AI-Powered Multi-Radar Sensor Fusion for In-Home Activity Recognition

Gold Sentinel aims to enhance in-home activity recognition by developing an AI model that effectively fuses data from multiple radar sensors. The challenge lies in integrating data from different radar sources to create a cohesive understanding of various in-home activities. This project will allow learners to apply their knowledge of AI and sensor technology to solve real-world problems. The goal is to create a model that accurately interprets and predicts household activities, improving the efficiency and reliability of smart home systems. By working on this project, learners will gain hands-on experience in AI model development, data fusion techniques, and sensor data analysis, all within a controlled and manageable scope.

AI-Driven Disease Monitoring and Prediction System

Gold Sentinel is seeking to develop an AI-based system to enhance the monitoring and prediction of diseases. The goal is to leverage artificial intelligence to analyze health data and identify patterns that could indicate the onset or spread of diseases. This project aims to create a prototype that can process data from various sources to provide timely insights. The system should be able to predict potential disease outbreaks and suggest preventive measures. By applying classroom knowledge of AI and data analysis, learners will contribute to a project that has the potential to improve public health outcomes. The project will focus on creating a user-friendly interface and ensuring data privacy and security.

AI Model Evaluation for Real-Time Sensor Applications

Gold Sentinel is exploring the integration of artificial intelligence to enhance the efficiency and accuracy of its sensor-based systems. The primary challenge is to identify the most suitable AI model that can process data from hundreds of sensors in real-time. The project aims to evaluate various AI models to determine which one offers the best performance in terms of speed, accuracy, and resource efficiency. Learners will be tasked with testing different AI models, analyzing their performance metrics, and recommending the most effective model for real-time applications. This project provides an opportunity for students to apply their knowledge of AI and machine learning, focusing on model evaluation and optimization in a practical, industry-relevant context.

Redis Data Storage Solution for Gold Sentinel

Gold Sentinel is seeking to enhance its data storage capabilities by implementing a Redis-based solution. The current system struggles with latency and scalability issues, impacting the efficiency of data retrieval and processing. The goal of this project is to design and implement a Redis-based data storage solution that addresses these challenges, providing faster data access and improved scalability. Learners will apply their knowledge of database management and distributed systems to create a robust and efficient storage solution. The project will involve understanding the existing data architecture, designing a Redis schema, and implementing the solution in a test environment. Key tasks include configuring Redis instances, optimizing data structures, and ensuring data consistency and reliability.