The Emotional Analysis Project utilizes ESP32 CAM technology and emotion recognition algorithms to predict intruders by analyzing facial expressions. Through machine learning, it interprets facial cues to classify emotions, enhancing security monitoring in real-time. This project integrates hardware and software to revolutionize surveillance methods with emotion recognition technology.
This project aims to predict the likelihood of stroke occurrence using a machine learning model developed in R. Leveraging a dataset containing various health parameters and historical stroke occurrences, the model makes accurate predictions. Through meticulous data preprocessing, model training, and evaluation, it provides valuable insights for early stroke detection and preventive healthcare measures.
This project endeavors to utilize Shor's algorithm on a quantum computer to factor large numbers efficiently. The algorithm leveraging the principles of quantum computation, the project seeks to demonstrate the potential of quantum computing in tackling computationally intensive tasks and cryptanalysis, with implications for cryptography and security protocols