Kalanjai Kollipara

Diligent learner with specializing in automation using Selenium, Java, and TestNG. Operate well in both individual and team capacities, leveraging a seasoned work ethic to quickly adapt to different processes and drive company objectives. Strong problem-solving skills and a commitment to continuous improvement.

• Attended the workshop on mobile app development and received certification
• Participated in workshop on Java full stack development.
• Attended a bootcamp by AICETE on Python programming and was certified in AI-ML by IIDT through Blackbugs.
• Developed an event management app using Android Studio
• Certified in Azure-AI Fundamentals by Microsoft.
• AWS cloud computing certification

• Title: HRMS Test Automation:

Developed and executed comprehensive test cases for an HRMS application, including user login, adding, editing, and deleting employee information. Automated test cases using Selenium WebDriver and TestNG, implemented a keyword-driven framework for enhanced test management, and generated detailed TestNG reports to track and analyze test results

• Title: Virtual system control using hand gestures

This project promotes an approach where cursor movement can be controlled using a real-time camera, it is an alternative to the current methods, including manual input of buttons or a mouse.This project proposes a cursor control system using hand gestures captured from a webcam, which works smoothly using Python and OpenCV, without any additional hardware requirements.

• Title: Unveiling brain tumor boundaries with advanced image segmentation

This project centers on the development of a sophisticated deep-learning model tailored for medical image segmentation. Specifically, the model aims to accurately delineate and highlight regions of interest, such as tumors, in medical images, enhancing the efficiency and accuracy of medical professionals in analyzing and diagnosing conditions from various imaging modalities. Implemented YOLO(You Only Look Once) for object detection using TensorFlow and Keras.Trained the model on annoted datasets and evaluated performance using accuracy, precision, recall and F1-score metrics. Deployed the model in real-time using OpenCV.

• NeuroSculpt: Unveiling Brain Tumor Boundaries with Advanced Image Segmentation IRJMETS Journal