Technologies: IoT, OpenCV, Machine Learning, Blockchain, Aadhar Authentication, Arduino, LSTM, Computer Vision
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Guided the implementation of an IoT and Computer Vision system for Vehicle Speed Regulation, utilizing real-time data processing to enhance traffic safety and compliance.
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Mentored the development of a Traffic Sign Detection system using OpenCV, achieving a 90% accuracy rate in sign recognition, significantly improving driver awareness and response times.
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Supervised the creation of a Bank Intruder Detection System leveraging Computer Vision, which reduced false alarm rates by 30% and enhanced security measures for financial institutions.
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Directed the design of a Blockchain-Based Regulated Sales Framework for alcohol sales, integrating Aadhar authentication to ensure compliance and safety, resulting in a 50% reduction in unauthorized transactions.
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Mentored a Smart Agriculture project focused on Crop Disease Detection using Machine Learning, leading to a 40% increase in early disease identification and improved crop yield.
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Supervised the development of an Online Payment Fraud Detection system, utilizing machine learning algorithms that decreased fraud incidents by 25% in the first year of implementation.
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Guided the implementation of a Dress Code Monitoring system through Object Detection, enhancing compliance in educational institutions with a 95% detection accuracy.
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Mentored a Weather Forecast Prediction project using advanced ML models, achieving a prediction accuracy of 85%, which improved decision-making for agricultural stakeholders.
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Supervised the development of an Image and Video Caption Generator with Voice Output, enhancing accessibility for visually impaired users and increasing engagement by 40%.
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Guided the creation of an AI-Based Weed Eliminator using Arduino, which improved weed management efficiency by 60%, reducing the need for chemical herbicides.
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Mentored an IoT-Based Drowsiness Detection System, which increased driver safety by providing real-time alerts, reducing drowsiness-related incidents by 20%.
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Supervised a Toxic Comment Classification project using LSTM, achieving a 92% accuracy rate in identifying harmful comments, thus enhancing online community safety.
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Guided the development of a Face Recognition-Based Smart Attendance System, improving attendance tracking efficiency by 70% and reducing manual errors.