Dr. Mohamed S. Darweesh

Associate Professor

Faculty Office Ext.

3025

Faculty Building

UB2

Office Number

S09

Bio

Biography

Mohamed Saeed Darweesh (M’17, SM’20) received his master’s and Ph.D. degrees (with honors) in Electronics and Electrical Communications Engineering from the Faculty of Engineering, Cairo University, Giza, Egypt, in 2013 and 2017, respectively. He is currently an Associate Professor at the School of Engineering and Applied Sciences, Nile University. Previously, he worked at the American University in Cairo (AUC), Zewail City (ZC) of Science and Technology, Arab Academy for Science and Technology and Maritime Transport (AASTMT), and Institute of Aviation Engineering and Technology (IAET).

Saeed is a Senior Member of the IEEE from 2020 and has served on the technical program committees of major IEEE conferences. Saeed has been elected as the IEEE Egypt Section Secretary 2024-2025, IEEE SIGHT Egypt since June 2023, IEEE Consultants Network Vice Chair since April 2023, and the IEEE CAS Egypt Treasurer since January 2023. For the global and regional IEEE positions, starting January 2024, he is appointed as a corresponding member in the IEEE Region 8 Young Professionals Subcommittee, a member in the IEEE HTB Proposal Evaluation Subcommittee, a member in the IEEE HTB Events Committee. He has been an IEEE HTB/SIGHT Projects Technical Reviewer since April 2023. Also, he was the IEEE Egypt Young Professionals Egypt Past Chair for 2022-2023.

Dr. Saeed actively serves as a reviewer in several journals and conference publications, including IEEE conferences and journals. Dr. Saeed has led multiple research projects in the fields of artificial intelligence, machine learning, healthcare, and Industry 4.0. He has authored/co-authored over +80 papers in international journals and conferences. Also, he is a Principal Investigator (PI), Co-PI, and Research Associate in several research projects funded by different agencies like the Science and Technology Development Fund (STDF), National Telecom Regularity Authority (NTRA), Information Technology Industry Development Agency (ITIDA), and Academy of Scientific Research and Technology (ASRT). He supervised over +90 graduation projects. He has a solid technical background with a strong interest in machine learning and artificial intelligence. His research interests focus on Wireless Communications, Self-Driving Cars, Vehicle to Vehicle (V2V) Systems, Narrow-Band IoT (NB-IoT), Biomedical Engineering (EEG Seizure Detection, Sleepiness Detection using EEG, Breast Cancer Classification, and Skin Cancer Prediction), Optimization Techniques, and Data Compression. Dr. Saeed also worked and trained with many telecom operators and suppliers like (MobiNil, Alcatel.Lucent, and Geniprocess) and has a strong background in computer networks and security.

Achievements

1) Best Paper Award (First Place) at NILES2020 “Comparative Analysis of Various Machine Learning Techniques for Epileptic Seizures Detection and Prediction Using EEG Data".

2) Best Paper Award (Third Place) at ICM2020 “Collision Probability Computation for Road Intersections based on Vehicle to Infrastructure Communication”.

3) Best Paper Award (Third Place) at NILES2021“Real-Time Fish Detection Approach on Self-Built Dataset Based on YOLOv3”.

4) Outstanding Paper Award at ICACT2022, “Relay Selection in NOMA-Based Diamond Relaying Networks".

5) Mohamed Saeed Darweesh ECE graduation project titled: “Enhanced Beauty Services Mobile Application Based on Artificial Intelligence” has been accepted at the AUC Science Slam competition and qualified for the finalists 2021.

6) Mohamed Saeed Darweesh was nominated to be an Expert at Phi Science Institute, 2021.

7) Mohamed Saeed Darweesh was elected as Chair for IEEE Young Professionals Egypt Section for 2022-2024.

Recent Publications

A seamless and fast handover from one cell to another is one of the main goals of long term evolution (LTE). Hence, the decision of handover is a critical part of the design process of handover. Then the selection of handover parameters must be in a careful and optimal way to have an efficient and successful handover. In this paper, a new optimized CoMP handover algorithm (CoMP HO) for LTE network

The Orthogonal Frequency Division Multiplexing (OFDM) has emerged as one of the promising techniques because of its robustness to multipath fading with high-speed data transmission. Classical bipolar OFDM cannot be used in intensity modulated with direct detection (IM/DD) optical communication systems, as visible light communication (VLC), so many optical modulation techniques as asymmetrical

Research Tracks

Artificial Intelligence
Machine Learning
Optimization Techniques
Wireless Communications
Self-Driving Cars
Biomedical Engineering Applications

Grants

Projects

Research Project

Low Power 3GPP (Release 14) NB-IoT Platform for Internet of Things Applications

Internet of Things (IoT) is a network of physical objects or people called things, where electronics, software, and network work together to open the door for these things to be able to collect and exchange data remotely. The IoT devices serve many applications such as smart cities, wearables and health monitoring, security, and surveillance. Low cost, long battery life, low data rate, low latency

Research Project

IoT-based Platform for Wearable Devices for Continuous Healthcare Monitoring

The ultimate goal of this project is to achieve diagnosis on the fly and predict abnormal health conditions and alert people before the condition happens. In order to achieve this, the following objectives should be fulfilled: Implementing wearable self-powered painless devices for healthcare monitoring. Implementing AI algorithm for predicting abnormal health conditions. Implementing painless

Research Project

Interactive Smart Application for Museums Education (iSAME)

Information and Communication Technologies (ICTs) play a huge role in developing and updating the functions and innovative activities for a promising economic performance of museums and cultural institutions, allowing museums to achieve remarkable success and reach their main goals of attracting people and spreading historical knowledge. As a result of the increasing numbers of technologies and

Research Project

An Underwater AR based System for Marine Life Detection and Classification for Divers and Tourists

Objective/Contributions: Collecting and annotating an extensive dataset of fish images representing the fish species in the red sea. Building a machine learning model capable of detecting and classifying fish species from a real-time video. Building a proof-of-concept prototype for the AR hardware that is capable of capturing the live video of marine life, running the classification model, and

Research Project

Pre-emergency Alert IoT System-based on Wearable Devices for Diabetic Patient

According to World Health Organization (WHO), there are more than 400 million diabetic adults worldwide. In Egypt, only there are about 8 million diabetic people, and the diabetes prevalence rate is estimated to be 13.3% by 2025, according to WHO. In most cases, the diabetic person requires regular visits to the hospital for health checks. However, due to the COVID pandemic, it has become risky

Research Project

Artificial Intelligence Based Cloud Computing for Autonomous Traffic Management

Automobile-related deaths rank as one of the most common causes of death in many places, particularly developing countries; Egypt loses about 12,000 lives due to road traffic crashes every year. The greatest danger to human beings is not cars but people themselves because cars are not dangerous if driven by care and more attention. Cell phone use, whether by talking on the phone or texting

Research Project

Low-Power Artificial Intelligence Framework for Internet of Things Applications

The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI). Due to the massive scalability of modern IoT networks and increased data privacy, traditional AI algorithms require centralized data collecting and processing, which may not be practical in real-world application