Guizani, Aiman Erbad, Abdulla Al-Ali

Rapid evolution of wireless medical devices and network technologies has fostered the growth of remote monitoring systems.

QoE-Aware Resource Allocation for Crowdsourced Live Streaming: A Machine Learning Approach

no code implementations20 Jun 2019 Fatima Haouari, Emna Baccour, Aiman Erbad, Amr Mohamed, Mohsen Guizani

This can be achieved by advocating a geo-distributed cloud infrastructure to allocate the multimedia resources as close as possible to viewers, in order to minimize the access delay and video stalls.

BIG-bench Machine Learning

Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey, and Future Directions

no code implementations23 Oct 2018 Reza Shakeri, Mohammed Ali Al-Garadi, Ahmed Badawy, Amr Mohamed, Tamer Khattab, Abdulla Al-Ali, Khaled A. Harras, Mohsen Guizani

We present and propose state-of-the-art algorithms to address design challenges with both quantitative and qualitative methods and map these challenges with important CPS applications to draw insightful conclusions on the challenges of each application.

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

no code implementations29 Jul 2018 Mohammed Ali Al-Garadi, Amr Mohamed, Abdulla Al-Ali, Xiaojiang Du, Mohsen Guizani

Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems.

Multimodal deep learning approach for joint EEG-EMG data compression and classification

no code implementations27 Mar 2017 Ahmed Ben Said, Amr Mohamed, Tarek Elfouly, Khaled Harras, Z. Jane Wang

In this paper, we present a joint compression and classification approach of EEG and EMG signals using a deep learning approach.

Data Compression EEG +3

Optimal Cooperative Cognitive Relaying and Spectrum Access for an Energy Harvesting Cognitive Radio: Reinforcement Learning Approach

no code implementations30 Mar 2014 Ahmed El Shafie, Tamer Khattab, Hussien Saad, Amr Mohamed

It manages the flow of the undelivered primary packets to its relaying queue using the appropriate actions over time slots.

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