K Srilaxmi

Aspiring Software Engineer with a strong interest in machine learning, cloud technologies, and automation. Proficient in Python and skilled in developing automated workflows and deploying ML models to enhance system performance and streamline operations. Passionate about leveraging cloud technologies to build scalable solutions and eager to grow into roles such as ML Engineer or Cloud Engineer. Highly motivated to tackle new challenges, contribute to innovative projects, and continuously expand technical skills.

Traffic Management System using Deep Learning

• Developed a deep learning-based traffic management system, optimizing traffic flow in urban environments and improving overall traffic efficiency by 20%.
• Implemented a Convolutional Neural Network (CNN) model for vehicle detection and classification, achieving a 30% increase in real-time traffic monitoring accuracy.
• Integrated real-time traffic density-based signal optimization, reducing congestion by 15% and enhancing average vehicle throughput.
• Achieved 95% accuracy in vehicle counting and classification, significantly enhancing system reliability in urban environments.

Object Detection:

• Developed a real-time object detection model using the YOLOV4-tiny algorithm, achieving 90% accuracy in detecting static and dynamic objects across various environments.
• Designed a model to accurately detect university identity cards, enhancing identification efficiency and security.
• Enhanced model performance by 12% through hyperparameter tuning and adjustments to meet specific project requirements.
• Conducted rigorous testing and analysis, reducing false positives by 5% and increasing processing speed by 20%.
• Trained the model on diverse datasets, resulting In a 30% reduction in processing time and improved real-time detection efficiency.