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- Implemented a full stack web application with a 99.9% uptime on Amazon EC2, ensuring continuous accessibility for users.
- Integrated Flask API for seamless data flow, expanding application capabilities
+ Created a user-friendly National Police Incident Database (NPID) full stack application using semantic web engineering.
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- Led data preprocessing with pandas, analyzing a 1900+ police incidents dataset. Generated 42,000+ triples, showcasing advanced
- RDF data management. Developed an ontology-based system for optimized data organization and query efficiency
+ Deployed on Amazon EC2 with a 99.9% uptime, ensuring continuous accessibility. Integrated Flask API for seamless data flow,
+ expanding application capabilities.
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- Accomplished a responsive user interface, reducing page load times by 99% through ReactJS optimization
+ Preprocessed data with pandas, analyzing a 1900+ police incidents dataset, generating 42,000+ triples. Built an ontology-based
+ system for optimized data organization and query efficiency.
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- Synthesized gaming experience with immersive audio and visual elements, and smooth camera transitions, resulting in a significant
- 25% increase in user satisfaction and a 15% boost in player engagement
+ Synthesized gaming experience with immersive audio and visual elements, and smooth camera transitions, resulting in a
+ significant 25% increase in user satisfaction and a 15% boost in player engagement.
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- Pioneered responsive player controls and refined enemy AI, contributing to a 35% improvement in overall user experience
+ Pioneered responsive player controls and refined enemy AI, contributing to a 35% improvement in overall user experience.
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- Solved intricate technical challenges through methodical debugging, accomplishing a substantial 40% decrease in critical bugs
+ Overcame intricate technical challenges through methodical debugging, accomplishing a 40% decrease in critical bugs.
- Face Recognition and detection system | python
+ Face Recognition and detection system
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- Built an advanced face detection and recognition system using OpenCV library and SVM (Support Vector Machine) algorithm
+ Built an advanced face detection and recognition system using OpenCV library and SVM (Support Vector Machine) algorithm.
+
+ -
+ Utilized advanced feature extraction algorithms to identify crucial facial components (eyes, ears, nose, and mouth).
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- Extracted key facial features(eyes, nose, and mouth) utilizing a feature extraction algorithm and delivered a precision of 91% by
- training an SVM classifier on extracted facial features to perform face recognition
+ Attained 91% precision by training an SVM classifier on extracted features for precise face recognition