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This project introduces an automated framework that generates high-quality multiple-choice questions (MCQs) using the large language model GPT-4 and prompt engineering techniques, focusing on minimizing common writing flaws. The framework is designed to create MCQs aligned with the first three cognitive levels of Bloom's Taxonomy: Remembering, Understanding, and Applying.

Developed a deep learning pipeline for Space Situational Awareness (SSA), leveraging Faster R-CNN V2 with ResNet-50 FPN to detect and classify space objects in orbit. Integrated Apache Spark for large-scale data processing and PyTorch for model training and inference. Fine-tuned a pre-trained COCO model on the SPARK dataset (150K RGB & depth images, 11 object classes), achieving high precision and recall, particularly for larger objects. Addressed challenges in detecting smaller objects, identifying key areas for future improvement.

Conducted an in-depth analysis of Ethereum’s security landscape, exploring its blockchain architecture, consensus mechanisms (Proof of Work & Proof of Stake), cryptographic techniques (Merkle trees, zero-knowledge proofs), and smart contract vulnerabilities. Investigated major past security incidents, including the DAO hack and DeFi bridge exploits, highlighting lessons learned and improvements in security practices. Assessed Ethereum 2.0’s impact on security, ongoing research in formal verification, and the challenges of scalability and interoperability. Provided insights into emerging threats and potential countermeasures to strengthen Ethereum’s long-term security and resilience.

This project focuses on developing a solution to streamline the manual processes involved in managing network component stock for large companies. The current workflow requires significant manual intervention, which is time-consuming and inefficient. The new application aims to automate much of the stock management process, simplifying the workflow for current and future users.