Shafeeq S

Created an image segmentation approach to identify tumor regions in MRI scans. Applied a PyTorch-based deep learning setup and designed a training pipeline for better boundary detection. The project reports improved segmentation quality versus conventional CNN-based approaches (about 15% boundary improvement).

Built a sentiment classification workflow for review text. Used NLP features and modeling approaches including TF-IDF and word embeddings alongside NLTK for text preprocessing. The solution was evaluated on a large set of reviews and reports about 88% accuracy.

Developed a deep-learning model to recognize emotions from facial images. Implemented a CNN-based pipeline using OpenCV for image handling and TensorFlow/Keras for model training. The project targets real-time emotion detection performance and reports about 80% classification accuracy.

Participated in the full software development lifecycle, including requirement analysis, development, testing, and maintenance of backend applications. Built and optimized Python backend logic to automate internal business processes, reducing manual processing time by 35%. Worked with cross-functional stakeholders to clarify requirements, troubleshoot issues, and deliver within deadlines, while maintaining code quality and version control with Git/GitHub.