Technologies: VB.NET, IoT, Machine Learning, Deep Neural Networks, OpenCV, Convolutional Neural Networks, RFID, Data Analytics
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Developed an advanced video streaming solution utilizing a Mesh-based architecture in VB.NET, enhancing data transmission reliability and reducing latency by 30% for over 1,000 concurrent users.
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Designed and implemented a Smart Solution for Railways leveraging IoT technologies, resulting in a 25% increase in operational efficiency through real-time monitoring and predictive maintenance.
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Executed a comprehensive Air Quality Prediction and Analysis system using Machine Learning algorithms, achieving 90% accuracy in forecasting pollution levels, thereby aiding local authorities in environmental decision-making.
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Created a Multipurpose Smart Card System integrating RFID technology, streamlining user access and transactions for over 5,000 users across multiple platforms.
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Engineered a Credit Card Fraud Detection system based on Deep Neural Networks, successfully identifying fraudulent transactions with a 95% detection rate, significantly reducing financial losses.
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Implemented an Animal Detection system using OpenCV, achieving real-time detection with 85% accuracy, contributing to wildlife conservation efforts.
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Developed a classification model for malaria-infected cells using Convolutional Neural Networks, improving diagnostic speed by 40% and accuracy by 20% compared to traditional methods.
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Launched an Air Quality Management system utilizing Machine Learning, which provided actionable insights to over 10,000 users, leading to a 15% improvement in local air quality metrics.