I am an Electrical Engineering graduate with a strong background in programming and embedded systems. I enjoy tackling practical engineering challenges and contributing effectively to team projects while continuously learning and developing my skills in a professional setting.
I have hands-on experience in Python, C++, C, and machine learning projects related to computer vision and embedded systems, and I am passionate about applying my academic knowledge to innovative real-world applications.
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• Developed an object detection model to identify and classify solid waste types (plastic, metal, cardboard, glass, paper, trash) in real-world environments.
• Leveraged the Faster R-CNN architecture with a ResNet50 backbone to achieve accurate multi-class detection.
• Preprocessed and annotated custom waste datasets.
• Achieved high precision and recall metrics across classes, facilitating automated waste segregation for smart city applications.
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