My Academic Projects
Welcome to my Academic Projects and Research Papers webpage. As a senior majoring in Computer Science at Trinity College, I have had the privilege of delving into various academic projects and engaging in cutting-edge research. This webpage serves as a platform to showcase my academic achievements, providing you with a glimpse into my scholarly pursuits. From developing innovative software solutions to exploring complex algorithms, my academic projects demonstrate my dedication to the field of computer science and my passion for pushing the boundaries of knowledge. Additionally, my research papers shed light on the important discoveries and contributions I have made in specific areas of interest.
| Name | Description | Language | Category | Course | Frameworks and Libraries | Source Code | Image preview |
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| Smart Home Appliance Database | Database model that of a system manages various aspects of appliances, rooms in a house, users, accounts, schedules, manufacturers, maintenance and repair records, insurance policies, and premium payments. | SQL | Database Development | Database fundamentals | Built-in Libraries and Frameworks | https://github.com/tarek-debug/Smart_Home_Appliance_Database |
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| Product Inventory Database | Java programs that allows users to manage a list of products, add new products, remove existing ones, and perform various queries on the inventory. | Java | Database Development | Data Structures & Algorithms | Built-in Libraries and Frameworks | https://github.com/tarek-debug/Product-Inventory-Database |
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| Solar Anomalies Analysis | This research aims to and utilizes observations from multiple NASA solar observatory missions to analyze and predict anomalies in solar observations through the application of unsupervised machine learning techniques, including cluster analysis. | Python | Machine Learning | Summer Research | Tensor Flow | https://github.com/tarek-debug/Solar-Anomalies-Analysis |
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| GraphGuard | This project develops a Diffusion-Based Graph Autoencoder and Anomaly Detection for detecting and mitigating cyberattacks in dynamic networks using synthetic graphs generated by GraphMaker and real-time anomaly detection via AnomRank. By simulating attack scenarios and applying targeted responses, the system aims to improve detection accuracy, reduce false positives, and enhance the resilience of evolving network structures. | Python, C++ | Machine Learning | Summer Research | PyTorch | Coming Soon |
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