I am seeking a challenging position in a growth-oriented organization where I can apply my skills, knowledge, and passion for continuous learning. My goal is to contribute meaningfully to team success while developing professionally. I am motivated to take on responsibilities, adapt quickly, and bring innovative solutions with a strong work ethic and a commitment to excellence.
I am seeking a challenging position in a growth-oriented organization where I can apply my skills, knowledge, and passion for continuous learning. My goal is to contribute meaningfully to team success while developing professionally. I am motivated to take on responsibilities, adapt quickly, and bring innovative solutions with a strong work ethic and a commitment to excellence.
- Developed an intelligent medicinal leaf classification system using Deep Learning and Transfer Learning techniques.
- Utilized a dataset containing 6,900+ images across 80 medicinal plant classes.
- Implemented CNN, MobileNet, and DenseNet121 models achieving 92%+ accuracy.
- Developed a scalable web-based assessment system using HTML5, CSS3, PHP, and MySQL.
- Designed frontend and backend modules for assessments, student management, and dashboards.
- Integrated responsive UI and database operations reducing manual work by 40%.
- Co-authored a research project to detect pneumonia using chest X-rays with a ResNet-50 CNN model and transfer learning.
- Achieved 90% accuracy on a benchmark dataset of 5,800+ chest X-ray images classified as normal or pneumonia-infected.
- Preprocessed data, applied image normalization, augmentation, and split data into training, validation, and test sets.
- Designed a full-stack web application for personalized fitness tracking using PHP, MySQL, HTML5, and CSS.
- Implemented user authentication and dynamic meal plan generation, achieving 85% user satisfaction in surveys.
- Developed CI/CD pipelines using Jenkins, automating build processes with a 70% reduction in release time.
- Containerized applications with Docker, cutting deployment errors by 80% and improving reliability by 60%.