I'm a 4th year Computer Science undergraduate at Effat University in Jeddah, Saudi Arabia, specializing in Artificial Intelligence. With a growing focus on Machine and Deep Learning, Data Science, and Intelligent Systems, I'm passionate about building smart solutions that bridge theory with real-world impact. My curiosity also extends to web development and design, where I like blending functionality with creativity to craft engaging, AI-powered applications.
Backed by a strong foundation in mathematics and analytical thinking, I'm eager to apply my skills in data-driven, AI-centric projects that solve meaningful problems and push the boundaries of what's possible.
Effat University
- Cumulative GPA: 3.95/4.0
- Achieved Effat College of Engineering's Dean's List for the academic years 2022-23 & 2023-24 & 2024-25
- Programming Club Co-leader: Led and coordinated club activities.
- Authored a research paper accepted for funding by the Deanship of Graduate Studies and Research (DGSR)
Cedar International School
- Graduated with a GPA of 3.99/4.0
- Consistent honor roll throughout high school, demonstrating academic excellence and dedication.
- Class representative 2020 and 2022, serving as a bridge between students and faculty, helping organize class-related matters.
Quantum
June 2025 - September 2025
Developed and deployed an AI-powered conversational agent built on n8n, combining workflow automation, RAG, and advanced prompt engineering to deliver contextual, human-like interactions. Designed scalable session management and personalization pipelines, enhancing user engagement, retention, and cross-platform adaptability.
An AI-driven application tailored to monitor Powerline Communication networks. The application provides real-time device monitoring, AI-based threat detection, automated bandwidth optimization, and energy usage tracking. Developed with a modular architecture, the system integrates machine learning for anomaly detection and features a user-friendly dashboard, role-based access, and secure data handling. It ensures improved reliability, responsiveness, and control over smart home and industrial PLC environments—bridging the gap between traditional monitoring tools and intelligent, adaptive network protection.
View Project →An innovative Raspberry Pi-based system that simulates Powerline Communication by combining physical charging with automatic Wi-Fi onboarding. This project showcases the integration of physical and digital communication layers, echoing the essence of PLC, where data and power are delivered over a single line. When a mobile device is plugged into a power outlet, it triggers the Raspberry Pi to broadcast a hotspot, automatically connecting the user to a captive portal without requiring manual input. It leverages hostapd for wireless AP management, dnsmasq for DNS spoofing, and iptables for network redirection, all pre-configured to auto-start using systemd upon power-up.
View Project →An intelligent, AI-powered intrusion detection system designed for securing IoT devices by accurately identifying and classifying a wide range of cyberattacks in real time. Developed in Python, the system implements a complete machine learning pipeline, including data cleaning, normalization, SMOTE oversampling, and dimensionality reduction using PCA, to enhance model efficiency and reliability. It harnesses the power of traditional and deep learning algorithms, including Random Forest, SVM, XGBoost , MLP , and CNN . The project highlights advanced model tuning, comparative performance analysis, and neural network design, making it an ideal demonstration of applied machine learning for network security.
View Project →A Python-based tool for solving differential equations using numerical methods like Euler, Taylor (2nd order), and Runge-Kutta (6th order). The project uses NumPy for computations and Matplotlib for visualizing approximations against the exact solution. Each method is implemented from scratch, allowing users to observe accuracy, convergence, and error behavior over different step sizes. The tool supports step-by-step analysis and comparison of methods, making it useful for understanding the practical application of numerical techniques in scientific computing.
View Project →A tool that uses data science techniques to predict football player performance, specifically the likelihood of a player scoring a goal in upcoming matches. Using Python libraries like Pandas for data manipulation, Seaborn for visualization, and Scikit-learn for machine learning, the project integrates player statistics, match outcomes, and other external factors into a unified dataset. By applying the Random Forest Regressor algorithm, the model predicts goal-scoring probabilities based on features like age, position, assists, and minutes played. The project also involves data preprocessing, visualization, and evaluation, making it a comprehensive approach to understanding player dynamics in football.
View Project →A simulated mini-country network in Cisco Packet Tracer, featuring 5 ministries represented by VLANs, each with its own servers, devices, and specialized protocols. The central hub; the Pink House, oversees all operations with a main router providing DHCP services across VLANs on distinct IP addresses. A default DNS server is centralized for all ministries, facilitating seamless communication. Additionally, Access Control Lists (ACLs) are implemented to regulate traffic flow between ministries, web servers, and FTP services. This setup ensures comprehensive interconnectivity between devices, and secure data management throughout the network.
View Project →A user-friendly website that allows users to discover top songs and artists from almost every country. Utilizing simple and interactive HTML, CSS, and JavaScript, Music Map offers users a beautiful and seamless experience, in addition to fun quizzes across different genres to test the user's musical knowledge. Music Map also provides a special user zone powered by PHP and SQL. This zone grants full access to the website's database exclusively to admins. Special users can create, update, or delete countries, artists, and songs.
View Project →A simple Java Swing application designed to organize daily tasks by grouping them into categories. Users can create, edit, or delete tasks and categories, choosing from three task types: Deadline Task, Recurring Task, and Simple Task. Each task has a title, due date, and priority level. Tasks must be added to a category to be displayed, and users can mark tasks as completed once finished, providing an efficient way to manage and prioritize activities.
View Project →This paper examines the integration of Artificial Intelligence into Powerline Communication (PLC) networks to address three core challenges: security vulnerabilities, performance optimization, and real-time monitoring. It reviews traditional approaches such as encryption and static filtering, and contrasts them with AI-driven solutions like adaptive threat detection, intelligent interference mitigation, and dynamic energy management. By analyzing recent studies and identifying key gaps, the research demonstrates how AI can transform PLC networks into more secure, efficient, and autonomous systems for modern smart infrastructures.
This paper explores the integration of Artificial Intelligence and Cybersecurity in Powerline Communication (PLC) systems, emphasizing the challenges and solutions arising from their convergence in smart grid and IoT environments. It examines key vulnerabilities introduced by AI-enabled and cloud-based PLC architectures, such as electromagnetic interference, physical access risks, and evolving cyber threats. The study reviews both traditional defenses and advanced techniques, including AI-driven anomaly detection and post-quantum cryptography, to secure data integrity, confidentiality, and availability. By analyzing current research and proposing an adaptive security framework, the paper underscores the critical role of intelligent, resilient protections for safeguarding next-generation PLC networks in critical infrastructure.
This paper examines the impact of quantum computing on cybersecurity, highlighting its ability to break widely-used encryption methods like RSA and ECC with quantum algorithms. It explores solutions such as post-quantum cryptography, quantum key distribution, and hybrid models to safeguard sensitive information. The research discusses ongoing challenges, global efforts, and the importance of transitioning to quantum-resistant systems to protect data and ensure secure digital communications in the quantum era.
This paper examines the integration of Artificial Intelligence in medical diagnostics, focusing on early disease detection and diagnostic accuracy through AI-driven systems. It highlights the use of multiple imaging modalities and deep learning models, addressing challenges in clinical workflows and emphasizing the need for standardized protocols and ethical considerations to enhance healthcare efficiency and patient care.
This paper examines the convergence of Artificial Intelligence and virtual environments, highlighting both the possibilities and challenges. It investigates the potential applications of AI-powered virtual environments across various fields, including education, healthcare, and social interaction, while emphasizing the importance of responsible development to safeguard human well-being and ethical standards. Through surveys and interviews, the study uncovers user excitement about these advancements, alongside concerns about social isolation and mental health impacts. The research advocates for clear guidelines, educational programs, and strong security measures to ensure safe and ethical use of AI in virtual environments, aiming to enhance lives and promote societal progress.
This paper examines Round Robin (RR) scheduling, a long-standing method in operating systems for allocating CPU time slices to processes fairly. While widely used, RR scheduling faces inefficiencies due to its fixed time allocation per process, regardless of varying computational needs. The paper explores innovative approaches to dynamically adjust time allocations based on process behavior, aiming to improve overall system efficiency. By addressing these challenges, the research seeks to optimize process management in operating systems for enhanced performance and resource utilization.
