Ruthvik Rao Allam

Software Tester effective at working in fast-paced testing environments to review software at each stage of development and production. Checks function, layout and operation for usability and visual design issues. Familiar with working in Agile-driven environments.

As a fitness enthusiast, I have developed a passion for physical activity and healthy living. This hobby has not only helped me maintain a healthy lifestyle, but has also taught me discipline, time management, and goal setting. I enjoy incorporating various forms of exercise into my routine, such as weightlifting, cardio, and outdoor activities, to keep my workouts interesting and challenging. In addition to personal satisfaction, being a fitness enthusiast has also allowed me to build a supportive community through group fitness classes and participation in local events. This hobby is an integral part of my daily routine and has greatly impacted my physical and mental well-being.

Project Experience:

Glaucoma Detection System (2021-2022):

• Designed and developed a computer vision system to detect early signs of glaucoma in retinal images.
• Implemented image pre-processing techniques, including denoising, contrast enhancement, and segmentation, to improve the accuracy of the detection process.
• Trained a deep learning model using a large dataset of retinal images, achieving an accuracy of 95% in detecting early signs of glaucoma.
• Developed a user-friendly interface to allow medical professionals to easily interpret the results and make more informed diagnostic decisions.

Wildlife Food Supply Website:

• Description: Designed a web application to manage the food supply for wildlife.
• Technologies Used: React (for the front-end), Vite, Node.js, and Express (for the back-end).
• Key Features:
• management, including authentication and authorization.
  Product management for tracking food items.
  Order management system for wildlife supply requests.
  Admin dashboard to manage stock and delivery records.
  

TensorFlow: This is an open-source framework for building machine learning models, and it is one of the most popular frameworks for deep learning. You can use TensorFlow to build and train deep neural networks for image classification, object detection, and segmentation, which can be used for glaucoma detection.

keras: This is a high-level neural network API that runs on top of TensorFlow. It is a user-friendly framework for building and training neural networks, and it is especially well-suited for small to medium-sized projects.

PyTorch: This is an open-source machine learning library for Python, and it is known for its simplicity and ease of use. PyTorch provides a wide range of tools and libraries for building and training deep learning models, including convolutional neural networks (CNNs), which are commonly used for image classification and segmentation tasks.

Scikit-learn: This is a machine learning library for Python that provides a wide range of algorithms for classification, regression, clustering, and dimensionality reduction. While it is not specifically designed for deep learning, you can use scikit-learn as a preprocessing tool to prepare your data before feeding it into a deep learning model.