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Dr. 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 a full-time associate professor at 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). Dr. Saeed has a research stay at Zewail City (ZC) of Science and Technology, American University in Cairo (AUC), and Faculty of Engineering, Cairo University. Dr. Saeed is a Senior Member of the IEEE and has served on the technical program committees of major IEEE conferences. Dr. Saeed has been the IEEE Egypt Young Professionals Egypt Chair since January 2022 and IEEE CAS Egypt Treasurer since January 2023.
Dr. Saeed actively serves as a reviewer in several journals and conference publications, including IEEE conferences and journals. Dr. Saeed leads multiple research times in the fields of Artificial Intelligence, Machine learning, Healthcare, and Industry 4.0. He has authored/co-authored over 70 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.
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.
Real-Time Fish Detection Approach on Self-Built Dataset Based on YOLOv3
Creating a model to detect freely moving fish underwater in real-time is a challenging process for two main reasons. First, the available datasets suffer from some limitations that severely affect the results of the detection models operating in challenging and blurry environments. These models should be able to capture all of the fish movement given different types of surroundings. Second
A Novel Power-Aware Task Scheduling for Energy Harvesting-Based Wearable Biomedical Devices Using FPA
Power management and saving in energy harvesting-based biomedical wearable devices are mandatory to ensure prolonged and stable operation under a stringent power budget. Thus, power-aware task scheduling can play a key role in minimizing energy consumption to improve system durability while maintaining device functionality. This paper proposes a novel biosensor task scheduling for optimizing
A Preprocessing Approach to Improve the Performance of Inception v3-based Face Shape Classification
Face shape classification is considered one of the trending topics in the artificial intelligence research field. Face shape classification can be employed in many broad-scoped projects, such as hairstyle recommendation systems in the beauty and fashion industry. In this paper, the inception v3 model was employed to reach the highest possible performance for classifying the different face shapes
A Deep Learning-Based Benchmarking Framework for Lane Segmentation in the Complex and Dynamic Road Scenes
Intelligent Arabic-Based Healthcare Assistant
Text classification has been one of the most common natural language processing (NLP) objectives in recent years. Compared to other languages, this mission with Arabic is relatively restricted and in its early stages, and this combination in the medical application area is rare. This paper builds an Arabic health care assistant, specifically a pediatrician that supports Arabic dialects, especially
Early breast cancer diagnostics based on hierarchical machine learning classification for mammography images
Breast cancer constitutes a significant threat to women’s health and is considered the second leading cause of their death. Breast cancer is a result of abnormal behavior in the functionality of the normal breast cells. Therefore, breast cells tend to grow uncontrollably, forming a tumor that can be felt like a breast lump. Early diagnosis of breast cancer is proved to reduce the risks of death by
Comparative Analysis of Various Machine Learning Techniques for Epileptic Seizures Detection and Prediction Using EEG Data
Epileptic seizures occur as a result of functional brain dysfunction and can affect the health of the patient. Prediction of epileptic seizures before the onset is beneficial for the prevention of seizures through medication. Electroencephalograms (EEG) signals are used to predict epileptic seizures using machine learning techniques and feature extractions. Nevertheless, the pre-processing of EEG
Real-Time Lane Instance Segmentation Using SegNet and Image Processing
The rising interest in assistive and autonomous driving systems throughout the past decade has led to an active research community in perception and scene interpretation problems like lane detection. Traditional lane detection methods rely on specialized, hand-tailored features which is slow and prone to scalability. Recent methods that rely on deep learning and trained on pixel-wise lane
Real-Time Collision Warning System Based on Computer Vision Using Mono Camera
This paper aims to help self-driving cars and autonomous vehicles systems to merge with the road environment safely and ensure the reliability of these systems in real life. Crash avoidance is a complex system that depends on many parameters. The forward-collision warning system is simplified into four main objectives: detecting cars, depth estimation, assigning cars into lanes (lane assign) and
- Artificial Intelligence
- Machine Learning
- Optimization Techniques
- Wireless Communications
- Self-Driving Cars
- Vehicle to Vehicle (V2V) Systems
- Narrow-Band IoT (NB-IoT) Applications
- Biomedical Engineering Applications