Assistant Professor, Mansoura University and Egypt
Mostafa Mahmoud El-Gayar is an Assistant Professor in Information Technology at Mansoura University, Egypt. He holds expertise in Cyber Security, Internet of Things (IoT), Image Processing, Computer Vision, and Machine Learning. He is recognized for his contributions to enhancing security measures in IoT systems, developing intelligent frameworks for machine learning, and deep learning applications in various domains. El-Gayar has made significant strides in the detection of phishing attacks, heart disease prediction, and the detection of deep fake videos. His work is highly regarded for its practical implications in modern technology, and his research continues to influence both academic and industrial communities. He is actively engaged in researching solutions for cybersecurity challenges, including IoT botnet detection, anomaly detection, and semantic-based search engines. El-Gayar’s extensive publication record reflects his significant impact on technology and innovation.
Profile :
Google Scholar
Education :
Mostafa Mahmoud El-Gayar completed his academic journey at Mansoura University, Egypt, where he earned his undergraduate and postgraduate degrees in Computer Science and Information Technology. His academic career is defined by a deep commitment to exploring the intersection of machine learning, artificial intelligence, and cybersecurity. El-Gayar has published extensively in prestigious journals and conferences, earning recognition for his research contributions. His doctoral research focused on areas like image processing, machine learning applications, and their impact on real-world security challenges. Throughout his career, he has continuously engaged in advanced studies to refine his knowledge and skills in the rapidly evolving domains of artificial intelligence and cybersecurity. As an academic leader, El-Gayar mentors students and researchers, fostering an environment of intellectual growth and innovation in his field. His educational foundation, coupled with his ongoing academic pursuits, positions him as a prominent figure in his areas of research.
Experience:
Dr. Mostafa Mahmoud El-Gayar has a distinguished career as an academic professional and researcher, currently serving as an Assistant Professor in Information Technology at Mansoura University, Egypt. His experience spans teaching, research, and consulting in cutting-edge areas of computer science, particularly focusing on cybersecurity, machine learning, and Internet of Things (IoT). Dr. El-Gayar has been actively involved in several research projects aimed at developing innovative solutions for real-world security problems, including IoT botnet attack detection, heart disease prediction, and deep fake detection. He has collaborated with numerous researchers and practitioners from both academia and industry, contributing to the development of advanced algorithms and frameworks. His work has had a significant impact on the academic community, as evidenced by his numerous high-citation publications in top-tier journals and conferences. In addition to his research, Dr. El-Gayar actively participates in academic mentoring, teaching courses, and supervising postgraduate students.
Research Focus :
Dr. Mostafa Mahmoud El-Gayar’s research focus lies at the intersection of Cyber Security, Machine Learning, Internet of Things (IoT), Image Processing, and Computer Vision. His work aims to address some of the most pressing challenges in modern computing, including anomaly detection, phishing attack mitigation, and deep fake video detection. El-Gayar is particularly interested in enhancing the security of IoT networks through the development of robust machine learning-based intrusion detection systems (IDS). He also explores semantic search engines, leveraging advanced algorithms to improve search accuracy and ranking using deep learning techniques. Additionally, El-Gayar is involved in innovative healthcare solutions, such as heart disease prediction using hybrid classifiers and genetic algorithms. His research contributes to safety, privacy, and efficiency across diverse fields, and he continues to push the boundaries of knowledge with his cutting-edge, interdisciplinary research.
Publication Titles :
- A Comparative Study of Image Low-Level Feature Extraction Algorithms 📷
- HDPF: Heart Disease Prediction Framework Based on Hybrid Classifiers and Genetic Algorithm ❤️🧬
- Enhanced Search Engine Using Proposed Framework and Ranking Algorithm Based on Semantic Relations 🔍🔗
- Enhancing IoT Botnets Attack Detection Using Machine Learning-IDS and Ensemble Data Preprocessing Technique 📡💻
- Efficient Proposed Framework for Semantic Search Engine Using New Semantic Ranking Algorithm 🔎🧠
- A Novel Approach for Detecting Deep Fake Videos Using Graph Neural Network 🎥🤖
- Resource Allocation in UAV-Enabled NOMA Networks for Enhanced Six-G Communications Systems 🚁📶
- Detection Technique and Mitigation Against a Phishing Attack 🔒💻
- A Computerized System for SEMG Signals Analysis and Classification 🧠📊
- Comparative Study Between Metaheuristic Algorithms for IoT Wireless Nodes Localization 🌐🔍
- Semantic Pneumonia Segmentation and Classification for Covid-19 Using Deep Learning Network 🦠💻
- Intelligent System for Ranking Big Data in Search Engine 📊🔍
- Automatic Generation of Image Caption Based on Semantic Relation Using Deep Visual Attention Prediction 🖼️🧠
- A Novel Model for Securing Seals Using Blockchain and Digital Signature Based on QR Codes 🔐📱
- A Novel Knowledge-Based Semantic Search Engine 🔍🧠
- Smart Collaborative Intrusion Detection System for Securing Vehicular Networks Using Ensemble Machine Learning Model 🚗🛡️
- Efficient Real-Time Anomaly Detection in IoT Networks Using One-Class Autoencoder and Deep Neural Network 🖥️💡
- Novel Biomarkers for Colorectal Cancer Prediction 🎗️🧬
- A Novel Model for Securing Seals Using Blockchain and Digital Signature Based on Quick Response Codes 🔐📲