MOBILE USER TRAJECTORY PREDICTION SYSTEM WITH EXTREME MACHINE LEARNING ALGORITHM

MOBILE USER TRAJECTORY PREDICTION SYSTEM WITH EXTREME MACHINE LEARNING ALGORITHM The global adoption of smartphones and location-based services has resulted in a massive and rapid increase in Mobile User data. Because of the large size of Mobile User data, new possibilities for determining the characteristics of Mobile User mobility patterns and making mobility predictions emerge. Predicting mobile user mobility is critical in a variety of modern applications, including personalized recommendation systems and 5G networks. The present invention disclosed herein is Mobile User Trajectory Prediction System with Extreme Machine Learning Algorithm comprising of Trajectory Dataset (101), Extreme Machine Learning (102), Sequence to Sequence (103), Trajectory Prediction (104); can predict the trajectory of the mobile user with high accuracy and low mean square error. The present invention disclosed herein uses Extreme Machine Learning (EML) Algorithm with Sequence to Sequence (Seq2Seq) Algorithm. The EML with Seq2Seq can predict the trajectories by next locations prediction with training the trajectories of their previous locations of single or multiple mobiles users. Predicting location of users plays an important role for 5G Internet networks as network service providers need to allocate nearest resources to users to process their mobile request data. The present invention disclosed herein can achieves good accuracy in predicting the trajectory of the mobile user with low Mean Square Error (MSE) of 0.00776, compared with the other existing inventions such as Long Term Short Term Memory (LSTM) in which MSE is 1.85185 and Gate Recurrent Unit (GRU) with MSE of 11.89521. The present invention, EML with Seq2Seq disclosed herein is having mobile user prediction accuracy of 95.47%. The Geolife real life trajectory movement dataset which consist of user's movement latitude, longitude and users id with each mobile user has 9 locations are considered for training the proposed present invention. MOBILE USER TRAJECTORY PREDICTION SYSTEM WITH EXTREME MACHINE LEARNING ALGORITHM 101 102 103 104 TRAJECTORY EXTREME MACHINE SEQUENCE TO TRAJECTORY DATASET LEARNING SEQUENCE PREDICTION Figure 1: Mobile User Trajectory Prediction System with Extreme Machine Learning Algorithm. UPLOAD DATASET 202 GENERATE EML MODEL 203 r ENTER USER PREDICT TRAJECTORY GENERATE MSE GRAPH J.206 Figure 2: Flow Chart of the present Invention.