AI and Machine Learning Project: User Authentication Using Acceleration-Based Features and Neural Networks (MATLAB)

Overview

This project focuses on implementing a machine learning solution for user authentication using acceleration-based features. The system utilizes supervised learning techniques, specifically a Feedforward Multi-Layer Perceptron (FFMLP) model, to classify users based on sensor data. The goal is to optimize the authentication performance through feature selection and classifier adjustment.

Features

Supervised Learning: The project trains a model using labeled acceleration data to authenticate users. Feature Selection: Techniques to identify and select the most relevant features for the classification task. Feedforward Multi-Layer Perceptron (FFMLP): A neural network model used to predict the identity of users based on acceleration data. Model Evaluation: The model's performance is evaluated using standard metrics such as accuracy, precision, recall, and F1-score. Technologies Used

Programming Language: MATLAB Machine Learning Toolbox: Neural Network Toolbox (for FFMLP model training) Data Processing: MATLAB functions for data manipulation and preprocessing Model Evaluation: Accuracy, Precision, Recall, F1-Score (calculated using MATLAB functions)