Radar-Based Activity Monitoring AI Model Development

Gold Sentinel is seeking to enhance its radar sensor technology by developing AI models capable of accurately monitoring and interpreting human activities. The project aims to leverage radar sensors to detect and classify various human movements and activities in real-time. This initiative will allow learners to apply their knowledge of AI, machine learning, and signal processing to a practical scenario. The primary goal is to create a robust AI model that can differentiate between different activities such as walking, running, sitting, and standing. The project will focus on optimizing the model for accuracy and efficiency, ensuring it can be implemented in real-world applications. Learners will work on data preprocessing, feature extraction, model training, and validation, using datasets provided by Gold Sentinel.

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.

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-Driven Care Planning Study Framework

This project focuses on developing a detailed study design to explore the potential of Artificial Intelligence (AI) in improving care planning for patients. The study will involve designing a research framework, identifying appropriate AI methodologies, and analyzing how AI models can assist in care planning by integrating patient data and predicting care needs. The primary objective is to create a comprehensive report and plan that outlines how AI can be effectively used in healthcare to improve outcomes, optimize resources, and support personalized care. The study design will emphasize ethical considerations, data privacy, and the practical implementation of AI solutions.