RAKESH S

Dynamic and results-driven intern with hands-on experience at Vcodez, where I developed and deployed machine learning models using Python and data analysis tools. Proven adaptability and strong problem-solving skills, complemented by a solid foundation in UI/UX design from Workiy Technologies, enhancing user engagement in CRM systems.

• Transparent AI Framework for Diagnosing Mild COVID-19 Using MRI Chest Images

This study proposes an AI-based system to differentiate mild COVID-19 from other respiratory diseases using MRI scans. It uses preprocessing techniques and CNNs for feature extraction, followed by SVM and Random Forest classifiers. LIME-based explanations enhance model transparency for clinical use. A user-friendly interface supports real-time diagnostics and telemedicine integration.

• Hybrid deep learning models for eye fundus segmentation and classification

Developed a deep learning-based diagnostic tool for eye fundus disease classification and segmentation using LeNet (89% accuracy) and U-Net (94% accuracy). The system classifies diseases like diabetic retinopathy and glaucoma, and segments eye fundus images Integrated the models into a Django framework, enabling users  to upload images for real-time detection. Optimized using backpropagation and gradient descent.  Tech Stack: Python, TensorFlow, Keras, Django, LeNet, U-Net

• Defending cyber-physical systems through reverse-engineering-based memory sanity check

This project develops a real-time, lightweight security system for Cyber-Physical Systems using memory sanity checks. It detects unauthorized memory changes by monitoring deviations from normal memory behavior. The solution ensures secure CPS operations across various critical sectors with minimal performance impact.

• College portal automated for checking grades

This project automates the process of checking student grades on a college portal using UiPath. It uses robotic process automation (RPA) to log in, navigate to the grades section, and extract results efficiently. The system saves time, reduces manual effort, and ensures accurate retrieval of academic records.