Machine Learning Strategies for the Implementation of a Surveillance Drone

Drone technology is utilized for a variety of reasons, including military, agricultural, aerial photography, surveillance, remote sensing, and more. Based on real-time processing techniques, a drone plane is presented for monitoring and targeting public area crime theft in this proposed work. Previously crime prediction model was developed using Artificial Neural Network (ANN) and Regressive Neural Network (RNN), as they suffer from inappropriate accuracy levels and long-time computation. Thus, to overcome this drawback, Cat boost machine learning has been implemented as it uses tree-shaped primitives for the prediction that makes classification faster for the IoT environment. Buffalo-based Cat boosts Crime Prediction System (BCPS) initially collects crime data, preprocessing them, and then extracting environmental features and context features, the features are given to cat boost machine learning. The features are combined and give results as trees, and to improve accuracy, African Buffalo optimization (ABO) has been employed here. By estimating the predictors, a result has been obtained that was used for learning purposes and the testing side shows the result of crime theft detection. Thus BCPS is evaluated for results and compared with previous techniques to show the supremacy of the proposed model.