An Intent Prediction System for Wireless Fidelity (Wi-Fi) Sensor Data Using an Emerging Neural Machine Intelligence

This paper presents a User Intent (UI) mining scheme based on an emerging neural machine intelligence technique called the Neuronal Auditory Machine Intelligence (NeuroAMI) and considering a Wi-Fi sessions dataset containing about 8000 data points for intent prediction. The results of simulations considering graded increase in samples from 50samples to 999samples showed that substantial increases in accuracies are achievable. When compared to the Long Short-Term Memory (LSTM) method, the results showed that the NeuroAMI will outperform the LSTM by a factor of about 2 considering percentage accuracies.